Non-fundamental Information Research Articles

Herding behavior and systemic risk in global stock markets

This paper provides new evidence of herding due to non- and fundamental information in global equity markets. Using quantile regressions applied to daily data for 33 countries, we investigate herding during the Eurozone crisis, China’s market crash in 2015–2016, in the aftermath of the Brexit vote and during the Covid-19 Pandemic. We find significant evidence of herding driven by non-fundamental information in case of negative tail market conditions for most countries. This study also investigates the relationship between herding and systemic risk, suggesting that herding due to fundamentals increases when systemic risk increases more than when driven by non-fundamentals. Granger causality tests and Johansen’s vector error-correction model provide solid empirical evidence of a strong interrelationship between herding and systemic risk, entailing that herding behavior may be an ex-ante aspect of systemic risk, with a more relevant role played by herding based on fundamental information in increasing systemic risk.

Dampening effect and market efficiency

The way informed traders use their privileged information affects the properties of asset prices. This paper analyzes the differences in market efficiency between the two most widely adopted informed trading paradigms (Grossman-Stiglitz-style and Kyle-style) in a unified three-period framework. The aggregate risk—the product of fundamental risk, liquidity trading risk, and traders’ risk aversion coefficient—uniquely determines three measures of market efficiencies, including price informativeness, return autocorrelations, and the unconditional expected utilities of uninformed market makers. In the Kyle-style model, a self-reinforcing dampening effect arises from informed traders’ trading on fundamental information and non-fundamental information about their prior positions, leading to less efficient markets, contrarian trading by informed traders, and a return reversal between the two trading periods. Our study proposes to estimate price informativeness using information from short-term return reversals.

Intraday return predictability in the cryptocurrency markets: Momentum, reversal, or both

This paper reports evidence of intraday return predictability, consisting of both intraday momentum and reversal, in the cryptocurrency market. Using high-frequency price data on Bitcoin from March 3, 2013, to May 31, 2020, it shows that the patterns of intraday return predictability change in the presence of large intraday price jumps, FOMC announcement release, liquidity levels, and the outbreak of the COVID-19. Intraday return predictability is also found in other actively traded cryptocurrencies such as Ethereum, Litecoin, and Ripple. Further analysis shows that the timing strategy based on the intraday predictors produces higher economic value than the benchmark strategy such as the always-long or the buy-and-hold. Evidence of intraday momentum can be explained in light of the theory of late-informed investors, whereas evidence of intraday reversal, which is unique to the cryptocurrency market, can be related to investors’ overreaction to non-fundamental information and overconfidence bias.

Herding on Fundamental/Nonfundamental Information During the COVID-19 Outbreak and Cyber-Attacks: Evidence From the Cryptocurrency Market

We provide an empirical analysis of herding behavior in cryptocurrency markets during COVID-19 and periods of cyber-attacks, differentiating between fundamental and nonfundamental herding. The results show that herding behavior is driven by fundamental information during the full sample period and the cyber-attack days. However, herding is not prevalent during the COVID-19 outbreak, either when reacting to fundamental or nonfundamental information. This finding suggests heterogeneity in the behaviors of participants in the cryptocurrency markets during the COVID-19 period.

Intentional and spurious herding during unconventional events and high sentiments

This study explores investors’ herding behavior toward the market consensus in the U.S. and Europe stock markets from January 5, 2006 to September 30, 2020. We find that the uncertainty triggered by the outbreak of the Covid-19 pandemic amplified the observed herding behavior. The occurrence of unconventional events induced herding for both markets and, moreover, this behavior is driven by non-fundamental information. We document strong evidence of herding during periods characterized by high sentiments. Finally, our analysis suggests that the ECB’s non-standard monetary policy announcements induced both spurious and intentional herding, while the Fed’s releases did not cause herding.

Valuation ratio style investing and economic sentiment: evidence from major Eurozone markets

This paper explores the role of sentiment in style investing for a sample of eight Eurozone markets and makes a distinction between fundamentals-driven sentiment and sentiment based on non-fundamental information. The findings indicate that style portfolio returns are statistically and economically significant, however, their return differential is not. Style portfolio returns are sensitive to changes in sentiment with value portfolios exhibiting higher sensitivity, and sentiment variance explains a significant percentage of style portfolio return variance. For example, for Germany during the US financial crisis the variance of fundamentals-driven sentiment accounts for 19.65% of the value portfolio variance and the variance of non-fundamental sentiment for a further 24.67%. The results are robust to the choice of valuation ratios in defining investment style. Finally, style portfolio returns tend to be higher, on average, during optimistic months (high sentiment), for the majority of the markets. For instance, during the US crisis, when high non-fundamental sentiment prevails, for five northern Eurozone markets, value portfolio monthly returns range between 2.54 and 3.87%.

Do Fama–French common risk-factor portfolio investors herd on a daily basis? Implications for common risk-factor regressions

Purpose The purpose of this paper is to examine whether Fama–French common risk-factor portfolio investors herd on a daily basis for five developed markets, namely, Europe, Japan, Asia Pacific ex Japan, North America and Globe. Design/methodology/approach To examine the herd behavior of common risk-factor portfolio investors, this paper utilizes the cross-sectional absolute deviations (CSAD) methodology, covering a daily data sampling period of July 1990 to January 2019 from Kenneth R. French-Data Library. CSAD driven by fundamental and non-fundamental information is assessed using Fama–French five-factor model. Findings The results do not provide evidence for herding under normal market conditions, either when reacting to fundamental information or non-fundamental information, for any region under consideration. However, Fama–French common risk-factor portfolio investors mimic the underlying risk factors in returns related to size and book-to-market value, size and operating profitability, size and investment and size and momentum of the equity stocks in European and Japanese markets during crisis period. Also, no considerable evidence is found for herding (on fundamental information) under crisis and up-market conditions except for Japan. Ancillary findings are discussed under conclusion. Research limitations/implications Further research on new risk factors explaining stock return variation may help improve the model performance. The performance can be improved by adding new risk factors that are free from behavioral bias but significant in explaining common stock return variation. Also, it is necessary to revisit the existing common risk factors in order to understand behavioral aspects that may affect cost of capital calculations (e.g. pricing errors) and valuation of investment portfolios. Originality/value This is the first paper that examines the herd behavior (fundamental and non-fundamental) of Fama–French common risk-factor investors using five-factor model.

Ponašanje gomile na tržištu kriptovalute - stvarno vs lažno gomilanje

This paper sets out to explore whether the investor herding in the cryptocurrency market induces correlations in cryptocurrency returns using the methodology of Chang et al. (2000) and Galariotis et al. (2015) from a daily data sampling period of 3/30/2015 to 5/24/2019. The initial regression results show that the cross-sectional absolute deviation of return can only be explained by GSCI oil and gold index return, but no relationship exists between cross-sectional absolute deviation of return and other regression variables, such as return on CCi30, US equity risk premium and US/Euro exchange rate return. The herding regression results under normal market condition show that a strong tendency exists to herd on non-fundamental information that explains cross-sectional absolute deviation of returns. As such, cryptocurrency returns cannot be predicted on the basis of fundamental economic information (e.g., major macroeconomic announcements). Herding on non-fundamental information is found to be more pronounced during an upward-trending period of the market and other than upward-trending period. No signs of herding on fundamental information could be observed under other market conditions. Although the theory suggests that herding on non-fundamental information results in more efficient outcomes, the above findings do not encourage the diversification of traditional assets with cryptocurrency on the basis of low correlation. Since cryptocurrency lacks intrinsic value, the exchange is shown to provide a pseudo-efficient trading platform for speculative investors. Implications for future research are discussed.

Herding behaviour of Chinese A- and B-share markets

Purpose The purpose of this paper is to examine the evidence of herding phenomenon, spill-over effects related to herding and whether herding is driven by fundamentals or non-fundamentals for various sub-periods and sub-samples. Design/methodology/approach The cross-sectional absolute deviation model is applied to China’s A- and B-share markets in combination with fundamental information. Findings Herding is prevalent on both A- and B-share markets. In detail, investors on A-share market herd for small and growth stock portfolios irrespective of market states while they only herd for large or value stocks in down market, therefore leading the whole herding behaviour to be pronounced in down market. Comparatively, on B-share market, herding is robust for various investment styles (small or large, value or growth) or market situations. Additionally, spill-over effects related to herding do not exist no matter from A-shares to B-shares or from B-shares to A-shares. Moreover, investors on B-share markets tend to herd as the response to non-fundamental information more frequently during financial crisis. Originality/value Investors on A- and B-share markets tend to herd as the response to non-fundamental information more frequently during financial crisis. Analysing the herding behaviours could be helpful in controlling the financial risk.

Information Acquisition: Fundamental and Non-Fundamental

We present a parsimonious information acquisition model in which two types of traders can produce either fundamental or non-fundamental information. Fundamental information is related to asset liquidation value, whereas non-fundamental information is related to the noise caused by traders sentiment. Opening access to non-fundamental information increases the coordination possibilities among sentiment-informed traders and can yield two equilibrium-displaying properties: substitutability and complementarity. We find that the dominated mass of one type of informed trader can attenuate their information advantage, resulting in low ex ante expected utility associated with such traders. We further find that there is a crowding-out effect in information acquisition between the two types of informed traders, which offers some significant insights in explaining why bubbles burst when market sentiment is dominant.

Learning about noise

Abstract This paper studies how acquisition of non-fundamental information (learning about noise) affects financial markets. We develop a rational expectations model with investors who are endowed with fundamental and non-fundamental information of heterogeneous quality and who optimally allocate learning capacity between fundamentals and noise. We demonstrate that learning about noise increases price informativeness, and the price can be the most informative when the majority of investors acquire non-fundamental information. We also find that i) investors whose prior fundamental information is relatively precise (imprecise) compared to their prior non-fundamental information learn only about fundamentals (noise) and ii) learning about fundamentals (noise) increases (decreases) the heterogeneity in the fundamental information quality across investors.

Catching Falling Knives: Speculating on Liquidity Shocks

Many market participants invest resources to acquire information about liquidity rather than fundamentals. I show that agents using such information can reduce the magnitude of short-lived pricing errors by trading against liquidity shocks. However, the short-run stabilizing effect of this behavior also makes it more difficult to identify liquidity shocks, a signal-jamming effect that slows down price discovery in the long run. As more agents invest in nonfundamental information, market prices become more resilient to liquidity shocks but also recover more slowly from temporary price deviations. This paper was accepted by Gustavo Manso, finance.

Strategic Trading with Endogenous Fundamental and Non-Fundamental Information Acquisition

We study information acquisition in a strategic setting where traders can produce either fundamental information or non-fundamental information. Fundamental information is related to the traded asset value. Non-fundamental information is related to noise trading (transitory random demand). Opening access to non-fundamental information reduces the equilibrium number of fundamental traders, therefore price informativeness and eventually the welfare of noise traders. Hence, favoring nonfundamental trading might be detrimental. Then, we assume that non-fundamental traders observe in addition the forthcoming aggregate demand from fundamental and noise traders. In this setting, non-fundamental information allows both to correct short-term mispricing and to draw back fundamental information from the order flow. Opening access to non-fundamental information still reduces price informativeness and eventually the welfare of noise traders, even when non-fundamental information is perfect. Moreover, in the latter case, there are two equilibria: a good (bad) equilibrium with many (few) fundamental traders and few (many) non-fundamental ones.

Learning About Noise

This paper studies how acquisition of non-fundamental information (learning about noise) affects financial markets. We develop a rational expectations model with investors who are endowed with fundamental information of heterogeneous quality and who optimally allocate learning capacity between fundamentals and noise. We demonstrate that learning about noise increases price informativeness, and the price can be the most informative when the majority of investors acquire non-fundamental information. We also find that i) investors endowed with high (low) quality information about fundamentals learn only about fundamentals (noise) and ii) learning about fundamentals (noise) increases (decreases) the heterogeneity of information quality across investors.

Feedback Trading between Fundamental and Nonfundamental Information

We develop a strategic trading model in which an insider exploits noise traders' overreaction. A feedback effect arises from the insider's trading on fundamental information (the expected growth rate of dividends) and nonfundamental information (insider's inventory or noise supply). We find that the stock price is not fully revealing; a faster mean-reverting noise supply leads to a more volatile price; the price impact can increase with insider's risk-aversion; and a risk-averse insider can trade more aggressively on fundamental information than a risk-neutral one does. Insider's current trade and his previous inventory exhibit simultaneously positive forecasting powers for future stock returns.

Spillover of fear: Evidence from the stock markets of five developed countries

This study investigates the spillover effect in five leading stock markets (i.e., the United States, the United Kingdom, Germany, Japan, and France). It estimates the spillover indices of these countries and finds that information transmission between these stock markets increases considerably after 1998. Germany and the United States are the main stock markets conveying information to other international markets. Germany primarily influences the French stock market, and the United States significantly influences many other stock markets. Results show that the US stock market shows three periods during which its net spillover effect exceeds zero: the period prior to 1997, the dot-com bubble from 2000 to 2002, and the subprime mortgage crisis and Lehman Brothers bankruptcy from 2007 to 2008. The fear index correlates significantly with the spillover of the US stock market into other markets. The spillover effect of the US stock market demonstrates asymmetry and the likelihood to spread positive fundamental information and non-fundamental information (e.g., fear).

Nobel Price for Father of the Efficient Market Hypothesis

Nobel Price for Father of the Efficient Market Hypothesis

How does market concern derived from the Internet affect oil prices?

With the acceleration of oil marketisation and the rapid development of electronic information carriers, external information shocks can be easily and quickly transmitted to the oil market through the Internet. This paper analyses the impact of short- and long-run market concerns, derived from search query volumes in Google for different domains around the oil market on oil volatility using co-integration and the modified EGARCH model. Empirical results suggest there is a long-term equilibrium relationship between oil prices and long-run market concern for oil prices and oil demand. The short-run market concerns for the 2008 financial crisis and the Libyan war convulsion have a significant and asymmetric influence on oil price volatility. This indicates that market concern transmitted through the Internet can strengthen the linkage between oil price changes and external events by influencing the expectation of market traders, and to some extent it can exaggerate the impact of nonfundamental information shocks.

Catching Falling Knives: Speculating on Market Overreaction

Many market participants invest resources to acquire information about liquidity rather than fundamentals. I show that agents using such information can reduce the magnitude of short-lived pricing errors by trading against liquidity shocks. However, the short-run stabilizing effect of this behavior also makes it more difficult to identify liquidity shocks, a signal-jamming effect that slows down price discovery in the long-run. As more agents invest in non-fundamental information, market prices become more resilient to liquidity shocks, but also recover more slowly from temporary price deviations.

Non-Fundamental Information and Market-Makers' Behavior During the NASDAQ Preopening Session

This paper examines whether NASDAQ dealers' preopening quotes might be related to non-fundamental information, that is, information about transient trading pressure unrelated to fundamentals. Preopening quotes posted by wholesalers (dealers specialized in market-making and thus presumably more exposed to inventory risks) are strongly related to opening price reversals, a measure of transitory price pressure. Wholesalers are more likely to post preopening quotes on days characterized by large liquidity shocks or days following larger order imbalances, but not on days of strong informational asymmetry about fundamentals (days of analyst recommendation releases, earnings announcements or merger announcements). These patterns do not hold for other intermediaries, namely institutional brokers providing sell-side coverage. I also nd that daily order imbalances (another trading pressure measure) are strongly related to the preopening activity of wholesalers but not to any other groups of market-makers with more diversi ed banking activities. Overall, I interpret this as evidence that non-fundamental information matters during the preopening session and impacts intermediaries' preopening behavior.