Search results

The literature on positive feedback trading has grown considerably in recent years. The purpose of this paper is to provide a review of the theoretical and empirical literature on positive feedback trading and especially the literature related to the Sentana and Wadhwani (1992) model.

This literature review covers theoretical and empirical work in this area and it points out shortcomings and potential extensions of the basic feedback model.

The evidence so far points in the direction of positive feedback trading being present in aggregate stock market indices, index futures, bond markets, foreign exchange markets and individual stocks. There are some important issues that require further investigation. For example, it is likely that feedback trading is a function of longer lags of past return. Likewise, asymmetric behavior during up and down markets appears to be the rule rather than the exception. More importantly, the models should allow for positive as well as negative feedback and be general enough to investigate feedback trading behavior in individual assets and not just the aggregate market.

The discussion points out theoretical and empirical limitations and shortcomings of the extant literature.

This is the first paper to review positive feedback trading, implications, limitations and need for future research.

Although some research has been carried out on feedback trading in different asset classes, there have been few empirical investigations that consider both major and emerging stock markets (Koutmos, 1997; Antoniou et al., 2005; Kim, 2009) stock index futures (Salm and Schuppli, 2010). In this study, the author examines positive/negative feedback trading in both developed-emerging-frontier-standalone (51) stock markets for 2010–2020 and sub-periods including COVID-19 period.

The hypothesis “feedback trading behaviour led the price boom/bust in the stock markets during the first quarter of COVID-19 pandemic” is tested by employing the Sentana and Wadhwani (1992) framework and using asymmetrical GARCH models (GJRGARCH, EGARCH) in accordance with the empirical literature.

The following conclusions can be drawn from the present study; (1) There is no evidence to support a significant distinction between developed, emerging, frontier or standalone markets or high/upper middle, lower middle income economies in the case of feedback trading. It is more likely to be a general phenomenon reflecting the outcomes of general human psychology (2) in the long term (2010–2020) based on the feedback trading results Asian stock markets appear to be far from efficiency.

Stock markets are selected based on data availability.

Several inferences can be drawn about overall results. First, investors and portfolio managers should beware of their investment decisions during bearish market conditions where volatility is on the rise and also when there is a strong reaction to bad news/negative shocks in the market. Moreover, investing in Asia stock markets may require more attention since those markets are reputed to be more “idiosyncratic”, less reliant on economic and corporate fundamentals in their pricing. Moreover, the impact of foreign investors on stock market volatility and returns and weaker implementation of regulations also affect the efficiency of the markets (Lipinsky and Ong, 2014).

To the best of the author’s knowledge, most studies in the field of feedback trading in stock markets have only focused on a small sample of countries and second, the effect of COVID-19 uncertainty on the stock markets have not been addressed in the literature with respect to feedback trading. This paper fills these literature gaps. This study is expected to provide useful insights for understanding the instabilities in stock markets particularly under conditions of high uncertainty and to fill the gap in the literature by comparing the results for a large sample of countries both in the long term and in the pandemic.

Highlights for review

1. This study has shown that feedback trading is more prevalent in Asian stock markets in the long run in Europe, America or Middle East for the period 2010–2020.

2. Positive feedback traders generally dominated most of the stock markets during the early period of COVID-19 pandemic.

3. Another major finding was that the stock markets in Malaysia, Japan, the Philippines, Estonia, Portugal and Ukraine are dominated by negative feedback traders which may be interpreted as “disposition effect” meaning that they sell the “past winners”.

4. In Indonesia, New Zealand, China, Austria, Greece, UK, Finland, Spain, Iceland, Norway, Switzerland, Poland, Turkey, Chile and Argentina neither positive nor negative feedback trading exists even under uncertain conditions.

This study has shown that feedback trading is more prevalent in Asian stock markets in the long run in Europe, America or Middle East for the period 2010–2020.

Positive feedback traders generally dominated most of the stock markets during the early period of COVID-19 pandemic.

Another major finding was that the stock markets in Malaysia, Japan, the Philippines, Estonia, Portugal and Ukraine are dominated by negative feedback traders which may be interpreted as “disposition effect” meaning that they sell the “past winners”.

In Indonesia, New Zealand, China, Austria, Greece, UK, Finland, Spain, Iceland, Norway, Switzerland, Poland, Turkey, Chile and Argentina neither positive nor negative feedback trading exists even under uncertain conditions.

This paper investigates the possibility that futures markets attract noise traders who engage in positive feedback trading, an especially destabilizing form of noise trading. The hypothesis is tested using data from four major national index futures markets. The empirical evidence is consistent across all index futures markets under examination. Specifically, there is significant evidence of positive feedback trading. More importantly, the feedback trading pattern exhibits significant long memory in the sense that it depends on longer lags of past prices. Because volatility is asymmetric, the implication is that feedback trading is also asymmetric, being more prevalent during down markets so that mispricing is more likely during those periods that feedback traders are more active.

The purpose of this paper is to comprehensively review a large and heterogeneous body of academic literature on investors' feedback trading, one of the most popular trading patterns observed historically in financial markets. Specifically, the authors aim to synthesize the diverse theoretical approaches to feedback trading in order to provide a detailed discussion of its various determinants, and to systematically review the empirical literature across various asset classes to gauge whether their feedback trading entails discernible patterns and the determinants that motivate them.

Given the high degree of heterogeneity of both theoretical and empirical approaches, the authors adopt a semi-systematic type of approach to review the feedback trading literature, inspired by the RAMESES protocol for meta-narrative reviews. The final sample consists of 243 papers covering diverse asset classes, investor types and geographies.

The authors find feedback trading to be very widely observed over time and across markets internationally. Institutional investors engage in feedback trading in a herd-like manner, and most noticeably in small domestic stocks and emerging markets. Regulatory changes and financial crises affect the intensity of their feedback trades. Retail investors are mostly contrarian and underperform their institutional counterparts, while the latter's trades can be often motivated by market sentiment.

The authors provide a detailed overview of various possible theoretical determinants, both behavioural and non-behavioural, of feedback trading, as well as a comprehensive overview and synthesis of the empirical literature. The authors also propose a series of possible directions for future research.

This paper aims to investigate feedback trading and autocorrelation behavior in the cryptocurrency market.

It uses the GJR-GARCH model to investigate feedback trading in the cryptocurrency market.

The findings show a negative relationship between trading volume and autocorrelation in the cryptocurrency market. The GJR-GARCH model shows that only the USD Coin and Binance USD show an asymmetric effect or leverage effect. Interestingly, other cryptocurrencies such as Ethereum, Binance Coin, Ripple, Solana, Cardano and Bitcoin Cash show the opposite behavior of the leverage effect. The findings of the GJR-GARCH model also show positive feedback trading for USD Coin, Binance USD, Ripple, Solana and Bitcoin Cash and negative feedback trading for Ethereum and Cardano only.

This paper contributes to the literature by extending Sentana and Wadhwani (1992) to explore the presence of feedback trading in the cryptocurrency market using a sample of the most active cryptocurrencies other than Bitcoin, namely, Ethereum, USD coin, Binance Coin, Binance USD, Ripple, Cardano, Solana and Bitcoin Cash.

The purpose of this paper is to research and analyze the influence of institutional investors in the present securities market due to behavior alienation with “running after rising and falling” and “herd behavior”.

A DeLong, Shleifer, Summers, and Waldmann (DSSW) model with positive feedback trading is established first to show the trading process, and these securities prices are calculated considering the investors' emotion. Through numerical analysis, the influence of institutional investors on securities price fluctuation is simulated. Further, the analysis of institutional investors' incomes is processed based on this model.

Through these analyses, the following conclusions are drawn: it lies on the scale of positive feedback traders and their sensitivity to past market performances whether the institutional investors can stabilize the market, and it is not necessary for the institutional investors to benefit from manipulating the market due to the existence of noise trader risk, so the positive feedback traders may survive in the security market over the long term.

The DSSW model considering positive feedback trading, presented in the paper, is more effective in analyzing the relation among the behavior of institutional investors, securities pricing and securities price fluctuation. The paper proposes some advice for policy decisions, which is helpful for government and institutions to maintain the stability of securities markets.

Purpose – The aggregate investment by foreign institutional investors (FIIs) in the Indian stock market is significant compared to that by domestic institutions and individual (retail) investors. The question of whether FIIs exhibit herding and positive feedback trading while investing in the Indian stock markets has not been examined so far. This study is an attempt to fill the gap and contribute to the existing evidence on foreign portfolio investment in India.

Methodology/approach – We have analyzed the daily data on purchases and sales of securities by FIIs sourced from the Securities and Exchange Board of India (SEBI), and the Bombay Stock Exchange (BSE). We have adopted the approach of Lakonishok et al. (1992), and Wermers (1999) to examine herding and positive feedback trading by foreign investors.

Findings – Our results suggest that FIIs exhibit herding and positive feedback trading during different phases of the stock market. This observed behavior is prominent in but not restricted to large cap stocks as they enjoy better liquidity.

Social implication – The herding and positive feedback trading by FIIs is a cause for concern for government of India, capital market regulator (SEBI), and the country's central bank (RBI) as it adversely affects stock prices and volatility. They are required to formulate and implement a suitable policy response given their objective of protecting the interests of small investors in the market. They may also have to monitor the purchases and sales of equities by FIIs in general and of better performing large cap stocks in particular.

This study aims to examine the relation between illiquidity, feedback trading and stock returns for several European markets, using panel regression methods, during the financial and the sovereign debt crises. The authors’ interest here lies twofold. First, the authors seek to compare the results obtained here under crisis conditions with those in the existing literature. Second, and of greater importance, the authors wish to examine the interaction between liquidity and feedback trading and their effect on stock returns.

The authors jointly model both feedback trading and illiquidity, which are typically considered in isolation. The authors use panel estimation methods to examine the relations across the European markets as a whole.

The key results suggest that in common with the literature, illiquidity has a negative impact upon contemporaneous stock returns, while supportive evidence of positive feedback trading is reported. However, in contrast to the existing literature, lagged illiquidity is not a priced risk, while negative shocks do not lead to greater feedback trading behaviour. Regarding the interaction between illiquidity and feedback trading, the study results support the view that greater illiquidity is associated with stronger positive feedback.

The study results suggest that when price changes are more observable, due to low liquidity, then feedback trading increases. Therefore, during the crisis periods that afflicted European markets, the lower levels of liquidity prevalent led to an increase in feedback trading. Thus, negative liquidity shocks that led to a fall in stock prices were exacerbated by feedback trading.

The purpose of this paper is to examine the hypothesis of feedback trading along with the short-term return dynamics of three size-based stock portfolios of Athens Stock Exchange during the Greek debt crisis period.

To this end, the authors employ for the first time in the literature two well-known models while the variance equation is modeled by means of a multivariate EGARCH specification. As a robustness test an innovative nested-EGARCH model is also employed.

The assumption that positive feedback trading is an important component of the short-term return movements across the three stock portfolios receives significant support. Moreover, the volatility interdependence, both in magnitude and sign, is almost similar across the three models. Finally, bad news originating from the portfolio of small stock appears to have a higher impact on the volatility of large and medium size stock returns than good news during the Greek debt crisis period.

The methodology is innovative and the authors test for the first time the feedback trading hypothesis across different size stocks. The authors believe that the results might entail significant policy implications for investors and market regulators.

The main purpose of this paper is to investigate autocorrelation structure of stock and portfolio returns in a unique market setting of Saudi Arabia, where nearly all active traders are the retail individuals and the market operates under severe limits to arbitrage. Specifically, the authors examine how return autocorrelation of Saudi Arabian stock market is related to factors such as the day of the week, stock trading, performance on the preceding day and volatility.

The sample consists of the daily stock price and index data of 159 firms listed in Tadawul (Saudi Arabian Stock Exchange) for the period from January 2004 through December 2015. The methodology of Safvenblad (2000) is primarily used to investigate the autocorrelation structure of individual stock and index returns. The authors also use the Sentana and Wadhwani (1992) methodology to test for the presence of feedback traders in the Saudi stock market.

Results show that there is significantly positive autocorrelation in individual stock, size portfolio and market returns and that the last two are almost always larger than the first. Return autocorrelation is negatively related to firm size. Interestingly, return autocorrelation is positively related to trading frequency. For portfolios, autocorrelation of returns following a high absolute return day is significantly higher than that following a low absolute return day. Similarly, return autocorrelation during volatile periods is generally larger than that during tranquil periods. Return correlation between weekdays is usually larger than that between the first and last days of the week. Overall, the results suggest that the possible reason for positive autocorrelation in stock returns could be the presence of negative feedback traders who are engaged in frequent profit-taking activities.

This is the first paper that thoroughly investigates the autocorrelation structure of the returns of the Saudi stock market using both index and individual stock returns. As this US$583bn (as of August 21, 2014) market opened to foreign institutional investors in June 2015, the results of this paper should be of significant value for the potential uninformed foreign investors in this relatively lesser known and previously closed yet highly prospective market.