Slow Diffusion Of Information Research Articles

Stock price delay and the cross-section of expected returns: A story of night and day

We examine how individual stock price reacts to intraday and overnight market information. By examining stock betas, we document a stark gap between day beta and night beta and nontrivial asynchronous beta, which captures the slow diffusion of information. We provide evidence that this information delay can predict future stock returns over a one-month period. Long-short portfolios sorted on the gap between day beta and night beta and asynchronous beta generate raw returns of 0.8% and 0.39% and risk-adjusted alphas of 0.77% and 0.28% per month. These results are robust to alternative asset pricing models and when controlling for firm characteristics, such as size, book-to-market ratios, liquidity, investor recognition and limits-to-arbitrage characteristics. We also explore the heterogeneity of the information delay premium and find that the predictive power of information delay for future return is stronger among small, value firms, less visible firms, illiquid firms and firms with higher limit-to-arbitrage. We also provide evidence that our measure of price delay indeed offers incremental information for cross-section return prediction after we explicitly control for the conventional measure of Hou and Moskowitz (2005)’s price delay.

Cross-cryptocurrency return predictability

Using data from Binance, we find strong evidence of cross-cryptocurrency return predictability. The lagged returns of other cryptocurrencies serve as significant predictors of focal cryptocurrencies. The results are robust across various methods, including the adaptive LASSO and principal component analysis. Furthermore, a long-short portfolio formed on the past returns of cryptocurrencies can generate a sizable return out-of-sample after accounting for transaction costs. Overall, our findings corroborate cross-cryptocurrency return predictability and are consistent with the spillover effect mechanism, where common shocks among cryptocurrencies coupled with the limited attention of investors lead to slow information diffusion across coins.

Financial Linkage via Idiosyncratic Shocks: A Case in an Emerging Market

ABSTRACT Slow diffusion of information in inefficient markets can lead to the transmission of idiosyncratic shocks and make some financial linkages among firms. Using monthly data from 250 firms on Tehran Stock Exchange, we predict the links originated from the idiosyncratic shocks. We use the extracted links for two purposes. In the first step, we examine whether considering the idiosyncratic shocks can lead to a positive abnormal return. In addition, we investigated how the idiosyncratic shocks can help to solve the puzzle of idiosyncratic volatility in the Tehran Stock Exchange. The results show that a portfolio with more shocks yields a significant positive abnormal return. The results also show that using the average idiosyncratic shocks cannot help to solve the puzzle of idiosyncratic volatility. Since the first step results show the significant effect of slow information diffusion on asset returns, we can examine the idiosyncratic shocks as a risk propagation channel in the financial network. Using the network theory, we investigate the idiosyncratic shocks contagion in the financial market. We consider the centrality measures to identify the most vulnerable and systemically important firms and sectors in the financial system. The results show that the portfolios consisting of vulnerable firms can make an abnormal positive alpha with the expense of high systemic risk.

Common analyst links and predictable returns: Evidence from China

Based on shared analyst coverage, we are the first to document the common-analyst momentum (CAM) effect in China. Empirically, we show that average returns of common-analyst peer firms have strong predictive power for future focal firm returns. Moreover, the CAM effect is stronger than other cross-asset momentum (XAM) effects. Interestingly but differently, the CAM cannot unify other XAM effects as the U.S. market does. Exploiting the underlying mechanism, we find that common-analyst-connected firms are fundamentally similar. Further, the CAM effect is stronger when inter-firm linkages are stronger when the information processing task is more complex and on earnings announcement dates. We conclude that sluggish analyst forecasting and investors’ attention constraint could contribute to the stronger CAM effect, and our results support the hypothesis that slow information diffusion generates the CAM effect.

High-Frequency Effects of Novel News on the EURUSD Exchange Rate

We study the intraday relationship between novel Euro-specific news and the EURUSD exchange rate with over 100 million news articles with millisecond precision. We show a predictive but not contemporaneous relationship between these news and their sentiment and EURUSD returns. This lends support to the slow information diffusion hypothesis as well as rational inattention to new news theories. Furthermore, we identify a regime change in the relationship between news and the EURUSD returns since the Global Financial Crisis (GFC). The positive relationship observed since the GFC between news sentiment and the EURUSD was particularly prominent during the Eurozone debt crisis.

Far away from home: Investors’ underreaction to geographically dispersed information

Using a novel data set to identify geographic peer firms based on the locations of both firms’ headquarters and material subsidiaries, we show that returns on geographical peers have strong predictive power for focal firm returns. A value-weighted long-short strategy that buys stocks with the highest geo-peer returns and shorts stocks with the lowest geo-peer returns generates a Fama and French(2015) five-factor alpha of 0.6% per month. This strategy is distinct from other cross-firm momentum strategies and cannot be explained by local economic conditions. The effect is more pronounced among firms that receive less investor attention and that are more costly to arbitrage, consistent with slow information diffusion in the geographic network into stock prices.

Concept links and return momentum

Unlike traditional asset categories (e.g., industry classifications) that are generally defined clearly, some groups of stocks are tied to certain loosely defined “concepts” (e.g., e-commerce). When investors find it difficult to analyze ambiguous concept-oriented information, information diffuses slowly, creating “concept momentum”. Based on unique concept data in the Chinese stock market, this study constructs a concept-momentum strategy that involves buying stocks from past winning concepts and selling stocks from past losing concepts, which can generate pronounced abnormal returns. Neither risk factors, firm-level momentum, nor industry-level momentum can explain concept momentum. Furthermore, we find that both the underreaction and cross-stock lead-lag effect channels can cause slow information diffusion and drive concept momentum. Moreover, the concept momentum effect is stronger for relatively ambiguous concepts, for concepts that attract less investor attention, and following high-sentiment periods.

Buy-Side Competition and Momentum Profits

Abstract We show that a new measure of buy-side competition explains momentum profits. The momentum quintile spread is 1.11% when competition is low and negligible when competition is high. Better alphas are attained with superior Sharpe and Sortino ratios, with no negative skewness, and in more investible strategies featuring value-weighted portfolios and large capitalization stocks. Stock characteristics traditionally related to momentum do not explain our results. Tests based on long-term reversals, the trading patterns of funds, their style peers, distant funds, and retail investors suggest that slow information diffusion explains the large momentum spreads and momentum reversals in low competition markets.

Informed Trading and Momentum in the Corporate Bond Market

Abstract Taking advantage of the different trading behaviors of investors on same-issuer bonds, we show that informed trading lies at the core of the momentum effect for corporate bonds. We split the firm-level bond cross-section into top (nontop) bonds that are characterized by higher (lower) volumes of institution-sized trades. We show that top bonds attract more informed trading and transmit information faster than nontop bonds. We design specific top and nontop bond momentum strategies to capitalize on this informational heterogeneity. The results indicate that fast news spreading yields short-lived momentum in top bonds, whereas momentum in nontop bonds is strong and drawn-out due to slow information diffusion. These differences are concentrated in bond-level information-intensive periods and are not explained by differences in liquidity levels, systematic risk (including liquidity risk), bond characteristics, and market states. In particular, bond-level liquidity affects the momentum effect only by altering the rate at which news spreads.

Where Does the Predictability from Sorting on Returns of Economically Linked Firms Come From?

AbstractCross-firm predictability among economically linked firms can arise when both firms exhibit their own momentum and their returns are contemporaneously correlated. We show that cross-firm predictability can last up to 10 years, which is hard to reconcile with an interpretation of slow information diffusion. However, it is consistent with the economically linked firms’ commonality in momentum. The contribution of each source can be found by decomposing leaders’ returns into the predictable (momentum) and news components. Sorting on each, we find that both sources contribute almost equally to 1-month predictability, whereas commonality in momentum is solely responsible for longer-horizon cross-firm predictability.

China Markets and Global Stock Return Predictability

This paper explores the China equity market's impact on the global equity markets through the lens of asset pricing. We study the predictive properties of the lagged China returns for global stock returns and find that the lagged China returns can significantly predict other markets' stock returns, but not vice versa. We augmented three selected global equity pricing models (Chen, Roll, and Ross's model (CRR), Ang and Bekaert's model (AB), and Rapach, Strauss, and Zhou's model (RSZ)) with the lagged China returns respectively, and find that these popular global stock return predictors cannot explain the predictive power of the lagged China returns. Moreover, we find that only the lagged China returns can lead the international returns at the one-month horizon but not the lagged US returns. Further evidence reveals it is due to that the information diffusion of the China market occurs at the longer-horizons (e.g. one-month) while the information diffusion of the US market occurs at the shorter-horizons (e.g. one-week). Lastly, we documented that the lagged China returns during the low investor-attention period have stronger predictability compared to the predictive performance during the high attention period, which is in line with the information friction theory that when the attention-constrained investors make investment decisions they fail to allocate attention to certain economic state variables, which causes the slow information diffusion across markets and leads to the cross-market return predictability. Overall, our results indicate that the lagged China returns should be regarded as a global state variable that helps us predict future stock returns.

Cross-momentum: Tracking idiosyncratic shocks

In the presence of slow diffusion of information, the transmission of idiosyncratic shocks through economically linked firms leaves footprints in the prices. In this paper, we estimate the links between firms using past prices and predict the average shock exerted to each firm in the entire cross-section. The long-short cross-momentum portfolio constructed based on average shocks yields significant alphas and performs well in explaining variation in returns.

Geographic spillover of dominant firms’ shocks

This paper shows that productivity shocks to the 100 largest U.S. firms (by revenue) contain systematic information. Specifically, shocks to the top-100 firms predict future shocks to geographically close firms. Intra-sector trade links are an important economic channel for spillover effects. However, these spillovers are not restricted to firms’ trade links only. Knowledge externalities and state income tax payments of the top-100 firms are other economic channels through which shocks propagate. Market participants do not fully incorporate the information contained in shocks to the top-100 firms. Consequently, a portfolio analysis that exploits the slow diffusion of information generates an annual risk-adjusted return of 5.4%. Overall, the results show how productivity shocks to the few largest firms in the U.S. spillover to other firms and potentially aggregate to affect the national economy.

Overlapping Momentum Portfolios

Different momentum investors use different time horizons, or formation periods, to evaluate prior stock performance. We show that this heterogeneity has important consequences on asset returns. We provide evidence that heightened trading pressure due to the concurrence of the heterogeneous momentum investors contributes to enhanced return autocorrelation. In particular, U.S. overlapping momentum stocks - stocks that are in the intersection of the 6-month and 12- month momentum portfolios - display superior medium-term returns to the momentum strategy that neglects the overlap. The differential returns of the overlapping momentum portfolio are robust to the inclusion of a broad set of risk factors and interpretations. The results provide insights on the underlying mechanisms of under and over-reaction in financial markets as the result of limited information investment rules and slow information diffusion.

Stock market anomalies and baseball cards

AbstractBaseball cards exhibit anomalies that are analogous to those that are documented in financial markets, namely, momentum, price drift in the direction of past fundamental performance, and initial public offering (IPO) underperformance. Momentum is higher among active players than retired players, and among newer sets than older sets. Regarding IPO underperformance, we find that newly issued rookie cards underperform newly issued cards of veteran players, and that newly issued sets underperform older sets. The results are broadly consistent with models of slow information diffusion and short‐selling constraints.

Asymmetric relationship between implied volatility, index returns and trading volume: an application of quantile regression model

The study explored a vital issue on market microstructure that is the relationship between volatility, index returns and trading volume within the Indian stock market. The daily closing prices of select indices, from 2 March 2009 to 31 July 2018, were taken. The investor’s fear index India Volatility Index (VIX) was used as an implied volatility measure popularly the investor’s fear index, whereas Nifty50 daily returns and trading volume were considered. Toda–Yamamoto causality test provided limited information about the causal relationship among the variables. The results of the Toda–Yamamoto test captured only the central values of the dependent variable’s distribution; therefore, quantile regression models (QRM) were estimated. Through QRM, the asymmetric impact of returns was estimated on volume and volatility changes. The asymmetric relationship of stock returns with changes in volatility and volume distribution was found significant. It was found to be stronger at extreme ends of the dependent variable’s distribution. The study supports the behavioural justification for a contemporaneous negative return–volatility relationship but conditionally. Evidence of investor’s heterogenous beliefs is found. The evidence of leverage effect was also significant for the lagged period. The contemporaneous negative relationship was found between volatility and volume changes highlighting that the investors in Indian markets are risk-averse. But the positive lagged effect of changes in volatility on trading volume supports sequential arrival of information hypothesis and affirmed the presence of noise traders. They make the information arrival sequential and allow the slow diffusion of information. India VIX potentially can be used for portfolio hedging purposes.

Buy-Side Competition and Momentum Profits

We show that a new measure of buy-side competition explains momentum profits. The momentum quintile spread is 1.11% when competition is low and negligible when competition is high. Better alphas are attained with superior Sharpe and Sortino ratios, no negative skewness and in more investible strategies featuring value-weighted portfolios and large capitalization stocks. Stock characteristics traditionally related to momentum do not explain our results. Tests based on long-term reversals, the trading patterns of funds, their style peers, distant funds, and retail investors suggest that slow information diffusion explains the large momentum spreads and momentum reversals in low competition markets.

The Information Content of 'Big' Firms' Idiosyncratic Shocks

This paper shows that productivity shocks to the 100 largest U.S. firms (by revenue) contain systematic information. Specifically, shocks to the top-100 firms predict future shocks to geographically close firms. Intra-sector trade links are an important economic channel for spillover effects. However, these spillovers are not restricted to firms' trade links only. Knowledge externalities and state income tax payments of the top-100 firms are other economic channels through which shocks propagate. Market participants do not fully incorporate the information contained in shocks to the top-100 firms. Consequently, a portfolio analysis that exploits the slow diffusion of information generates an annual risk-adjusted return of 5.4%. Overall, the results show how productivity shocks to the few largest firms in the U.S. spillover to other firms and potentially aggregate to affect the national economy.

Slow diffusion of information and price momentum in stocks: Evidence from options markets

This paper investigates the source of price momentum in the stock market using information from options markets. We provide direct evidence of the gradual information diffusion model in Hong and Stein (1999): momentum profits are larger for stocks whose information diffuses slowly into the stock market. We exploit the options markets to identify stocks with slow information diffusion speed. As informed traders trade options to realize the information that has not been fully incorporated in the stock price, we are able to enhance the momentum strategy by selecting winner/loser stocks with high growth/large drop in call option implied volatility. Our empirical strategy generates a risk-adjusted alpha of 1.8% per month over the 1996–2011 period, during which the simple momentum strategy fails to perform. The results are robust to the impact of earnings announcement, transaction costs, industry concentration, and choice of options’ moneyness and time-to-maturity. Finally, our finding is not driven by existing stock- or option-related characteristics that are known to improve momentum.

Do Chinese Industries Predict the Stock Market Due to Slow Diffusion of Information? Analysis of the Shanghai Stock Exchange

Returns predictability and cross autocorrelations is an important consideration in behavioral finance and is of considerable interest to investors and financial economists. This area gained much attention due to developments in behavioral finance during the last two decades. Previously, lead-lag patterns were supported by argument of rational expectations, however, it lacked much empirical evidence. According to behaviorists' views, it is human inability to process all sorts of information and thus, information moves diffuse slowly from one market to another. In this study, we tested this slow diffusion of information using an extensive set of data over a period from 2004-2013. We found that the market was led by industry returns up to a period of four weeks. This predictive power of industries decreased as the prediction horizon increased. These findings are robust, even if we control for industry size and trading volume. Thus, these findings add considerable support to the slow diffusion of information hypothesis.