Narrow Results

In January 1999, the authors published a quantitative prediction that the Nikkei index should recover from its 14-year low in January 1999 and reach ≈20 500 a year later. The purpose of the present paper is to evaluate the performance of this specific prediction as well as the underlying model: the forecast, performed at a time when the Nikkei was at its lowest (as we can now judge in hindsight), has correctly captured the change of trend as well as the quantitative evolution of the Nikkei index since its inception. As the change of trend from sluggish to recovery was estimated quite unlikely by many observers at that time, a Bayesian analysis shows that a skeptical (resp. neutral) Bayesian sees prior belief in our model amplified into a posterior belief 19 times larger (resp. reach the 95% level).

We propose that the herding behavior of traders leads not only to speculative bubbles with accelerating over-valuations of financial markets possibly followed by crashes, but also to "anti-bubbles" with decelerating market devaluations following all-time highs. For this, we propose a simple market dynamics model in which the demand decreases slowly with barriers that progressively quench in, leading to a power law decay of the market price characterized by decelerating log-periodic oscillations. We document this behavior of the Japanese Nikkei stock index from 1990 to present and of the gold future prices after 1980, both after their all-time highs. We perform simultaneously parametric and nonparametric analyses that are fully consistent with each other. We extend the parametric approach to the next order of perturbation, comparing the log-periodic fits with one, two and three log-frequencies, the latter providing a prediction for the general trend in the coming years. The nonparametric power spectrum analysis shows the existence of log-periodicity with high statistical significance, with a preferred scale ratio of λ≈3.5 for the Nikkei index and λ≈1.9 for the Gold future prices, comparable to the values obtained for speculative bubbles leading to crashes.

Shortfalls in infrastructure expenditure represent a ubiquitous problem in all Australian local government systems as well as in many other countries. In this paper, we use an evolutionary model to describe how local government investment decisions are made. We demonstrate that fear of reputational damage among elected councilors could cause herding behavior resulting in convergent and overly cautious investment behavior by councils. Under these conditions, divergent viewpoints amongst council members are discouraged and local government may become moribund in its decision-making. We show how this may result in “gaps” in infrastructure investment.

In view of explosive trends and excessive trades in the cryptocurrency markets, this paper contributes to the existing literature by bringing in the limelight the effect of liquidity on the herding behavior in the cryptocurrency market. Results from a first applied herding model including contemporaneous and lagged squared market returns demonstrated that market-wide herding exists within falling markets. The incorporation of liquidity highlights further evidences on herding behavior across cryptocurrencies during high and low liquid days, which varies across percentiles. Our findings bring handy implications for topics of portfolio and risk management, as well as regulation.

Since the pioneer contributions due to Vandewalle and Ausloos, the Hurst exponent has been applied by econophysicists as a useful indicator to deal with investment strategies when such a value is above or below $0.5$, the Hurst exponent of a Brownian motion. In this paper, we hypothesize that the self-similarity exponent of financial time series provides a reliable indicator for herding behavior (HB) in the following sense: if there is HB, then the higher the price, the more the people will buy. This will generate persistence in the stocks which we shall measure by their self-similarity exponents. Along this work, we shall explore whether there is some connections between the self-similarity exponent of a stock (as a HB indicator) and the stock’s future performance under the assumption that the HB will last for some time. With this aim, three approaches to calculate the self-similarity exponent of a time series are compared in order to determine which performs best to identify the transition from random efficient market behavior to HB and hence, to detect the beginning of a bubble. Generalized Hurst Exponent, Detrended Fluctuation Analysis, and GM2 algorithms have been tested. Traditionally, researchers have focused on identifying the beginning of a crash. We study the beginning of the transition from efficient market behavior to a market bubble, instead. Our empirical results support that the higher (respectively the lower) the self-similarity index, the higher (respectively the lower) the mean of the price change, and hence, the better (respectively the worse) the performance of the corresponding stock. This would imply, as a consequence, that the transition process from random efficient market to HB has started. For experimentation purposes, S&P500 stock Index constituted our main data source.

We study the statistics of earning forecasts of US, EU, UK and JP stocks during the period 1987–2004. We confirm, on this large data set, that financial analysts are on average over-optimistic and show a pronounced herding behavior. These effects are time dependent, and were particularly strong in the early 1990s and during the Internet bubble. We furthermore find that their forecast ability is, in relative terms, quite poor and comparable in quality, a year ahead, to the simplest "no change" forecast. As a result of herding, analysts agree with each other five to ten times more than with the actual result. We have shown that significant differences exist between US stocks and EU stocks, that may partly be explained as a company size effect. Interestingly, herding effects appear to be stronger in the US than in the Eurozone. Finally, we study the correlation of errors across stocks and show that significant sectorization occurs, some sectors being easier to predict than others. These results add to the list of arguments suggesting that the tenets of Efficient Market Theory are untenable.

We apply the Kalman filter method to estimate nine Asian markets and find evidence that stock return dispersions decline as markets experience stress conditions, supporting the existence of herding. This paper finds that herding behavior is time-varying and comoving across markets. Both linear and nonlinear Granger causality tests conclude that there is strong bilateral causality between herding and returns for all nine Asian markets. For markets in Japan, South Korea, and Thailand, we consistently find strong two-way causality exists in pairwise variables among herding, stock returns, and illiquidity. No consistent evidence can be drawn from other markets for other pairwise variables.

In this paper, we examine the evidence of herding behavior on the Chinese stock market. Our main findings are as follows. First, we find strong evidence of herding behavior on both the Shanghai and Shenzhen stock exchanges. Second, we document evidence of asymmetric herding behavior with greater magnitude of herding behavior on up markets than on down markets. Third, our findings suggest that herding behavior is sector-specific and predominant in the industrial and properties sectors. Finally, we unravel strong evidence suggesting that herding behavior is time-varying and in some sectors time-varying herding behavior is more prevalent than in other sectors.

An extensive, in-depth study of risk factors seems to be of crucial importance in the research of the financial market in order to prevent (or reduce) the chance of developing this return. It represents market anomalies. This study confirms that the $q$-factors model is better than the other traditional asset pricing models in explaining individual stock return in the US over the 2000–2017 period. The main focus of data analysis is, on the use of models, to discover and understand the relationships between different factors of risk market anomaly. Recently, Fama and French presented a five-factor model that captures the size, value, profitability, and investment patterns in average stock market returns better than their three-factor model presented previously in 1993. This paper explores a shred of new empirical evidence to assess the asset pricing model through an extension of Fama and French model and a report on applying Bayesian Network (BN) modeling to discover the relationships across different risk factor. Furthermore, the induced BN was used to make inference taking into account sensibility and the application of BN tools has led to the discovery of several direct and indirect relationships between different parameters. For this reason, we introduce additional factors that are related to behavioral finance such as investor’s sentiment to describe a behavior return, confidence index, and herding. It is worth noting that there is an interaction between these various factors, which implies that it is interesting to incorporate them into the model to give more effectiveness to the performance of the stock market return. Moreover, the implemented BN was used to make inferences, i.e., to predict new scenarios when different information was introduced.

Microscopic models dealing with the decisions of traders on the market have tried to reproduce real market behaviour. Possibly the simplest of these models is the herding approach of Cont and Bouchaud. Variations include letting the concentration varying between zero and unity (or zero and percolation threshold); changing the price proportionally not to the difference between demand and supply, but to the square root of this difference; influencing the buy/sell decisions by the actual price and price change. As a result, the probability to find a market change greater than some R was found to vary as R^-2.9; this distribution gets wings which might correspond to outliers like the 1929 crash on Wall Street; bubbles lead to sharp peaks separated by flat valleys; and the log-periodic variations after the Japanese crash of 1990 were reproduced to get rich from the prediction made in January 1999 by Johansen and Sornette that Nikkei will rise appreciably during 1999. As it did.

This paper investigates the relationship between deposition effect and escalation of commitment and herding behavior. First, this paper ranks the mutual fund into five groups by herding style, and we examine deposition spread (DISP) and escalation of commitment dispread (ESCA) zero investment portfolios in each herding style portfolio. Then, we investigate whether the paper gain ratio or paper loss ratio impacts on herding behavior after controlling other characteristics. Our results can be summarized as follow: First, deposition effect and escalation of commitment has negative impact on mutual fund performance. Second, in buy herding style fund, the deposition effect and escalation of commitment has the most negative impact on performance, however, in sell herding style fund, the negative effect disappears. Finally, we find the deposition effect indeed impacts the herding behavior.

Based on the unique data from a popular P2P website in China, this paper investigates the borrowers’ herding behavior and its influencing factors, e.g., platform attribute and moderating events. We mainly find that: (i) there exists herding behavior at the platform level and that the loan amount, loan interest rate and loan duration are positively related to the magnitude of herding; (2) the borrowers’ herding behavior is increased by a platform’s market share and the number of participants; (3) regulatory events have negative effects on the herding behavior. Our findings have practical implications for P2P participants, platforms, and policymakers.

Recently, Zou et al. (2017) proposed a novel covariance regression model to study the relationship between the covariance matrix of responses and their associated similarity matrices induced by auxiliary information. To estimate the covariance regression model, they introduced five estimators: the maximum likelihood, ordinary least squares, constrained ordinary least squares, feasible generalized least squares and constrained feasible generalized least squares estimators. Among these five, they recommended the constrained feasible generalized least squares estimator due to its estimation efficiency and computational convenience. Under the normality assumption, they further demonstrated the theoretical properties of these estimators. However, the data in the area of finance and accounting may exhibit heavy tails. Hence, to broaden the usefulness of the covariance regression model, we relax the normality assumption and employ Lee’s (2004) approach to obtain inferences for covariance regression parameters based on the five estimators proposed by Zou et al. (2017). Two empirical examples are presented to illustrate the practical applications of the covariance regression model in analyzing stock return comovement and herding behavior of mutual funds.

The phenomenon of information cascade widely exists in the financial market, mainly manifested in the herd behavior of investors. With the development of online social networks, stock forums provide conditions for cascading effects. Taking the stock bar forum as the research scenario, this paper used a Python Web crawler to collect forum data and empirically tested the cascading effect of the stock bar Forum. Research shows that, as proxy variables, stock popularity ranking and opinion leader posts are related to investors’ herding behavior.

Microscopic models dealing with the decisions of traders on the market have tried to reproduce real market behaviour. Possibly the simplest of these models is the herding approach of Cont and Bouchaud. Variations include letting the concentration varying between zero and unity (or zero and percolation threshold); changing the price proportionally not to the difference between demand and supply, but to the square root of this difference; influencing the buy /sell decisions by the actual price and price change. As a result, the probability to find a market change greater than some R was found to vary as R^−2.9; this distribution gets wings which might correspond to outliers like the 1929 crash on Wall Street; bubbles lead to sharp peaks separated by flat valleys; and the log-periodic variations after the Japanese crash of 1990 were reproduced to get rich from the prediction made in January 1999 by Johansen and Sornette that Nikkei will rise appreciably during 1999. As it did.