ارائه الگوریتم قاعده محور برای شناسایی رژیم‌ها در بازارهای افتان‌وخیزان

نوع مقاله : علمی - پژوهشی

نویسنده

استادیار، گروه مدیریت مالی، دانشگاه پیام نور، تهران، ایران.

10.48308/jfmp.2024.104193

چکیده

پژوهش حاضر ضمن توسعه ادبیات مالی مرتبط با شناسایی رژیم‌های بازارها مالی، شیوه قاعده محور جدیدی را برای انتخاب نقاط تغییر در چرخه‌های کسب‌وکار پیشنهاد می‌کند که در آن ذهنیت‌گرایی را در فرآیند طبقه‌بندی رژیم‌های بازار حذف می‌کند. الگوریتم پیشنهادشده از یک رویکرد اکتشافی برخوردار بوده و هیچ شرطی برای تعیین دوره رژیم‌ها یا دامنه بازدهی آن‌ها اعمال نمی‌شود. همچنین برای مقایسه بین الگوریتم پیشنهادی و الگوریتم‌های پاگان، لیونه از آزمون بوت استرپ وایت در دارایی‌های مختلف شامل شاخص بورس اوراق بهادار تهران، فلزات مس و طلا و کالای نفت از نسبت شارپ به‌عنوان اندازه‌گیری عملکرد در داده‌های برون‌نمونه‌ای استفاده‌ شده است. نتایج حاصل نشان می‌دهد الگوریتم پیشنهادی در شناسایی رژیم‌های برون‌نمونه‌ای به‌خصوص در سری‌های زمانی که متفاوت از داده‌های شاخص بازار سرمایه باشد، عملکردی بهتر یا مساوی با سایر روش‌های شناسایی دارد. نتایج به‌دست‌آمده توفیق الگوریتم پیشنهادی را نسبت به سایر الگوریتم‌ها نشان می‌دهد. ساختار صرفه‌جو الگوریتم پیشنهادی با ارائه روشی مشخص از نوسان‌های بالقوه‌ای که در بهینه‌سازی پارامتر ایجاد می‌شود اجتناب کرده و در شناسایی رژیم‌های متفاوت در مجموعه‌های گوناگونی ازسری‌های زمانی بدون تغییر در پارامترها مفید و کاربردی است.

کلیدواژه‌ها

عنوان مقاله [English]

Designing a Rule-Based Algorithm to Identify Bull and Bear Markets Regimes

نویسنده [English]

  • Seyed Jalal Tabatabaei
Assistant Prof. Department of Financial Management, Payamenoor University, Tehran, Iran
چکیده [English]

To develop the financial literature related to the identification of financial market regimes, the present research proposes a new rule-based method for selecting turning points in business cycles, which eliminates subjectivity in the process of classifying market regimes. The proposed algorithm has a heuristic approach. No conditions are imposed to determine the duration of the regimes or the amplitude of their returns. Also, for the comparison between the proposed algorithm and the algorithms of Pagan, Lunde, White's bootstrap test has been used in various assets, including the Tehran Stock Exchange index, copper and gold metals, and oil commodities, and the Sharpe ratio has been used as a performance measure in out-of-sample data. The results show that the proposed algorithm has better or equal performance with other identification methods in identifying out-of-sample regimes, especially in time series that are different from capital market index data. The obtained results show the success of the proposed algorithm compared to other algorithms. The straightforward structure of the proposed algorithm avoids the potential fluctuations that occur in parameter optimization by providing a specific method and is useful and practical in identifying different regimes in various sets of time series without changing the parameters.

کلیدواژه‌ها [English]

  • Financial Market
  • Market Regimes
  • Financial Cycle
  • Bull Market
  • Bear Market
  • Rule-Based Algorithm

مراجع

  1. Albuquerque, R., Eichenbaum, M., Papanikolaou, D., & Rebelo, S. (2015). Long-run bulls and bears. Journal of Monetary Economics, 76, 21-36.
  2. Bohl, M. T., Siklos, P. L., & Werner, T. (2007). Do central banks react to the stock market? The case of the Bundesbank. Journal of Banking & Finance, 31(3), 719-733.
  3. Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of empirical finance, 11(1), 1-27.
  4. Bry, G., & Boschan, C. (1971). Front matter to" Cyclical Analysis of Time Series: Selected Procedures and Computer Programs". In Cyclical analysis of time series: Selected procedures and computer programs (pp. -13--12). NBEr.
  5. Candelon, B., Piplack, J., & Straetmans, S. (2008). On measuring synchronization of bulls and bears: The case of East Asia. Journal of Banking & Finance, 32(6), 1022-1035.
  6. Cashin, P., McDermott, C. J., & Scott, A. (2002). Booms and slumps in world commodity prices. Journal of development Economics, 69(1), 277-296.
  7. Chauvet, M., & Potter, S. (2001). Nonlinear risk. Macroeconomic Dynamics, 5(4), 621-646.
  8. Chen, S.-S. (2009). Predicting the bear stock market: Macroeconomic variables as leading indicators. Journal of Banking & Finance, 33(2), 211-223.
  9. Claessens, S., Kose, M. A., & Terrones, M. E. (2012). How do business and financial cycles interact? Journal of International economics, 87(1), 178-190.
  10. Cunado, J., Gil-Alana, L., & de Gracia, F. P. (2010). Mean reversion in stock market prices: New evidence based on bull and bear markets. Research in International Business and Finance, 24(2), 113-122.
  11. Diebold, F. X., & Inoue, A. (1999). Long memory and structural change. Available at SSRN 267789.
  12. Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
  13. Fabozzi, F. J., & Francis, J. C. (1977). Stability tests for alphas and betas over bull and bear market conditions. Journal of Finance, 32(4), 1093-1099.
  14. Gordon, S., & St-Amour, P. (2000). A preference regime model of bull and bear markets. American Economic Review, 90(4), 1019-1033.
  15. Gregg, P., Washbrook, E., Propper, C., & Burgess, S. (2005). The effects of a mother’s return to work decision on child development in the UK. Economic Journal, 115(501), F48-F80.
  16. Hamilton, J. D. (2011). Calling recessions in real time. International journal of forecasting, 27(4), 1006-1026.
  17. Hanna, A. J. (2018). A top-down approach to identifying bull and bear market states. International Review of Financial Analysis, 55, 93-110.
  18. Harding, D., & Pagan, A. (2002). Dissecting the cycle: a methodological investigation. Journal of Monetary Economics, 49(2), 365-381.
  19. Jansen, D. W., & Tsai, C.-L. (2010). Monetary policy and stock returns: Financing constraints and asymmetries in bull and bear markets. Journal of empirical finance, 17(5), 981-990.
  20. Kaminsky, G. L., & Schmukler, S. L. (2003). Short-run pain, long-run gain: the effects of financial liberalization. In: National Bureau of Economic Research Cambridge, Mass., USA.
  21. Kim, M. K., & Zumwalt, J. K. (1979). An analysis of risk in bull and bear markets. Journal of Financial and Quantitative analysis, 14(5), 1015-1025.
  22. Kole, E., & Van Dijk, D. (2017). How to identify and forecast bull and bear markets? Journal of Applied Econometrics, 32(1), 120-139.
  23. Lo, A. W. (2004). The adaptive markets hypothesis. Journal of Portfolio Management, 30(5), 15-29.
  24. Lunde, A., & Timmermann, A. (2004). Duration dependence in stock prices: An analysis of bull and bear markets. Journal of Business & Economic Statistics, 22(3), 253-273.
  25. Maheu, J. M., & McCurdy, T. H. (2000). Identifying bull and bear markets in stock returns. Journal of Business & Economic Statistics, 18(1), 100-112.
  26. Maheu, J. M., McCurdy, T. H., & Song, Y. (2012). Components of bull and bear markets: bull corrections and bear rallies. Journal of Business & Economic Statistics, 30(3), 391-403.
  27. Nguyen, L., Novák, V., & Mirshahi, S. (2020). Trend‐cycle Estimation Using Fuzzy Transform and Its Application for Identifying Bull and Bear Phases in Markets. Intelligent Systems in Accounting, Finance and Management, 27(3), 111-124.
  28. Ntantamis, C. (2009). A duration hidden markov model for the identification of regimes in stock market returns. Available at SSRN 1343726.
  29. Ntantamis, C., & Zhou, J. (2015). Bull and bear markets in commodity prices and commodity stocks: Is there a relation? Resources Policy, 43, 61-81.
  30. Nyberg, H. (2010). Dynamic probit models and financial variables in recession forecasting. Journal of Forecasting, 29(1‐2), 215-230.
  31. Pagan, A. R., & Schwert, G. W. (1990). Alternative models for conditional stock volatility. Journal of econometrics, 45(1-2), 267-290.
  32. Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18(1), 23-46.
  33. Politis, D. N., & Romano, J. P. (1994). The stationary bootstrap. Journal of the American Statistical association, 89(428), 1303-1313.
  34. Rigobon, R., & Sack, B. (2003). Measuring the reaction of monetary policy to the stock market. quarterly journal of Economics, 118(2), 639-669.
  35. Rydén, T., Teräsvirta, T., & Åsbrink, S. (1998). Stylized facts of daily return series and the hidden Markov model. Journal of Applied Econometrics, 13(3), 217-244.
  36. Satchell, S., & Timmermann, A. (1995). An assessment of the economic value of non‐linear foreign exchange rate forecasts. Journal of Forecasting, 14(6), 477-497.
  37. Shiller, R. J. (1995). Conversation, information, and herd behavior. American economic review, 85(2), 181-185.
  38. Veronesi, P. (1999). Stock market overreactions to bad news in good times: a rational expectations equilibrium model. Review of Financial Studies, 12(5), 975-1007.
  39. White, H. (2000). A reality check for data snooping. Econometrica, 68(5), 1097-1126.
  40. Woodward, G., & Marisetty, V. B. (2005). Introducing non-linear dynamics to the two-regime market model: Evidence. Quarterly Review of Economics and Finance, 45(4-5), 559-581.
  41. Wruck, K. H. (1989). Equity ownership concentration and firm value: Evidence from private equity financings. Journal of Financial economics, 23(1), 3-28.
  42. Wu, S.-J., & Lee, W.-M. (2015). Predicting severe simultaneous bear stock markets using macroeconomic variables as leading indicators. Finance Research Letters, 13, 196-204.
چشم انداز مدیریت مالی
دوره 13، شماره 43
1402
صفحه 51-78

فایل ها

هم رسانی

ارجاع به این مقاله

آمار