FRJ University of Tehran تحقیقات مالی 1024-8153 University of Tehran 51 10.22059/jfr.2015.51914 49. مدل‌های سری زمانی رابطۀ بین‌دوره‌ای ریسک و بازده با استفاده از همبستگی‌های شرطی پویا و تغییرات زمانی بتا رابطۀ بین‌دوره‌ای ریسک و بازده با استفاده از همبستگی‌های شرطی پویا و تغییرات زمانی بتا باقرزاده حجت الله دکتری اقتصاد مالی، دانشکدۀ اقتصاد دانشگاه تهران، ایران سالم علی اصغر استادیار دانشکدۀ اقتصاد دانشگاه علامه طباطبائی،‌ تهران، ایران 21 03 2015 17 1 1 20 07 02 2014 08 11 2014 Copyright © 2015, University of Tehran. 2015 https://jfr.ut.ac.ir/article_51914.html

در این مقاله‌ مدل قیمت‌گذاری بین‎‌دوره‌ای، دارایی‌های سرمایه‌ای در بورس اوراق بهادار تهران بررسی شده است. همبستگی بین بازده پورتفولیوها و بازده بازار‌ با اجرای روش همبستگی‌های شرطی پویا تخمین زده شد و بتا که در مدل بین‌‎دوره‌ای، ضریب ریسک‎گریزی محسوب می‌شود و در طول زمان تغییر می‎کند، به‎کمک روش کالمن فیلتر برآورد شد. یافته‎های پژوهش، ضرایب ریسک‌گریزی نسبی را در مدل قیمت‌گذاری بین‎‌دوره‌ای دارایی‌های سرمایه‌ای بین 013/0 و 28/0 (متوسط 20/0) نشان می‎دهد که با توجه به بی‌معنا بودن عرض ‌از مبدأ در اکثر معادلات، می‎توان گفت در بورس اوراق بهادار تهران، مدل‌ قیمت‌گذاری بین‎‌دوره‌ای دارایی‌های سرمایه‌ای برقرار است. همچنین دارایی‌هایی که با تلاطم شرطی بازار همبستگی زیادی دارند،‌ در دورۀ بعد از بازدۀ انتظاری کمتری برخوردارند. به بیانی،‌ ریسک تلاطم بازار بر بازدۀ انتظاری چنین‌ دارایی‌هایی اثر منفی می‎گذارد. دارایی‌هایی که با رشد قیمت ارز همبستگی زیادی دارند، ‌پاداش ریسک مثبتی اضافه بر پاداش ریسک بازار کسب می‌کنند، بنابراین در دورۀ مبادلاتی بعد، بازده انتظاری بیشتری به‎دست می‎آورند.

 روش کالمن فیلتر روش همبستگی شرطی پویا قیمت‌گذاری بین‌دوره‌ای دارایی‌های سرمایه‌ای واریانس‌‌ها و کواریانس‌های شرطی پویا Adrian, T. & Franconia, F. (2009). Learning about beta: time-varying factor loadings, expected returns, and the conditional CAPM. Journal of Empirical Finance, 16 (4): 537-556. Ang, A. & Chen, J. (2007). CAPM over the long run: 1926–2001. Journal of Empirical Finance, 14 (1): 1–40. Bali, T., Cakici, N., Yan, X. & Zhang, Z. (2005). Does idiosyncratic risk really matter? The Journal of Finance, 60 (2): 905-929.  Bali, T.G., (2008). The intertemporal relation between expected returns and risk. Journal of Financial Economics, 87 (1): 101-131. Bali, T.G. & Engle, R.T. (2010). The intertemporal capital asset pricing model with dynamic conditional correlations. Journal of Monetary Economic, 57 (4): 377-390. Bollerslev, T. (1990). Modeling the coherence in short-Run nominal exchange rates: A multivariate generalized ARCH model. Review of Economics and Statistics, 72 (3): 498-505. Bollerslev, T., Engle, R.F. & Wooldridge, M. (1988). A capital asset pricing model with time varying covariances. The Journal of Political Economy, 96 (1): 116-131. Campbell, J. Y., (1993). Intertemporal asset pricing without consumption data. The American Economic Review, 83 (3): 487- 512. Engle, R., (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20 (3): 339-350. Engle, R., (2009). Anticipating correlations. A new Paradigm for risk management . Princeton University Press, USA. Epstein, L. G. & Zin, S. (1989). Substitution, risk aversion, and the temporal behavior of consumption and asset returns: A theoretical framework. Econometrica, 57 (4): 937-969. Epstein, L. G. & Zin, S. (1991). Substitution, risk aversion, and the temporal behavior of consumption and asset returns: An empirical analysis. Journal of Political Economy, 99 (2): 263-286. Fama, E. F. & French, K. R. (1992). The cross section of expected stock returns. The Journal of Finance, 47 (2): 427- 465. Fama, E. F. & French, K. R. (1993). Common risk factor in the returns on stocks and bonds. Journal of Financial Economics, 33 (1): 3-56. French, K., Schwert, G. & Stambaugh, R. (1987). Expected stock returns and volatility. Journal of Financial Economics, 19 (1): 3-29. Ghysels, E., Santa-Clara, P. & Valkanov, R. (2005). There is a risk-return trade-off after all. Journal of Financial Economics, 76 (3) 509-548. Giovannini, A. & Weil, P., (1989). Risk aversion and intertemporal substitution in the capital asset pricing model. NBER Working Paper Series, No. 2824. Glosten, L., Jagannathan, R. & Runkle, D. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48 (5): 1779 -1801. Goyal, A. & Santa- Clara, P. (2003). Idiosyncratic risk Matters! The Journal of Finance, 58 (3): 975- 1007. Hardouvelis, G. A., Kim D. & Wizman T.A., (1996). Asset pricing models with and without consumption data: An empirical evaluation. Journal of Empirical Finance, 3 (3): 267-301. Harrison, P. & Zhang, H. (1999). An Investigation Risk and Return Relation at Long Horizon. The Review of Economics and Statistics, 81 (3): 399-408. Jegadeesh, N. & Titman, S. (1993). Returns to buying winners and selling losers: implications for stock market efficiency. Journal of Finance, 48 (1): 65–91. Lewellen, J. & Nagel, S. (2006). The conditional CAPM does not explain asset-pricing anomalies. Journal of Financial Economics, 82 (2): 289–314. Lundblad, C. (2007). The risk - return tradeoff in the long run: 1836–2003. Journal of Financial Economics, 85 (1): 123–150. Merton, R., (1973). An intertemporal capital asset pricing model. Econometrica, 41 (5): 867-887. Raee, R., Farhadi, R. & Shirvani, A. (2011). Intertemporal relationship between return and risk; Evidence of Intertemporal capital asset pricing model. Accounting and financial management perspective. 2(2):125-140. (in Persian) Sabunchi, M., Fallahpur, S. & Mohammadi, Sh. (2014). Comparison of conditional capital asset pricing model with time - varying beta and the standard capital asset pricing model. Published online: http://jfr.ut.ac.ir/article. (in Persian) Tehrani, R. & SadeghiSharir, S. (2004). Conditional capital asset pricing model in Tehran stock market. Financial research, 18 (2): 41-75. (in Persian) Turner, C., Startz, R. & Nelson, C. (1989). A markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics, 25 (1): 3-22. Weil, P. (1989). The equity premium puzzle and the risk-free rate puzzle. Journal of Monetary Economics, 24 (3): 401- 421. Weil, P. (1990). Non- expected utility in macroeconomics. Quarterly Journal of Economics, 105 (1): 29-42. Adrian, T. & Franconia, F. (2009). Learning about beta: time-varying factor loadings, expected returns, and the conditional CAPM. Journal of Empirical Finance, 16 (4): 537-556. Ang, A. & Chen, J. (2007). CAPM over the long run: 1926–2001. Journal of Empirical Finance, 14 (1): 1–40. Bali, T., Cakici, N., Yan, X. & Zhang, Z. (2005). Does idiosyncratic risk really matter? The Journal of Finance, 60 (2): 905-929.  Bali, T.G., (2008). The intertemporal relation between expected returns and risk. Journal of Financial Economics, 87 (1): 101-131. Bali, T.G. & Engle, R.T. (2010). The intertemporal capital asset pricing model with dynamic conditional correlations. Journal of Monetary Economic, 57 (4): 377-390. Bollerslev, T. (1990). Modeling the coherence in short-Run nominal exchange rates: A multivariate generalized ARCH model. Review of Economics and Statistics, 72 (3): 498-505. Bollerslev, T., Engle, R.F. & Wooldridge, M. (1988). A capital asset pricing model with time varying covariances. The Journal of Political Economy, 96 (1): 116-131. Campbell, J. Y., (1993). Intertemporal asset pricing without consumption data. The American Economic Review, 83 (3): 487- 512. Engle, R., (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20 (3): 339-350. Engle, R., (2009). Anticipating correlations. A new Paradigm for risk management . Princeton University Press, USA. Epstein, L. G. & Zin, S. (1989). Substitution, risk aversion, and the temporal behavior of consumption and asset returns: A theoretical framework. Econometrica, 57 (4): 937-969. Epstein, L. G. & Zin, S. (1991). Substitution, risk aversion, and the temporal behavior of consumption and asset returns: An empirical analysis. Journal of Political Economy, 99 (2): 263-286. Fama, E. F. & French, K. R. (1992). The cross section of expected stock returns. The Journal of Finance, 47 (2): 427- 465. Fama, E. F. & French, K. R. (1993). Common risk factor in the returns on stocks and bonds. Journal of Financial Economics, 33 (1): 3-56. French, K., Schwert, G. & Stambaugh, R. (1987). Expected stock returns and volatility. Journal of Financial Economics, 19 (1): 3-29. Ghysels, E., Santa-Clara, P. & Valkanov, R. (2005). There is a risk-return trade-off after all. Journal of Financial Economics, 76 (3) 509-548. Giovannini, A. & Weil, P., (1989). Risk aversion and intertemporal substitution in the capital asset pricing model. NBER Working Paper Series, No. 2824. Glosten, L., Jagannathan, R. & Runkle, D. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48 (5): 1779 -1801. Goyal, A. & Santa- Clara, P. (2003). Idiosyncratic risk Matters! The Journal of Finance, 58 (3): 975- 1007. Hardouvelis, G. A., Kim D. & Wizman T.A., (1996). Asset pricing models with and without consumption data: An empirical evaluation. Journal of Empirical Finance, 3 (3): 267-301. Harrison, P. & Zhang, H. (1999). An Investigation Risk and Return Relation at Long Horizon. The Review of Economics and Statistics, 81 (3): 399-408. Jegadeesh, N. & Titman, S. (1993). Returns to buying winners and selling losers: implications for stock market efficiency. Journal of Finance, 48 (1): 65–91. Lewellen, J. & Nagel, S. (2006). The conditional CAPM does not explain asset-pricing anomalies. Journal of Financial Economics, 82 (2): 289–314. Lundblad, C. (2007). The risk - return tradeoff in the long run: 1836–2003. Journal of Financial Economics, 85 (1): 123–150. Merton, R., (1973). An intertemporal capital asset pricing model. Econometrica, 41 (5): 867-887. Raee, R., Farhadi, R. & Shirvani, A. (2011). Intertemporal relationship between return and risk; Evidence of Intertemporal capital asset pricing model. Accounting and financial management perspective. 2(2):125-140. (in Persian) Sabunchi, M., Fallahpur, S. & Mohammadi, Sh. (2014). Comparison of conditional capital asset pricing model with time - varying beta and the standard capital asset pricing model. Published online: http://jfr.ut.ac.ir/article. (in Persian) Tehrani, R. & SadeghiSharir, S. (2004). Conditional capital asset pricing model in Tehran stock market. Financial research, 18 (2): 41-75. (in Persian) Turner, C., Startz, R. & Nelson, C. (1989). A markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics, 25 (1): 3-22. Weil, P. (1989). The equity premium puzzle and the risk-free rate puzzle. Journal of Monetary Economics, 24 (3): 401- 421. Weil, P. (1990). Non- expected utility in macroeconomics. Quarterly Journal of Economics, 105 (1): 29-42.
FRJ University of Tehran تحقیقات مالی 1024-8153 University of Tehran 51 10.22059/jfr.2015.54612 21. مالی رفتاری بررسی رفتار معاملاتی و عملکرد معاملاتی انواع سرمایه گذاران در بورس اوراق بهادار تهران بررسی رفتار معاملاتی و عملکرد معاملاتی انواع سرمایه گذاران در بورس اوراق بهادار تهران بدری احمد استادیار دانشکدۀ مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران عزآبادی بهاره کارشناس‎ارشد مدیریت مالی، دانشگاه شهید بهشتی، تهران، ایران 21 03 2015 17 1 21 38 09 03 2014 01 12 2014 Copyright © 2015, University of Tehran. 2015 https://jfr.ut.ac.ir/article_54612.html

در این پژوهش با استفاده از مدل خودرگرسیون برداری و مقیاس عملکرد هفتگی پرتفوی گرینبلت و تیتمن، الگوهای معاملاتی و اجزای تشکیل‎دهندۀ عملکرد معاملاتی سرمایه‎گذاران حقیقی و حقوقی در بورس اوراق بهادار تهران در سال‎های 1387 تا 1391 بررسی شده است. نتایج نشان داد در مجموع سرمایه‎گذاران حقیقی رفتاری جمعی دارند، اما سرمایه‎گذاران حقوقی راهبرد  معاملاتی معکوس را در پیش می‎گیرند. با وجود این، شواهدی در اتخاذ راهبرد مومنتوم از سوی سرمایه‎گذاران حقیقی و حقوقی به‎دست نیامد. سرمایه‎گذاران حقوقی با اتخاذ راهبرد معکوس، در اغلب فواصل معاملاتی، عملکرد معاملاتی بهتری داشتند و بخش شایان توجهی از بازده به‎دلیل زمان‎بندی معاملاتی مناسب بوده است؛ اگرچه انتخاب سهام نامناسب، بخشی از بازدۀ آنها را تحت‎الشعاع قرار داد. در مقابل، برای سرمایه‎گذاران حقیقی با وجود عملکرد مناسب از بُعد انتخاب سهام، بخشی از بازده عملکرد معاملاتی به‎دلیل زمان‎بندی معاملاتی نامناسب، خنثی شده است.  

 خودرگرسیون برداری راهبرد معکوس راهبرد مومنتوم رفتار جمعی Ahmad Walid, M.A. (2014). The Trading Patterns and Performance of Individual vis-à-vis Institutional Investors in the Qatar Exchange. Review of Accounting and Finance, 13 (1): 24-42. Ahmadpour, A. & Ghorbani, H. (2013). Investigating the Stock Selection Ability among Institutional and Individual Investors. Quarterly Journal of Investment Knowledge, 2 (5): 45-60. (in Persian( Barber, B., Lee, Y.T., Liu, Y.J. & Odean, T. (2004). Who gains from trade? Evidence from Taiwan. Working Paper, University of California and National Chengchi University. Etemadi, H., Daghani, R., Azizkhani, M. & Farahbakhsh, S. (2014). Timing in Portfolio Evaluation: Evidence of capital market. Journal of Financial Research, 16(1): 25-36. (in Persian) Griffin, J.M., Harris, J.H. & Topaloglu, S. (2003). The Dynamics of Institutional and Individual Trading. Journal of Finance, 58 (6): 285-2320. Grinblatt, M. & Keloharju, M. (2000). The Investment Behavior and Performance of Various Investor-Types: A study of Finland's unique data set. Journal of Financial Economics, 55 (1): 43-67. Grinblatt, M. & Titman, S. (1989). Mutual Fund Performance: an Analysis of Quarterly Portfolio Holdings. Journal of Business, 62 (3): 394-415. Islami Bidgoli, G. & Shahriyari, S. (2007). Study and Test of Investors’ Herding Behavior using Deviations of Stock Return from market return in Tehran Stock Exchange during the years of 2001-2005. Journal of the Accounting and Auditing Review, 14(3): 25-44. (in Persian) Kamali, H. (2007). Investigation and Comparison of a Momentum Strategy with a Contrarian Strategy in Tehran Stock Exchange. (Unpublished thesis dissertation). AL Zahra University, Tehran, Iran. (in Persian) Keshavarz Haddad, G. & Rezai, M. (2011). Herding Behavior among Institutional Investors in Tehran Stock Exchange. Quarterly Iranian Economic Research, 15(45): 103-137. (in Persian) Che, L. (2011). Investors Performance and Trading Behavior on the Norwegian Stock Market. Working Paper, Norwegian Business School. Mehrani, S. & Nonahal Nahr, A. (2008). An Investigation of Implementing Contrarian Trading Strategy in Tehran Stock Exchange. Journal of the Accounting and Auditing Review, 15(1): 117-136. (in Persian) Nikbakht, M. & Moradi, M. (2005). Evaluating the Overreaction of Shareholders in Tehran Stock Exchange. Journal of the Accounting and Auditing Review, 12(2): 97-123. (in Persian) Nofsinger, J. R. & Sias, R. W. (1999). Herding and Feedback Trading by Institutional and Individual Investors. Journal of Finance, 54 (6): 2263-2295. Phansatan, S., Powell, J.G., Tanthanongsakkun, S. & Treepongkaruna, S. (2012). Investor Type Trading Behavior and Trade Performance: Evidence from the Thai stock market. Pacific Basin finance Journal, 20 (1):1-23. Saeedi, A. & Bagheri, S. (2010). Contrarian Strategy in Tehran Stock Exchange. Journal of Financial Research, 12 (30):75-94. (in Persian) Saeedi, A. & Farahanian, M. (2011). To Study the Investor Herd Behavior in Tehran Stock Exchange. Quarterly Journal of Securities Exchange, 4(16): 175-198. (in Persian) Shariat Panahi, M., Sohrabi Araghi, M. & Shariati, A. (2014). Contrarian Investment Strategy based on Reward-Risk Stock Selection Criteria. Journal of Financial Research, 16(1): 113-128. (in Persian)_||_ Ahmad Walid, M.A. (2014). The Trading Patterns and Performance of Individual vis-à-vis Institutional Investors in the Qatar Exchange. Review of Accounting and Finance, 13 (1): 24-42. Ahmadpour, A. & Ghorbani, H. (2013). Investigating the Stock Selection Ability among Institutional and Individual Investors. Quarterly Journal of Investment Knowledge, 2 (5): 45-60. (in Persian( Barber, B., Lee, Y.T., Liu, Y.J. & Odean, T. (2004). Who gains from trade? Evidence from Taiwan. Working Paper, University of California and National Chengchi University. Etemadi, H., Daghani, R., Azizkhani, M. & Farahbakhsh, S. (2014). Timing in Portfolio Evaluation: Evidence of capital market. Journal of Financial Research, 16(1): 25-36. (in Persian) Griffin, J.M., Harris, J.H. & Topaloglu, S. (2003). The Dynamics of Institutional and Individual Trading. Journal of Finance, 58 (6): 285-2320. Grinblatt, M. & Keloharju, M. (2000). The Investment Behavior and Performance of Various Investor-Types: A study of Finland's unique data set. Journal of Financial Economics, 55 (1): 43-67. Grinblatt, M. & Titman, S. (1989). Mutual Fund Performance: an Analysis of Quarterly Portfolio Holdings. Journal of Business, 62 (3): 394-415. Islami Bidgoli, G. & Shahriyari, S. (2007). Study and Test of Investors’ Herding Behavior using Deviations of Stock Return from market return in Tehran Stock Exchange during the years of 2001-2005. Journal of the Accounting and Auditing Review, 14(3): 25-44. (in Persian) Kamali, H. (2007). Investigation and Comparison of a Momentum Strategy with a Contrarian Strategy in Tehran Stock Exchange. (Unpublished thesis dissertation). AL Zahra University, Tehran, Iran. (in Persian) Keshavarz Haddad, G. & Rezai, M. (2011). Herding Behavior among Institutional Investors in Tehran Stock Exchange. Quarterly Iranian Economic Research, 15(45): 103-137. (in Persian) Che, L. (2011). Investors Performance and Trading Behavior on the Norwegian Stock Market. Working Paper, Norwegian Business School. Mehrani, S. & Nonahal Nahr, A. (2008). An Investigation of Implementing Contrarian Trading Strategy in Tehran Stock Exchange. Journal of the Accounting and Auditing Review, 15(1): 117-136. (in Persian) Nikbakht, M. & Moradi, M. (2005). Evaluating the Overreaction of Shareholders in Tehran Stock Exchange. Journal of the Accounting and Auditing Review, 12(2): 97-123. (in Persian) Nofsinger, J. R. & Sias, R. W. (1999). Herding and Feedback Trading by Institutional and Individual Investors. Journal of Finance, 54 (6): 2263-2295. Phansatan, S., Powell, J.G., Tanthanongsakkun, S. & Treepongkaruna, S. (2012). Investor Type Trading Behavior and Trade Performance: Evidence from the Thai stock market. Pacific Basin finance Journal, 20 (1):1-23. Saeedi, A. & Bagheri, S. (2010). Contrarian Strategy in Tehran Stock Exchange. Journal of Financial Research, 12 (30):75-94. (in Persian) Saeedi, A. & Farahanian, M. (2011). To Study the Investor Herd Behavior in Tehran Stock Exchange. Quarterly Journal of Securities Exchange, 4(16): 175-198. (in Persian) Shariat Panahi, M., Sohrabi Araghi, M. & Shariati, A. (2014). Contrarian Investment Strategy based on Reward-Risk Stock Selection Criteria. Journal of Financial Research, 16(1): 113-128. (in Persian)
FRJ University of Tehran تحقیقات مالی 1024-8153 University of Tehran 51 10.22059/jfr.2015.51432 08. ادغام؛ تملک؛ تجدید ساختار؛ حاکمیت شرکتی 13. مالی رفتاری تأثیر سازوکارهای حاکمیت شرکتی و مالی بر تجدید ارائۀ صورت‎های مالی شرکت‎های پذیرفته‎شده در بورس اوراق بهادار تهران حساس یگانه یحیی دانشیار حسابداری، دانشگاه علامه طباطبائی، تهران، ایران تقی زاده سمیه کارشناس‎ارشد حسابداری، دانشگاه علوم اقتصادی، تهران، ایران 21 03 2015 17 1 39 58 06 10 2013 28 10 2014 Copyright © 2015, University of Tehran. 2015 https://jfr.ut.ac.ir/article_51432.html

هدف این پژوهش بررسی تأثیر برخی سازوکارهای راهبری شرکتی (ساختار هیئت‎مدیره و ساختار مالکیت) و متغیرهای مالی (سطح بدهی، اندازۀ شرکت) بر تجدید ارائۀ صورت‎های مالی شرکت‎های پذیرفته‎شده در بورس اوراق بهادار تهران است. برای این منظور، اطلاعات 48 شرکت با بیشترین تجدید ارائۀ صورت‎های مالی و 46 شرکت (گروه کنترل) با کمترین تجدید ارائۀ صورت‎های مالی جمع‎آوری شده است. تجزیه‎وتحلیل اطلاعات از طریق آزمون t-test، همبستگی (کندال و اسپیرمن) و ایجاد مدل رگرسیون لجستیک انجام گرفته است. نتایج نشان می‎دهد بین دوگانگی وظیفۀ مدیرعامل و تجدید ارائۀ صورت‎های مالی رابطۀ مثبتی وجود دارد، بین اندازۀ هیئت‎مدیره و تجدید ارائۀ صورت‎های مالی رابطۀ مثبتی برقرار است و از میان متغیرهای مالی، سطح بدهی با تجدید ارائۀ صورت‎های مالی رابطۀ مثبتی دارد.

 تجدید ارائۀ صورت‎های مالی تعدیلات سنواتی راهبری شرکتی Abdolmohammadi, M. J., Read, W. J. (2010). Corporate Governance Ratings and Financial Restatements: Pre and Post Sarbanes-Oxley Act . Journal of Forensic & Investigative Accounting, 2(1): 1-44. Aghaei, M. & Chalaki, P. (2010). Studding the Relationship between Corporate Governance attributes and the Information Content of Accounting Earnings: The Role of Earnings Management. Iranian journal of management sciences, 4 (16): 27-58. (in Persian) Agrawal, A. & Chadha, S. (2005). Corporate Governance and Accounting Scandals. Journal of Law and Economics. 48(2): 371-406. Alves, S. (2012). Ownership Structure and Earnings Management: Evidence from Portugal. Australasian Accounting Business and Finance Journal, 6 (1): 47-57. Audit organization. (2010).Accounting standards. Accounting standards setting committee, Tehan. (in Persian) Baber, R. B., Liang, L. & Zhu, Z (2012). Associations between Internal and External Corporate Governance Characteristics: Implications for Investigating Financial Accounting Restatements, American Accounting Association, 26(2): 219-237. Bolo, GH. Moameni, S. (2012). Relationship between financial restatement statement and earnings management and earnings quality: Evidence from Tehran Stock Market. Master's thesis, Tehran, Allameh Tabatabai University. (in Persian) Flanagan, D.J. (2008). An overview of accounting restatement activity in the United States. International Journal of Commerce and Management, 18 (4): 363-381 GAO. (2002). Financial statement restatements; trends, market impacts, regulatory responses, and remaining challenges. Report to the chairman, committee on banking, housing, and urban affairs, U.S. Senate. GAO. (2006). Financial restatements; update of public company trends, market impacts, and regulatory enforcement activities. Report to the chairman, committee on banking, housing, and urban affairs, U.S. Senate. Ghalibafasl, H. & Rezaei, F. (2009). A Study of the Relationship between Board Composition and Firm Performance in TSE.Journal of financial research, 9 (2) (in Persian) Hassas  Yeganeh, Y. & Baghomiyan, R. (2006). Corporate Governance and Financial Reporting Quality. CPA Journal, 45-86. (in Persian) Hassas Yeganeh, Y . & Nadighomi, V. (2007). The role of transparency in the effectiveness of corporate governance. Auditor magazine, 21(179): 32-37. (in Persian) Marcdiukaityte, D., Szewczyk, S.H. & Vama, R. (2009). Voluntary vs. Forced Financial Restatements: The Role of Board Independence. Financial Analysts Journal, 65(5). Available at SSRN: http://ssrn.com/abstract=1484824. Mehrani, M., Hoseini, A. & Heidari, H. (2013). Investigation of the Effects of Ownership Structure on Firms Value: Evidence from Tehran Stock Market.Journal of financial research, 15 (1): 129-148. (in Persian) Nikbakht, M. & Rafiei, A. (2012). A model of effective factors in financial restatements in Iran. Journal of accounting knowledge, 3(9): 167-194. (in Persian) Sajjadi, H. (2005). The annual adjustments of the financial statements related to Stock price, and life-size of the firms listed in the Tehran Stock Exchanged.Quarterly Journal of Quantitative Economics, 1(1): 63-87. (in Persian) Shamsul-Nahar, A. (2010). Financial restatements and corporate governance among Malaysian listed companies. Managerial Auditing Journal, 25(6): 526-552. Shariatpanahi, M. & Kazemi, H. (2011). Model to assess the effects of restatement of financial statements in the information content of earnings. PHD thesis, Tehran, Allameh Tabatabai University. (in Persian) Shelton, S.W., Owens-Jackson, L.A., Robinson, D.R. (2011). IFRS and U.S. GAAP: Assessing the impact of reporting incentives on firm restatements in foreign and U.S. markets, Advances in Accounting, incorporating Advances in International Accounting Journal, 27 (1): 187-192.   _||_ Abdolmohammadi, M. J., Read, W. J. (2010). Corporate Governance Ratings and Financial Restatements: Pre and Post Sarbanes-Oxley Act . Journal of Forensic & Investigative Accounting, 2(1): 1-44. Aghaei, M. & Chalaki, P. (2010). Studding the Relationship between Corporate Governance attributes and the Information Content of Accounting Earnings: The Role of Earnings Management. Iranian journal of management sciences, 4 (16): 27-58. (in Persian) Agrawal, A. & Chadha, S. (2005). Corporate Governance and Accounting Scandals. Journal of Law and Economics. 48(2): 371-406. Alves, S. (2012). Ownership Structure and Earnings Management: Evidence from Portugal. Australasian Accounting Business and Finance Journal, 6 (1): 47-57. Audit organization. (2010).Accounting standards. Accounting standards setting committee, Tehan. (in Persian) Baber, R. B., Liang, L. & Zhu, Z (2012). Associations between Internal and External Corporate Governance Characteristics: Implications for Investigating Financial Accounting Restatements, American Accounting Association, 26(2): 219-237. Bolo, GH. Moameni, S. (2012). Relationship between financial restatement statement and earnings management and earnings quality: Evidence from Tehran Stock Market. Master's thesis, Tehran, Allameh Tabatabai University. (in Persian) Flanagan, D.J. (2008). An overview of accounting restatement activity in the United States. International Journal of Commerce and Management, 18 (4): 363-381 GAO. (2002). Financial statement restatements; trends, market impacts, regulatory responses, and remaining challenges. Report to the chairman, committee on banking, housing, and urban affairs, U.S. Senate. GAO. (2006). Financial restatements; update of public company trends, market impacts, and regulatory enforcement activities. Report to the chairman, committee on banking, housing, and urban affairs, U.S. Senate. Ghalibafasl, H. & Rezaei, F. (2009). A Study of the Relationship between Board Composition and Firm Performance in TSE.Journal of financial research, 9 (2) (in Persian) Hassas  Yeganeh, Y. & Baghomiyan, R. (2006). Corporate Governance and Financial Reporting Quality. CPA Journal, 45-86. (in Persian) Hassas Yeganeh, Y . & Nadighomi, V. (2007). The role of transparency in the effectiveness of corporate governance. Auditor magazine, 21(179): 32-37. (in Persian) Marcdiukaityte, D., Szewczyk, S.H. & Vama, R. (2009). Voluntary vs. Forced Financial Restatements: The Role of Board Independence. Financial Analysts Journal, 65(5). Available at SSRN: http://ssrn.com/abstract=1484824. Mehrani, M., Hoseini, A. & Heidari, H. (2013). Investigation of the Effects of Ownership Structure on Firms Value: Evidence from Tehran Stock Market.Journal of financial research, 15 (1): 129-148. (in Persian) Nikbakht, M. & Rafiei, A. (2012). A model of effective factors in financial restatements in Iran. Journal of accounting knowledge, 3(9): 167-194. (in Persian) Sajjadi, H. (2005). The annual adjustments of the financial statements related to Stock price, and life-size of the firms listed in the Tehran Stock Exchanged.Quarterly Journal of Quantitative Economics, 1(1): 63-87. (in Persian) Shamsul-Nahar, A. (2010). Financial restatements and corporate governance among Malaysian listed companies. Managerial Auditing Journal, 25(6): 526-552. Shariatpanahi, M. & Kazemi, H. (2011). Model to assess the effects of restatement of financial statements in the information content of earnings. PHD thesis, Tehran, Allameh Tabatabai University. (in Persian) Shelton, S.W., Owens-Jackson, L.A., Robinson, D.R. (2011). IFRS and U.S. GAAP: Assessing the impact of reporting incentives on firm restatements in foreign and U.S. markets, Advances in Accounting, incorporating Advances in International Accounting Journal, 27 (1): 187-192.
FRJ University of Tehran تحقیقات مالی 1024-8153 University of Tehran 51 10.22059/jfr.2015.52008 006. مدیریت ریسک تخمین ارزش در معرض ریسک بازده بورس اوراق بهادار تهران با استفاده از آنالیز موجک تخمین ارزش در معرض ریسک بازده بورس اوراق بهادار تهران با استفاده از آنالیز موجک رستمی نوروزآباد مجتبی دانشجوی دکتری مدیریت مالی، دانشگاه آزاد اسلامی واحد تهران شمال، تهران، ایران شجاعی عبدالناصر استادیار گروه اقتصاد، دانشگاه آزاد اسلامی، واحد سنندج، سنندج، ایران خضری محسن دانشجوی دکتری اقتصاد، دانشگاه تربیت مدرس، تهران، ایران رحمانی نوروزآباد سامان کارشناس ‎ارشد مدیریت بازرگانی (مالی)، دانشگاه آزاد اسلامی، واحد سنندج، سنندج، ایران 21 03 2015 17 1 59 82 08 10 2013 22 11 2014 Copyright © 2015, University of Tehran. 2015 https://jfr.ut.ac.ir/article_52008.html

شرکت‌های مالی همواره در معرض خطرهای ناشی از ریسک قرار دارند. در چند سال گذشته بنا به ‎دلایلی، اندازه‎گیری ارزش در معرض ریسک (VaR)، از اهمیت روزافزونی برای شرکت‌های مالی برخوردار شده است. این پژوهش از بین معیارهای متعدد ریسک، معیار VaR را با رویکرد جدیدی برای محاسبۀ ریسک بازارها ارائه می‌کند. رویکردهای معمول اندازه‌گیری ریسک به‎دلیل ماهیت پیچیده، غیرخطی و در حال تغییر ریسک، از قدرت توضیحی ضعیف و عملکرد محدودی برخوردارند. بنابراین پژوهش پیش رو، پارادایم شبه‎پارامتریکی جدیدی با ترکیب آنالیز موجک و مدل‌های GARCH پیشنهاد کرده است که با استفاده از آنالیز موجک به بررسی خواص چندمقیاسی داده‌ها می‌پردازد. نتایج تجربی حاکی از برتری روش پیشنهادی این مقاله نسبت به رویکردهای سنتی است؛ به‎طوری که این روش، تخمین‌هایی با درجۀ اطمینان و صحت بیشتری از ارزش در معرض ریسک را به‎دست می‎دهد.

 آنالیز موجک ارزش در معرض ریسک بورس اوراق بهادار تهران Abad, P. & Benito, S. (2009). A Detailed Comparison of Value at Risk in International Stock Exchanges. Fundación De Las Cajas De Ahorros, Documento De Trabajo (452/2009): 1-45. Bates, J.M. & Granger, C.W.J. (1969). The combination of forecasts. Operations Research Society, 20 (4): 451–468. Bauwens, L., Omrane, W. B. & Rengifo, E. (2006). Intra-Daily FX Optimal Portfolio Allocation. CORE Discussion Paper (2006/10): 1-27. Bubák, V. (2008). Value-at-Risk on Central and Eastern European Stock Markets: An Empirical Investigation Using GARCH Models; Working Paper, IES (Institute of Economic Studies) (18/2008): 1-28. Chan, N.H., Deng, S.J., Peng, L. & Xia, Z. (2005). Interval estimation of Value at Risk based on GARCH models with heavy- tailed innovations. Journal of Econometrics, 137 (2): 556-576. Chi, X. & Kai-jian, H. (2006). Wavelet denoised value at risk estimate. International Conference on Management Science & Engineering, 5-7 Oct. pp: 1552–1557. 10.1109/ICMSE.2006.314034. Cifter, A. (2011). Value-at-risk estimation with wavelet-based extreme value theory: Evidence from emerging markets. Physica A: Statistical Mechanics and its Applications, 390(12): 2356-2367. Conlon, T., Crane, M. & Ruskin, H. J. (2008). Wavelet multiscale analysis for hedge funds: Scaling and strategies. Physica A: Statistical Mechanics and its Applications, 387 (21): 5197-5204. Fadaiinezhad, M.I. & Eghbalnia, M. (2006). Designing a model for the management of investment risk In the Tehran Stock Exchange Using the concept of value at risk, Fourth International Conference on Management. 18: 71-90 (in Persian) Fallah-Pour, S. & Ahmadi, E. (2014). Oil and gold portfolio VaR estimates using Copula-GARCH, Journal of Financial Research, Accepted.15 (2): 269-288 (in Persian) Fernandez, V. (2006). The CAPM and value at risk at different time-scales. International Review of Financial Analysis, 15 (3): 203-219. Giot, P. & Laurent, S. (2004). Modeling daily Value at Risk using realized volatility and ARCH type models. Journal of Empirical Finance, 11 (3): 379-398. Gold Smith, W. Reymond. (1969). Financial Structure and Development New Hower, CT: Uyale U. Press. Grigore, A. (2008). Value-at-Risk. Measurement and Evaluation Methods for Market Risk. Paper presented at the International Conference, Financial and monetary policies in European Union. He, K., Xie, C., Chen, S. & Lai, K. K. (2010). Estimating VaR in crude oil market: A novel multi-scale non-linear ensemble approach incorporating wavelet analysis and neural network. Neurocomputing, 72 (16-18): 3428-3438 Hendricks, D. (1996). Evaluation of Value at Risk models using Historical Data. FRBNY Economic Policy Review. 2 (1): 39-70. In, F. & Kim, S. (2007). A note on the relationship between Fama-French risk factors and innovations of ICAPM state variables. Finance Research Letters. 4 (3): 165-171. In, F., Kim, S., Marisetty, V. & Faff, R. (2008). Analysing the performance of managed funds using the wavelet multiscaling method. Review of Quantitative Finance and Accounting. 31(1): 55-70. Jensen, G.R., Johnson, R.R. and Mercer, J.M. (2002). Tactical asset allocation and commodity futures. Journal of Portfolio Management, 28(4): 100-111. Karunaratne, N.D. & Bhar, R. (2011). Regime-shifts and post-float inflation dynamics of Australia. Economic Modeling, 28 (405): 1941–1949. King, R.G. & Levine, R. (1993). Financial Intermediation and Economic Development, financial Intermediation in the Construction of Europe, Eds: Colin Mayer and Xavier vives. London: Center for economic Policy, PP: 89-153. Kupiec, P.H. (1995). Techniques for verifying the accuracy of risk measurement models. The Journal of Derivatives, 3 (2): 73–84. Lai, K.K., Kaijian, H., Chi, X. & Chen, S. (2006). Market risk measurement for crude oil: a wavelet based VaR approach. IJCNN. pp. 2129-2136. DOI: 10.1109/IJCNN.2006.246984. Lai, K.K., Kaijian, H., Xie, C. & Chen, S. (2006). Market risk for nonferrous metals: a wavelet based VaR approach, ISDA, 1: 1179-1184. Levine, R. & Zervos, S. (1996). Stock Mankats, Danks and Economic Growth, Working paper. World Bank Policy Research. Available at SSRN: http://ssrn.com/abstract=60141. Liu, Y. (2005). Value-at-risk model combination using artificial neural networks. Working Paper. Emory University. Available at: http://citeseerx.ist.psu. edu/viewdoc/download?doi=10.1.1.113.8523&rep=rep1&type=pdf. Manganeli, S. & Engle, R. T. (2001). Value at Risk models in finance. Working Papers. European Central Bank. Nason, G.P. & Von Sachs, R. (1999). Wavelets in time series analysis. Philosophical Transactions of the Royal Society of London A357, 2511–2526. Available at: http://www.maths.bris.ac.uk/~magpn/Research/papers/roysoc.pdf. Norouzzadeh, P. (2006). Performance of VaR measure In the Tehran stock exchange. Futures conference, technology and development prospects. Tehran - Amirkabir University of Technology. Novrang, A.R., Hezaveh, A. & Ghorbani Salanghooch, M. (2006). Value at risk is a prerequisite for value engineering In the multi-project companies. Fourth National Conference on Value Engineering 2008-08-19 - 2008-08-20, Tehran, Iran (in Persian) Ozun, A. & Cifter, A. (2007). Aided-computer evaluation of nonlinear combination of financial forecast with genetic algorithm. International Review on Computers and Software, 2 (3): 276–284. Pagan, A.A. & Schwert, G.W. (1990). Alternative models for conditional stock volatility. Journal of Econometrics, 45 (1-2): 267-290. Patrick, H. (1966). Financial Development and Economic growth in underdeveloped countries. Economic development and cultural change, 12 (2):174-189. Peykarjoo, K., Shahriar, B. & Noorallahy, N. (2009). Corporate Finance Risk and financial institutions Measurement using VaR methodology. Economic Bulletin, 9 (5): 195-221. (in Persian) Pojarliev, M. & Polasek, M. (2000). Volatility forecasts and Value at Risk Evaluation for the MSCI North America Index. From: http://www. goliramundi.org. Ramsey, J. (2002). Wavelets in economics and finance: past and future. Studies in Nonlinear Dynamics and Econometrics, 6 (3): 1558-3708. Ramsey, J.B. (1999). The contribution of wavelets to the analysis of economic and financial data. Philosophical Transactions of the Royal Society of London Series A-Mathematical Physical and Engineering Sciences, 357 (1760): 2593–2606. Rostami, M.R. & Haghighi, F. (2013). Comparing performance Multivariate GARCH model of risk of the portfolio. Journal of Financial Research, 15 (2): 215-228. (in Persian) Saeedi, A. & Afkhami, A. (2012). Examine the relationship between risk, return and liquidity with free float shares on the Tehran Stock Exchange. Journal of Financial Research, 14 (2): 65-80. (in Persian) Saidi, P. & Amiri, A. (2008). Examine the relationship between macroeconomic variables with Tehran Stock Exchange Overall Index. Journal of Economic Modeling, 2 (2): 111-130. (in Persian) Show, E.S. (1973). Financial Deepening in Economic Development. New York: oxford university press. So, M. K. P. & Yu, P. L.H. (2005). Empirical analysis of GARCH models in Value at Risk estimation. International Financial Markets.Institutions and Money, 16 (2): 180-197. Tan, Z., Zhang, J. Wang, J. & Xu, J. (2010). Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models, Applied Energy, 87 3606-3610. Yaghoobnezhad, A., Saidi, A. & Rozeei, M. (2010). Estimating the market risk premium with Market Leverage in the Tehran Stock Exchange. 11, 11 (28): 105-120. (in Persian) Yiu, K.F.C. (2003). Optimal portfolios under a value-at-risk constraint. Journal of Economic Dynamics and Control. 28 (7): 1317-1334. Young, H.K. & Ingall, L. (2007). Exploring Monte Carlo Simulation Applications for Project Management. Risk Management, 9 (1): 44-57._||_ Abad, P. & Benito, S. (2009). A Detailed Comparison of Value at Risk in International Stock Exchanges. Fundación De Las Cajas De Ahorros, Documento De Trabajo (452/2009): 1-45. Bates, J.M. & Granger, C.W.J. (1969). The combination of forecasts. Operations Research Society, 20 (4): 451–468. Bauwens, L., Omrane, W. B. & Rengifo, E. (2006). Intra-Daily FX Optimal Portfolio Allocation. CORE Discussion Paper (2006/10): 1-27. Bubák, V. (2008). Value-at-Risk on Central and Eastern European Stock Markets: An Empirical Investigation Using GARCH Models; Working Paper, IES (Institute of Economic Studies) (18/2008): 1-28. Chan, N.H., Deng, S.J., Peng, L. & Xia, Z. (2005). Interval estimation of Value at Risk based on GARCH models with heavy- tailed innovations. Journal of Econometrics, 137 (2): 556-576. Chi, X. & Kai-jian, H. (2006). Wavelet denoised value at risk estimate. International Conference on Management Science & Engineering, 5-7 Oct. pp: 1552–1557. 10.1109/ICMSE.2006.314034. Cifter, A. (2011). Value-at-risk estimation with wavelet-based extreme value theory: Evidence from emerging markets. Physica A: Statistical Mechanics and its Applications, 390(12): 2356-2367. Conlon, T., Crane, M. & Ruskin, H. J. (2008). Wavelet multiscale analysis for hedge funds: Scaling and strategies. Physica A: Statistical Mechanics and its Applications, 387 (21): 5197-5204. Fadaiinezhad, M.I. & Eghbalnia, M. (2006). Designing a model for the management of investment risk In the Tehran Stock Exchange Using the concept of value at risk, Fourth International Conference on Management. 18: 71-90 (in Persian) Fallah-Pour, S. & Ahmadi, E. (2014). Oil and gold portfolio VaR estimates using Copula-GARCH, Journal of Financial Research, Accepted.15 (2): 269-288 (in Persian) Fernandez, V. (2006). The CAPM and value at risk at different time-scales. International Review of Financial Analysis, 15 (3): 203-219. Giot, P. & Laurent, S. (2004). Modeling daily Value at Risk using realized volatility and ARCH type models. Journal of Empirical Finance, 11 (3): 379-398. Gold Smith, W. Reymond. (1969). Financial Structure and Development New Hower, CT: Uyale U. Press. Grigore, A. (2008). Value-at-Risk. Measurement and Evaluation Methods for Market Risk. Paper presented at the International Conference, Financial and monetary policies in European Union. He, K., Xie, C., Chen, S. & Lai, K. K. (2010). Estimating VaR in crude oil market: A novel multi-scale non-linear ensemble approach incorporating wavelet analysis and neural network. Neurocomputing, 72 (16-18): 3428-3438 Hendricks, D. (1996). Evaluation of Value at Risk models using Historical Data. FRBNY Economic Policy Review. 2 (1): 39-70. In, F. & Kim, S. (2007). A note on the relationship between Fama-French risk factors and innovations of ICAPM state variables. Finance Research Letters. 4 (3): 165-171. In, F., Kim, S., Marisetty, V. & Faff, R. (2008). Analysing the performance of managed funds using the wavelet multiscaling method. Review of Quantitative Finance and Accounting. 31(1): 55-70. Jensen, G.R., Johnson, R.R. and Mercer, J.M. (2002). Tactical asset allocation and commodity futures. Journal of Portfolio Management, 28(4): 100-111. Karunaratne, N.D. & Bhar, R. (2011). Regime-shifts and post-float inflation dynamics of Australia. Economic Modeling, 28 (405): 1941–1949. King, R.G. & Levine, R. (1993). Financial Intermediation and Economic Development, financial Intermediation in the Construction of Europe, Eds: Colin Mayer and Xavier vives. London: Center for economic Policy, PP: 89-153. Kupiec, P.H. (1995). Techniques for verifying the accuracy of risk measurement models. The Journal of Derivatives, 3 (2): 73–84. Lai, K.K., Kaijian, H., Chi, X. & Chen, S. (2006). Market risk measurement for crude oil: a wavelet based VaR approach. IJCNN. pp. 2129-2136. DOI: 10.1109/IJCNN.2006.246984. Lai, K.K., Kaijian, H., Xie, C. & Chen, S. (2006). Market risk for nonferrous metals: a wavelet based VaR approach, ISDA, 1: 1179-1184. Levine, R. & Zervos, S. (1996). Stock Mankats, Danks and Economic Growth, Working paper. World Bank Policy Research. Available at SSRN: http://ssrn.com/abstract=60141. Liu, Y. (2005). Value-at-risk model combination using artificial neural networks. Working Paper. Emory University. Available at: http://citeseerx.ist.psu. edu/viewdoc/download?doi=10.1.1.113.8523&rep=rep1&type=pdf. Manganeli, S. & Engle, R. T. (2001). Value at Risk models in finance. Working Papers. European Central Bank. Nason, G.P. & Von Sachs, R. (1999). Wavelets in time series analysis. Philosophical Transactions of the Royal Society of London A357, 2511–2526. Available at: http://www.maths.bris.ac.uk/~magpn/Research/papers/roysoc.pdf. Norouzzadeh, P. (2006). Performance of VaR measure In the Tehran stock exchange. Futures conference, technology and development prospects. Tehran - Amirkabir University of Technology. Novrang, A.R., Hezaveh, A. & Ghorbani Salanghooch, M. (2006). Value at risk is a prerequisite for value engineering In the multi-project companies. Fourth National Conference on Value Engineering 2008-08-19 - 2008-08-20, Tehran, Iran (in Persian) Ozun, A. & Cifter, A. (2007). Aided-computer evaluation of nonlinear combination of financial forecast with genetic algorithm. International Review on Computers and Software, 2 (3): 276–284. Pagan, A.A. & Schwert, G.W. (1990). Alternative models for conditional stock volatility. Journal of Econometrics, 45 (1-2): 267-290. Patrick, H. (1966). Financial Development and Economic growth in underdeveloped countries. Economic development and cultural change, 12 (2):174-189. Peykarjoo, K., Shahriar, B. & Noorallahy, N. (2009). Corporate Finance Risk and financial institutions Measurement using VaR methodology. Economic Bulletin, 9 (5): 195-221. (in Persian) Pojarliev, M. & Polasek, M. (2000). Volatility forecasts and Value at Risk Evaluation for the MSCI North America Index. From: http://www. goliramundi.org. Ramsey, J. (2002). Wavelets in economics and finance: past and future. Studies in Nonlinear Dynamics and Econometrics, 6 (3): 1558-3708. Ramsey, J.B. (1999). The contribution of wavelets to the analysis of economic and financial data. Philosophical Transactions of the Royal Society of London Series A-Mathematical Physical and Engineering Sciences, 357 (1760): 2593–2606. Rostami, M.R. & Haghighi, F. (2013). Comparing performance Multivariate GARCH model of risk of the portfolio. Journal of Financial Research, 15 (2): 215-228. (in Persian) Saeedi, A. & Afkhami, A. (2012). Examine the relationship between risk, return and liquidity with free float shares on the Tehran Stock Exchange. Journal of Financial Research, 14 (2): 65-80. (in Persian) Saidi, P. & Amiri, A. (2008). Examine the relationship between macroeconomic variables with Tehran Stock Exchange Overall Index. Journal of Economic Modeling, 2 (2): 111-130. (in Persian) Show, E.S. (1973). Financial Deepening in Economic Development. New York: oxford university press. So, M. K. P. & Yu, P. L.H. (2005). Empirical analysis of GARCH models in Value at Risk estimation. International Financial Markets.Institutions and Money, 16 (2): 180-197. Tan, Z., Zhang, J. Wang, J. & Xu, J. (2010). Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models, Applied Energy, 87 3606-3610. Yaghoobnezhad, A., Saidi, A. & Rozeei, M. (2010). Estimating the market risk premium with Market Leverage in the Tehran Stock Exchange. 11, 11 (28): 105-120. (in Persian) Yiu, K.F.C. (2003). Optimal portfolios under a value-at-risk constraint. Journal of Economic Dynamics and Control. 28 (7): 1317-1334. Young, H.K. & Ingall, L. (2007). Exploring Monte Carlo Simulation Applications for Project Management. Risk Management, 9 (1): 44-57.
FRJ University of Tehran تحقیقات مالی 1024-8153 University of Tehran 51 10.22059/jfr.2015.50707 09. سیاست‌های تقسیم سود 12. مدیریت مالی کوتاه مدت 14. ارزیابی عملکرد مالی شرک‍ت‌ها؛ عوامل موثر بر عملکرد مالی شرکت‌ها انحراف مقطعی در عدم تقارن زمانی جریان‌های نقد عملیاتی و تأثیر آن بر محافظه‌کاری شرطی انحراف مقطعی در عدم تقارن زمانی جریان‌های نقد عملیاتی و تأثیر آن بر محافظه‌کاری شرطی سجادی سید حسین استاد حسابداری، دانشکدۀ اقتصاد و علوم اجتماعی، دانشگاه شهید چمران، اهواز، ایران رشیدی باغی محسن دانشجوی دکتری حسابداری، دانشکدۀ اقتصاد و علوم اجتماعی، دانشگاه شهید چمران، اهواز، ایران نیک کار جواد دانشجوی دکتری حسابداری، دانشکدۀ اقتصاد و علوم اجتماعی، دانشگاه شهید چمران، اهواز، ایران 21 03 2015 17 1 83 102 03 05 2014 11 11 2014 Copyright © 2015, University of Tehran. 2015 https://jfr.ut.ac.ir/article_50707.html

 هدف از این پژوهش، بررسی انحراف مقطعی در عدم تقارن زمانی جریان نقد و تأثیر آن بر محافظه­کاری شرطی است. در این پژوهش با استفاده از داده‌های 120 شرکت پذیرفته‎شده در بورس اوراق بهادار تهران در سال‌های 1384 تا 1390، به بررسی این موضوع پرداخته شد. الگوی رگرسیون پژوهش با استفاده از روش داده‌های تابلویی با رویکرد اثرهای ثابت، بررسی و آزمون شد. متداول­ترین معیار محافظه­کاری شرطی در حسابداری، عدم تقارن زمانی در شناسایی سودها و زیان­ها است. این معیار، ترکیبی از عدم تقارن زمانی اقلام تعهدی و جریان­های نقد عملیاتی است. با توجه به اینکه عدم تقارن جریان­های نقد عملیاتی به دامنۀ شناسایی سودها و زیان­ها تعلق ندارد، موجب انحراف در آزمون محافظه­کاری می‎شود. در این پژوهش با توجه به اخبار بد و خوب، نشان داده شد عدم تقارن جریان­های نقد عملیاتی به‎طور مقطعی با متغیرهای چرخۀ عمر شرکت (اندازه، عمر، رشد و مخارج سرمایه­ای) تفاوت دارد. نتایج نشان می­دهد در مراحل اولیۀ چرخۀ عمر شرکت، محافظه­کاری بیشتر است.

 جریان نقد عملیاتی چرخۀ عمر شرکت عدم تقارن زمانی محافظه کاری Ahmed, A. & Duellman, S. (2007). Accounting conservatism and board of director characteristics: An empirical analysis. Journal of Accounting and Economics, 43 (2-3): 411-437. Anthony, J. & Ramesh, K. (1992). Association between accounting performance measures and stock prices: A test of the life cycle hypothesis. Journal of Accounting and Economics, 15(2-3): 203–227. Ball, R., Kothari, S. & Nikolaev, P. (2009). Econometrics of the Basu asymmetric timeliness coefficient and accounting conservatism. Working paper, University of Chicago and MIT. Ball, R., Kothari, S. & Robin, A. (2000). The effect of international institutional factors on properties of accounting earnings. Journal of Accounting and Economics, 29 (1): 1-51. Ball, R., Kothari, S., & Nikolaev, P. (2012). On Estimating Conditional Conservatism. Working paper, University of Chicago and MIT. Basu, S. (1997). The Conservatism Principle and The Asymmetric Timeliness of Earnings. The Journal of Accounting and Economics, 24 (1): 3- 37. Bushman, R., Smith, Z. & Zhang, F. (2011). Investment Cash Flow Sensitivities Really Reflect Related Investment Decisions. Working paper, University of North Carolina at Chapel Hill, University of Chicago and Yale School of Management. Chandra U., Wasley, C. & Waymire, G. (2004). Income conservatism in the U.S. technology sector. Working Paper, University of Rochester. Chowdhury, S. & Watts, R.L. (2007). Asymmetric Timelines of Earnings, Market -to -Book and Conservatism in Financial Reporting. The Journal of Accounting and Economics, 44 (1-2): 2-31. Collins, D.W., Hribar, P. & Xiaoli, T. (2012). Cross Sectional Variation in Cash Flow Asymmetric Timeliness and Its Effect on the Earnings-Based Measure of Conditional Conservatism. Electronic copy available at: http://ssrn. com/abstract=2120677. Dechow, P., Ge, W. & Schrand, C. (2010). Understanding earnings quality: A review of the proxies, their determinants and their consequences. Journal of Accounting and Economics, 50 (2-3): 344- 401. Dickinson, V. (2011). Cash flow patterns as a proxy for life cycle. The Accounting Review, 86 (6): 1969-1994. Dietrich, D., Muller, K. & Riedl, E. (2007). Asymmetric timeliness tests of accounting conservatism. Review of Accounting Studies, 12(1): 95-124. Garcia Lara, J.M., Garcia Osma, B. & Penalva, F. (2010). Accounting Conservatism and Firm Investment Efficiency. Available at SSRN: http://ssrn.com/ abstract=1383642. Garcia Lara, J.M., Garcia Osma, B. & Penalva, F. (2011). Conditional conservatism and cost of capital, Review of Accounting Studies 16 (2): 247-271. Givoly, D., Hayn, C. & Natarajan, A. (2007). Measuring reporting conservatism. The Accounting Review, 82(1): 65-106. Hribar, P. & Collins, D.W. (2002). Errors in estimating accruals: implications for empirical research. Journal of Accounting Research, 40(1): 105–134. Keller, G. & Warrack, B. (2003). Statistics for management and economics. 6th ed. Pacific Grove, CA: Duxbury Press. Khodamipor, A., Deldar, M. & Chopani, M. (2012). Effect of information asymmetry and the life cycle of the future returns of the company on shares of companies listed in Tehran Stock Exchange. Experimentalstudy offinancialaccounting, 11 (38): 143-147. Kordestani, Gh. & Langrody, H. (2008). Conservatism in financial reporting: a review of the relationship between time asymmetry profit and MTB as Two measure of conservatism. Review of Accounting and Auditing, 15 (52): 89-106. Lafond, R. & R. Watts. (2008). The information role of conservatism. The Accounting Review, 83 (2): 447-478. Lafond, R. & Roychowdhury, S. (2008). Managerial ownership and accounting conservatism. Journal of Accounting Research, 46 (1): 101-135. Lundholm, R. & Sloan, R. (2007). Equity valuation and analysis, 2nd Edition, McGraw-Hill, Irwin. Mayar, S. (1995). The Information Content of Financial cash flow of listed companies in Tehran Stock Exchange. FinancialResearch, 3 (9- 10): 84-69. McNichols, M. & Wilson, G. P. (1988). Evidence of Earnings Management from the Provision for Bad Debts. Journal of Accounting Research, Supplement, 26: 1–31. Pae, J., Thornton, D. & Walker, M. (2005). The Link between Earnings Conservatism and the Price to Book Ratio. Contemporary Accounting Research, 22 (3): 693–717. Patatoukas, P. N. & Thomas, J. K. (2011). More evidence of bias in the differential timeliness measure of conditional conservatism. The Accounting Review, 86 (5): 1765-1793. Ryan, S. (2006). Identifying Conditional Conservatism. European Accounting Review, 15(4): 511-525. Sloan, R. (1996). Do stock prices fully reflect information in accruals and cash flows about future earnings? The Accounting Review, 71(3): 289-315. Steele, L. (2011). On the Asymmetric Timeliness of Operating Cash Flows. Working paper. University of Connecticut. Vaus, D. (2002). Analyzing social science data. 1st Ed. London: SAGE Publications Ltd. Wang, D. (2006). Founding family ownership and earnings quality. Journal of Accounting Research, 44 (3): 619-656. Watts, R.L. (2003). Conservatism in accounting part II: Evidence and research opportunities. Accounting Horizons, 17 (4): 287-301. Wilson, G.P. (1986). The relative information content of accruals and cash flows: combined evidence at the earnings announcement and annual report release date. Journal of Accounting Research, 24: 165-200.  _||_ Ahmed, A. & Duellman, S. (2007). Accounting conservatism and board of director characteristics: An empirical analysis. Journal of Accounting and Economics, 43 (2-3): 411-437. Anthony, J. & Ramesh, K. (1992). Association between accounting performance measures and stock prices: A test of the life cycle hypothesis. Journal of Accounting and Economics, 15(2-3): 203–227. Ball, R., Kothari, S. & Nikolaev, P. (2009). Econometrics of the Basu asymmetric timeliness coefficient and accounting conservatism. Working paper, University of Chicago and MIT. Ball, R., Kothari, S. & Robin, A. (2000). The effect of international institutional factors on properties of accounting earnings. Journal of Accounting and Economics, 29 (1): 1-51. Ball, R., Kothari, S., & Nikolaev, P. (2012). On Estimating Conditional Conservatism. Working paper, University of Chicago and MIT. Basu, S. (1997). The Conservatism Principle and The Asymmetric Timeliness of Earnings. The Journal of Accounting and Economics, 24 (1): 3- 37. Bushman, R., Smith, Z. & Zhang, F. (2011). Investment Cash Flow Sensitivities Really Reflect Related Investment Decisions. Working paper, University of North Carolina at Chapel Hill, University of Chicago and Yale School of Management. Chandra U., Wasley, C. & Waymire, G. (2004). Income conservatism in the U.S. technology sector. Working Paper, University of Rochester. Chowdhury, S. & Watts, R.L. (2007). Asymmetric Timelines of Earnings, Market -to -Book and Conservatism in Financial Reporting. The Journal of Accounting and Economics, 44 (1-2): 2-31. Collins, D.W., Hribar, P. & Xiaoli, T. (2012). Cross Sectional Variation in Cash Flow Asymmetric Timeliness and Its Effect on the Earnings-Based Measure of Conditional Conservatism. Electronic copy available at: http://ssrn. com/abstract=2120677. Dechow, P., Ge, W. & Schrand, C. (2010). Understanding earnings quality: A review of the proxies, their determinants and their consequences. Journal of Accounting and Economics, 50 (2-3): 344- 401. Dickinson, V. (2011). Cash flow patterns as a proxy for life cycle. The Accounting Review, 86 (6): 1969-1994. Dietrich, D., Muller, K. & Riedl, E. (2007). Asymmetric timeliness tests of accounting conservatism. Review of Accounting Studies, 12(1): 95-124. Garcia Lara, J.M., Garcia Osma, B. & Penalva, F. (2010). Accounting Conservatism and Firm Investment Efficiency. Available at SSRN: http://ssrn.com/ abstract=1383642. Garcia Lara, J.M., Garcia Osma, B. & Penalva, F. (2011). Conditional conservatism and cost of capital, Review of Accounting Studies 16 (2): 247-271. Givoly, D., Hayn, C. & Natarajan, A. (2007). Measuring reporting conservatism. The Accounting Review, 82(1): 65-106. Hribar, P. & Collins, D.W. (2002). Errors in estimating accruals: implications for empirical research. Journal of Accounting Research, 40(1): 105–134. Keller, G. & Warrack, B. (2003). Statistics for management and economics. 6th ed. Pacific Grove, CA: Duxbury Press. Khodamipor, A., Deldar, M. & Chopani, M. (2012). Effect of information asymmetry and the life cycle of the future returns of the company on shares of companies listed in Tehran Stock Exchange. Experimentalstudy offinancialaccounting, 11 (38): 143-147. Kordestani, Gh. & Langrody, H. (2008). Conservatism in financial reporting: a review of the relationship between time asymmetry profit and MTB as Two measure of conservatism. Review of Accounting and Auditing, 15 (52): 89-106. Lafond, R. & R. Watts. (2008). The information role of conservatism. The Accounting Review, 83 (2): 447-478. Lafond, R. & Roychowdhury, S. (2008). Managerial ownership and accounting conservatism. Journal of Accounting Research, 46 (1): 101-135. Lundholm, R. & Sloan, R. (2007). Equity valuation and analysis, 2nd Edition, McGraw-Hill, Irwin. Mayar, S. (1995). The Information Content of Financial cash flow of listed companies in Tehran Stock Exchange. FinancialResearch, 3 (9- 10): 84-69. McNichols, M. & Wilson, G. P. (1988). Evidence of Earnings Management from the Provision for Bad Debts. Journal of Accounting Research, Supplement, 26: 1–31. Pae, J., Thornton, D. & Walker, M. (2005). The Link between Earnings Conservatism and the Price to Book Ratio. Contemporary Accounting Research, 22 (3): 693–717. Patatoukas, P. N. & Thomas, J. K. (2011). More evidence of bias in the differential timeliness measure of conditional conservatism. The Accounting Review, 86 (5): 1765-1793. Ryan, S. (2006). Identifying Conditional Conservatism. European Accounting Review, 15(4): 511-525. Sloan, R. (1996). Do stock prices fully reflect information in accruals and cash flows about future earnings? The Accounting Review, 71(3): 289-315. Steele, L. (2011). On the Asymmetric Timeliness of Operating Cash Flows. Working paper. University of Connecticut. Vaus, D. (2002). Analyzing social science data. 1st Ed. London: SAGE Publications Ltd. Wang, D. (2006). Founding family ownership and earnings quality. Journal of Accounting Research, 44 (3): 619-656. Watts, R.L. (2003). Conservatism in accounting part II: Evidence and research opportunities. Accounting Horizons, 17 (4): 287-301. Wilson, G.P. (1986). The relative information content of accruals and cash flows: combined evidence at the earnings announcement and annual report release date. Journal of Accounting Research, 24: 165-200.
FRJ University of Tehran تحقیقات مالی 1024-8153 University of Tehran 51 10.22059/jfr.2015.52759 13. مالی رفتاری بررسی واکنش سرمایه‌گذاران نسبت به رویدادهای ناگهانی در بورس اوراق بهادار تهران بررسی واکنش سرمایه‌گذاران نسبت به رویدادهای ناگهانی در بورس اوراق بهادار تهران سعیدا اردکانی سعید دانشیار مدیریت بازرگانی، دانشگاه یزد، ایران بهلکه آی ناز دانشجوی کارشناسی ارشد، مدیریت بازرگانی ـ گرایش مالی، دانشگاه یزد، ایران میرزاد نگار دانشجوی کارشناسی ارشد، مدیریت بازرگانی ـ گرایش مالی، دانشگاه یزد، ایران توسلی طاهره السادات دانشجوی کارشناسی ارشد، مدیریت بازرگانی ـ گرایش مالی، دانشگاه یزد، ایران 21 03 2015 17 1 103 122 04 10 2013 14 02 2015 Copyright © 2015, University of Tehran. 2015 https://jfr.ut.ac.ir/article_52759.html

 یکی از عوامل مهم در تصمیم‎های سرمایه‎گذاران، رویدادهای ناگهانی اقتصادی و سیاسی است که در زمان خریدوفروش سهام بر تصمیم‎گیری تأثیر می‎گذارد و قیمت سهام را در جهت مطلوب یا نامطلوب تغییر می‎دهد. هدف پژوهش حاضر، بررسی واکنش سرمایه­گذاران نسبت به رویدادهای ناگهانی در بورس اوراق بهادار تهران است. برای دستیابی به این هدف، به‎مثابۀ نمونه­ای از رفتار سرمایه­گذاران، بازده روزانه از سال­های 1386- 1391 بر اساس فرضیۀ اطلاعات مبهم بررسی شده است. یافته­ها نشان می‎دهد با وقوع رویدادهای ناگهانی، نوسان‎های بازده افزایش می­یابد. همچنین بر اساس نتایج پژوهش، دربارۀ اخبار مطلوب واکنش سرمایه‎گذاران منطبق بر پیش­بینی فرضیۀ اطلاعات مبهم است؛ به این معنا که بازده‌های غیرعادی سهام طی دورۀ بعد از ورود اطلاعات، مطلوب مثبت است، ولی دربارۀ اخبار نامطلوب این فرضیه صدق نمی­کند. به بیانی، رفتار بعد از اخبار خوب پیرو فرضیۀ اطلاعات مبهم است، اما بعد از اخبار بد از این فرضیه پیروی نمی‎کند. همچنین برای هردو حالت رویداد مطلوب و نامطلوب، تعدیل قیمت سهام رو به پایین بوده است.

 بورس اوراق بهادار رویدادهای غیرمنتظره فرضیۀ اطلاعات مبهم واکنش سرمایه‌گذاران Abde Albaghi, A. M. & Badri, A. (2010). An introduction on financial econometrics. Tehran: Nas Publications. (in Persian) Ahmed Heshmat, N. (2012). Non-professional investors' behavior: an empirical study of female Saudi investors. International Journal of Commerce and Management, 22 (1): 75-90. Ajayi, R. A. & Mehdian, S. (1994). Rational Investors'reaction to uncertainty: evidence from the world's major markets. Journal of Business Finance & Accounting, 21 (4): 533-545. Al-Tamimi, H. (2006). Factors influencing individual investor behavior: an empirical study of the UAE financial markets. The Business Review, 5 (2): 225-233. Badri, A. & Asilzadeh, M. (2011). Cross-reactive and amplitude Prices: Evidence from Tehran Stock Exchange. Accounting and auditing research, 9 (3): 56-73. (in Persian) Brown, K. D., Harlow, W. V. & Tinic, S. M. (1988). Risk Aversion, Uncertain Information, and Market Efficiency. Journal of Financial Economics, 22 (2): 355−385. Brown, K. D., Harlow, W. V. & Tinic, S. M. (1993). The Risk and Required Rate of Common Stock Following Major Innovations. Journal of Financial and Quantitative Analysis, 28 (1): 101−116. Daniel, K., Hirshleifer, D. & Subrahmanyam, A. (1998). Investor Psychology and Security Market Under-and Overreactions. Journal of Finance, 3 (6): 1839–1885. Debondt, W. F. & Thaler, R. H. (1985). Does The Stock Market Overreact? Journal of Finance, 40 (3): 793−805. Debondt, W. F. & Thaler, R. H. (1987). Further Evidence on Investor Overreaction And Stock Market Seasonality. Journal of Finance, 42 (3): 557−581. Flahpoor, S., & Abdullahi, Gh. (2011). Identifying and weighting bias of investor behavior in Tehran Stock Exchange market: AHP approach. Financial Research, 13 (31): 99-120. (in Persian) Jahankhany, A., Noferesti, M. & Qharaguzloo, F. (2009). Review of excessive confidence of investors and trading volume in the Tehran Stock Exchange. Prespective of Management, 30 (8): 105-123. (in Persian) Khajawi, SH. & Ghasemi, M. (2006). Hypothesis of effective market and Behavioural finance. Quarterly of Financial Research, 20(7): 49-69. (in Persian) Lashkari, M. & Mortazi, H. (2011). Theory of behavioral finance and its impact on the volume of investment in the Tehran Stock Exchange. Strategy magazine, 26: 48-63. (in Persian)  Maditinos, D. I. & Sevic, Z. (2007). Theriou, Nikolaos G.; Investors’ Behaviour in the AthensStock Exchange (ASE). Studies in Economics and Finance, 24 (1): 32-50. Muradoglu, G. & Harvey, N. (2012). Behavioural finance: the role of psychological factors in financial decisions. Review of Behavioral Finance, 4 (2): 68-80. Nazari, M. & Farzanegan, E. (2011). Irregnaritics of periods in returns of Stock Exchange of Tehran. (Nonparametric bootstrap sampling method). Financial Research, 13 (31): 147-167. (in Persian) Raai, R., & Flahpoor, S. (2004). Behavioral finance, a different approach in the financial area. Financial Research, 18(6): 77-106. (in Persian) Ritter, J.R. (2003). Behavioral Finance. Pacific-Basin Finance Journal, 4 (11): 429-437. Saadi, R., Gholipoor, A. & Gholipoor, F. (2010). Consideration of impact of investors’ personality and cognitive errors on their investment in the Tehran Stock Exchange. Financial Research, 12 (29): 41-58. (in Persian) Sedghi Khorasgani, H. (2005). The identification and characterization of the dominant styles Tehran stock market investors, University of Imam Sadiq, Tehran, MSc thesis. (in Persian) Shajari, H. & Imam, S. (2012). Identification and analysis on factors affecting the decision making investors in the Tehran Stock Exchange. Commercial Consideration, 53: 1-13. (in Persian) Talane, A., Mahmoodi, M. & Sharafi, K. (2013). Abnormal trading volume of content companies in Tehran Stock Exchange. Financial Research, 16 (1): 1-16. (in Persian) Talangi, A. (2004). Confrontation of modern financial theory and behavioral finance. Financial Research, 6 (1): 3-25. (in Persian) Venezia, I. & Sharpia, Z. (2007). On the Behavioral Differences between Professional and Amateur Investors after the Weekend. Journal of Banking & Finance, 31 (5): 1417-1426. Yousefi, R. & Shahrabadi, A. (2009). Evaluate and test the behavior of investors in the Stock Exchange. Management of development and evolution, 2: 57-64. (in Persian)_||_ Abde Albaghi, A. M. & Badri, A. (2010). An introduction on financial econometrics. Tehran: Nas Publications. (in Persian) Ahmed Heshmat, N. (2012). Non-professional investors' behavior: an empirical study of female Saudi investors. International Journal of Commerce and Management, 22 (1): 75-90. Ajayi, R. A. & Mehdian, S. (1994). Rational Investors'reaction to uncertainty: evidence from the world's major markets. Journal of Business Finance & Accounting, 21 (4): 533-545. Al-Tamimi, H. (2006). Factors influencing individual investor behavior: an empirical study of the UAE financial markets. The Business Review, 5 (2): 225-233. Badri, A. & Asilzadeh, M. (2011). Cross-reactive and amplitude Prices: Evidence from Tehran Stock Exchange. Accounting and auditing research, 9 (3): 56-73. (in Persian) Brown, K. D., Harlow, W. V. & Tinic, S. M. (1988). Risk Aversion, Uncertain Information, and Market Efficiency. Journal of Financial Economics, 22 (2): 355−385. Brown, K. D., Harlow, W. V. & Tinic, S. M. (1993). The Risk and Required Rate of Common Stock Following Major Innovations. Journal of Financial and Quantitative Analysis, 28 (1): 101−116. Daniel, K., Hirshleifer, D. & Subrahmanyam, A. (1998). Investor Psychology and Security Market Under-and Overreactions. Journal of Finance, 3 (6): 1839–1885. Debondt, W. F. & Thaler, R. H. (1985). Does The Stock Market Overreact? Journal of Finance, 40 (3): 793−805. Debondt, W. F. & Thaler, R. H. (1987). Further Evidence on Investor Overreaction And Stock Market Seasonality. Journal of Finance, 42 (3): 557−581. Flahpoor, S., & Abdullahi, Gh. (2011). Identifying and weighting bias of investor behavior in Tehran Stock Exchange market: AHP approach. Financial Research, 13 (31): 99-120. (in Persian) Jahankhany, A., Noferesti, M. & Qharaguzloo, F. (2009). Review of excessive confidence of investors and trading volume in the Tehran Stock Exchange. Prespective of Management, 30 (8): 105-123. (in Persian) Khajawi, SH. & Ghasemi, M. (2006). Hypothesis of effective market and Behavioural finance. Quarterly of Financial Research, 20(7): 49-69. (in Persian) Lashkari, M. & Mortazi, H. (2011). Theory of behavioral finance and its impact on the volume of investment in the Tehran Stock Exchange. Strategy magazine, 26: 48-63. (in Persian)  Maditinos, D. I. & Sevic, Z. (2007). Theriou, Nikolaos G.; Investors’ Behaviour in the AthensStock Exchange (ASE). Studies in Economics and Finance, 24 (1): 32-50. Muradoglu, G. & Harvey, N. (2012). Behavioural finance: the role of psychological factors in financial decisions. Review of Behavioral Finance, 4 (2): 68-80. Nazari, M. & Farzanegan, E. (2011). Irregnaritics of periods in returns of Stock Exchange of Tehran. (Nonparametric bootstrap sampling method). Financial Research, 13 (31): 147-167. (in Persian) Raai, R., & Flahpoor, S. (2004). Behavioral finance, a different approach in the financial area. Financial Research, 18(6): 77-106. (in Persian) Ritter, J.R. (2003). Behavioral Finance. Pacific-Basin Finance Journal, 4 (11): 429-437. Saadi, R., Gholipoor, A. & Gholipoor, F. (2010). Consideration of impact of investors’ personality and cognitive errors on their investment in the Tehran Stock Exchange. Financial Research, 12 (29): 41-58. (in Persian) Sedghi Khorasgani, H. (2005). The identification and characterization of the dominant styles Tehran stock market investors, University of Imam Sadiq, Tehran, MSc thesis. (in Persian) Shajari, H. & Imam, S. (2012). Identification and analysis on factors affecting the decision making investors in the Tehran Stock Exchange. Commercial Consideration, 53: 1-13. (in Persian) Talane, A., Mahmoodi, M. & Sharafi, K. (2013). Abnormal trading volume of content companies in Tehran Stock Exchange. Financial Research, 16 (1): 1-16. (in Persian) Talangi, A. (2004). Confrontation of modern financial theory and behavioral finance. Financial Research, 6 (1): 3-25. (in Persian) Venezia, I. & Sharpia, Z. (2007). On the Behavioral Differences between Professional and Amateur Investors after the Weekend. Journal of Banking & Finance, 31 (5): 1417-1426. Yousefi, R. & Shahrabadi, A. (2009). Evaluate and test the behavior of investors in the Stock Exchange. Management of development and evolution, 2: 57-64. (in Persian)
FRJ University of Tehran تحقیقات مالی 1024-8153 University of Tehran 51 10.22059/jfr.2015.50708 24. بانک‌ها؛ سایر موسسات سپرده‌پذیر؛ تامین مالی خرد؛ اوراق رهنی 36. تبدیل به اوراق بهادار کردن دارایی‌ها؛ اوراق بهادار به پشتوانه دارایی‌ها 38. ریسک اعتباری؛ رتبه‌بندی اعتباری؛ امتیاز‌بندی اعتباری 53. شبکه‌های عصبی؛ یادگیری ماشینی و موضوعات مرتبط؛ سایر مدل‌های داده‌کاوی 54. روش‌های پیش‌بینی؛ روش‌های شبیه‌سازی 56. تئوری بازی‌ها طراحی بهینۀ اوراق بهادارسازی در رابطه کارفرما-کارگزار براساس رویکرد استنباط بیزین از مخاطرۀ اخلاقی طراحی بهینۀ اوراق بهادارسازی در رابطه کارفرما-کارگزار براساس رویکرد استنباط بیزین از مخاطرۀ اخلاقی عباسیان عزت اله دانشیار اقتصاد، دانشکدۀ اقتصاد و علوم اجتماعی، دانشگاه بوعلی سینا، همدان، ایران ابراهیمی محسن دانشیار اقتصاد، دانشکدۀ اقتصاد و علوم اجتماعی، دانشگاه بوعلی سینا، همدان، ایران فرزانگان الهام دانشجوی دکتری اقتصاد، دانشکدۀ اقتصاد و علوم اجتماعی، دانشگاه بوعلی سینا، همدان، ایران 21 03 2015 17 1 123 140 02 05 2014 11 11 2014 Copyright © 2015, University of Tehran. 2015 https://jfr.ut.ac.ir/article_50708.html

در فرایند اوراق بهادارسازی، بانی با فروش وام به سرمایه­گذاران ریسک­پذیر، می­تواند ریسک وام­های رهنی را به آنها تخصیص دهد. در این صورت ممکن است بانی انگیزه­ای برای غربال‎ قرض­گیرندگان نداشته باشد، بنابراین مشکل مخاطرۀ اخلاقی به‎وجود می­آید. این نوشتار در قالب رابطۀ کارفرما ـ کارگزار، این نوع مسئلۀ عاملیت را بررسی می‎کند؛ بدین صورت که سرمایه­گذار برای کاهش عدم تقارن اطلاعات از برنامۀ جبران برای ایجاد انگیزه به بانی استفاده می‎کند و به استنباط ابعاد مختلف تلاش انجام‎گرفته، از قاعدۀ بیز کمک می‎گیرد و اعتقادهای پسین مشترک خود از مشاهدات وضعیت اعتباری ادغام وام­ها و ابعاد مختلف تلاش را در مسئلۀ طراحی قرارداد لحاظ می‎کند. نتایج نشان می‎دهد شکل قرارداد بهینه، تابعی از محتوای اطلاعاتی مشاهده‎های سرمایه­گذار و اطلاعات استنباط‌شده است و حاکی از آن است که استفاده از اطلاعات اضافی از فرصت­طلبی­های بانی جلوگیری می‎کند و بانی به احتمال بیشتر وظایف تعیین‎شده را هنگام اعطای وام به متقاضیان انجام می­دهد.

 استنباط بیزین ناپارامتریک اوراق بهادار با پشتوانۀ وام‌های رهنی طراحی بهینۀ برنامۀ جبران چندبعدی فرایند تصادفی بوفۀ هندی مخاطرۀ اخلاقی Barnard, C. I. (1938). The Economy of Incentives. Cambridge, MA: Harvard University Press. Bond, P. & Gomes, A. (2009). Multitask Principal-Agent Problems: Optimal Contracts, Fragility, and Effort Misallocation. Journal of Economic Theory,144(1): 175-211. DeMarzo, P. (2005). The Pooling and Tranching of Securities: a Model of Informed Intermediation. Review of Financial Studies, 18(1): 1-35. DeMarzo, P. & Fishman, M. (2007). Optimal Long-Term Financial Contracting. Review of Financial Studies,20 (6): 2079-2128. DeMarzo, P. & Sannikov, Y. (2006). Optimal Security Design and Dynamic Capital Structure in a Continuous – Time Agency Model. Journal of Finance, 61 (6): 2681-2724. Drucker, S.  & Puri, M. (2009). On Loan Sales, Loan Contracting, and Lending Relationships. Review of Financial Studies,22(7): 2835-2872. Duffie, D. & Rahi, R. (1995). Financial Market Innovation and Security Design: an Introduction. Journal of Economic Theory,65(1): 1-42. Gorton, G. B. & Pennacchi, G.G. (1995). Banks and Loan Sales: Marketing Nonmarketable Assets. Journal of Monetary Economics,35(3): 389-411. Griffiths, T. L. & Ghahramani, Z. (2006). Infinite Latent Features Models and the Indian Buffet Process. In Advances in Neural Information Processing Systems 18 (NIPS 2005). Available in: http://mlg.eng.cam.ac.uk/zoubin/ papers/ibp-nips05.pdf. Grossman, S. J. & Hart, O.D. (1983). An Analysis of the Principal–Agent Problem. Econometrica,51 (1):7–45. Harris, M. & Raviv, A. (1978). Some Results on Incentives Contracts with Application to Education and Employment, Health Insurance, and Law Enforcement. American Economic Review,68(1): 20-30. Hart, D. O. & Holmstrom, B. (1987). The Theory of Contracts. In: Bewley, T. Ed. Advances in Economic Theory. Cambridge: Cambridge University Press. Hartman-Glaser, B., Piskorski, T. & Tchistyi, A. (2011). Optimal Securitization with Moral Hazard. Journal of Financial Economics,104(1): 186-202. Holmstrom, B. (1979). Moral Hazard and Observability. The Bell Journal of Economics,10(1): 74–91. Keeney, R.L. (1973). Risk Independence and Multiattributed Utility Functions. Econometrica,41(1): 27-34. Laffont, J. J. & Martimort, D. (2002). The Theory of Incentives. Princeton: Princeton University Press. March, J. G. & Simon, H.A. (1958). Organization. New York: John Wiley & Sons. Mas-Colell, A., Whinston, M. & Green, J. (1995). Microeconomic Theory. Oxford: Oxford University Press. Mayer,C., Pence, K. & Sherlund, S.M. (2009). The Rise in Mortgage Defaults. Journal of Economic Perspectives,23(1): 27–50. Parlour, C. A. & Plantin, G. (2008). Loan Sales and Relationship Banking. The Journal of Finance,63 (3): 1291-1314. Pennacchi, G. G. (1988). Loan Sales and the Cost of Bank Capital. Journal of Finance,43(2): 375–396. Pouyanfar, A. & Safabakhsh, S. (2011). Collateralized Mortgage Obligations Optimization. Journal of Financial Research, 12(30), 1-22. (in Persian) Riddiough, T. J. (1997). Optimal Design and Governance of Asset-Backed Securities. Journal of Financial Intermediation,6 (2): 121-152. Shavell, S. (1979). Risk Sharing and Incentives in the Principal and Agent Relationship. The Bell Journal of Economics, 10(1): 53-73. Shin, H. S. (2009). Securitization and Financial Stability. The Economic Journal,119 (536): 309-332. Sufi, A. (2007). Information Asymmetry and Financing Arrangements: Evidence from Syndicated Loans. The Journal of Finance,62(2): 629–668. The, Y. W., Gorur, D. & Ghahramani, Z. (2007).Stick Breaking Construction for the Indian Buffet Process, Proceedings of the 11th Conference on Artificial Intelligence and Statistic (AISTATS-07). Available in:  http://mlg.eng.cam. ac.uk/zoubin/papers/TehGorGha07.pdf._||_ Barnard, C. I. (1938). The Economy of Incentives. Cambridge, MA: Harvard University Press. Bond, P. & Gomes, A. (2009). Multitask Principal-Agent Problems: Optimal Contracts, Fragility, and Effort Misallocation. Journal of Economic Theory,144(1): 175-211. DeMarzo, P. (2005). The Pooling and Tranching of Securities: a Model of Informed Intermediation. Review of Financial Studies, 18(1): 1-35. DeMarzo, P. & Fishman, M. (2007). Optimal Long-Term Financial Contracting. Review of Financial Studies,20 (6): 2079-2128. DeMarzo, P. & Sannikov, Y. (2006). Optimal Security Design and Dynamic Capital Structure in a Continuous – Time Agency Model. Journal of Finance, 61 (6): 2681-2724. Drucker, S.  & Puri, M. (2009). On Loan Sales, Loan Contracting, and Lending Relationships. Review of Financial Studies,22(7): 2835-2872. Duffie, D. & Rahi, R. (1995). Financial Market Innovation and Security Design: an Introduction. Journal of Economic Theory,65(1): 1-42. Gorton, G. B. & Pennacchi, G.G. (1995). Banks and Loan Sales: Marketing Nonmarketable Assets. Journal of Monetary Economics,35(3): 389-411. Griffiths, T. L. & Ghahramani, Z. (2006). Infinite Latent Features Models and the Indian Buffet Process. In Advances in Neural Information Processing Systems 18 (NIPS 2005). Available in: http://mlg.eng.cam.ac.uk/zoubin/ papers/ibp-nips05.pdf. Grossman, S. J. & Hart, O.D. (1983). An Analysis of the Principal–Agent Problem. Econometrica,51 (1):7–45. Harris, M. & Raviv, A. (1978). Some Results on Incentives Contracts with Application to Education and Employment, Health Insurance, and Law Enforcement. American Economic Review,68(1): 20-30. Hart, D. O. & Holmstrom, B. (1987). The Theory of Contracts. In: Bewley, T. Ed. Advances in Economic Theory. Cambridge: Cambridge University Press. Hartman-Glaser, B., Piskorski, T. & Tchistyi, A. (2011). Optimal Securitization with Moral Hazard. Journal of Financial Economics,104(1): 186-202. Holmstrom, B. (1979). Moral Hazard and Observability. The Bell Journal of Economics,10(1): 74–91. Keeney, R.L. (1973). Risk Independence and Multiattributed Utility Functions. Econometrica,41(1): 27-34. Laffont, J. J. & Martimort, D. (2002). The Theory of Incentives. Princeton: Princeton University Press. March, J. G. & Simon, H.A. (1958). Organization. New York: John Wiley & Sons. Mas-Colell, A., Whinston, M. & Green, J. (1995). Microeconomic Theory. Oxford: Oxford University Press. Mayer,C., Pence, K. & Sherlund, S.M. (2009). The Rise in Mortgage Defaults. Journal of Economic Perspectives,23(1): 27–50. Parlour, C. A. & Plantin, G. (2008). Loan Sales and Relationship Banking. The Journal of Finance,63 (3): 1291-1314. Pennacchi, G. G. (1988). Loan Sales and the Cost of Bank Capital. Journal of Finance,43(2): 375–396. Pouyanfar, A. & Safabakhsh, S. (2011). Collateralized Mortgage Obligations Optimization. Journal of Financial Research, 12(30), 1-22. (in Persian) Riddiough, T. J. (1997). Optimal Design and Governance of Asset-Backed Securities. Journal of Financial Intermediation,6 (2): 121-152. Shavell, S. (1979). Risk Sharing and Incentives in the Principal and Agent Relationship. The Bell Journal of Economics, 10(1): 53-73. Shin, H. S. (2009). Securitization and Financial Stability. The Economic Journal,119 (536): 309-332. Sufi, A. (2007). Information Asymmetry and Financing Arrangements: Evidence from Syndicated Loans. The Journal of Finance,62(2): 629–668. The, Y. W., Gorur, D. & Ghahramani, Z. (2007).Stick Breaking Construction for the Indian Buffet Process, Proceedings of the 11th Conference on Artificial Intelligence and Statistic (AISTATS-07). Available in:  http://mlg.eng.cam. ac.uk/zoubin/papers/TehGorGha07.pdf.
FRJ University of Tehran تحقیقات مالی 1024-8153 University of Tehran 51 10.22059/jfr.2015.52036 15. نتخاب پرتفوی؛ تصمیمات سرمایه‌گذاری؛ تصمیمات تخصیص دارایی بهینه سازی سبد سهام با استفاده از روش تبرید شبیه‌ سازی شده بهینه سازی سبد سهام با استفاده از روش تبرید شبیه‌ سازی شده قدوسی سعید کارشناس‎ارشد مدیریت مالی، دانشگاه تهران، تهران، ایران تهرانی رضا دانشیار مدیریت مالی، دانشکدۀ مدیریت دانشگاه تهران، تهران، ایران بشیری مهدی دانشیار مهندسی صنایع، دانشکدۀ فنی دانشگاه شاهد، تهران، ایران 21 03 2015 17 1 141 158 06 05 2014 01 03 2015 Copyright © 2015, University of Tehran. 2015 https://jfr.ut.ac.ir/article_52036.html

 مسئلۀ بهینه­سازی مارکویتز و تعیین مرز کارای سرمایه­گذاری، هنگامی­که وضعیت و محدودیت­های دنیای واقعی در نظر گرفته شود، به سادگی با استفاده از شیوه­های دقیق ریاضی، مانند برنامه­ریزی درجۀ دوم، حل نمی­شود. از سوی دیگر، اغلب مدیران ترجیح می­دهند به جای مدیریت سبد بسیار بزرگ، سبد کوچکی از دارایی­ها را اداره کنند. این مسئله را می‎توان به محدودیت­های کاردینال، یعنی محدودیت­های حداقل و حداکثر تعداد دارایی­های سبد تشبیه کرد. پژوهش پیش رو با بهره‎مندی از الگوریتم فرا­ابتکاری تبرید شبیه‎سازی‎شده، به حل مسئلۀ بهینه­سازی سبد با محدودیت­های کاردینال پرداخته است. بدین منظور با استفاده از اطلاعات سهام پنجاه شرکت فعال­تر در بورس اوراق بهادار تهران در فاصلۀ زمانی اول فروردین 1389 تا پایان فروردین 1391، مرز کارای سبدهای مختلف 10 تا 50 سهمی ترسیم شده است. نتایج پژوهش موفقیت الگوریتم تبرید شبیه­سازی‎شده را در حل مسئلۀ فوق نشان می‎دهد. همچنین با انتخاب درست سهام و تعیین وزن­های مناسب از آن، می­توان سبد­های کوچک‎تری که عملکرد مناسب­تری دارند، انتخاب کرد.

 بهینه سازی سبد تبرید شبیه سازی‎شده محدودیت های کاردینال مدل میانگین ـ واریانس مرز کارا Abdul Ali Zadeh Shahir, S. & Eshghi, K. (2003). Application of genetic algorithms to select assets in the stock exchange. Journal of Economic Research, (17): 175-192. (in Persian) Anagnostopoulos, K.P., Mamanis, G. (2010). A portfolio optimization model with three objectives and discrete variables. Computers & Operations Research, 37 (7): 1285-1297. Bashiri M. & Karimi, H. (2010). Application of heuristic and meta-heuristic algorithm for designing industrial systems. ­Tehran: Shahed University. (in Persian) Bertsimas, D., Shioda, R. (2009). Algorithm for cardinality-constrained quadratic optimization. Computational Optimization and Applications (43): 1–22. Cerny, V. (1985). A thermodynamically approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Application, (45): 41-45. Chang, T.J., Meade, N., Beasley, J.E., Sharaiha, Y.M., (2000). Heuristics for cardinality constrained portfolio optimisation. Computers & Operations Research, (27): 1271–1302. Chang, T.J., Yang, S.C., Chang, K.J. (2009). Portfolio optimization problems in different risk measures using genetic algorithm. Expert Systems with Applications, (36): 10529–10537. Crama, Y., Schyns, M. (2003). Simulated annealing for complex portfolio selection problems. European Journal of Operational Research, (150): 546-571. Deng, G.F., Lin, W.T. & Lo, Ch.Ch. (2012). Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization. Expert Systems with Applications, 39 (4): 4558-4566. Elahi, M., Yousefi, Zare Mehrjerdi, Y. (2014). Portfolio optimization with mean-variance approach using hunting search meta-heuristic algorithm. Financial Research, 16 (1): 37-56. (in Persian) Eslami Bidgoli, GH., Vafi Sani, J., Bajelan, M. (2009). Portfolio Optimization and Examination of the Effect of Diversification on Its Performance through Using Ant Colony Algorithm. Quarterly Journal of Securities Exchange, 2 (5): 57-75. (in Persian) Fernandez, A., Gomez, S. (2007). Portfolio selection using neural networks. Computers & Operations Research, (34): 1177–1191. Gulpinar, N., An, L.T.H., Moeini, M. (2010). Robust investment strategies with discrete asset choice constraints using DC programming. Optimization, (59): 45-62. Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, (220): 671-680. Maringer, D. (2005). Portfolio Management with Heuristic Optimization .Germany: Graw-Hill. Published by Springer. Markowitz, H. (1952). Portfolio selection. Journal of Finance, (7):  77-91. Markowitz, H.M. (1956). The optimization of a quadratic function subject to linear constraints. Naval Research Logistics Quarterly, (3): 111–133. Modares, SA. & Estakhri Nazanin, M. (2007). Selecting a portfolio from­ listed companies in Tehran Stock Exchange by using Optimized­ Genetic Algorithm. Journal of Development and Investment, 1(1): 71-92. (in Persian) Moral-Escudero, R., Ruiz-Torrubiano, R., Suarez, A. (2006). Selection of optimal investment portfolios with cardinality constraints. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation, 2382–2388. DOI: 10.1109/CEC.2006.1688603. Raei, R. & Alibeiki, H. (2010). Portfolio optimization using particle swarm optimization method. Financial Research, 12 (29): 21-40. (in Persian) Raei, R., Mohammadi, S., Alibeiki, H. (2011). Mean-Semivariance Portfolio Optimization Using Harmony Search Method. Management Research in Iran, 15 (3): 105-128. (in Persian) Shaw, D.X., Liu, S. & Kopman, L. (2008). Lagrangian relaxation procedure for cardinality-constrained portfolio optimization. Optimization Methods & Software, (23): 411-420. Soleimani, H., Golmakani, H.R., Salimi, M.H. (2009). Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm. Expert Systems with Applications, (36): 5058-5063. Taqavifard, M., Mansouri, T. & Khosh-Tinat, M. (2007). A Meta-heuristic Algorithm for Portfolio Selection Problem under Cardinality and Bounding Constraints. The Economic Research, 7 (4): 49-69. (in Persian) Vielma, J.P., Ahmed, S., Nemhauser, G.L. (2008). A lifted linear programming branchand-bound algorithm for mixed-integer conic quadratic programs. INFORMS Journal on Computing,  (20): 438-450. Woodside-Oriakhi, M., Lucas, C. & Beasley, J.E. (2011). Heuristic algorithms for the cardinality constrained efficient frontier. European Journal of Operational Research, 213 (3): 538-550. Zhu, H., Wang, Y., Wang, K., Chen, Y. (2011). Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem. Expert Systems with Applications 38 (8): 10161–10169._||_ Abdul Ali Zadeh Shahir, S. & Eshghi, K. (2003). Application of genetic algorithms to select assets in the stock exchange. Journal of Economic Research, (17): 175-192. (in Persian) Anagnostopoulos, K.P., Mamanis, G. (2010). A portfolio optimization model with three objectives and discrete variables. Computers & Operations Research, 37 (7): 1285-1297. Bashiri M. & Karimi, H. (2010). Application of heuristic and meta-heuristic algorithm for designing industrial systems. ­Tehran: Shahed University. (in Persian) Bertsimas, D., Shioda, R. (2009). Algorithm for cardinality-constrained quadratic optimization. Computational Optimization and Applications (43): 1–22. Cerny, V. (1985). A thermodynamically approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Application, (45): 41-45. Chang, T.J., Meade, N., Beasley, J.E., Sharaiha, Y.M., (2000). Heuristics for cardinality constrained portfolio optimisation. Computers & Operations Research, (27): 1271–1302. Chang, T.J., Yang, S.C., Chang, K.J. (2009). Portfolio optimization problems in different risk measures using genetic algorithm. Expert Systems with Applications, (36): 10529–10537. Crama, Y., Schyns, M. (2003). Simulated annealing for complex portfolio selection problems. European Journal of Operational Research, (150): 546-571. Deng, G.F., Lin, W.T. & Lo, Ch.Ch. (2012). Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization. Expert Systems with Applications, 39 (4): 4558-4566. Elahi, M., Yousefi, Zare Mehrjerdi, Y. (2014). Portfolio optimization with mean-variance approach using hunting search meta-heuristic algorithm. Financial Research, 16 (1): 37-56. (in Persian) Eslami Bidgoli, GH., Vafi Sani, J., Bajelan, M. (2009). Portfolio Optimization and Examination of the Effect of Diversification on Its Performance through Using Ant Colony Algorithm. Quarterly Journal of Securities Exchange, 2 (5): 57-75. (in Persian) Fernandez, A., Gomez, S. (2007). Portfolio selection using neural networks. Computers & Operations Research, (34): 1177–1191. Gulpinar, N., An, L.T.H., Moeini, M. (2010). Robust investment strategies with discrete asset choice constraints using DC programming. Optimization, (59): 45-62. Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, (220): 671-680. Maringer, D. (2005). Portfolio Management with Heuristic Optimization .Germany: Graw-Hill. Published by Springer. Markowitz, H. (1952). Portfolio selection. Journal of Finance, (7):  77-91. Markowitz, H.M. (1956). The optimization of a quadratic function subject to linear constraints. Naval Research Logistics Quarterly, (3): 111–133. Modares, SA. & Estakhri Nazanin, M. (2007). Selecting a portfolio from­ listed companies in Tehran Stock Exchange by using Optimized­ Genetic Algorithm. Journal of Development and Investment, 1(1): 71-92. (in Persian) Moral-Escudero, R., Ruiz-Torrubiano, R., Suarez, A. (2006). Selection of optimal investment portfolios with cardinality constraints. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation, 2382–2388. DOI: 10.1109/CEC.2006.1688603. Raei, R. & Alibeiki, H. (2010). Portfolio optimization using particle swarm optimization method. Financial Research, 12 (29): 21-40. (in Persian) Raei, R., Mohammadi, S., Alibeiki, H. (2011). Mean-Semivariance Portfolio Optimization Using Harmony Search Method. Management Research in Iran, 15 (3): 105-128. (in Persian) Shaw, D.X., Liu, S. & Kopman, L. (2008). Lagrangian relaxation procedure for cardinality-constrained portfolio optimization. Optimization Methods & Software, (23): 411-420. Soleimani, H., Golmakani, H.R., Salimi, M.H. (2009). Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm. Expert Systems with Applications, (36): 5058-5063. Taqavifard, M., Mansouri, T. & Khosh-Tinat, M. (2007). A Meta-heuristic Algorithm for Portfolio Selection Problem under Cardinality and Bounding Constraints. The Economic Research, 7 (4): 49-69. (in Persian) Vielma, J.P., Ahmed, S., Nemhauser, G.L. (2008). A lifted linear programming branchand-bound algorithm for mixed-integer conic quadratic programs. INFORMS Journal on Computing,  (20): 438-450. Woodside-Oriakhi, M., Lucas, C. & Beasley, J.E. (2011). Heuristic algorithms for the cardinality constrained efficient frontier. European Journal of Operational Research, 213 (3): 538-550. Zhu, H., Wang, Y., Wang, K., Chen, Y. (2011). Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem. Expert Systems with Applications 38 (8): 10161–10169.
FRJ University of Tehran تحقیقات مالی 1024-8153 University of Tehran 51 10.22059/jfr.2015.52861 18. پیش‌بینی و شبیه‌سازی قیمت و بازده پیش بینی سقوط بازار سهام با استفاده از شبکه های عصبی نگاشت خود سازمان ده پیش بینی سقوط بازار سهام با استفاده از شبکه های عصبی نگاشت خود سازمان ده محمد علی زاده آرش دکتری مدیریت مالی، دانشگاه تهران، تهران، ایران راعی رضا استاد گروه مدیریت مالی، دانشگاه تهران، تهران، ایران محمدی شاپور دانشیار گروه مدیریت مالی، دانشگاه تهران، تهران، ایران 21 03 2015 17 1 159 178 28 01 2014 15 06 2014 Copyright © 2015, University of Tehran. 2015 https://jfr.ut.ac.ir/article_52861.html

سقوط بازار پدیده­ای است که سبب از دست رفتن ثروت و دارایی سرمایه‎گذاران در بازۀ زمانی نسبتاً کوتاهی می­شود، از این رو تلاش برای پیش­بینی آن از اهمیت زیادی برای سرمایه­گذاران، سیاست‎گذاران، نهادهای مالی و دولت برخوردار است. بررسی اجمالی تئوری­ها و مدل‎های ارائه‎شدۀ پیش­بینی سقوط در بازار سهام نشان می­دهد میان پژوهشگران دربارۀ الگوهای مشاهده‎شدۀ متغیرها، مانند حجم معامله، بازده‎ها، نوسان‎پذیری، عوامل بنیادی، شاخص­های رفتاری و غیره در بازارهای سهام پیش از وقوع سقوط، اتفاق نظری وجود ندارد. یکی از روش‎های بسیار مناسب پیشنهادشده برای یافتن الگوهایی که در داده­های شبکه­های عصبی وجود دارد، نگاشت خودسازمان‎ده است که روشی ناپارامتریک و غیرخطی محسوب می‎شود. در این پژوهش با استفاده از شبکه­های عصبی نگاشت خوسازمان‎ده، روشی برای پیش‎بینی سقوط در بازار سهام ایران ارائه شده است. نتایج اجرای مدل و پیش‎بینی برون‎نمونه‎ای حاکی از این است که مدل عملکرد به‎نسبت قابل قبولی را در پیش‎بینی دوره­های پیش از سقوط در بازار سهام به‎دست آورده است.

 پیش بینی سقوط بازار سهام شبکه های عصبی نگاشت خودسازمان‎ده Barlevya, G. & Veronesib, P. (2003). Rational panics and stock market crashes. Journal of Economic Theory, 110(2): 234-263. Barunik, J. & Vosvrda, M. (2009). Can a Stochastic Cusp Catastrophe Model Explain Stock Market Crashes? Journal of Economic Dynamics & Control, 33(10): 1824-1836. Blanchard, O. J. & Watson, M. W. (1982). Bubbles, Rational Expectations and Financial Markets. NBER Working Paper Series, Working Paper No. 9115.  DOI: 10.3386/w0945. Bolgorian, M. & Raei, R. (2010). Convergence of Fundamentalists and Chartists' Expectations: An Alarm for Stock Market Crash. Physica A: Statistical Mechanics and its Applications, 389(18): 3822-3827. Bree, D.S. & Joseph, N. (2007). The mechanism underlying Log Periodic Power Law fits to financial crashes. Symposium on agent-based modeling, risk, and finance, Fribourg, 8-9 November 2007. Cajueiro, D.O., Tabakb, B.M. & Wernecka, F.K. (2009). Can we predict crashes? The case of the Brazilian stock market. Physica A: Statistical Mechanics and its Applications, 388 (8): 1603-1609. Cecchetti, S.C., Lam, P.S. & Mark, N.C. (1988). Mean Reversion in Equilibrium Asset Prices. NBER Working Paper Series. Working Paper No. 2762. Chen, J., Hong, H. & Stein, J. C. (2001). Forecasting Crashes: Trading Volume, Past Returns, and Conditional Skewness in Stock Prices. Journal of Financial Economics, 61(3): 345–381. Choudhry, T. (1996). Stock Market Volatility and the Crash of 1987: Evidence from Six Emerging Markets. Journal of International Money and Finance, 15(6): 969-981. Fama, E.F. & French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2): 247-273. Fischer, B. (1988). An Equilibrium Model of the Crash. In NBER Macroeconomics Annual 1988. Cambridge, MA: MIT Press, 269-275. Garber, P.M. (1992). Crashes. In: Newman, P. and al. (eds.): The New Palgrave Dictionary of Money and Finance. I., Macmillan Reference, London, 1992. pp. 511-513. Gençay, R. & Gradojevic, N. (2010). Crash of '87 - Was it expected? Aggregate Market Fears and Long-Range Dependence. Journal of Empirical Finance, 17(2): 270-282. Gennotte, G. & Leland, H. (1990). Market Liquidity, Hedging, and Crashes. The American Economic Review, 80(5): 999-1021. Giovanis, E. (2010). Application of logit model and self-organizing maps (SOMs) for the prediction of financial crisis periods in US economy. Journal of Financial Economic Policy, 2(2): 98 – 125. Grossman, S. J. (1988). An Analysis of the Implications for Stock and Futures Price Volatility of Program Trading and Dynamic Hedging Strategies. Journal of Business, 61(3): 275-298. Harmon, D., de Aguiar, M.A.M., Chinellato, D.D., Braha, D., Epstein, I.R. & Bar-Yam, Y. (2011). Predicting Economic Market Crises Using Measures of Collective Panic. Arxiv preprint arXiv: 11022620. available in: http://arxiv.org/pdf/1102.2620.pdf. Hong, H. & Stein, J. C. (1999). Differences of Opinion, Rational Arbitrage and Market Crashes. NBER Working Paper. DOI: 10.3386/w7376. Jacklin, C., Kleidon, A., Pfleiderer, P. (1992). Underestimation of Portfolio Insurance and the Crash of October 1987. Review of Financial Studies, 5(1): 35-63. Johansen, A., Ledoit, O., &Sornette, D. (2000). Crashes as Critical Points. International Journal of Theoretical and Applied Finance, 2(3): 219-255. Kleidon, A. W. (1995). Stock market crashes, in Finance, R. A. Jarrow, V. Maksimovic, and W. T. Ziemba, editors, Handbooks in Operations Research and Management Science 9, 465-495, Elsevier Science, Amsterdam and New York. Koh, S.K., Fong, W.M. & Chan, F. (2007). A Cardan’s Discriminate Approach to Predicting Currency Crashes. Journal of International Money and Finance, 26(1): 131-148. Kohonen, T. (2001). The Self-Organizing Maps. Heidelberg: Springer. Levy, M., Levy, H. & Solomon, S. (1994). A Microscopic Model of the StockMarket: Cycles, Booms, and Crashes. Economics Letters, 45(1): 103-111. Lux, T. (1995). Herd Behaviour, Bubbles and Crashes. The Economic Journal, 105(431): 881-896. Lux, T. (2008). Applications of Statistical Physics in Finance and Economics. Kiel Working Paper No. 1425. DOI: 10.1.1.163.6265. Madrigal, V. & Scheinkman, J.A. (1997). Price Crashes, Information Aggregation, and Market-Making. Journal of Economic Theory, 75(1): 16-63. Nadin, M. (2005). Anticipating Extreme Events the Need for Faster than Real Time Models. in Extreme Events in Nature and Society, Frontiers Collection. New York/Berlin: Springer Verlag, 21-45. Namazi, M. & Kiamehr, M. (2008). Predicting Daily Stock Returns of Companies listed in Tehran Stock Exchange Using Artificial Neural Networks. Journal of Financial Research, 9(3): 115-134. Raei, R., &Chavoshi, K. (2003). Prediction of stock return in Tehran Stock Market: Artificial neural network model and multi factor model. Journal of Financial Research, 5(1), 97-120. Raei, R. & Fallahpour, S. (2004). Prediction of financial distress of firms using artificial neural network. Journal of Financial Research, 6(1): 39-69. Sarlin, P. & Marghescu, D. (2010). Visual Predictions of Currency Crises using Self-Organizing Maps. IEEE International Conference on Data Mining Workshops.Dec 13, Sydney, NSW. Schluter, C. & Trede, M. (2008). Identifying Multiple Outliers in Heavy-Tailed Distributions, Journal of Empirical Finance, 15(4), 700–713. Schwert, G. W. (1989). Business Cycles, Financial Crises, and Stock Volatility. Carnegie-Rochester Conference Series on Public Policy, 31: 83-126. Shiller, R.J. (1989). Market Volatility. Cambridge, MA: MIT Press. Shirkavand, S., Mohammadi, S. & Dolati, N. (2009). An Investigation on the Presence of Mean Reversion in Stock Prices in Tehran Stock Exchange. Journal of Financial Research, 9(4): 41-56. Vandewalle, N., Boveroux, P., Minguet, A. & Ausloos, M. (1998). The crash of October 1987 seen as a phase transition: amplitude and universality. Physica A: Statistical Mechanics and its Applications, 255 (1-2): 201-210. Westerhoff, F. H. (2004). Greed, Fear and Stock Market Dynamics. Physica A: Statistical Mechanics and its Applications, 343(1): 635-642. Zeira, J. (1999). Informational Overshooting, Booms, and Crashes. Journal of Monetary Economics, 43 (1): 237-257._||_ Barlevya, G. & Veronesib, P. (2003). Rational panics and stock market crashes. Journal of Economic Theory, 110(2): 234-263. Barunik, J. & Vosvrda, M. (2009). Can a Stochastic Cusp Catastrophe Model Explain Stock Market Crashes? Journal of Economic Dynamics & Control, 33(10): 1824-1836. Blanchard, O. J. & Watson, M. W. (1982). Bubbles, Rational Expectations and Financial Markets. NBER Working Paper Series, Working Paper No. 9115.  DOI: 10.3386/w0945. Bolgorian, M. & Raei, R. (2010). Convergence of Fundamentalists and Chartists' Expectations: An Alarm for Stock Market Crash. Physica A: Statistical Mechanics and its Applications, 389(18): 3822-3827. Bree, D.S. & Joseph, N. (2007). The mechanism underlying Log Periodic Power Law fits to financial crashes. Symposium on agent-based modeling, risk, and finance, Fribourg, 8-9 November 2007. Cajueiro, D.O., Tabakb, B.M. & Wernecka, F.K. (2009). Can we predict crashes? The case of the Brazilian stock market. Physica A: Statistical Mechanics and its Applications, 388 (8): 1603-1609. Cecchetti, S.C., Lam, P.S. & Mark, N.C. (1988). Mean Reversion in Equilibrium Asset Prices. NBER Working Paper Series. Working Paper No. 2762. Chen, J., Hong, H. & Stein, J. C. (2001). Forecasting Crashes: Trading Volume, Past Returns, and Conditional Skewness in Stock Prices. Journal of Financial Economics, 61(3): 345–381. Choudhry, T. (1996). Stock Market Volatility and the Crash of 1987: Evidence from Six Emerging Markets. Journal of International Money and Finance, 15(6): 969-981. Fama, E.F. & French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2): 247-273. Fischer, B. (1988). An Equilibrium Model of the Crash. In NBER Macroeconomics Annual 1988. Cambridge, MA: MIT Press, 269-275. Garber, P.M. (1992). Crashes. In: Newman, P. and al. (eds.): The New Palgrave Dictionary of Money and Finance. I., Macmillan Reference, London, 1992. pp. 511-513. Gençay, R. & Gradojevic, N. (2010). Crash of '87 - Was it expected? Aggregate Market Fears and Long-Range Dependence. Journal of Empirical Finance, 17(2): 270-282. Gennotte, G. & Leland, H. (1990). Market Liquidity, Hedging, and Crashes. The American Economic Review, 80(5): 999-1021. Giovanis, E. (2010). Application of logit model and self-organizing maps (SOMs) for the prediction of financial crisis periods in US economy. Journal of Financial Economic Policy, 2(2): 98 – 125. Grossman, S. J. (1988). An Analysis of the Implications for Stock and Futures Price Volatility of Program Trading and Dynamic Hedging Strategies. Journal of Business, 61(3): 275-298. Harmon, D., de Aguiar, M.A.M., Chinellato, D.D., Braha, D., Epstein, I.R. & Bar-Yam, Y. (2011). Predicting Economic Market Crises Using Measures of Collective Panic. Arxiv preprint arXiv: 11022620. available in: http://arxiv.org/pdf/1102.2620.pdf. Hong, H. & Stein, J. C. (1999). Differences of Opinion, Rational Arbitrage and Market Crashes. NBER Working Paper. DOI: 10.3386/w7376. Jacklin, C., Kleidon, A., Pfleiderer, P. (1992). Underestimation of Portfolio Insurance and the Crash of October 1987. Review of Financial Studies, 5(1): 35-63. Johansen, A., Ledoit, O., &Sornette, D. (2000). Crashes as Critical Points. International Journal of Theoretical and Applied Finance, 2(3): 219-255. Kleidon, A. W. (1995). Stock market crashes, in Finance, R. A. Jarrow, V. Maksimovic, and W. T. Ziemba, editors, Handbooks in Operations Research and Management Science 9, 465-495, Elsevier Science, Amsterdam and New York. Koh, S.K., Fong, W.M. & Chan, F. (2007). A Cardan’s Discriminate Approach to Predicting Currency Crashes. Journal of International Money and Finance, 26(1): 131-148. Kohonen, T. (2001). The Self-Organizing Maps. Heidelberg: Springer. Levy, M., Levy, H. & Solomon, S. (1994). A Microscopic Model of the StockMarket: Cycles, Booms, and Crashes. Economics Letters, 45(1): 103-111. Lux, T. (1995). Herd Behaviour, Bubbles and Crashes. The Economic Journal, 105(431): 881-896. Lux, T. (2008). Applications of Statistical Physics in Finance and Economics. Kiel Working Paper No. 1425. DOI: 10.1.1.163.6265. Madrigal, V. & Scheinkman, J.A. (1997). Price Crashes, Information Aggregation, and Market-Making. Journal of Economic Theory, 75(1): 16-63. Nadin, M. (2005). Anticipating Extreme Events the Need for Faster than Real Time Models. in Extreme Events in Nature and Society, Frontiers Collection. New York/Berlin: Springer Verlag, 21-45. Namazi, M. & Kiamehr, M. (2008). Predicting Daily Stock Returns of Companies listed in Tehran Stock Exchange Using Artificial Neural Networks. Journal of Financial Research, 9(3): 115-134. Raei, R., &Chavoshi, K. (2003). Prediction of stock return in Tehran Stock Market: Artificial neural network model and multi factor model. Journal of Financial Research, 5(1), 97-120. Raei, R. & Fallahpour, S. (2004). Prediction of financial distress of firms using artificial neural network. Journal of Financial Research, 6(1): 39-69. Sarlin, P. & Marghescu, D. (2010). Visual Predictions of Currency Crises using Self-Organizing Maps. IEEE International Conference on Data Mining Workshops.Dec 13, Sydney, NSW. Schluter, C. & Trede, M. (2008). Identifying Multiple Outliers in Heavy-Tailed Distributions, Journal of Empirical Finance, 15(4), 700–713. Schwert, G. W. (1989). Business Cycles, Financial Crises, and Stock Volatility. Carnegie-Rochester Conference Series on Public Policy, 31: 83-126. Shiller, R.J. (1989). Market Volatility. Cambridge, MA: MIT Press. Shirkavand, S., Mohammadi, S. & Dolati, N. (2009). An Investigation on the Presence of Mean Reversion in Stock Prices in Tehran Stock Exchange. Journal of Financial Research, 9(4): 41-56. Vandewalle, N., Boveroux, P., Minguet, A. & Ausloos, M. (1998). The crash of October 1987 seen as a phase transition: amplitude and universality. Physica A: Statistical Mechanics and its Applications, 255 (1-2): 201-210. Westerhoff, F. H. (2004). Greed, Fear and Stock Market Dynamics. Physica A: Statistical Mechanics and its Applications, 343(1): 635-642. Zeira, J. (1999). Informational Overshooting, Booms, and Crashes. Journal of Monetary Economics, 43 (1): 237-257.
FRJ University of Tehran تحقیقات مالی 1024-8153 University of Tehran 51 10.22059/jfr.2015.52760 43. تامین مالی و سرمایه‌گذاری مستغلات بررسی اثرهای ساختار هیئت‎مدیره بر محدودیت در تأمین مالی شرکت های پذیرفته‎شدۀ بورس اوراق بهادار تهران بررسی اثرهای ساختار هیئت‎مدیره بر محدودیت در تأمین مالی شرکت های پذیرفته‎شدۀ بورس اوراق بهادار تهران مهدوی غلامحسین دانشیار حسابداری، دانشگاه شیراز، شیراز، ایران رضایی غلامرضا دانشجوی دکتری حسابداری، دانشگاه شیراز، شیراز، ایران 21 03 2015 17 1 179 198 05 05 2014 14 02 2015 Copyright © 2015, University of Tehran. 2015 https://jfr.ut.ac.ir/article_52760.html

هدف این مقاله بررسی اثرهای ساختار هیئت‎مدیره بر محدودیت در تأمین مالی شرکت­های پذیرفته‎شده در بورس اوراق بهادار تهران است. به همین منظور، از شاخص کپلن و زینگالس (KZ) بومی­شده برای شاخص محدودیت در تأمین مالی استفاده شده است. جامعۀ آماری این پژوهش متشکل از 56 شرکت پذیرفته‎شده در بورس اوراق بهادار تهران (392 سال ـ شرکت) طی دورۀ زمانی 1385 تا 1391 است. آزمون فرضیه­های پژوهش به‎کمک تحلیل آماری رگرسیون لجستیک چندمتغیره به اجرا درآمد. نتایج آزمون آماری فرضیه­های پژوهش، رابطۀ معناداری بین ساختار هیئت‎مدیره (استقلال، اندازه، مدرک تحصیلی، تغییر در اعضا و یکسانی مدیر عامل و رئیس هیئت‎مدیره) با محدودیت در تأمین مالی، گزارش نداد. همچنین، نتایج پژوهش در خصوص متغیرهای کنترلی نشان داد بین اندازۀ شرکت­ها و محدودیت در تأمین مالی آنها رابطۀ منفی معناداری وجود دارد؛ در حالی‎که بین عمر شرکت­ها و محدودیت در تأمین مالی آنها رابطۀ مثبت معناداری برقرار است.

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