台灣股市近十年（2015-2025）異常效應回顧研究

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Abstract This study examines whether institutional herds, as defined by Chung and Kim, influence future excess returns across different market states in Taiwan stock market. Using specialized trading data aggregated from three major institutional investor groups, we focus on institutional herding behavior during market collapses and booms. Fama–MacBeth regression results show that overall institutional herding positively contributes to future excess returns during market downturns and across the full sample, but negatively during market booms. By dividing stocks into quintiles based on herding intensity, we find that concentrated herding (top 20%) tends to reduce returns during downturns, whereas broader herding helps stabilize the market. In booms, leading herding may enhance returns, while widespread herding tempers excessive gains. Portfolios with the highest institutional herding scores also yield higher cumulative returns than those with the lowest scores (bottom 20%). These findings underscore the critical role of institutional herding, particularly high-intensity herds in shaping market dynamics under extreme conditions, providing a comprehensive understanding of how institutional herds influence the Taiwan stock market. Impact Statement This study offers empirical insights into how institutional herding, particularly under extreme market conditions, influences future excess returns in Taiwan’s stock market. By distinguishing herding intensity and market phases, the findings reveal that herding behavior enhances return predictability during downturns but dampens performance during booms. These results contribute to a deeper understanding of institutional dynamics and their implications for market efficiency and asset pricing.

This study investigates the financial determinants of high stock returns, defined as quarterly returns exceeding 20%, in the Taiwan equity market. Using binary logistic regression models, we assess the predictive power of valuation ratios (P/E, P/B, and P/S) and profitability (ROE), under both contemporaneous and one-period lagged specifications. The empirical results reveal that ROE is a consistently strong predictor, with odds ratios indicating a 1–3% increase in the likelihood of high returns per unit increase in profitability. The P/B ratio also shows statistically significant and economically meaningful effects in contemporaneous models, but its predictive value weakens in lagged models. In contrast, the P/E and P/S ratios provide little forward-looking information, especially when lagged. Industry classification, as captured by a dummy variable for electronics firms (ELEC), adds explanatory power, with odds ratios suggesting that electronics firms are significantly more likely to deliver high returns. Overall, the findings emphasize the superior predictive value of profitability over valuation ratios in identifying near-term outperformers.

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ABSTRACT Liquidity plays a crucial role in the risks associated with capital flows. This study points out that, in addition to transaction costs that may hinder liquidity, increased external risk can also contribute to a reduction in liquidity. A slowdown in liquidity can lead to abnormal returns in the stock market, resulting in sharp increases or decreases. The research investigates the relationship between liquidity of capital flows and abnormal returns within the Taiwan stock market, with particular emphasis on the periods preceding and following the US-China trade conflict and the COVID-19 pandemic. The results reveal that both events exerted a considerable influence on the risk dynamics within Taiwan’s stock market. Specifically, during the US-China trade war, there was a notable increase in capital liquidity in Taiwan’s stock market. Conversely, the period of the COVID-19 pandemic was characterized by a decrease in liquidity. Additionally, our analysis indicates that illiquidity significantly impacts abnormal returns.

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It is well-believed that most trading activities tend to herd. Herding is an important topic in finance. It implies a violation of efficient markets and hence, suggests possibly predictable trading profits. However, it is hard to test such a hypothesis using aggregated data (as in the literature). In this paper, we obtain a proprietary data set that contains detailed trading information, and as a result, for the first time it allows us to validate this hypothesis. The data set contains all trades transacted in 2019 by all the brokers/dealers across all locations in Taiwan of all the equities (stocks, warrants, and ETFs). Given such data, in this paper, we use swarm intelligence to identify such herding behavior. In particular, we use two versions of swarm intelligence—Boids and PSO (particle swarm optimization)—to study the herding behavior. Our results indicate weak swarm among brokers/dealers.

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This study examines the impact of the COVID-19 outbreak on the Taiwan stock market and investigates whether companies with a commitment to corporate social responsibility (CSR) were less affected. This study uses a selection of companies provided by CommonWealth magazine to classify the listed companies in Taiwan as CSR and non-CSR companies. The event study approach is applied to examine the change in the stock prices of CSR companies after the first COVID-19 outbreak in Taiwan. The empirical results indicate that the stock prices of all companies generated significantly negative abnormal returns and negative cumulative abnormal returns after the outbreak. Compared with all companies and with non-CSR companies, CSR companies were less affected by the outbreak; their stock prices were relatively resistant to the fall and they recovered faster. In addition, the cumulative impact of the COVID-19 on the stock prices of CSR companies is smaller than that of non-CSR companies on both short- and long-term bases. However, the stock price performance of non-CSR companies was not weaker than that of CSR companies during times when the impact of the pandemic was lower or during the price recovery phase.

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This study investigates group common factors within six anomaly groups using a factor model with time‐varying coefficients and stochastic volatility. We explore the time‐varying relative contributions of group common factors in explaining the variation of each anomaly's return. We demonstrate that the relative importance is heterogeneous across the anomaly groups. The relative importance of the value group common factor decreased during the global financial crisis (GFC) and the COVID‐19 pandemic in 2020 because the GFC and the pandemic were associated with cash flow and earnings. Moreover, we reveal that business cycles have heterogeneous impacts on the group common factors.

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The increase in global economic policy uncertainty (EPU), volatility or stock market uncertainty (VIX), and geopolitical risk (GPR) has affected gold prices (GD), crude oil prices (WTI), and stock markets, which present challenges for investors. Sustainable stock investments in emerging markets may minimize and diversify investor risk. We applied the non-linear autoregressive distributed lag (NARDL) model to examine the effects of EPU, VIX, GPR, GD, and WTI on sustainable stocks in seven emerging markets (Thailand, Malaysia, Indonesia, Brazil, South Africa, Taiwan, and South Korea) from January 2012 to June 2023. EPU, VIX, GPR, GD, and WTI showed non-linear cointegration with sustainable stocks in seven emerging markets and possessed different asymmetric effects in the short and long run. Change in EPU increases the return of Thailand’s sustainable stock in the long run. The long-run GPR only affects the return of Indonesian sustainable stock. All sustainable stocks are negatively affected by the VIX and positively affected by GD in the short and long run. Additionally, long-run WTI negatively affects the return of Indonesia’s sustainable stocks. Our findings contribute to rational investment decisions on sustainable stocks, including gold and crude oil prices, to hedge the asymmetric effect of uncertainty.

The Efficient-Market Hypothesis (EMH) is one of the important theories in financial markets. Under this hypothesis, developing a robust profitable strategy is infeasible because the market price fluctuates immediately following any new information and is thus unpredictable. However, many empirical studies have shown that certain trading strategies in the financial markets are profitable, and the Momentum Strategy is one of the major strategies among them. With four momentum strategies, this paper uses the real-world data points (intra-day data of one-minute time frame) for back-testing the Taiwan Stock Exchange Capitalization Weighted Stock Index Futures (TAIEX Futures) during the period from January 04, 2010 to March 25, 2015. Numerical comparisons among the four strategies reveal that there exist market inefficiencies in Taiwan stock market. We verified the momentum effect of Taiwan Index Futures market through different stop-loss and stop-profit mechanisms. In conclusion, the management of stop-loss and stop-profit is crucial in the profit/loss of the trading strategy. The technique can be applied to many trading methodologies in improving the quality of strategies. Money management provides another path for strategy planning other than purely focusing on the technical mechanisms.

Abstract For the Taiwanese stock market, evidence from the present study documents significant reversal in January-February, but strong momentum in March-December when there are increases of lagged M1B. Moreover, the M1B-induced momentum manifests only over economic expansion, rather than economic recession. Both the reversal and the momentum can be partly explained by unrealized capital gains, implying the disposition effect to some extent driving both phenomena since Grinblatt and Han (2005) used unrealized capital gains as a proxy for the disposition effect. We further find the reversal primarily occurring in January, implying reverse disposition trading occurring before the beginning of a year. As there are no capital gain taxes levied in Taiwan, the reverse disposition trading cannot be related to tax-loss selling as in U.S. Furthermore, time-varying market risk exposure cannot explain the reversal in most cases. For the March-December momentum, apart from unrealized capital gains, the CAPM and the Fama-French 3-factor models can each to some extent explain the momentum. JEL classification numbers: G10, G11, G19. Keywords: Price momentum; Reversal; Disposition effect; Emerging stock market.

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