برآورد احتمال معاملات آگاهانه تعدیل شده؛ مطالعه موردی بخش مالی

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشیار اقتصاد، گروه اقتصاد نظری، دانشکده اقتصادی، دانشگاه علامه طباطبائی

چکیده

سطح عدم تقارن اطلاعات در بازارهای مالی تأثیر بسیاری بر تشکیل بازار، قیمت سهام و ریسک سرمایه‌‌گذاری دارد. علاوه بر این، طراحی سیاست‌های بهینه به ویژه اعمال محدودیت‌های معاملاتی توسط سیاستگذاران و تعیین استراتژی معاملاتی توسط معامله‌گران مستلزم آگاهی از میزان عدم تقارن اطلاعات است. در مقاله حاضر با استفاده از مدل احتمال معاملات آگاهانه (PIN) و احتمال معاملات آگاهانه تعدیل شده (AdjPIN) در چارچوب ریزساختار بازار مالی، میزان عدم تقارن اطلاعات در 69 شرکت فعال در حوزه واسطه‌گری مالی طی دوره 1396:1 تا 1402:3 برآورد می‌شود. طبق یافته‌ها، اولاً متوسط عدم تقارن اطلاعات طبق معیارهای PIN و AdjPIN به ترتیب 26 و 21 درصد بوده است. ثانیاً عدم تقارن اطلاعات در سال 1399 و به موازات با شکل‌گیری حباب در بازار سهام در تمامی زیربخش‌های مالی به شدت افزایش یافته است. ثالثاً بخش‌های بانک و سرمایه‌گذاری (با سهم 11 درصدی از ارزش بازار سهام)، عدم تقارن اطلاعات کمتری را در مقایسه با بخش‌های بیمه و لیزینگ (با سهم 5/0 درصدی از ارزش بازار سهام) تجربه‌ نموده‌اند. رابعاً احتمال معاملات آگاهانه در شرکت‌های بزرگ (نظیر بانک تجارت، بانک ملت، بیمه ما، سرچشمه، لیزینگ سایپا) کمتر از شرکت‌های کوچک (نظیر بانک دی، بیمه کارآفرین، سرمایه‌گذاری کوثر بهمن، لیزینگ پارسیان) است. این یافته‌ها بر اهمیت درک عدم تقارن اطلاعات برای سیاستگذاری مؤثر و تصمیمات سرمایه‌گذاری در بازارهای مالی تأکید می‌کنند.

کلیدواژه‌ها

عنوان مقاله [English]

Estimating the Adjusted Probability of Informed Trading Model; the Case Study of Financial Sector

نویسندگان [English]

  • Reza Taleblou
  • Parisa Mohajeri
Associate Professor in Economics, Department of Economics, University of Allemeh Tabataba’i University
چکیده [English]

The level of information asymmetry in financial markets significantly influences market structure, stock prices, and investment risk. In addition, optimal policy design, particularly the implementation of trading constraints by regulators and the determination of trading strategies by traders, requires an awareness of the extent of information asymmetry. In this paper, using two models, Probability of Informed Trading (PIN) and Adjusted Probability of Informed Trading (AdjPIN), within the framework of financial market microstructure, we estimate the level of information asymmetry for 69 active companies in the financial intermediation sector during the period from 1396:Q1 to 1402:Q3. According to our findings, first, the average information asymmetry, as measured by PIN and AdjPIN, stands at 26% and 21% respectively. Second, information asymmetry surged notably in 2019, coinciding with the stock market bubble across all financial sub-sectors. Third, the banking and investment sectors, comprising 11% of stock market value, exhibit lower information asymmetry compared to the insurance and leasing sectors, which collectively contribute only 0.5% to the stock market value. Forth, large companies such as Bank Tejarat, Bank Mellat, Bimah Ma, Sarcheshme, and Saipa Leasing demonstrate lower probability of informed tradings compared to smaller counterparts like Bank Day, Bimah Karafarin, Kausar Bahman Investment, and Parsian Leasing. These findings underscore the significance of understanding information asymmetry for effective policy-making and investment decisions in financial markets.

کلیدواژه‌ها [English]

  • Financial Market Microstructure
  • Probability of Informed Trading (PIN)
  • Adjusted Probability of Informed Trading (AdjPIN)
  • Financial Intermediation

مراجع

Akerlof, G. A. (1970). The Market for ‘Lemons’: Quality Uncertainty and the Market Mechanism. Quarterly Journal of Economics, 84 (3), 488-500.
Aslan, H., Easley, D., Hvidkjaer, S., & O’Hara, M. (2011). The Characteristics of Informed Trading: Implications for Asset Pricing. Journal of Empirical Finance, 18, 782-801.
Atilgan, Y. (2014). Volatility Spreads and Earnings Announcement Returns. Journal of Banking and Finance, 38, 205-215.
Berkman, H., Koch, P.D., Westerholm, P.J. (2014). Informed Trading through the Accounts of Children. Journal of Finance, 69(1), 363–404.
Bongaerts, D., Rösch, D. & Van Dijk, M.A. (2014). Cross-Sectional Identification of Informed Trading. SSRN Electronic Journal.
Brown, S., Hillegeist, S. A., & Lo, K. (2008). The Effect of Earnings Surprises on Information Asymmetry. Journal of Accounting and Economics, 47 (3), 208-225.
Brown, S. & Hillegeist, S.A. (2007). How Disclosure Quality Affects the Level of Information Asymmetry. Review Accounting Studies, 12(2–3), 443–477.
Brown, S., Hillegeist, S.A. & Lo, K. (2004). Conference Calls and Information Asymmetry. Journal of Accounting and Economics, 37(3), 343–366.
Cepoi, C.O., Dragota, V., Trifan, R. & Lordache, A. (2023). Probability of Informed Trading during the COVID-19 Pandemic: the Case of the Romanian Stock Market. Financial Innovation, 9(34), 1-27.
Chang, S.S., Chang, V.L. & Wang, F.A. (2014), A Dynamic Intraday Measure of the Probability of Informed Trading and Firm-Specific Return Variation. Journal of Empirical Finance, 29, 80–94.
Chen, Y., & Zhao, H. (2012). Informed Trading, Information Uncertainty, and Price Momentum. Journal of Banking and Finance, 36(7), 2095-2109.
Cruces, J. J., & Kawamura, E. (2005). Insider Trading and Corporate Governance in Latin America. Research Network Working Paper, R-513, Inter-American Development Bank.
Davallou, M. & Azizi, N. (2017). The Investigation of Information Risk Pricing; Evidence from Adjusted Probability of Informed Trading Measure. Financial Research Journal, 19(3), 415-438. (In Persian)
Dennis, P.J. and Weston, J. (2001). Who’s Informed? An Analysis of Stock Ownership and Informed Trading. American Finance Association 2002 Atlanta Meetings, Chicago, IL.
Duarte, J. & Young, L. (2009). Why Is Pin Priced? Journal of Financial Economics, 91(2), 119–138.
Duarte, J., Han, X., Harford, J. & Young, L. (2008). Information Asymmetry, Information Dissemination and the Effect of Regulation FD on the Cost of Capital. Journal of Financial Economics, 87(1), 24–44.
Easley D., & O’Hara, M. (1992). Time and the Process of Security Price Adjustment. Journal of Finance, 47(2), 577–605
Easley D., Kiefer NM, O’Hara M, Paperman JB (1996) Liquidity, Information, and Infrequently Traded stocks. The Journal of Finance, 51(4), 1405.
Easley, D., Kiefer, N.M. & O’Hara, M. (1997). The Information Content of the Trading Process. Journal of Empirical Finance, 4(2-3), 159–186.
Easley, D., O’Hara, M. & Paperman, J.B. (1998). Financial Analysts and Information-Based Trades. Journal of Financial Markets, 1(2), 175–201.
Easley, D., O’Hara, M. & Saar, G. (2001). How Stock Splits Affect Trading: A Microstructure Approach. The journal of Financial and Quantitative Analaysis., 36(1), 25–51.
Easley, D., Hvidkjaer, S. & O’Hara, M. (2002). Is Information Risk a Determinant of Asset Returns? The Journal of Finance, 57(5), 2185–2221.
Easley, D., Hvidkjaer, S. & O’Hara, M. (2010). Factoring Information into Returns. Journal of Financial and Quanti- tative Analysis, 45(2), 293–309.
Ellul, A. & Pagano, M. (2006). IPO Underpricing and after-Market Liquidity. Review of Financial Studies, 19(2), 381–421.
Ersan, O. (2016). Multilayer Probability of Informed Trading. SSRN Electronic Journal.
Fama, E. F. (1970). Efficient Capital Markets: a Review of Theory and Empirical Work. The Journal of Finance, 25 (2), 383-417
Fama, E. F. (1991). Efficient Capital Markets: II. The Journal of Finance, 46 (5), 1575-1617.
Ghachem M. & Ersan O. (2023). PINstimation: An R Package for Estimating Models of Probability of Informed Trading. SSRN Electronic Journal.
Gan, Q., Wei, W. C., & Johnstone, D. (2015). A Faster Estimation Method for the Probability of Informed Trading using Hierarchical Agglomerative Clustering. Quantitative Finance, 15(11), 1805-1821.
Ghachem M. & Ersan O. (2023). PINstimation: An R Package for Estimating Models of Probability of Informed Trading. SSRN Electronic Journal.
Guo, H. & Qiu, B. (2016). A Better Measure of Institutional Informed Trading. Contemporary Accounting Research, 33(2), 815–850.
Henry, T. R., Kisgen, D. J., & Wu, J. J. (2015). Equity Short Selling and Bond Rating Downgrades. Journal of Financial Intermediation, 24, 89-111.
Hsieh, W.l.G. & He, H.R. (2014). Informed Trading, Trading strategies and the Information Content of Trading Volume: Evidence from the Taiwan Index Options Market. Journal of International Financial Markets, Institutions and Money, 31(1), 187–215.
Hwang, L. S., Lee, W. J., Lim, S. Y., & Park, K. H. (2013). Does Information Risk Affect the Implied Cost of Equity Capital? An Analysis of PIN and Adjusted PIN. Journal of Accounting and Economics, 55(2-3), 148-167.
Jayaraman, S., & Shuang Wu, J. (2020). Should I Stay or Should I Grow? Using Voluntary Disclosure to Elicit Market Feedback. The Review of Financial Studies, 33(8), 38543888.
Jensen, M. C., & Meckling, W. H. (1976). Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure. Journal of Financial Economics, 3 (4), 305-360.
Kang, M. (2010). Probability of Information-Based Trading and the January Effect. Journal of Banking and Finance, 34(12), 2985-2994.
Lee, C. M., & Ready, M. J. (1991). Inferring Trade Direction from Intraday Data. The Journal of Finance, 46(2), 733-746.
Leland, H. E., & Pyle, D. H. (1977). Information Asymmetries, Financial Structure, and Financial Intermediation. The Journal of Finance, 32(2), 371-387.
Ma, R., Marshall, B. R., Nguyen, H. T., Nguyen, N. H., & Visaltanachoti, N. (2022). Climate Events and Return Comovement. Journal of Financial Markets, 61, 100731.
Martins, O.S. & Paulo, E. (2014). Information Asymmetry in Stock Trading, Economic and Financial Characteristics and Corporate Governance in the Brazilian Stock Market. Accounting & Finance Review, 25(64), 33-45.
Meng X.L. & Rubin, D. B. (1993). Maximum Likelihood Estimation via the ECM Algorithm: A General Framework. Biometrika, 80(2), 267-278.
Mohajeri, P. & Taleblou, R. (2023). Investigating the Dynamic of Volatility Spillovers across Sector’s Return Utilizing a Time-Varying Parameter Vector Autoregressive Connectedness Approach; Evidence from Iranian Stock Market. Economic Research (Tahghighat-E-Eghtesadi), 57(2), 321-359. (In Persian)
Ng, S.K., Krishnan, T. & McLachlan, G.J. (2012). The EM Algorithm. In: Handbook of Computational Statistics, Springer,139–172.
Odders-White, E. & Ready, M.J. (2006). Credit Ratings and Stock Liquidity. Review of Financial Studies, 19(1), 119–157.
Pan, J. & Poteshman, A.M. (2006). The Information in Option Volume for Future Stock Prices. Review of Financial Studies, 19(3), 871–908.
Taleblou, R. & Rahmaniani, M. (2017). Measuring Probability of Informed Trading in Tehran Stock Exchange. Journal of Economic Modeling Reasearch, 8(29), 73-98. (In Persian)
Taleblou, R., Shakeri, A. & Rahmaniani, M. (2019). Comparing Different Methods of Estimation for Probability of Informed Trading in Tehran Stock Exchange. Iranian Journal of Economic Reasearch, 24(78), 1-29. (In Persian)
Taleblou, R. & Mohajeri, P. (2021). Modeling the Transmission of Volatility in the Iranian Stock Market; Space-State Nonlinear Approach. Economic Research, 55(4), 963-990. (In Persian)
Taleblou, R. & Mohajeri, P. (2022). Connectedness and Risk Spillovers in Iranian Stock Market: Using TVP-VAR in a Sectoral Analysis. Journal of Econometric Modeling, 7(3), 95-125. (In Persian)
Taleblou, R. & Mohajeri, P. (2023). Measuring the Information Content of Stock Trade; Evidence from Iranian Capital Market. Economic Research, 58(3), 433-1458. (In Persian)
Vega, C. (2006). Stock Price Reaction to Public and Private Information. Journal of Financial Economics, 82(1), 103-133.
Yan, Y. & Zhang, S. (2012). An Improved Estimation Method and Empirical Properties of the Probability of Informed Trading. Journal of Banking and Finance, 36 (2), 454–467.
Yin, X. & Zhao, J. (2015). A hidden Markov Model Approach to Information-Based Trading: Theory and Applications. Journal of Applied Econometrics, 30(7), 1210–1234.

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