Political Risk and Foreigners' Trading: Evidence from an Emerging Stock Market

This paper analyzes the impact of political risk on foreign investors' trading in emerging stock markets, market-wide and for industry portfolios, using quantified political risk ratings reported in the International Country Risk Guide and foreign flows data compiled by the Istanbul Stock Exchange. We also track the differential effect of political risk upgrades and downgrades. Political risk is shown to affect stock returns, net foreign flows, and macroeconomic variables. Foreigners' reaction to upgrades (downgrades) is slow (immediate) and smaller in magnitude. Foreigners' reaction to political risk varies with industry's sensitivity to market risk, except for the tourism sector, where their response is particularly salient. Local investors appear to provide liquidity to foreigners, who respond to information.     

Do Individual Investors Have Asymmetric Information Based on Work Experience?

Using a novel data set covering all individual investors' stock market transactions in Norway over 10 years, we analyze whether individual investors have a preference for professionally close stocks, and whether they make excess returns on such investments. After excluding own‐company stock holdings, investors hold 11% of their portfolio in stocks within their two‐digit industry of employment. Given the poor hedging properties of such investments, one would expect abnormally high returns. In contrast, all estimates of abnormal returns are negative, in many cases statistically significant. Overconfidence seems the most likely explanation for the excessive trading in professionally close stocks.     

Institutional investor’ proportions and inactive trading

We focus on a typical market anomaly-inactive trading: trading volume shrinks while stock price abnormally jumps. We calibrate a theoretical model with variance ambiguity heterogeneous among investors and illustrate that ambiguity averse investors' proportions enhance trading volume shrinkage and abnormal price jumps. We provide a cross-section analysis of stocks' inactive trading by introducing institutional investors' proportions to measure investor structures' differences among stocks. We also empirically measure relative inactive trading for constituent stocks in S&P 500 from 2014 to 2019 and demonstrate that institutional investors' proportion is negatively related to inactive trading. Finally, we demonstrate that higher proportions of institutional investors lead to less inactive trading anomalies.     

Stock market entry timing and retail investors' disposition effect

This paper explores the long-lasting impact of investors' first impression of participation in the stock market and the disposition effect. Using a unique dataset of account-level transaction records from a large brokerage firm in China, we find strong evidence for the existence of a disposition effect of Chinese retail investors. Our empirical results suggest that investors exhibit a stronger disposition effect if they initially participate in the stock market in periods of lower market returns, lower investor sentiment, higher market volatility, or higher economic policy uncertainty (EPU). The evidence enhances our understanding of the role of investors' early experience in stock market decisions and the determinants of behavioural biases in investment decisions.     

Weather,institutional investors and earnings news

We examine how pre-announcement weather conditions near a firm's major institutional investors affect stock market reactions to firms' earnings announcements. We find that unpleasant weather experienced by institutional investors leads to more delayed market responses to subsequent earnings news. Moreover, unpleasant weather of institutional investors is associated with higher earnings announcement premia. The influence of institutional investors' weather is robust after controlling for New York City weather, extreme weather conditions, and firm local weather. Additional cross-sectional evidence suggests that the strength of this weather effect is related to institutional investors' trading behavior.     

Trading behaviour and market sentiment: Firm-level evidence from an emerging Islamic market

We provide firm-level evidence from an emerging Islamic market that individual investors' trading behaviour causes weekend sentiment. Using data for 285 companies listed on the Dhaka Stock Exchange (DSE) for the period from 2002 to 2019 and applying appropriate econometric techniques, the paper has found evidence of weekend effect both on return and volatility. The results confirm that individual investors' sentiment drives the weekend effect in DSE. ‘Information content theory’ and ‘information processing hypothesis’ work for investors so that the market return and volatility become significantly different on Sunday. The market sentiment effect is significant for smaller firms and low dividend yield firms where individual investors are prevalent, suggesting that trading behaviour of individual investors determines weekend sentiment. A positive feedback relationship exists between returns on Sunday and the previous Thursday for both institutions and individuals. Our results are robust in various alternative specifications.     

Announcing the announcement

What drives investors’ attention? We study how far in advance earnings calendars are pre-announced and find that investors are more attentive to earnings news when such details are disclosed well ahead of time. This variation in investors' attention affects short-run and long-run stock returns, thereby creating incentives for firms to strategically pre-announce the report date on short notice when the earnings news is bad. Consistent with this idea, firms pre-announce their report dates well ahead of time when earnings are good and do it at the very last moment when earnings are bad. A trading strategy that exploits such variations yields abnormal returns of 1.5% per month.     

The change in stock-selection risk and stock market returns

Following Jiang et al. (2021), who propose a stock-selection opportunity (SSO) measurement by the absolute average positive alpha of individual stocks to reflect stock-selection timing, we construct a stock-selection risk (SSR) measure from the perspective of negative alphas of individual stocks. Then, we investigate the predictive abilities of SSO, SSR, the change of SSO (CSSO), and the change of SSR (CSSR) on stock market returns. By using data from 2003 to 2020 in China, we find that only CSSR significantly predicts future one-month market returns. Moreover, considering other popular predictors, our in-sample and out-of-sample results and a series of robustness checks support the proposal that CSSR provides unique information for predicting market returns that reduces forecast errors and increases economic value for investors. Furthermore, our trading activity evidence shows that CSSR predicts stock market returns due to investors' underreaction to the information of CSSR.     

Leverage Is a Double-Edged Sword

We use proprietary data on intraday transactions at a futures brokerage to analyze how implied leverage influences trading performance. Across all investors, leverage is negatively related to performance, due partly to increased trading costs and partly to forced liquidations resulting from margin calls. Defining skill out-of-sample, we find that relative performance differentials across unskilled and skilled investors persist. Unskilled investors' leverage amplifies losses from lottery preferences and the disposition effect. Leverage stimulates liquidity provision by skilled investors, and enhances returns. Although regulatory increases in required margins decrease skilled investors' returns, they enhance overall returns, and attenuate return volatility.     

Institutional trading,information production,and corporate spin-offs

We use a large sample of transaction-level institutional trading data to analyze, for the first time in the literature, the role of institutional investors as producers of information around corporate spin-offs. Our results may be summarized as follows. First, there is a significant imbalance in post-spin-off institutional trading between the equity of new parent firms versus subsidiaries, suggesting that spin-offs increase institutional investors' welfare by relaxing a trading constraint. This imbalance in institutional trading is driven by differences in information asymmetry across the two spun-off firm divisions. Second, institutional trading around spin-offs has significant predictive power for the announcement effect of a spin-off and for post-spin-off long-run stock returns. Third, institutional investors are able to realize significant abnormal profits by trading in the subsidiary firm equity in the first quarter post-spin-off. Overall, we show that spin-offs enhance information production by institutional investors, who profit from this enhanced information production.     

Are investors consistent in their trading strategies? An examination of individual investor-level data

While it has been demonstrated that momentum or contrarian trading strategies can be profitable in a range of institutional settings, less evidence is available concerning the actual trading strategies investors adopt. Standard definitions of momentum or contrarian trading strategies imply that a given investor applies the same strategy to both their buy and sell trades, which need not be the case. Using investor-level, transaction-based data from China, where tax effects are neutral, we examine investors' buy-sell decisions separately to investigate how past returns impact differentially on the trading strategies investors adopt when buying and selling stock. After controlling for a wide range of stock characteristics, extreme price changes and portfolio value, a clear asymmetry in trading is observed; with investors displaying momentum behavior when buying stocks, but contrarian behavior when selling stocks. This asymmetry in behavior is not driven purely by reactions to stock characteristics or extreme stocks. We discuss behavioral and cultural explanations for our findings.     

Can the probability of extreme returns be the basis for profitable portfolios? Evidence from China

Based on the generalized logit predicting model from Jang and Kang (2019), this paper estimates the ex-ante probability of extreme returns and finds that the significantly negative (positive) influence of the predicted crash (jackpot) probability is robust, whether based on the traditional portfolio construction, orthogonalized portfolio construction and Fame-Macbeth cross-section regression. Further analyses show both the behavioral speculators' trading and rational investors' arbitrage limits could be the sources of mispricing caused by extreme returns. Overall, this paper applies a predicting model to estimate the probabilities of the future extreme returns, and figures out the significant influence and possible sources of the crash and jackpot probabilities in China. Portfolios based on extreme return probabilities can be profitable and steady bases for uninformed investors in the Chinese stock market.     

Pandemic-induced fear and stock market returns: Evidence from China

We construct a pandemic-induced fear (PIF) index to measure fear of the COVID-19 pandemic using Internet search volumes of the Chinese local search engine and empirically investigate the impact of fear of the pandemic on Chinese stock market returns. A reduced-bias estimation approach for multivariate regression is employed to address the issue of small-sample bias. We find that the PIF index has a negative and significant impact on cumulative stock market returns. The impact of PIF is persistent, which can be explained by mispricing from investors' excessive pessimism. We further reveal that the PIF index directly predicts stock market returns through noise trading. Investors' Internet search behaviors enhance the fear of the pandemic, and pandemic-induced fear determines future stock market returns, rather than the number of cases and deaths caused by the COVID-19 pandemic.     

Futures Market Sentiment and Institutional Investor Behavior in the Spot Market: The Emerging Market in Taiwan

This paper investigates whether and how futures market sentiment and stock market returns heterogeneously affect the trading activities of institutional investors in the spot market in Taiwan. Our empirical results suggest that foreign investors are net sellers whenever futures market sentiment is bullish and net buyers when investor sentiment is bearish. The two types of domestic institutional investors have poor sentiment timing abilities and the price-pressure effect may account for the behavioral differences among institutional investors. In addition, all three institutional investors are momentum traders. Nevertheless, the momentum trading of foreigners is consistent with an information-based model and that of two local institutional investors, as behavior-based models suggest. This indicates that the same trading momentum strategy can lead to different outcomes for different investors, and both information- and behavior-based momentum trading can exist contemporaneously in the Taiwanese stock market.     

The Effects of Stock Characteristics on the Direction and Extent of Herding by Foreign Institutional Investors in the Taiwan Stock Exchange

We use a dynamic herding measure to explore the causes of foreign institutional investor (FII) herding in the Taiwan stock market and examine the effects of stock characteristics on the direction and extent of such herding. We find that FII herding primarily results from cascades rather than habit investing or momentum trading. The result of a panel smooth transition regression shows that FIIs' negative cascades focus on their largest net purchases of stocks, but FIIs' positive cascades focus on winner and small-sized stocks. To increase portfolio returns, investors can use FIIs' cascades to inform their stock purchases.     

Stock market return predictability: A combination forecast perspective

Based on traditional macroeconomic variables, this paper mainly investigates the predictability of these variables for stock market return. The empirical results show the mean combination forecast model can achieve superior out-of-sample performance than the other forecasting models for forecasting the stock market returns. In addition, the performances of the mean combination forecast model are also robust during different forecasting windows, different market conditions, and multi-step-ahead forecasts. Importantly, the mean combination forecast consistently generates higher CER gains than other models considering different investors' risk aversion coefficients and trading costs. This paper tries to provide more evidence of combination forecast model to predict stock market returns.     

Information salience,investor sentiment,and stock returns: The case of British soccer betting

Soccer clubs listed on the London Stock Exchange provide a unique way of testing stock price reactions to different types of news. For each firm, two pieces of information are released on a weekly basis: experts' expectations about game outcomes through the betting odds, and the game outcomes themselves. The stock market reacts strongly to news about game results, generating significant abnormal returns and trading volumes. We find evidence that the abnormal returns for the winning teams do not reflect rational expectations but are high due to overreactions induced by investor sentiment. This is not the case for losing teams. There is no market reaction to the release of new betting information although these betting odds are excellent predictors of the game outcomes. The discrepancy between the strong market reaction to game results and the lack of reaction to betting odds may not only be the result from overreaction to game results but also from the lack of informational content or information salience of the betting information. Therefore, we also examine whether betting information can be used to predict short-run stock returns subsequent to the games. We reach mixed results: we conclude that investors ignore some non-salient public information such as betting odds, and betting information predicts a stock price overreaction to game results which is influenced by investors' mood (especially when the teams are strongly expected to win).     

Opening price manipulation and its value influences

We investigate how opening price manipulation influences market behaviors and investors' returns. Analyzing direct evidence comprising 87 opening price manipulation cases, and indirect evidence consisting of 19,003 suspected cases detected by an opening price manipulation identification model that we construct, we examine the impact of manipulation on mispricing, investors' welfare, trading activity and price volatility. Our results indicate that manipulated stocks experience significantly lower returns and a higher probability of price reversal after manipulation. Investors who purchase manipulated stocks at their opening price, or the volume-weighted average price, on the manipulation day make losses on their investments. Further, manipulation increases market trading activity and price volatility due to the influx of retail investors. Our additional analysis demonstrates that enhancing the intensity of external supervision and internal governance can mitigate mispricing caused by opening price manipulation. Our study provides novel evidence of the economic consequences of open market manipulation and policy implications for governments and regulators to develop effective supervisory processes to reduce manipulation and mitigate its impact on efficient markets.     

The turn-of-the-year effect and tax-loss-selling by institutional investors

Prior studies attribute the turn-of-the-year effect whereby small capitalization stocks earn unusually high returns in early January to tax-loss-selling by individual investors and window-dressing by institutional investors. My results suggest that a significant portion of the effect on turn-of-the-year returns that prior studies attribute to window-dressing is actually attributable to tax-loss-selling by institutional investors. Among small capitalization stocks, I find that institutional investors with strong tax incentives and weak window-dressing incentives realize significantly more losses in the fourth quarter than in the first three quarters of the calendar year, and that their fourth quarter realized losses have a significant impact on turn-of-the-year returns. A one percentage point change in these institutional investors' fourth quarter realized losses scaled by a firm's market capitalization results in an increase of 47 basis points in the firm's average daily return over the first three trading days of January, which represents a 46 percent change for the mean firm.     

Why Does Return Predictability Concentrate in Bad Times?

We build an equilibrium model to explain why stock return predictability concentrates in bad times. The key feature is that investors use different forecasting models, and hence assess uncertainty differently. As economic conditions deteriorate, uncertainty rises and investors' opinions polarize. Disagreement thus spikes in bad times, causing returns to react to past news. This phenomenon creates a positive relation between disagreement and future returns. It also generates time‐series momentum, which strengthens in bad times, increases with disagreement, and crashes after sharp market rebounds. We provide empirical support for these new predictions.