本研究探討股價的錯估程度是否會隨著交易量提升而放大，其中股價高估的股票在釋出正面資訊後，樂觀投資人的看法得到支持同時財富增加，進而帶動市場對於股票價格的預期往更樂觀的方向移動，反之，股價低估的股票在負面資訊釋出後市場預期往更悲觀的方向移動，因此股價錯估程度的不同會影響投資人意見不一致程度和股價之間的關係。
本研究以 2012-2023 年台灣上市公司股價資料共計 92,705 個樣本觀察值為樣本， 透過將股票交易量歸因爲投資人的意見不一致，並且以綜合 11 個財務和市場指標的 MISP 分數衡量股價的錯估程度，結果顯示樣本期間內台灣上市股票存在交易量放大 效果，亦即交易量和股價報酬率的關係將取決於股價的錯估程度，若股價低估則交易 量和下一期報酬率呈正相關，反之，若股價高估則交易量和下一期報酬率呈負相關， 同時交易量越大的股票其股價錯估的程度也越大，這項結果支持了在預期偏誤下投資 人意見的不一致可以預測股價報酬率這一觀點。本研究進一步將投資組合持有時間從 一個月延長至兩個月和三個月，並發現交易量放大股價錯估的程度也隨之提升。

This study explores whether the degree of stock mispricing amplifies with an increase in trading volume. When positive information is disclosed, it reinforces optimistic investors' perspectives and leads to growth in their wealth, which in turn drives the market's expectations bias for overvalued stock. Conversely, negative information release tends to shift market bias towards a more pessimistic outlook on undervalued stocks. As a result, varying degrees of mispricing can impact the link between investor disagreement levels and stock prices.
Using the stock price data of Taiwan-listed companies from 2012 to 2023, with a total of 92,705 observations, this study links trading volume to investor disagreements and measures mispricing using the MISP score, combining 11 financial and market indicators. Results suggest a positive relation between trading volume and next-period return if the stock is underestimated; conversely, an overvalued stock correlates with negative returns and higher trading volumes. The results of this study are consistent with the notion that investor disagreement forecasts stock returns based on expectation bias. This study further extends the portfolio holding period from one month to two and three months, and find that the degree of volume amplification also elevate as holding period extended.

Atmaz, A., & Basak, S. (2018). Belief dispersion in the stock market. The Journal of Finance, 73(3), 1225-1279.
Back, K. (2010). Asset pricing and portfolio choice theory. Oxford University Press.
Banerjee, S. (2011). Learning from prices and the dispersion in beliefs. The Review of Financial Studies, 24(9), 3025-3068.
Barberis, N., Greenwood, R., Jin, L., & Shleifer, A. (2018). Extrapolation and bubbles. Journal of Financial Economics, 129(2), 203-227.
Blume, L., Easley, D., & O'hara, M. (1994). Market statistics and technical analysis: The role of volume. The journal of finance, 49(1), 153-181.
Brockman, P., Chung, D. Y., & Pérignon, C. (2009). Commonality in liquidity: A global perspective. Journal of Financial and Quantitative Analysis, 44(4), 851-882.
Chang Tzu-Pu, Wu Meng-Tao, Lin Chen-Fu. (2014). An Empirical Analysis of the Liquidity of Taiwan Stock Market: A Cross-Country Comparison. Cross-Sreait Banking and Finance, (2:2), 45-68.
Hung Chen-Chien. (2011). Volume-return Relation and Trading Strategy. Sun Yat-Sen Management Review, 19(2), 305-342.
Wu Chen-Hui, Wang Tzu-Yun. (2020). The Effects of Reducing the Securities Transaction Tax on Day Trading: Evidence from the Taiwan Stock Market. Journal of Accounting Review, 70, 135-169.
Hsieh Chin-Shan, We-Shin. (2016). The Relationship between Stock Trading Volume and Price Volatility with the Rate of Return on TWSE. Management Information Computing, 5, 169-180.
Chung Yu-Hsuan, Huang Shaio-Yan, Chi Wu-Chun. (2013). The Effect of Conference Calls on Information Asymmetry. Journal of Management, 30(4), 295-322.
Cochrane, J. H. Efficient markets today. Talk at Conference of Chicago Economics, 2007.
Cochrane, J.H. Macro-finance. Rev. Finance 21, 945–985, 2017.
Conrad, J. S., Hameed, A., & Niden, C. (1994). Volume and autocovariances in short‐horizon individual security returns. The journal of finance, 49(4), 1305-1329.
Daniel, K., & Hirshleifer, D. (2015). Overconfident investors, predictable returns, and excessive trading. Journal of Economic Perspectives, 29(4), 61-88.
Diether, K. B., Malloy, C. J., & Scherbina, A. (2002). Differences of opinion and the cross section of stock returns. The journal of finance, 57(5), 2113-2141.
Engelberg, J., McLean, R. D., & Pontiff, J. (2018). Anomalies and news. The Journal of Finance, 73(5), 1971-2001.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of financial economics, 116(1), 1-22.
Fama, E. F., & French, K. R. (2016). Dissecting anomalies with a five-factor model. The Review of Financial Studies, 29(1), 69-103.
Gervais, S., Kaniel, R., & Mingelgrin, D. H. (2001). The high‐volume return premium. The journal of finance, 56(3), 877-919.
Han, Y., Huang, D., Huang, D., & Zhou, G. (2022). Expected return, volume, and mispricing. Journal of financial economics, 143(3), 1295-1315.
Hong, H., & Stein, J. C. (2007). Disagreement and the stock market. Journal of Economic perspectives, 21(2), 109-128.
Hou, K., Xue, C., & Zhang, L. (2020). Replicating anomalies. The Review of financial studies, 33(5), 2019-2133.
Kelley, E. K., & Tetlock, P. C. (2013). Why do investors trade?. Available at SSRN 2064144.
Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica: Journal of the Econometric Society, 1315-1335.
Lee, C. M., & Swaminathan, B. (2000). Price momentum and trading volume. the Journal of Finance, 55(5), 2017-2069.
Lo, A. W., & Wang, J. (2010). Stock market trading volume. In Handbook of financial econometrics: Applications (pp. 241-342). Elsevier.
Mayew, W. J. (2008). Evidence of management discrimination among analysts during earnings conference calls. Journal of Accounting Research, 46(3), 627-659.
Stambaugh, R. F., Yu, J., & Yuan, Y. (2015). Arbitrage asymmetry and the idiosyncratic volatility puzzle. The Journal of Finance, 70(5), 1903-1948.
Wang, J. (1994). A model of competitive stock trading volume. Journal of political Economy, 102(1), 127-168.
Elliott, W. B., Loftus, S., & Winn, A. (2023). To read or to listen? Does disclosure delivery mode impact investors' reactions to managers' tone language?. Contemporary Accounting Research.