Gradual Information Diffusion Research Articles

Gradual information diffusion across commonly owned firms

This paper studies how common institutional ownership (CIO) affects information diffusion in the stock market. My findings suggest that CIO can exacerbate the slow spread of information across firms. With over 50% of institutional investors holding concentrated stock portfolios, I infer a fundamental connection among firms with CIO. These firms exhibit cross-predictability in monthly stock returns, leading to a CIO-based peer momentum strategy that outperforms Ali and Hirshleifer's (2020) shared-analyst momentum strategy. This anomaly stems primarily from institutional investors with fewer stock holdings, who employ passive asset management characterized by lower portfolio turnover and more delegated investment.

Do industries predict stock market volatility? Evidence from machine learning models

In a novel take on the gradual information diffusion hypothesis of Hong et al. (2007), we examine the predictive role of industries over aggregate stock market volatility. Using high frequency data for U.S. industry indexes and various heterogeneous autoregressive (HAR) type and machine learning models, we show that most industries are informative for future aggregate market volatility in out-of-sample tests. While the oil and gas industry plays a more dominant role for the component of aggregate market volatility that is associated with discount rate fluctuations, consumer services are most informative over market volatility that is attributable to cash flow fluctuations. More importantly, we find that the predictive information captured by industries not only helps improve the volatility forecasts for the stock market, but can also be used to generate significant economic benefits for investors who use these volatility forecasts in their asset allocation strategies.

Is There Smart Money? How Information in the Commodity Futures Market Is Priced into the Cross Section of Stock Returns with Delay

AbstractWe document a new empirical phenomenon in which the aggregate positions of money managers, who are sophisticated speculators in the commodity futures market, as disclosed by the Disaggregated Commitments of Traders reports, can predict the cross section of commodity producers’ stock returns in the subsequent week. We employ a number of cross-sectional methods, including calendar-time regression analysis, single-sort, double-sort, and Fama–MacBeth regressions, to confirm the predictability results. The results are more pronounced in firms with higher information asymmetry. We thus add more empirical evidence to the literature on costly information processing, which leads to gradual information diffusion across asset markets.

Innovation links, information diffusion, and return predictability: Evidence from China

Based on the activities of patent citation in China, a novel type of cross-firm innovation links is generated to investigate the gradual diffusion of information along the innovation chain via tests of cross-sectional return predictability. Various signals are created to represent the value of the information contained in the innovation links; these signals are demonstrated to have robust cross-predictability for stock returns in both the cross-sectional regression model and portfolio strategies. The effect of predictability is found to be stronger for stocks with high institutional ownership and analyst coverage. Considering the minimum number of steps required to establish the cross-firm linkage, innovation links are further partitioned to represent different proximity of the linked firms. It is then found that information diffuses faster across closely-linked firms than across distantly-linked firms. Sophisticated investors are found to be able to properly process the relevant information and benefit from innovation links.

Maritime Stock Prices and Information Flows: A Cointegration Study

In this study, the issue of how global maritime stock prices influence the stock prices of large transportation companies in the U.S. and other large markets is examined. Maritime stocks are chosen because they are central in global trade and thus may be good indicators of future global stock market and economic trends. Maritime companies are often owned by families or governments and are traded in stock markets with lower standards of accountability, hence information flows from maritime stocks may be slower than flows from other stocks. Cointegration and vector error-correction analysis is used to analyze the short-term and long-term relationships between maritime stocks, rail stocks, and trucking stocks. Evidence is found of a gradual diffusion of information from maritime stock prices to large rail or trucking stocks. This suggests that price changes in maritime stocks may help predict changes in prices in non-maritime transportation stocks.

Cross-cryptocurrency Return Predictability

Using the minute-frequency data of the top 30 coins listed on Binance, which represent 86% of the total dollar trading volume of the cryptocurrency market, we document strong evidence of cross-cryptocurrency return predictability. The lagged returns of other cryptocurrencies serve as significant predictors of focal cryptocurrencies up to ten minutes without return reversals, in line with gradual information diffusion. To alleviate statistical issues, we employ the adaptive LASSO method to identify informative predictors and find consistent results. Furthermore, even after accounting for the highest transaction costs on Binance, a long-short portfolio formed based on our findings with a ten-minute rebalancing frequency generates a significant out-of-sample return of 1.14 bps on a minute basis for the futures trading, indicating a profitable investment strategy.

Oil Prices and Firm Returns in an Emerging Market

This study examines the oil price effect on Turkish stock market as an emerging country on firm level data. After controlling short term interest rate, nominal exchange rate and crude oil price, we find that firms behave differently to a change in oil prices. The findings include these: i) variations in oil prices do not significantly affect Turkish firm returns. Out of 153, only 38 firms are affected significantly by oil price after controlling exchange rate and interest rate; ii) oil prices influence stock returns of Turkish firms, suggesting that under reaction and gradual information diffusion hypotheses may hold. iii) small and middle-sized firms are more affected negatively from oil price changes, where large-sized firms affected more positively. The empirical findings of this study have potential implications and offer significant insights for both practitioners and policy makers.

Disagreement after News: Gradual Information Diffusion or Differences of Opinion?

Abstract This paper considers the puzzle of increased trading volume around information releases through the lens of canonical models of disagreement. I use a unique data set of clicks on news by key finance professionals to simultaneously measure gradual information diffusion and differences of opinion. I find that neither channel subsumes the other and that the two are complementary in generating trading volume around news events. Their relative strengths depend on the characteristics of the underlying information: gradual information diffusion matters more for straightforward news, while differences of opinion play a larger role around textually ambiguous news. (JEL G12, G14, G41, D84) Received January 12, 2020; editorial decision: November 24, 2020 by Editor: Jeffrey Pontiff. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Cointegration, information transmission, and the lead‐lag effect between industry portfolios and the stock market

AbstractThis paper shows that lagged information transmission between industry portfolio and market prices entails cointegration. We analyze monthly industry portfolios in the US market for the period 1963–2015. We find cointegration between six industry portfolio and market prices. We show that the equilibrium error, the long‐term common factor between industry portfolio and market cumulative returns, has strong predictive power for excess industry portfolio returns. In line with gradual information diffusion across connected industries, the equilibrium error proxies for changes in the investment opportunity set that lead to industry return predictability by informed investors. Forecasting models including the equilibrium error have superior forecasting performance relative to models without it, illustrating the importance of cointegration between the industry portfolio and market prices. Overall, our findings have important implications for investment and risk‐management decisions, since the out‐of‐sample explanatory power of the equilibrium error is economically meaningful for making optimal portfolio allocations.

Global integration of the Gulf cooperation council markets

PurposeThis paper investigates the global financial integration of the Gulf Cooperation Council markets, which is important for financial economists, global investors and policymakers.Design/methodology/approachThe first step is to estimate a benchmark one-factor model and multifactor models over the entire sample period to obtain the time-invariant global integration estimates for the Gulf Cooperation Council markets. Because the global integration of the Gulf Cooperation Council markets may be time varying, the second step is to use 24-month rolling regressions to estimate the time-varying integration estimates. To explicitly test for structural breaks in global integration, this study applies a supremum Wald test to endogenously search for structural breaks.FindingsEmpirically, consistent evidence suggests that the Gulf Cooperation Council markets are increasingly integrated with international equity markets at different levels of financial development and from different regions. However, compared to other emerging and frontier markets, the global integration of the Gulf Cooperation Council markets is still relatively low, suggesting that these markets still offer significant diversification benefits for global investors.Originality/valueThis study contributes to the literature by systematically investigating the global integration of the Gulf Cooperation Council markets with monthly data (to account for the gradual information diffusion in international equity markets) and a longer sample period (to more robustly identify the trend in the global integration).

Does program trading contribute to excess comovement of stock returns?

Abstract Daily returns of stocks with high program trading comove more with each other but less with others. This significant comovement is disconnected with market movements and news of fundamentals and becomes stronger when market uncertainty is higher. It can be explained by neither the hypotheses of gradual information diffusion and liquidity provision nor the effects of quantitative trading signals, earnings announcements and index fund trading. Its non-fundamental nature is further demonstrated by the observation of program trading stimulating return reversals. Underlying this comovement is the high persistence of program trading. Our findings support the theory of habitat investing and demonstrate program trading creates a distinct source of excess return comovement.

Speculator activity and the cross-asset predictability of FX returns

This paper tests the gradual information diffusion hypothesis, which suggests that information spreads gradually across asset markets, to explain the role of speculator activity in the cross-asset return predictability of foreign exchange (FX) market strategies. We argue that the activity of speculators increase the rate of information diffusion across asset markets. Hence, we expect the predictive effect from the equity and commodity markets on FX market strategies to be weaker when speculators are active in the FX market. Our results show that, when speculator activity is high, the equity market's ability to predict the FX market dissipates, but not to the same extent as for the commodity market. Our findings suggest that speculators play a vital role in enhancing informational efficiency in the FX market.

Retail Investors’ Contrarian Behavior Around News and the Momentum Effect

We study the role of retail investors in the gradual diffusion of information in financial markets. We show that retail investors tend to trade as contrarians after large earnings surprises, and such contrarian trading contributes to sluggish price adjustment and to momentum. Retail traders are particularly active for small loser stocks. As a robustness check, we double sort stocks in quintiles based on momentum and the strength of retail contrarian trading, and find that the momentum phenomenon arises only in the 4th and 5th quintile of contrarian trading intensity. We further investigate the timing and the horizon of the traders, the role of segmentation in stock ownership, and the role of attention and browsing behavior in generating contrarian trading. Alternative hypotheses, such as the disposition effect, and stale limit orders, do not explain the phenomenon.

Institutional Trading on Information Diffusion across Fundamentally Related Firms

This paper provides a new explanation to the gradual information diffusion puzzle from the perspective of information asymmetry. I document a strong cross-predictability of stock returns driven by institutional investors' private information about firms' fundamental relations. A value-weighted arbitrage portfolio yields a monthly alpha of 1.65%. Contrary to the limited attention hypothesis, the magnitude of predicted returns increases with firm size, number of institutional shareholders, and institutional trading intensity while not decreasing with analysts following. Further evidence confirm that institutional investors strategically trade a stock in response to shocks to its peers, which subsequently causes lagged permanent price movements.

The Dynamic Industry Return Predictability: Evidence from Chinese Stock Markets

ABSTRACT This paper examines the dynamics, direction, and determinants of industry return predictability in Chinese stock markets during the period 1993–2015. Using the dynamic approach, we find that industry portfolio predictability is time varying and has wide variations across industries. Lagged returns in four industries (banking, real estate, leasing, and information technology) are positively associated with aggregate market returns, while lagged returns for traditional industries are largely inversely associated with market returns. Our findings are consistent with gradual information diffusion across economically-linked industries. The likelihood of industry predictability increases by 4.5–8% in a bull market over that in the bear market. Our results advise investors to distinguish industries and stock market conditions to better time the market.

Industry Return Predictability: A Machine Learning Approach

In this article, the authors use machine learning tools to analyze industry return predictability based on the information in lagged industry returns. Controlling for post-selection inference and multiple testing, they find significant in-sample evidence of industry return predictability. Lagged returns for the financial sector and commodity- and material-producing industries exhibit widespread predictive ability, consistent with the gradual diffusion of information across economically linked industries. Out-of-sample industry return forecasts that incorporate the information in lagged industry returns are economically valuable: Controlling for systematic risk using leading multifactor models from the literature, an industry-rotation portfolio that goes long (short) industries with the highest (lowest) forecasted returns delivers an annualized alpha of over 8%. The industry-rotation portfolio also generates substantial gains during economic downturns, including the Great Recession. <b>TOPICS:</b>Big data/machine learning, analysis of individual factors/risk premia, portfolio construction, performance measurement

The Nature of Volatility Spillovers Across the International Capital Markets

This paper studies the nature of volatility spillovers across countries from the perspective of network theory and by relying on data of US-listed ETFs. I use a Lasso-related technique to estimate the International Volatility Network (IVN) where the nodes correspond to large-cap international stock markets while the links account for significant volatility lead-lags. Also included in the analysis is the International Trade Network (ITN), whose links measure bilateral export-import flows thus, capturing fundamental interconnections between countries. I find that the IVN and the ITN resemble each other closely pointing out that volatility does not disseminate randomly but tends to spread across fundamentally related economies. I also note that the amount of lagged volatility reactions in the IVN is consistent with the notion of gradual diffusion of information across investors who are subject to limited attention and home bias. I formally test this hypothesis by using as a direct proxy of investors’ attention the aggregate search frequency in Google. The empirical results support this intuition indicating that higher volatility surprises in key foreign markets predict higher domestic attention upon those markets in subsequent days. Once domestic attention is captured by such external shocks, it is contemporaneously transformed into higher domestic volatility.

Impacts of oil implied volatility shocks on stock implied volatility in China: Empirical evidence from a quantile regression approach

This paper investigates the impacts of changes in the implied volatility index of the oil market (OVX) on the changes in the implied volatility index of the Chinese stock market (VXFXI). A quantile regression approach is applied to our empirical analysis, as this approach can perform a more detailed investigation under different market conditions. Moreover, we test whether the VXFXI changes would respond with lags and asymmetry to the OVX changes. Our empirical results show that the impacts of the OVX changes on the VXFXI changes are positive and tend to be stronger in bearish markets. Furthermore, the results of testing lagged effects reveal that strong linkages between the two variables are transient in different market conditions, which don't support the gradual information diffusion hypothesis very well. Finally, we find that the OVX changes can asymmetrically affect the VXFXI changes. Specifically, the negative OVX changes have stronger effects under bullish market conditions, while the positive OVX changes play a more important role during bearish periods.

Dynamic Return Dependencies Across Industries: A Machine Learning Approach

We use machine learning tools to analyze industry return predictability based on the information in lagged industry returns from across the entire economy. Controlling for post-selection inference and multiple testing, we nd significant in-sample evidence of industry return predictability. Lagged returns for the financial sector and commodity- and material-producing industries exhibit widespread predictive ability, consistent with the gradual diffusion of information across economically linked industries. Out-of- sample industry return forecasts that incorporate the information in lagged industry returns are economically valuable: controlling for systematic risk using leading multi- factor models from the literature, an industry-rotation portfolio that goes long (short) industries with the highest (lowest) forecasted returns delivers an annualized alpha of over 8%. The industry-rotation portfolio also generates substantial gains during economic downturns, including the Great Recession.

Speculator Activity and Cross-Asset Predictability of FX Returns

The gradual information diffusion hypothesis (GIDH) suggests that information flows slowly across investors and asset markets and thus generates return predictability. We examine cross-asset return ...