Determinants of Stock Prices: A Quantitative Literature Review
Main Article Content
Keywords
stock prices, macroeconomic indicators, firm fundamentals, investor sentiment, behavioral finance
Abstract
Stock price behavior remains a central focus in financial economics, spanning macroeconomics, corporate finance, and behavioral finance. Traditional theory posits that stock prices reflect discounted future cash flows; however, empirical evidence shows that prices are also shaped by macroeconomic conditions, firm-level fundamentals, and investor sentiment. This paper provides a quantitative literature review of the determinants of stock prices, categorizing them into macroeconomic, microeconomic, and behavioral factors. Macroeconomic variables-such as interest rates, inflation, GDP growth, and policy interventions-affect market-wide valuations and volatility. Firm-specific fundamentals, including earnings, cash flow, leverage, and corporate governance, explain cross-sectional differences in returns, as captured by models like the Fama-French factor frameworks. Behavioral factors, such as overconfidence, herding, and loss aversion, account for deviations from fundamental values and market anomalies. Evidence suggests that no single factor alone fully explains stock price dynamics; instead, their interaction produces complex market behavior. The study highlights the need for integrated quantitative models that combine econometric, machine learning, and behavioral approaches to improve the understanding and modeling of stock price dynamics. Additionally, it identifies research gaps, including high-frequency data integration, cross-market spillovers, and the influence of algorithmic trading, pointing to future directions for more holistic frameworks in financial analysis.
References
- [1] Fama, E. F. and French, K. R. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. 1993, 33(1), pp. 3-56. https://doi.org/10.1016/0304-405X(93)90023-5.
- [2] Baker, M. and Wurgler, J. Investor Sentiment and the Cross-Section of Stock Returns. The Journal of Finance. 2006, 61(4), pp. 1645-1680. https://doi.org/10.1111/j.1540-6261.2006.00885.x.
- [3] Brown, Gregory W. and Cliff, Michael T. Investor Sentiment and Asset Valuation. The Journal of Business. 2005, 78(2), pp. 405-440. https://doi.org/10.1086/427633.
- [4] Bernanke, B. S. and Kuttner, K. N. What Explains the Stock Market's Reaction to Federal Reserve Policy? The Journal of Finance. 2005, 60(3), pp. 1221-1257. https://doi.org/10.1111/j.1540-6261.2005.00760.x.
- [5] Fama, E. F. and Schwert, G. W. Asset returns and inflation. Journal of Financial Economics. 1977, 5(2), pp. 115-146. https://doi.org/10.1016/0304-405X(77)90014-9.
- [6] Chen, N.-F., Roll, R. and Ross, S. A. Economic Forces and the Stock Market. The Journal of Business. 1986, 59(3), pp. 383-403.
- [7] Adler, M. and Dumas, B. Exposure to Currency Risk: Definition and Measurement. Financial Management. 1984, 13(2), pp. 41-50. https://doi.org/10.2307/3665446.
- [8] Dechow, P., Ge, W. and Schrand, C. Understanding earnings quality: A review of the proxies, their determinants and their consequences. Journal of Accounting and Economics. 2010, 50(2), pp. 344-401. https://doi.org/10.1016/j.jacceco.2010.09.001.
- [9] Hribar, P. and Craig Nichols, D. The Use of Unsigned Earnings Quality Measures in Tests of Earnings Management. Journal of Accounting Research. 2007, 45(5), pp. 1017-1053. https://doi.org/10.1111/j.1475-679X.2007.00259.x.
- [10] Lee, C. M. C. and Swaminathan, B. Price Momentum and Trading Volume. The Journal of Finance. 2000, 55(5), pp. 2017-2069. https://doi.org/10.1111/0022-1082.00280.
- [11] Barth, M. E., Landsman, W. R. and Lang, M. H. International Accounting Standards and Accounting Quality. Journal of Accounting Research. 2008, 46(3), pp. 467-498. https://doi.org/10.1111/j.1475-679X.2008.00287.x.
- [12] Horton, J., Serafeim, G. and Serafeim, I. Does Mandatory IFRS Adoption Improve the Information Environment? Contemporary Accounting Research. 2013, 30(1), pp. 388-423. https://doi.org/10.1111/j.1911-3846.2012.01159.x.
- [13] Graham, J. R. and Leary, M. T. A Review of Empirical Capital Structure Research and Directions for the Future. Annual Review of Financial Economics. 2011, 3(Volume 3, 2011), pp. 309-345. https://doi.org/10.1146/annurev-financial-102710-144821.
- [14] Wang, W., Su, C. and Duxbury, D. Investor sentiment and stock returns: Global evidence. Journal of Empirical Finance. 2021, 63, pp. 365-391. https://doi.org/10.1016/j.jempfin.2021.07.010.
- [15] Zhang, C., Wu, X., Deng, H. and Zhang, H. A time-varying study of Chinese investor sentiment, stock market liquidity and volatility: Based on deep learning BERT model and TVP-VAR model. arXiv preprint arXiv:2205.05719. 2022. https://doi.org/10.48550/arXiv.2205.05719.
- [16] Li, T., Chen, H., Liu, W., Yu, G. and Yu, Y. Understanding the role of social media sentiment in identifying irrational herding behavior in the stock market. International Review of Economics & Finance. 2023, 87, pp. 163-179. https://doi.org/10.1016/j.iref.2023.04.016.
- [17] Manfield, J., Lukacsko, D. and Souza, T. T. P. Bull bear balance: A cluster analysis of socially informed financial volatility. In 2017 Computing Conference, London, UK, 2017; pp. 421-428. https://doi.org/10.1109/SAI.2017.8252134.
- [18] Bouri, E., Demirer, R., Gupta, R. and Nel, J. COVID-19 Pandemic and Investor Herding in International Stock Markets. Risks. 2021, 9(9), p. 168. https://doi.org/10.3390/risks9090168.
- [19] Guo, X., Gu, C., Zhang, C. and Li, S. Institutional herding and investor sentiment. Journal of Financial Markets. 2024, 68, p. 100891. https://doi.org/10.1016/j.finmar.2024.100891.
- [20] Da, Z., Engelberg, J. and Gao, P. The Sum of All FEARS Investor Sentiment and Asset Prices. The Review of Financial Studies. 2015, 28(1), pp. 1-32. https://doi.org/10.1093/rfs/hhu072.
Article Sidebar
This work is licensed under a Creative Commons Attribution 4.0 International License.