News Vacancy and Stock Movement: Uncovering Trading Opportunities With Idiosyncratic Price Changes
Keywords:
accounting, finance, price corrections, market efficiency, news coverage, behavioral finance, trading strategiesAbstract
This study examines whether stock price movements without accompanying news revert to prior levels. A portion of stock price volatility cannot be directly attributed to macroeconomic announcements, firm disclosures, or news reports. Instead, many fluctuations appear idiosyncratic—driven by firm-specific trading dynamics, investor sentiment, or liquidity imbalances rather than fundamental value changes. Using daily stock prices and scraped news data, this research quantifies trading opportunities arising from these idiosyncratic price shifts. Our results show that price movements accompanied by new information exhibit stronger reversals than those without news, particularly following large gains. Stocks experiencing sudden increases without news sustain elevated prices longer before correcting, whereas declines exhibit weaker, less predictable reversals. These results suggest that public information plays a critical role in market efficiency by shaping investor reactions and price adjustments. By highlighting the role of news— or its absence—in price discovery, this study deepens our understanding of short-term market inefficiencies and provides insights for both academic research and trading strategies.
References
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4), 1645–1680.
Ball, R., & Brown, P. (1968). An Empirical Evaluation of Accounting Income Numbers. Journal of Accounting Research, 6(2), 159–178.
Boudoukh, J., Feldman, R., Kogan, S., & Richardson, M. (2012). Which News Moves Stock Prices? A Textual Analysis. Retrieved from https://www.aqr.com/About-Us/AQR-Insight-Award/2013/Honorable-Mention-3
Chan, W.C. (2003). Stock price reaction to news and no-news: Drift and reversal after headlines. Journal of Financial Economics, 70(2), 223–260.
Clapham, B., Haferkorn, M., & Zimmermann, K. (2023). The Impact of High-Frequency Trading on Modern Securities Markets: An Analysis Based on a Technical Interruption. Business and Information Systems Engineering, 65(1), 7–24.
Coval, J., & Stafford, E. (2007). Asset fire sales (and purchases) in equity markets. Journal of Financial Economics, 86(2), 479–512.
Gissler, S. (2015). Slow capital, fast prices: Shocks to funding liquidity and stock price reversals. Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C.
Lee, C.M.C., Shleifer, A., & Thaler, R.H. (1991). Investor Sentiment and the Closed-End Fund Puzzle. The Journal of Finance, 46(1), 75.
Li, B., Rajgopal, S., & Venkatachalam, M. (2014). R2 and Idiosyncratic Risk Are Not Interchangeable. The Accounting Review, 89(6), 2261–2295.
Milgrom, P.R. (1981). Good News and Bad News: Representation Theorems and Applications. The Bell Journal of Economics, 12(2), 380.
Murphy, C.B. (2024). What Are Penny Stocks? Investopedia.com (Dotdash Meredith Publishing). Retrieved from https://www.investopedia.com/terms/p/pennystock.asp
Python Software Foundation. (n.d.). What is Python? Opensource.com. Retrieved from https://opensource.com/resources/python
Roll, R. (1988). R2. The Journal of Finance, 43(3), 541–566.
Segal, T. (2014). Fundamental Analysis: Principles, Types, and How to Use It. Retrieved from https://www.investopedia.com/terms/f/fundamentalanalysis.asp.
Sulcas, A. (2023). Beautiful Soup Tutorial - How to Parse Web Data With Python. oxylabs.io. Retrieved from https://oxylabs.io/blog/beautiful-soup-parsing-tutorial
Taft, D.K. (2010, October 14). JetBrains Strikes Python Developers with PyCharm 1.0 IDE. eWeek.
The Harvard Law School Forum on Corporate Governance. (2014). High-Frequency Trading: An Innovative Solution to Address Key Issues. Governance, The Harvard Law School Forum on Corporate Governance.
