Development of Asset Pricing Model Based on Price Impact: Comparison of High-frequency-based Measures

Document Type : Original Article

Authors
1 PhD Student of International Finance, Department of Financial Management and Accounting, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Assistant Professor, Department of Financial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 Associate Professor Of Accounting And Management department, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran
4 Assistant Professor, Department of Management, Shargh Tehran Branch, Islamic Azad University, Tehran,Iran
10.22034/jik.2025.78343.4704
Abstract
The purpose of this article is to develop the asset pricing model with the price impact approach in the stock market. In this regard, 4 price impact parameters of Kyle (1985), Glosten and Harris (1988), Foster and Viswanathan (1993) and Sadka (2006) were considered as representatives of high frequency data and 2 illiquidity proxy of Amihud (2002) and Pastor and Stambaugh (2003) as representatives of low frequency data. The number of 140 companies in the Tehran Stock Exchange was examined as a statistical sample of the research during the period of 1394 to 1402, and in order to estimate the price effect parameters based on abundant data, the monthly information of the companies' stock transactions was used. The risk factor of stock liquidity based on the parameters and based on the strategy of forming a win-loss portfolio was tested as the sixth risk factor in the Fama and French five-factor model. The results of this analysis showed that the three price effect parameters of Kyle, Foster, Viswanathan and Sadka have a significant role in the asset pricing model and have a significant explanatory power of the expected return and stock price. Also, all three parameters of the mentioned price effect improve the explanatory power of the share price compared to the 5-factor model.

  1. ابزری، مهدی؛ کبیری پور، وحید؛ سهیلی، سیروس (1392). تحلیل تأثیر نقدشوندگی بر بازده سهام با کنترل سبک‌های سرمایه‌گذاری رویکردی جدید با معیاری چند بعدی. دانش حسابداری 15، 104-79.
  2. پویان فر، احمد؛ راعی، رضا؛ محمدی، شاپور (1388). فرایند شکل‌گیری قیمتها در بورس تهران– رویکرد ریزساختاری. بررسیهای حسابداری و حسابرسی، 16(56)، 21-38 .
  3. حبیبی ثمر، جواد؛ تهرانی، رضا؛ انصاری، کامبیز (1393). بررسی رابطه بین ریسک نقدشوندگی وریسک بازار با بازده سهام رشدی و ارزشی با رویکرد مدل AHP در بورس اوراق بهادار تهران. مهندسی مالی و مدیریت اوراق بهادار، 23، 58-39.
  4. خجسته، محمدعلی؛ تهرانی، رضا (1396). ارتقای مدل قیمت‌گذاری سهام مبتنی بر عامل ریسک نقدشوندگی. دانش مالی تحلیل اوراق بهادار، 35(10)، 17-1.
  5. علیزاده، صدیقه؛ شهیکی تاش، محمدنبی؛ روشن، رضا (1399). استفاده از اختلاف قیمت پیشنهادی خرید و فروش به عنوان پراکسی برای هزینه معاملاتی در تعدیل مدل قیمت‌گذاری دارایی سرمایه‌ای مبتنی بر مصرف. مهندسی مالی و مدیریت اوراق بهادار (مدیریت پرتفوی)، 11(44 )، 256-227.
  6. عیوضلو، رضا (1391). طراحی مدل سنجش ریسک اطلاعات با استفاده از مدلهای مبتنی بر اطلاعات و ارزیابی ریسک غیرسیستماتیک. پایان نامه دکتری، دانشگاه تهران.
  7. Acharya, V., & Pedersen, L. H. (2005). Asset Pricing with Liquidity Risk. Journal of Financial Economics, 77, 375-410.
  8. Aitken, M. J., Ji, S., Mollica, V., & Wang, X. (2017). The Impact of the Shanghai–Hong Kong Connect on Market Liquidity and Price Divergence. In 8th Conference on Financial Markets and Corporate Governance (FMCG).
  9. Amihud, Y., & Levi, S. (2023). The effect of stock liquidity on the firm’s investment and production. The Review of Financial Studies36(3), 1094-1147.
  10. Ball, R., Sadka, G., & Tseng, A. (2022). Using accounting earnings and aggregate economic indicators to estimate firm-level systematic risk. Review of accounting studies27(2), 607-646.
  11. Ballco, P., Jaafer, F., & de Magistris, T. (2022). Investigating the price effects of honey quality attributes in a European country: evidence from a hedonic price approach. Agribusiness38(4), 885-904.
  12. Ben-Rephael, A., Carlin, B.I., Da, Z., Israelsen, R. D. (2017). Demand for Information and Asset Pricing. NBER Working Papers 23274, National Bureau of Economic Research, Inc.
  13. Brennan, M., Huh, S. W., & Subrahmanyam, A. (2013). An analysis of the Amihud illiquidity premium. The Review of Asset Pricing Studies3(1), 133-176.
  14. Chen, S., Wilson, W. W., Larsen, R., & Dahl, B. (2015). Investing in agriculture as an asset class. Agribusiness31(3), 353-371.
  15. Cho, T. (2020). Turning alphas into betas: Arbitrage and endogenous risk. Journal of Financial Economics137(2), 550-570.
  16. Chordia, T., Green, T. C., & Kottimukkalur, B. (2018). Rent seeking by low-latency traders: Evidence from trading on macroeconomic announcements. The Review of Financial Studies31(12), 4650-4687.
  17. Comin, D., Lashkari, D., & Mestieri, M. (2021). Structural change with long‐run income and price effects. Econometrica89(1), 311-374.
  18. Das, K. K., & Yaghoubi, M. (2023). Stock liquidity and firm-level political risk. Finance Research Letters51, 103419.
  19. Eisdorfer, A., Froot, K., Ozik, G., & Sadka, R. (2022). Competition links and stock returns. The Review of Financial Studies35(9), 4300-4340.
  20. Fama, E. & French, K. (1993), "Common risk factors in the returns on stocks and bonds", Journal of Financial Economics 33, 3–56.
  21. Foster, D. & Viswanathan, S. (1993), "Variations in trading volume, return volatility, and trading costs: evidence on recent price formation models", Journal of Finance 48, 187–211.
  22. Glosten, L. & Harris, L. (1988) "Estimating the components of the bid-ask spread", Journal of Financial Economics 21, 123–142.
  23. Glosten, L. R. (2020). Economics of the stock exchange business: Proprietary market data. Available at SSRN 3533525.
  24. Gu, S., Kelly, B., & Xiu, D. (2021). Autoencoder asset pricing models. Journal of Econometrics222(1), 429-450.
  25. Hollstein, F., Prokopczuk, M., & Wese Simen, C. (2019). The conditional CAPM revisited: evidence from high-frequency betas. Management Science.
  26. Kerr, J., Sadka, G., & Sadka, R. (2020). Illiquidity and price informativeness. Management Science66(1), 334-351.
  27. Kyle, A. (1985) "Continuous auctions and insider trading", Econometrica 53, 1315–1335.
  28. Le, H., & Gregoriou, A. (2020). How do you capture liquidity? A review of the literature on low‐frequency stock liquidity. Journal of Economic Surveys34(5), 1170-1186.
  29. Lischewski, J., & Voronkova, S. (2012). Size, value and liquidity. Do they really matter on an emerging stock market?. Emerging Markets Review13(1), 8-25.
  30. Luo, D. (2022). ESG, liquidity, and stock returns. Journal of International Financial Markets, Institutions and Money78, 101526.
  31. Miłobędzki, P., & Nowak, S. (2022). The components of bid-ask spread on the Warsaw Stock Exchange. In Handbook of Banking and Finance in Emerging Markets(pp. 131-151). Edward Elgar Publishing.
  32. Nadarajah, S., Duong, H. N., Ali, S., Liu, B., & Huang, A. (2021). Stock liquidity and default risk around the world. Journal of financial markets55, 100597.
  33. Naik, P., & Reddy, Y. V. (2021). Stock market liquidity: A literature review. Sage Open11(1), 2158244020985529.
  34. O`Hara, M. (1995). "Market Microstructure Theory", Blackwell Publishers, Cambridge, MA.
  35. Shang, C. (2020). Trade credit and stock liquidity. Journal of Corporate Finance62, 101586.
  36. Viswanathan, M., Umashankar, N., Sreekumar, A., & Goreczny, A. (2021). Marketplace literacy as a pathway to a better world: Evidence from field experiments in low-access subsistence marketplaces. Journal of Marketing85(3), 113-129.
  37. Zheng, D., Dai, X., Lan, T., Zhang, W., & Mou, J. (2021). The negative effect of share pledging by controlling shareholders under COVID-19. Emerging Markets Finance and Trade57(10), 2826-2837.
  38. Zhou, D., & Wang, W. (2020). Insider, outsider and information heterogeneity. The North American Journal of Economics and Finance53, 101193.