Gradual Information Diffusion Research Articles

Momentum in the European Corporate Bond Market: The Role of Bond-Specific Returns

This paper analyzes momentum patterns in the European corporate bond market. We study a broad sample of Euro-denominated investment grade and noninvestment grade bonds covering the period January 2004 to October 2016. Our empirical findings reveal that momentum is mainly concentrated among noninvestment grade bonds. Furthermore, the composition of the momentum portfolios varies over time and is related to bond characteristics. Taking this into account, we apply characteristics-based adjustments in context with performance measurement and find momentum profits to remain robust. Most importantly, bond-specific return components seem to drive momentum patterns, indicating gradual information diffusion in bond prices.

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

We document a new empirical phenomenon in which the positions of money managers (MM), who are sophisticated speculators in the commodity futures market, as disclosed by the CFTC Disaggregated Commitments of Traders (DCOT) reports, can predict the cross-section of commodity producers' stock returns in the subsequent week. We employ cross-sectional methodologies including single-sort, Jensen's alpha analysis, double-sort, and Fama-Macbeth regressions to confirm the predictability results. The results are more pronounced in firms with higher information asymmetry, proxied by analyst dispersion and historical volatility. We thus provide more empirical evidence to the literature on costly information processing which leads to market segmentation and gradual information diffusion across asset markets, as demonstrated in the lead-lag relationship.

Information diffusion of upstream and downstream industry-wide earnings surprises and its implications

This study presents new evidence that industry-wide earnings surprises diffuse gradually across the supply chain at both industry and individual-firm levels. This evidence provides fundamental support for studies in the literature of gradual information diffusion, commonly using lagged returns as a proxy for information. To allow for the possibility that firms react differently to the industry-wide earnings surprises, this study measures how a stock’s returns respond to the part of its main customer or supplier industry’s lagged returns that are associated with earnings surprises. A long/short equity strategy that combines the firm’s response coefficient and the prior month’s main customer/supplier industry return is shown to be profitable. The strategy tends to select medium-sized firms across industries. Firms in the winner portfolio are more likely to have a positive earning response coefficient and to be less capital intensive and financially constrained. Winners also experience positive responses to both positive and negative shocks while losers experience negative responses to both types of shocks.

THE IMPACTS OF POLICY REFORM ON INFORMATION DIFFUSION IN CHINESE REAL ESTATE STOCKS

The study focuses on the process of information diffusion in Chinese real estate stocks. By investigating whether and how policy reforms that occurred during 2002 to 2013 affected the process of information diffusion, this study finds there is a significant lead-lag relationship between big and small Chinese real estate stocks, generated by the gradual information diffusion. This study further discovers that this lead-lag effect is enhanced along with main policy reforms of Chinese real estate market during the full sample period, which implies the delay degree of information diffusion becomes greater over time. Therefore, policy reforms in Chinese real estate market increase friction of information diffusion. However, continuously decreasing information volatility of information diffusion suggests information environment and transparency of market have improved over time. Therefore, the results support the notion that, along with the market development, policy reforms of Chinese real estate market have brought about transparency to the market to some degree.Keywords: Policy Reform; Information Diffusion; Real Estate Stocks; China.

Lead-lag patterns in the Spanish and other European equity markets

The predictability of market performance is a matter of interest not only for traders and investors in financial market instruments but also for those attempting to understand the dynamics of these markets. According to the efficient market hypothesis, the price of an asset is a perfect reflection of all the information available, and consequently, it is not possible to capitalize on “undervalued or overvalued” asset; thus making market price prediction practically impossible. However, there are several groups of reasons (for example, transaction costs) that have led some economists to believe that prices are at least partially predictable. In this context, this study tries to evaluate the gradual information diffusion theory proposed by Hong et al. (2007) where industries with valuable, fundamental economic information tend lead the equity market as well as the economic activity. This hypothesis is not supported in the case of Spain, where company characteristics, and especially size, may be more relevant in understanding lead-lag patterns.

Economic Links and Cross-Predictability of Stock Returns: Evidence from Characteristic-Based “Styles”*

Prior research has shown that information diffuses gradually across stocks that are economically linked at the industry level. I document a similar pattern when stock portfolios are formed based on characteristics that are used in the anomaly literature (e.g., size, value, asset growth). Specifically, characteristics are useful to identify economic links, and earnings surprises contain information about future returns of other firms that share similar characteristics (i.e., “similar-style†firms). Such style-based earnings surprises can be used to predict style returns in the time series. For the cross-section of stocks, I create a composite style-based earnings surprise measure (SESM), which generates an equal-weighted (value-weighted) long–short strategy return of 167 (101) basis points per month. I do not find that industry spillovers, the traditional post-earnings announcement drift, unconditional abnormal style returns or risk can explain the return predictability. My findings suggest a further channel of gradual information diffusion in security markets.

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.

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

This paper explores the long-standing empirical fact of increased trading volume around news releases through the lens of canonical models of gradual information diffusion and differences of opinion. I use a unique dataset of clicks on news by key finance professionals to distinguish between trading among investors who see the news at different times and trading among investors who see the same news but disagree regarding its interpretation. Consistent with gradual information diffusion, dispersion in the timing of investors' attention is strongly predictive of daily volume around earnings announcements and volume within minutes of individual news articles. Furthermore, delayed attention is predictive of minute-level return continuation, daily-level post-earnings-announcement drift, and monthly-level return momentum. The results are robust to using the unpredictable component of news positioning as an instrument for delayed attention. Differences of opinion, measured as heterogeneity in the investors clicking on the news, is generally weaker in explaining trading volume around news, but plays a larger role when the news is more textually ambiguous.

Analysts' Reinitiations of Coverage and Market Underreaction

Do analysts add value and can they discover new information? I study the informativeness of reinitiations of coverage, which are defined as the resumption of coverage of a stock by a broker after more than six months of interruption. Reinitiations are associated with a significant short-term market response, in particular when the same analyst is assigned to the stock. Moreover, this market response is incomplete, and I document the existence of a six-month drift. The anomaly is sizeable, and a trading strategy delivers significant positive abnormal returns after transaction costs with a six-month horizon. These characteristics clearly differentiate reinitiations from other recommendations like initiations or upgrades. The results are consistent with an informed analyst hypothesis: the analyst who resumes the coverage of a firm benefits from his prior knowledge of the company and its sector, which helps him discover information that had been neglected by investors in an inefficient market, and time the issuance of his recommendation. Indeed, reinitiations coincide with future improvements in firms' profitability. Besides, when I test the implications of gradual information diffusion models, it appears that firms that were initially subject to a lower level of initial attention experience stronger cumulative abnormal returns after the reinitiation takes place.

Cross-asset return predictability: Carry trades, stocks and commodities

Equity returns predict carry trade profits from shorting low interest rate currencies. Commodity price changes predict profits from longing high interest rate currencies. The gradual information diffusion hypothesis (Hong & Stein, 1999) provides a ready explanation for these predictability results. These results cannot be explained by time-varying risk premia as stock returns and commodity price changes significantly predict negative carry trade profits. The predictability is one-directional, from commodities to high interest rate currencies, from commodities to stocks and from stocks to low interest rate currencies.

Risk-return trade-off, information diffusion, and U.S. stock market predictability

Recent academic literature in finance documents both risk-return trade-off and gradual information diffusion (ID). Motivated by these two financial theories, this paper proposes the ARCH-M model augmented by an ID indicator to forecast the U.S. stock market returns. Empirical studies performed on the monthly S&P500 index show that our approach is useful in both statistical and economic sense. Further analysis shows that the ID provides complementary information to risk-return trade-off effect. Our findings confirm that financial theories are valuable for stock return forecasting.

Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion.

Clinical annotations, such as voxel-wise binary or probabilistic tissue segmentations, structural parcellations, pathological regions-of-interest and anatomical landmarks are key to many clinical studies. However, due to the time consuming nature of manually generating these annotations, they tend to be scarce and limited to small subsets of data. This work explores a novel framework to propagate voxel-wise annotations between morphologically dissimilar images by diffusing and mapping the available examples through intermediate steps. A spatially-variant graph structure connecting morphologically similar subjects is introduced over a database of images, enabling the gradual diffusion of information to all the subjects, even in the presence of large-scale morphological variability. We illustrate the utility of the proposed framework on two example applications: brain parcellation using categorical labels and tissue segmentation using probabilistic features. The application of the proposed method to categorical label fusion showed highly statistically significant improvements when compared to state-of-the-art methodologies. Significant improvements were also observed when applying the proposed framework to probabilistic tissue segmentation of both synthetic and real data, mainly in the presence of large morphological variability.

Industry Interdependencies and Cross-Industry Return Predictability

We use the adaptive LASSO from the statistical learning literature to identify economically connected industries in a general predictive regression framework. The framework permits complex industry interdependencies, including both direct and indirect sectoral links. Consistent with gradual information diffusion across economically connected industries, we find extensive evidence that lagged returns of interdependent industries are significant predictors of individual industry returns. Using network analysis, we detect a significant relation between an industry’s importance in the U.S. production network and the pervasiveness of the predictive power of the industry’s lagged return. We also compute out-of-sample industry return forecasts based on the lagged returns of interdependent industries and show that crossindustry return predictability is economically valuable: an industry-rotation portfolio that goes long (short) industries with the highest (lowest) forecasted returns exhibits limited exposures to a variety of equity risk factors, delivers substantial alpha, and performs very well during the recent Global Financial Crisis.

Public Information and Price Discovery: Trade Time vs Clock Time

I estimate the impulse response function of stock prices to news shocks using quarterly earnings surprises as an instrument for changes in firm value. I find that price discovery occurs through trading, independent of the passage of time. Prices converge to fundamental value faster when markets are more liquid. Nearly all cross-sectional variation in the speed of convergence measured in clock time disappears when measured in trade time. Price discovery requires minimal trading volume when liquidity is low, consistent with uninformed agents learning from order flow. I show that the path of asset prices in response to news is better explained by models of strategic informed trading than by gradual information diffusion.

Gradual Information Diffusion in the Stock Market: Evidence from U.S. Multinational Firms

I use industry-level returns in foreign markets to examine the hypothesis that value-relevant foreign information slowly diffuses into the stock prices of U.S. multinational firms. A trading strategy that exploits foreign information generates abnormal returns of 0.8% monthly. The return predictability is not driven by industry momentum or anticipated changes in future risks. The effect is more pronounced in periods with lower media coverage of foreign news. Controlling for media coverage, I find that the market responds more slowly to information from more linguistically and culturally distant countries. These results show that both investors’ limited attention and their lack of understanding of foreign information matter for the return effects. I further separate these two mechanisms by examining market responses to earnings surprises.

How profitable is the Indian stock market?

In this paper, using a range of technical trading and momentum trading strategies, we show that the Indian stock market is profitable. We find robust evidence that investing in some sectors is relatively more profitable than investing in others. We show that sectoral heterogeneity with respect to profitability is a result of the gradual diffusion of information from the market to the sectors. Specifically, we show that while the market predicts returns of sectors, the magnitude of predictability varies with sectors. Our results are robust to a range of trading strategies.

Investor attention, information diffusion and industry returns

Using the monthly data for more than 1700 Australian stocks over the period from 1990 to 2009, we investigate whether industry portfolio returns predict the aggregate market. We find that a few industries significantly lead the market even controlling for well-recognized market predictors. However, unlike U.S. studies, we do not find that the ability of an industry to predict the market is closely related to its propensity to forecast economic growth. Instead, we find that the capacity of an industry to lead the market is significantly moderated by proxies for investor attention. In general, more neglected industries are more informative in leading the markets due to delayed investor attention to the information content of these industries; and the information contained in industry portfolio returns is incorporated into the market return more slowly during economic recession when investors pay less attention to the stock markets. Our research provides new empirical evidence in support of the gradual information diffusion hypothesis from a market that differs from the U.S. stock market.

Supply Chain Network Structure and Firm Returns

The complexity and opacity of the network of interconnections among firms and their supply chains inhibits understanding of the impact of management decisions concerning the boundaries of the firm and the number and intensity of its relationships with suppliers and customers. Using recently available data on the relationships of public US firms, this paper investigates the effects of supply chain connections on firm performance as reflected in stock returns. The paper finds that supply chain structure is closely related to firm returns at two levels, a first-order effect from direct connections and a second-order impact from systemic exposures through the network. For the first order effect, using a cross-sectional data set of the supply chain network and monthly returns, we show that a firm’s return can be explained by its concurrent supplier returns, concurrent customer returns, own momentum, and supplier momentum, whereas customer momentum has little impact. A long-short equity strategy based on the supplier momentum yields monthly abnormal returns of 56 basis points. This result implies investors’ limited attention to supplier firms relative to customer firms and gradual diffusion of information downstream as opposed to upstream in the supply chain. For the second-order effect, we find a market anomaly by grouping firms according to their centrality in the supply chain. Specifically, manufacturing firms that are more central in the network earn lower returns, while logistics firms that are more central in the network earn higher returns. This result holds for both eigenvector centrality and in-degree centrality (number of suppliers). We argue that centrality and multiplicity of suppliers have different risk implications for firms operating in different industries. More central firms in manufacturing choose their suppliers to operationally hedge shocks transmitted from other firms and earn lower returns due to lower systematic risk. On the contrary, more central firms in logistics are shock aggregators, earning higher returns due to their exposure to greater systematic risk. Our results are robust after controlling for common asset pricing factors.

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 the improve momentum.

Asset Pricing in the Dark: The Cross-Section of OTC Stocks

Compared to listed stocks, over-the-counter (OTC) stocks are far less liquid, disclose less information, and exhibit lower institutional holdings. We exploit these different market conditions to test theories of cross-sectional return premiums. Compared to return premiums in listed markets, the OTC premium for illiquid stocks is several times higher, the OTC premiums for size, value, and volatility are similar, and the OTC premium for momentum is three times lower. The OTC premiums for illiquidity, size, value, and volatility are largest among stocks that are held almost exclusively by retail investors and those that do not disclose financial information. Theories of differences in investors’ opinions and short sales constraints help to explain these return premiums. Our momentum results are most consistent with Hong and Stein’s (1999) theory based on the gradual diffusion of information.