Studying investor’s behavior in selecting technology levels and Stock return predictability by incorrect pricing theory.
Quarterly Journal of Securities Exchange

Quarterly Journal of Securities Exchange

Studying investor’s behavior in selecting technology levels and Stock return predictability by incorrect pricing theory.

Document Type : Original Article

Authors
1 Student of Accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
2 Assistant Professor, Department of Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran. (Author)
3 Assistant Professor, Department of Business Management, Rasht Branch, Islamic Azad University, Rasht, Iran.
Abstract
The goal of this study is to separate investor’s role based on risky and no risky approach on share return predictability on the basis of technology different levels. Technology different levels have been measured through number ratio average of staff having higher education, number ratio of production staff, ratio of production value added, and ratio of capital growth. In this study, concentration is on using three regression models with replacement ability in variable coefficient of technology level that from investor’s view can separate share return predictability to risky and no risky. Study sample includes 113 companies from listed companies in Tehran stock exchange that includes a ten years time period from the beginning of 2008 to the end of 2017. Results show that in share return predictability, investor’s behavior in using technology different levels is different, so that when only technology level is average, there is share return predictability for investors based on risky approach, and versus when technology level is up and down, share return predictability is done on the basis of no risky approach of investors. As a result, technology levels have important role on the basis of no risky approach basics, and when it is used as no risky factor by investors, it increases share return predictability.
Keywords

  1. . بادآور نهندی، یونس؛ سرافراز، الهه. (1397). "ارتباط بین قیمت گذاری نادرست سهام و میزان سرمایه گذاری های شرکت با تاکید بر نقش محدودیت های مالی و افق زمانی سرمایه گذاران". فصلنامه علمی پژوهشی دانش مالی تحلیل اوراق بهادار، سال یازدهم، شماره سی و هفتم. بهار.

    1. پاکیزه، کامران و بشیری جویباری، مهدی. (1392). "تاثیر ارزشیابی نادرست بر تصمیمات سرمایه گذاری". فصلنامه مدیریت دارایی و تامین مالی. سال اول، شماره سوم، شماره پیاپی (3)، زمستان. صفحه 91-98.
    2. خدادادی، ولی؛ نوروزی، محمد. (1395). "قیمت گذاری نادرست سهام و رفتار سرمایه گذاری شرکت ها. شواهدی از نظریه ی ارضای سهامداران". مجله پیشرفت های حسابداری دانشگاه شیراز، دوره هشتم، شماره دوم، پاییز و زمستان، پیاپی 3/71. صفحه های93-122.
    3. رضوی ایرد موسی، حمید. (1395). "بررسی رابطه بین گرایشات رفتاری سرمایه گذاران و سرمایه گذاری در مخارج تحقیق و توسعه در شرکت های پذیرفته شده در بورس اوراق بهادار تهران و تاثیر دو گانگی مدیرعامل بر این رابطه". ماهنامه پژوهش های مدیریت و حسابداری، شماره سی و دو.
    4. ستایش، محمدحسین و شمس الدینی، کاظم. (1395). "بررسی رابطه بین گرایش احساسی سرمایه گذاران و قیمت سهام شرکت های پذیرفته شده در بورس اوراق بهادار تهران". مجله پیشرفت های حسابداری، دوره هشتم، شماره اول، پیاپی 70، 103-125.
    5. Arrow, K., (1969). “Classificatory notes on the production and transmission of technological knowledge”. American Economic Review 59: 29–35.
    6. Baker, M., & Wurgler, J. (2006). “Investor sentiment and the cross-section of stock returns”. The Journal of Finance, LXI(4), 1645e1680. https://doi.org/ 10.1111/j.1540-6261.2006.00885.x.
    7. Bloom, N., M. Schankerman, and J. Van Reenen, )2013(. “Identifying technology spillovers and product market rivalry”. Econometrica 81: 1347–1393.
    8. Brown, G.W., Cliff, M.T., )2004(. “Investor sentiment and the near-term stock market.’ J. Empir. Finance 11 (1), 1–27.

    10.Daniel, Kent and Sheridan Titman, )1997(, “Evidence on the characteristics of cross-sectional variation in stock returns”, Journal of Finance 52, 1-33.

    1. Brown, G. W., and M. T. Cliff. (2005). “Investor sentiment and asset valuation”. Journal of Business 78(2):405–40. doi:10.1086/ 427633.
    2. Daniel, K., Titman, S. and Wei, K.C. (2001), “Explaining the cross-section of stock returns in Japan: factors or characteristics?”, Journal of Finance, Vol. 56 No. 2, pp. 743-766.
    3. Davis, J.L., Fama, E.F. and French, K.R. (2000), “Characteristics, covariances, and average returns: 1929 to 1997”, Journal of Finance, Vol. 55 No. 1, pp. 389-406.
    4. Drake, M. S., Roulstone, D. T., & Thornock, J. R. (2012). “Investor information demand: Evidence from Google searches around earnings announcements”. Journal of Accounting research, 50(4), 1001-1040.
    5. Fama, E.F. and French, K.R. (1992), “The cross-section of expected stock returns”, Journal of Finance, Vol. 47 No. 2, pp. 427-465.
    6. Fama, E.F. and French, K.R. (1993), “Common risk factors in the returns on stocks and bonds”, Journal of Financial Economics, Vol. 33 No. 1, pp. 3-56.
    7. He, Z., He, L., & Wen, F. (2019). “Risk compensation and market returns: The role of investor sentiment in the stock market”. Emerging Markets Finance and Trade, 55(3), 704-718.
    8. Hirshleifer, D., Hsu, P. H., & Li, D. (2013). “Innovative efficiency and stock returns”. Journal of Financial Economics, 107(3), 632-654.
    9. Hsu, P., (2011). “Technological innovation spillovers and stock returns”. Working Paper, University of Hong Kong.
    10. Huang, D., Jiang, F., Tu, J., Zhou, G., )2015(. “Investor sentiment aligned: A powerful predictor of stock returns”. Review of Financial Studies. 28, 791-837.
    11. Jaffe, A., (1986). “Technological opportunity and spillovers of R&D: Evidence from firms' patents, profits, and market value”. American Economic Review 76: 984–1001
    12. Jiang, Y., Y. Qian, and T. Yao, (2016). “R&D spillover and predictable returns”. Review of Finance 20: 1769–1797.
    13. Kumar, A., & Lee, C. M. (2006). “Retail investor sentiment and return comovements”. The Journal of Finance, 61(5), 2451-2486.
    14. Kim, Taehyuk & Ha. Aejin, (2010) “Investor Sentiment and Market Anomalies” 23rd Australasian Finance and Banking Conference 2010 Paper, Available at www.ssrn.com.
    15. Lewellen, J. W. (2000). “On the predictability of stock returns: theory and evidence” (Doctoral dissertation, University of Rochester. William E. Simon School of Business Administration).
    16. Li, D. (2011). “Financial constraints, R&D investment, and stock returns”. The Review of Financial Studies, 24(9), 2974-3007
    17. Li, X., Shen, D., Xue, M., Zhang, W., )2017(. “Daily happiness and stock returns: The case of Chinese company listed in the United States”. Economic Modelling 64, 496-501.
    18. Lin, Q., )2018(. “Technical analysis and stock return predictability: An aligned approach”. Journal of financial markets.38, 103-123.
    19. Mansour, S. B., Jouini, E., & Napp, C. (2006). “Is there a “pessimistic” bias in individual beliefs? Evidence from a simple survey”. Theory and Decision, 61(4), 345-362.
    20. Merton, R.C. (1974), “On the pricing of corporate debt: the risk structure of interest rates”, Journal of Finance, Vol. 29 No. 2, pp. 449-470.
    21. Narayan, P.K., Bannigidadmath, D., )2017(. “Does financial news predict stock returns? New evidence from Islamic and non-Islamic stocks”. Pacific-Basin Finance Journal .42, 24-45.
    22. Neely, C.J., Rapach, D.E., Tu, J., Zhou, G., )2014(. “Forecasting the equity risk premium: The role of technical indicators”. Management science. 60, 1772-1791.
    23. Narayan, P.K., Phan, D.H.B., Narayan, S., Bannigidadmath, D., )2017.(“ b. Is there a financial news risk premium in Islamic stocks?” Pacific-Basin Finance Journal .42, 158-170.
    24. Ni, Z.-X., Wang, D.-Z., Xue, W.-J., )2015(. “Investor sentiment and its nonlinear effect on stock returns—New evidence from the Chinese stock market based on panel quantile regression model”. Economic Modelling. 50, 266-274.
    25. Petit, J. J. G., Lafuente, E. V., & Vieites, A. R. (2019). “How information technologies shape investor sentiment: A web-based investor sentiment index”. Borsa Istanbul Review.
    26. Prombutr, W., & Phengpis, C. (2019). “Behavioral-related firm characteristics, risks and determinants of stock returns”. Review of Accounting and Finance, 18(1), 95-112.
    27. Oh, J. M. (2017). “Absorptive capacity, technology spillovers, and the cross-section of stock returns”. Journal of Banking & Finance, 85, 146-164.
    28. Rapach, D.E., Ringgenberg, M.C., Zhou, G., )2016(. “Short interest and aggregate stock returns”. Journal of Financial Economics. 121, 46-65.
    29. Romer, P., (1986). Increasing returns and long-run growth. Journal of Political Economy 94:1002–1037
    30. Schumpeter, J., (1934). “The theory of economic development”. Cambridge, MA: Harvard University Press.
    31. Yang, C., Yan, W., Zhang, R., )2013(. “Sentiment approach to negative expected return in the stock market”. Economic Modelling. 35, 30-34.
    32. Zhang, Y., Zeng, Q., Ma, F., & Shi, B. (2019). “Forecasting stock returns: Do less powerful predictors help?”. Economic Modelling, 78, 32-39.
    33. Zhu, B., & Niu, F. (2016). “Investor sentiment, accounting information and stock price: Evidence from China”. Pacific-Basin Finance Journal, 38 (3), 125-134.