This paper shows that for firms in Pacific time zone of the United States, the effect on weekly returns from overnight returns would be 23% stronger than firms in Eastern time zone. This asymmetrical impact is documented to be associated with degrees of information transparency due to firm's different timings of information releases. This paper demonstrates that such asymmetric effect contributes to the post earnings announcement drifts where the 1-day overnight returns at announcement day lead to higher cumulative returns of firms in the East coast; suggesting that for firms in the West, lower information asymmetry leads to weaker drift of returns.

We integrate classic financial variables with financial-network structures in a Graph Convolutional Network (GCN) framework to address transparency in machine-learning applications to finance. By stratifying and grouping data based on known variables, our GCN model improves both the interpretability and accuracy of financial analysis. This approach clarifies the interconnected nature of financial markets and shows the predictive value of combining traditional financial insights with advanced modeling techniques.

This paper evaluates the method of naive risk parity (RP) in portfolio trading, particularly with Standard & Poor's 500 stocks as components. Several sample selection criteria based on prevalent risk factors are applied, and the dynamic technique of RP is examined. Three performance risk-adjusted ratios and four downside risk measurements are used for evaluation. It is found that both conventional and dynamic versions of RP portfolios generally outperform traditional value-weighted and equal-weighted portfolios. In particular, when firm size is used as a sample selection criterion, RP portfolios show the largest improvements compared with other portfolio types. Various combinations of formation and holding periods are investigated, and the empirical results for subperiods are produced and discussed. When the economy is in a downturn, RP portfolios with a selection criterion based on stock momentum outperform other strategies, presumably due to volatilities among the stocks being highly correlated. This paper sheds light on the application of the RP strategy with components only from stocks, and it shows that for investments with a long horizon, RP constitutes an effective and profitable alternative risk-adjusted strategy.

In financial literature, institutional investors hold profound influence on stock dynamics. In Taiwan's stock market, institutional investors dominate largely due to the nation's unique financial regulations mandating daily trading data disclosure. This high-frequency data, distinct to Taiwan, offers an unparalleled opportunity for in-depth market analysis. Despite their importance, scant research employs machine learning to predict these investors' dynamic movements. Our study fills this gap, leveraging Taiwan's unique 2019-2022 transactional data with machine learning techniques. Impressively, insights derived yielded an 8.8% average cumulative return in just 20 days, highlighting the potential of understanding and leveraging these dynamics.

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This paper examines the relationship between product life cycle and book-to-market effect on cross-sectional stock returns. While previous papers suggest that the book-to-market effect is related to a firm’s market value and fundamental value, this paper examines the product life cycle, which directly affects future cash flows. We find that the book-to-market effect is stronger for firms with a long product life cycle, which is consistent with the mispricing story in explaining the book-to-market effect. We further find that the role of product life cycle is more critical for firms with high investor limited attention, and that the product life cycle in part explains intangible returns.

Using measurement of overnight returns, this article documents that trading behaviors due to information shocks and investor sentiments contribute to distress anomaly. We find that stocks with the highest (lowest) overnight returns are accompanied with the greatest (smallest) distress risk premium, and accordingly, a conditional distress probability portfolio composed with double sort of overnight returns and distress probability would yield significant profitability. The regression outcomes demonstrate that probability of default significantly interacts with average overnight returns in explaining the future stock returns. The conventional distress anomaly is subsumed by the conditional distress factor, but not vice versa; and only profits of conventional distress-probability portfolios, not of the conditional portfolios, mainly come from stocks with characteristics that are associated with stock mispricing. The empirical results indicate that investors hold distressed stocks longer than expected because positive information shocks occur with high level of information supply and good information quality.

This paper documents significant relationship between overnight returns and future stock returns in the long-term where high averages of overnight returns lead to low future stock returns, with formation periods ranging from 1 month to 1 year. On the other hand, variations in overnight returns lead to different reactions of future stock returns, depending on the levels of past return performances and stabilities of momentum effects. Return reversals are strongest for stocks with extreme past returns. When momentum effects are volatile, higher variations of overnight returns lead to higher future stock returns. When momentum effects are stable, lower variations of overnight returns lead to higher future stock returns for stocks with extreme positive past returns; for stocks that perform worst in the past few months, the two variables have a non-linear relationship. A set of sample sorting criteria according to above relationship are found to significantly enhance the profitability of momentum trading strategy.

Prior studies have documented that information presence (absence) leads to price continuation (reversal) when a stock price experiences extreme shock. The authors investigate whether the level of investor attention have an impact on stock price dynamics following the shock. They show that when shocks are not accompanied by new information, the price generally reverses, and the magnitude of the reversal is stronger for stocks with lower degree of investor attention. The asymmetric effect on the magnitude of reversal is stronger for stock with higher return volatility. In addition, for the price shocks accompanied by new information, stock price would continue for a short run, and such continuation is statistically significant and stronger when investors are optimistic toward to the stocks.