مدلسازی اثر معاملات نویزی بر بازده حدی سهام بر مبنای رهیافت رگرسیون چندکی
محورهای موضوعی : مهندسی مالیسولماز سلامی 1 , عبدالمجید عبدالباقی عطاآبادی 2 , روح الله فرهادی 3
1 - گروه مدیریت مالی, واحد تهران مرکز, دانشکاه آزاد اسلامی، تهران، ایران
2 - گروه مدیریت, دانشگاه صنعتی شاهرود, شاهرود, ایران
3 - گروه مدیریت دولتی, واحد تهران مرکز , دانشگاه آزاد اسلامی, تهران, ایران
کلید واژه: بازده, رگرسیون چندکی, معاملات نویزی, شرایط حدی,
چکیده مقاله :
معاملات نویزی در تعیین نوسانات بازار، بازده و حرکت قیمتی سهام دارای نقشی انکارناپذیر هستند لذا در این پژوهش به بررسی تأثیر معاملات نویزی بر بازده سهام با هدف ارائه تصویر مناسبی از نحوه تاثیرگذاری آنها در شرایط حدی پرداخته شده است. جامعه آماری این پژوهش شامل تمامی شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران در دوره زمانی 1387-1396 است که نمونه‌ای شامل 150 شرکت و 13717داده ماهانه را شامل می‌شود. فرضیه اصلی این پژوهش ارزیابی اثرات حدی معاملات نویزی بر بازده سهام، به روش رگرسیون چندکی است. یافته‌های مربوط به آمار توصیفی نشان دهنده افزایش سطح فعالیت‌های نویزی با افزایش سطح بازده است. همچنین نتایج مربوط به آزمون فرضیه نشان دهنده تأثیر مثبت شاخص معاملات نویزی با ضریب 0001/0 بر بازده است که در شرایط حدی بازده (از مقدار 0.000037 تا 0.000069 ) نسبت به مقادیر میانی این اثر بیشتر و مؤید تشدید فعالیت معاملات نویزی در دوره‌های رشد و افول قیمت سهام است.
Noise traders have an undeniable role in determining market volatility, returns and stock price movements. Therefore, In this paper, the effect of noise traders on the stock returns of companies with the aim of presenting an appropriate picture of how they are affected in extreme situations.The statistical population of this study includes all companies listed in Tehran Stock Exchange during the period of 2009-2017. The sample of 13717 data from 150 companies listed on the stock exchange monthly. The main hypothesis of this study is to evaluate the Extreme Effects of noise trading on stock returns by quantile regression was used to analyze the data. The findings of the research show that the level of noise activity increases with the level of efficiency Moreover,the positive effect of the noise trading index on returns with a coefficient of 0.0001.Under extreme returns, this effect is greater than the intermediate values and reflects the intensification of noise trading activity in periods of decline and market growth.
کل
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(10) Dontoh, A., Radhakrishnan, S., Ronen, J. (2004). The declining value relevance of accounting information and non-information-based trading: an empirical analysis Contemporary Accounting Research. 21, 795–812.
(11) Felix, H., Nicolas, P., Simon, A. (2018) Noise Trader Behavior–A Disaggregated Approach to Understanding News Reception in Financial Markets, Twenty-Sixth European Conference on Information Systems, Portsmouth, UK.
(12) Feng, J., Lin, D., Yan, X. (2014). Research on measure of noise trading in stock market based on EGARCH-M model. Paper presented at the Management Science & Engineering (ICMSE), 2014 International Conference.
(13) Fernandes, J., Luiz B., Ignacio Pena, J., Miranda Tabak, B. (2010). Behavior finance and risk estimation in stochastic portfolio optimization. Applied Financial Economics, 20(9), 719-738.
(14) Forbes, W. (2009). Behavioral finance: John Wiley & Sons.
(15) Gemmill, G., Thomas, Dylan C. (2002), Noise-Trading, Costly Arbitrage, and Asset Prices: Evidence from Closed-End Funds, Journal of finance, 57, 6, 2571-2594.
(16) Hens, Th., Schenk, H., Klaus, R. (2009) Handbook of Financial Markets: Dynamics and Evolution, 1 edition, North Holland.
(17) Kim, Taehyuk and Ha, Aejin. (2010). Investor sentiment and market anomalies. 23rd Australasian Finance and Banking Conference, 23.
(18) Lin, Ch. (2010). The Effects of Investor Sentiment on Returns and Idiosyncratic Risk in the Japanese Stock Market. International Research Journal of Finance and Economics, 60, 29-43.
(19) Qiang, Zh., Shu-e, Y. (2009). Noise trading, investor sentiment volatility, and stock returns. Systems Engineering-Theory & Practice, 29(3), 40-47.
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(20) Shleifer, Andrei and Summers, Lawrence H. (1990). The noise trader approach to finance. The Journal of Economic Perspectives, 4(2), 19-33.
(21) Stambaugh, R.F. and Yu, J. and Yuan, Y. (2015), Arbitrage asymmetry and the idiosyncratic volatility puzzle. Journal of Finance 70, 1903-1948.
(22) Verma, R., Verma, P (2006), Noise trading and stock market volatility, Journal of Multinational Financial Management, 17, 3, P 231-243.
(23) Zhu, Zh., Sun, L., Chen, M. (2018) Noise Trading, Slow Diffusion of Information, and Short-Term Reversals: A Fundamental Analysis Approach. Available at SSRN: https://ssrn.com/abstract=3097420.
(3) Abbasian A., Farzangan A. and Ansir al-Islami. Irregularity of price bubble in Tehran Stock Exchange: limit approach in arbitrage, economic researches and policies, 76, 75-92. 2014
(4) Nikbakht M., Hosseinpour A., Islamabadi H. Investigating the effect of investors' emotional behavior and accounting information on stock prices. Accounting Experimental Research, 6(2), 219-255. 2015
(5) Abo, T., Pantzalis, Ch., Park, J. (2017), Idiosyncratic volatility: An indicator of noise trading?, Journal of Banking and Finance, 75, 136–151. www.elsevier.com/locate/jbf.
(6) Amini, Sh., Cai, X (2017), Nominal Price Level and Noise Trading, Leeds University Business School Working Paper No. 08-17.
(7) Black, F. (1986) Noise, journal of finance, 41(3), 528-777.
(8) Brunnermeier, Markus K. (2016). bubbles. In Garrett Jones (Ed.), Banking Crises: Perspectives from The New Palgrave Dictionary (pp. 28-36).
(9) De Long, J and et al. (1990). Noise Trader Risk in Financial Markets. Journal of Political Economy 98: 703-738.
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(1) Saranj A., Tehrani R., Abbasi Mosulu, Kh., and Mandiri. Identifying trading behaviors and risk of disruptive traders in the Iranian stock market, Financial Management Strategy Quarterly, 6(3), 31-58, 2017.
(10) Dontoh, A., Radhakrishnan, S., Ronen, J. (2004). The declining value relevance of accounting information and non-information-based trading: an empirical analysis Contemporary Accounting Research. 21, 795–812.
(11) Felix, H., Nicolas, P., Simon, A. (2018) Noise Trader Behavior–A Disaggregated Approach to Understanding News Reception in Financial Markets, Twenty-Sixth European Conference on Information Systems, Portsmouth, UK.
(12) Feng, J., Lin, D., Yan, X. (2014). Research on measure of noise trading in stock market based on EGARCH-M model. Paper presented at the Management Science & Engineering (ICMSE), 2014 International Conference.
(13) Fernandes, J., Luiz B., Ignacio Pena, J., Miranda Tabak, B. (2010). Behavior finance and risk estimation in stochastic portfolio optimization. Applied Financial Economics, 20(9), 719-738.
(14) Forbes, W. (2009). Behavioral finance: John Wiley & Sons.
(15) Gemmill, G., Thomas, Dylan C. (2002), Noise-Trading, Costly Arbitrage, and Asset Prices: Evidence from Closed-End Funds, Journal of finance, 57, 6, 2571-2594.
(16) Hens, Th., Schenk, H., Klaus, R. (2009) Handbook of Financial Markets: Dynamics and Evolution, 1 edition, North Holland.
(17) Kim, Taehyuk and Ha, Aejin. (2010). Investor sentiment and market anomalies. 23rd Australasian Finance and Banking Conference, 23.
(18) Lin, Ch. (2010). The Effects of Investor Sentiment on Returns and Idiosyncratic Risk in the Japanese Stock Market. International Research Journal of Finance and Economics, 60, 29-43.
(19) Qiang, Zh., Shu-e, Y. (2009). Noise trading, investor sentiment volatility, and stock returns. Systems Engineering-Theory & Practice, 29(3), 40-47.
(2) Abbasian and Elham Farzangan. Behavior of disruptive and bubble traders in Tehran Stock Exchange. Journal of Economic Research, 46(3), 133-153. 1390.
(20) Shleifer, Andrei and Summers, Lawrence H. (1990). The noise trader approach to finance. The Journal of Economic Perspectives, 4(2), 19-33.
(21) Stambaugh, R.F. and Yu, J. and Yuan, Y. (2015), Arbitrage asymmetry and the idiosyncratic volatility puzzle. Journal of Finance 70, 1903-1948.
(22) Verma, R., Verma, P (2006), Noise trading and stock market volatility, Journal of Multinational Financial Management, 17, 3, P 231-243.
(23) Zhu, Zh., Sun, L., Chen, M. (2018) Noise Trading, Slow Diffusion of Information, and Short-Term Reversals: A Fundamental Analysis Approach. Available at SSRN: https://ssrn.com/abstract=3097420.
(3) Abbasian A., Farzangan A. and Ansir al-Islami. Irregularity of price bubble in Tehran Stock Exchange: limit approach in arbitrage, economic researches and policies, 76, 75-92. 2014
(4) Nikbakht M., Hosseinpour A., Islamabadi H. Investigating the effect of investors' emotional behavior and accounting information on stock prices. Accounting Experimental Research, 6(2), 219-255. 2015
(5) Abo, T., Pantzalis, Ch., Park, J. (2017), Idiosyncratic volatility: An indicator of noise trading?, Journal of Banking and Finance, 75, 136–151. www.elsevier.com/locate/jbf.
(6) Amini, Sh., Cai, X (2017), Nominal Price Level and Noise Trading, Leeds University Business School Working Paper No. 08-17.
(7) Black, F. (1986) Noise, journal of finance, 41(3), 528-777.
(8) Brunnermeier, Markus K. (2016). bubbles. In Garrett Jones (Ed.), Banking Crises: Perspectives from The New Palgrave Dictionary (pp. 28-36).
(9) De Long, J and et al. (1990). Noise Trader Risk in Financial Markets. Journal of Political Economy 98: 703-738.
