Noise Traders Research Articles

NOISE TRADING BEFORE AND AFTER REVERSE SPLITS

ABSTRACT This paper uses a trivariate structural vector autoregressive model (SVAR) to identify the effects of noise trading on returns, volatility, and volume before and after reverse splits of AIG and Citigroup. We find a persistent increase in volatility due to noise trading for many days after reverse splits, and the impact on volatility is greater after the reverse splits. We find that reverse splits attract more noise traders. Keywords noise trading, reverse splits, SVAR model

Fundamental volatility and informative trading volume in a rational expectations equilibrium

This paper presents a tractable model of asset price with informative trading volume. Investors condition demands on their rational expectations of both the equilibrium price and trading volume. We suggest a new information role of trading volume, which enables the investors to extract continuous information regarding the liquidation value volatility of the risky asset. The liquidation value volatility determines the relative importance of fundamental value and noise trading in the price function. It also monotonically increases the price volatility. We provide an explanation of the negative and nonlinear relationship between price volatility and trading volume. We also examine the effects of information accuracy and investor composition on the equilibrium price distribution and trading volume.

Shock Transfer in Futures and Spot Markets: An Agent-Based Simulation Modelling Method

There have been heated debates about the role of stock index futures in the financial market, especially during the crash periods. In this paper, a multiagent spot-futures market model is developed to analyze the micromechanism of shock transfer across spot and futures markets. We assume that there are two stocks and one stock index futures contract in the spot-futures market. Agents are heterogeneous, including fundamentalists, chartists, noise traders, and arbitragers. The spot market and the futures market are linked by arbitragers. The simulation results show that our spot-futures market model can reproduce various important stylized facts, including the price co-movement between stock index prices and index futures prices and the fat-tailed distribution of the returns of risky assets and the basis. Further analysis shows that when we introduce an exogenous fundamental shock to one of the stocks, the backwardation phenomenon appears in the futures market and the shock is widespread across the whole market by means of index futures. Moreover, the backwardation gradually disappears when the number of arbitragers increases. Besides, when there are few arbitragers or when there are sufficient arbitragers, shocks cannot be transferred to other stocks via the futures market, while an intermediate level of arbitrage will amplify the shock transfer and hurt market stability. These findings underscore that arbitragers play an important role in spot-futures market interaction and shock transfer, and adequate arbitrage trading during crises may help eliminate the positive basis and halt the further spread of the crises.

Do investors feedback trade in the Bitcoin—and why?

We empirically examine whether feedback traders are active in the Bitcoin and the extent to which their presence is affected by a series of noise-related factors (sentiment; volume; liquidity) at three different frequencies (hourly; daily; weekly) for the April 2013–July 2019 period based on Bitstamp data. Our findings suggest that positive feedback trading grows stronger for higher (hourly; daily) frequencies, with its presence manifesting itself mainly during periods of high/improving sentiment and high/rising volume/liquidity. Additional tests reveal that the significance of hourly feedback trading is identified during hours corresponding to the trading hours of major European/North American markets. Overall, our results confirm extant literature evidence on the prevalence of noise trading in cryptocurrencies, while further showcasing that the factors motivating feedback trading in other asset classes (equities; ETFs; futures) exhibit similar effects over the presence of feedback traders in the cryptocurrency market.

Are the least successful traders those most likely to exit the market? A survival analysis contribution to the efficient market debate

Concerns regarding the assumptions of the Efficient Market Hypothesis have led to a greater emphasis on how the behaviour of different groups of traders might impact the evolution of financial markets; ideas encapsulated in the Adaptive Markets Hypothesis (AMH). A key assumption of the AMH is that the dynamics of competition and natural selection will drive ‘noise traders’, those least likely to push prices to efficient levels because they follow sub-optimal trading strategies, to exit the market. To test the key assumptions of the AMH, survival analysis is employed to examine the behaviour of retail spread-traders, a group who are widely reported to include many noise traders. Analysis of the trades executed by 5,164 individuals in the period 24th March 2006 to 7th February 2012 found that the least profitable and those who adopted ill-disciplined trading strategies tended to cease trading sooner than others. These findings are consistent with the AMH. However, profitable traders were also found to be more likely to cease trading than the average trader and a V-shaped relationship was found between a trader's Sharpe ratio and their likelihood of ceasing to trade (cf. the average trader). Furthermore, during the financial crisis of 2008–09, the disposition effect of traders and the proportion of noise traders increased and throughout the period of the study, the ill-discipline of new generations of traders increased. The results suggest that the forces underpinning the AMH are complex and the move towards market efficiency may not be as straightforward as some expect.

Assessing the reversal of investor sentiment

Assessing the reversal of sentiment in stock markets is needed because, according to the social mood cycle, the change of social mood over time is an antecedent of price movements. The purpose of this study is to empirically assess reversal of investor sentiment, to show the phases of social mood cycle from increasing mood to decreasing mood, and to explain the dynamic change in market inefficiency from increasing to decreasing. Growth modeling, developed particularly for dealing with the change over time, is used in this study for assessing the reversal of investor sentiment. The autocovariance structure of errors and the variances/covariances of the random coefficients are all taken into account in the model. The results have indicated that the change in investor sentiment over time is inverted U-shaped for the entire market. Moreover, arbitrage constraint and stock characteristics exert a joint moderating effect on sentiment reversal. Less arbitrage constraint can strengthen sentiment reversal only when the market for individual stocks is dominated by noise traders. Based on the results obtained, we discuss asset pricing, liquidity management, and market intervention.

The Impact of Investor Sentiment on Bitcoin Returns and Conditional Volatilities during the Era of Covid-19

This paper studies the impact of investor sentiment on the Bitcoin returns and conditional volatility taking into account the Covid-19 outbreak by using different investor sentiment proxies and by employing the EGARCH model. Estimation results show that investor sentiment has a positive impact on the Bitcoin returns and their volatility, especially after the Covid-19 outbreak. The VAR model is employed to investigate whether investor sentiment and Bitcoin returns are related in a dynamic setting and to make distinguish between rational and irrational investor sentiments. The results from the VAR model show that both rational and irrational investor sentiments have an impact on Bitcoin returns indicating that the Bitcoin market is also driven by emotions and noise traders have an impact on the data generating process of Bitcoin returns. The positive impact of investor sentiment can be attributed to the fear of missing out (FOMO) behavior of speculative and irrational investors.

The sustainability of stock price fluctuations: Explanation from a recursive dynamic model.

The sustainability of stock price fluctuations indicated by many empirical studies hardly reconciles with the existing models in standard financial theories. This paper proposes a recursive dynamic asset pricing model based on the comprehensive impact of the sentiment investor, the information trader and the noise trader. The dynamic process of the asset price is characterized and a numerical simulation of the model is provided. The model captures the features of the actual stock price that are consistent with the empirical evidence on the sustainability of stock price fluctuations. It also offers a partial explanation for other financial anomalies, for example, asset price’s overreaction, asset bubble and the financial crisis. The major finding is that investor sentiment is the key factor to understand the sustainability of stock price fluctuations.

Disclosure, competition, and learning from asset prices

We study voluntary information disclosure by oligopoly firms in a setting in which firms learn information from asset prices to guide their production decisions. A firm that discloses information risks losing a competitive advantage over its rivals but may benefit from learning valuable information from a more informative asset market. Considering the financial market helps the product market escape a nondisclosure equilibrium with low total surplus. Firms' disclosure decisions can exhibit strategic complementarity, leading to multiple equilibria. Firms' endogenous disclosure behavior also gives rise to two novel comparative statics: fiercer competition in the product market can reduce consumer and total surplus, and increased noise trading in the financial market can improve price informativeness.

Do Investors Feedback Trade in the Bitcoin—And Why?

We empirically examine whether feedback traders are active in the Bitcoin and the extent to which their presence is affected by a series of noise-related factors (sentiment; volume; liquidity) at three different frequencies (hourly; daily; weekly) for the April 2013–July 2019 period based on Bitstamp data. Our findings suggest that positive feedback trading grows stronger for higher (hourly; daily) frequencies, with its presence manifesting itself mainly during periods of high/improving sentiment and high/rising volume/liquidity. Additional tests reveal that the significance of hourly feedback trading is identified during hours corresponding to the trading hours of major European/North American markets. Overall, our results confirm extant literature evidence on the prevalence of noise trading in cryptocurrencies, while further showcasing that the factors motivating feedback trading in other asset classes (equities; ETFs; futures) exhibit similar effects over the presence of feedback traders in the cryptocurrency market.

Strategic Trading with Information Acquisition and Long-Memory Stochastic Liquidity

This paper investigates the strategic interaction of information acquisition and noise trading patterns, as well as its significant implications in market equilibrium outcomes. We consider a market where the strategic trader can dynamically acquire costly information about an asset's payoff via private signals instead of endowing him with its full information. Particularly, the noise trading, which provides necessary liquidity for the market, has the feature of long-range dependence. We provide closed-form characterizations of the equilibriums in both opaque and transparent markets depending on whether or not the trader discloses his order flow. We then find that: (1) the endogenous information acquisition improves the price informativeness over time, while the information efficiency is decreasing; (2) the long-memory stochastic liquidity renders excessive price volatility, enlarges the price impact, and slows down information learning; (3) in a transparent market, the trader shall adopt a mixed strategy by adding an additional noise in his trading strategy to prevent rapid information leakage.

Trading Cryptocurrencies as a Pandemic Pastime: COVID-19 Lockdowns and Bitcoin Volume

This paper examines the impact of COVID-19 lockdowns on Bitcoin trading volume. Using data from Apple mobility trends and several time-series econometric models, we find that investors became active participants during the COVID-19 pandemic period and traded more bitcoins on days with low mobility associated with lockdown mandates. These results remain robust after controlling for stocks and gold returns, the VIX index, and the level of attention and sentiment toward Bitcoin, as measured by Google search frequencies and the tone of Tweets discussing Bitcoin. These results suggest that when individual investors have ample free time on their hands, they trade cryptocurrencies as a pastime and use the Bitcoin market as a form of entertainment. Moreover, our results have important implications concerning investors’ herding behavior and overconfidence leading to noise trader risks and bubbles typically accompanied by high trading volume in cryptocurrency markets.

Noise trading and market stability

Noise traders are often thought to be detrimental to market stability, increasing volatility and the risk of bubbles and crashes. The effect of noise traders on the learning and development of informed traders, however, has received little attention. We consider a computational model of a derivatives market containing informed traders and noise traders with the former group having to learn to price the traded asset. We demonstrate that noise traders have a beneficial effect on market stability: an increase in the amount of noise traders makes the market more resilient to shocks. Noise traders by pushing the price away from fundamentals create opportunities for learning, increasing the proportion of informed traders possessing high levels of trading skills in turn protecting the market.

Insider Trading with Memory under Random Deadline

In this paper, we study a model of continuous-time insider trading in which noise traders have some memories and the trading stops at a random deadline. By a filtering theory on fractional Brownian motion and the stochastic maximum principle, we obtain a necessary condition of the insider’s optimal strategy, an equation satisfied. It shows that when the volatility of noise traders is constant and the noise traders’ memories become weaker and weaker, the optimal trading intensity and the corresponding residual information tend to those, respectively, when noise traders have no any memory. And, numerical simulation illustrates that if both the trading intensity of the insider and the volatility of noise trades are independent of trading time, the insider’s expected profit is always lower than that when the asset value is disclosed at a finite fixed time; this is because the trading time ahead is a random deadline which yields the loss of the insider’s information.

Individual investors’ trading behavior in Moscow Exchange and the COVID-19 crisis

This article presents the first study of Russian individual investors’ aggregate equity trading behavior using novel data from Moscow Exchange, with a focus on the COVID-19 episode. Aggregate Russian individual bought the dip during the COVID crash in March–April 2020. While this can be accounted for by their regular contrarian trading traits, they remained as net buyers until the market fully recovered, a sign of sophistication in striking contrast to views that characterized individual investors as noise traders. Our analysis suggests that this outcome was driven by a combination of regular contrarian traits and a unique positive shock to individual investor demand for equities, with weaker evidence of exploiting a negative bubble.

Downside systematic risk in Pakistani stock market: role of corporate governance, financial liberalization and investor sentiment

PurposeThe purpose of this paper is to examine the impact of corporate governance, investor sentiment and financial liberalization on downside systematic risk and the interplay of socio-political turbulence on this relationship through static and dynamic panel estimation models.Design/methodology/approachThe evidence is based on a sample of 230 publicly listed non-financial firms from Pakistan Stock Exchange (PSX) over the period 2008–2018. Furthermore, this study analyzes the data through Blundell and Bond (1998) technique in the full sample as well sub-samples (big and small firms).FindingsThe authors document that corporate governance mechanism reduces the downside risk, whereas investor sentiment and financial liberalization increase the investors’ exposure toward downside risk. Particularly, the results provide some new insights that the socio-political turbulence as a moderator weakens the impact of corporate governance and strengthens the effect of investor sentiment and financial liberalization on downside risk. Consistent with prior studies, the analysis of sub-samples reveals some statistical variations in large and small-size sampled firms. Theoretically, the findings mainly support agency theory, noise trader theory and the Keynesians hypothesis.Originality/valueStock market volatility has become a prime area of concern for investors, policymakers and regulators in emerging economies. Primarily, the existence of market volatility is attributed to weak governance, irrational behavior of market participants, the liberation of financial policies and sociopolitical turbulence. Therefore, the present study provides simultaneous empirical evidence to determine whether corporate governance, investor sentiment and financial liberalization hinder or spur downside risk in an emerging economy. Furthermore, the work relates to a small number of studies that examine the role of socio-political turbulence as a moderator on the relationship of corporate governance, investor sentiment and financial liberalization with downside systematic risk.

Lottery preferences and retail short selling

Unlike developed countries, the prominent participants in the Taiwanese stock market are individual investors. This study found that retail short-sellers in Taiwan can predict stock prices and perform better with stocks held primarily by retail investors with a preference for lottery-like payoffs. Moreover, short-sellers make lower profits with stocks with lottery features in the absence of the uptick rule. The findings are robust when considering the effect of institutional short selling, alternative lottery-like measures such as retail trading activity, arbitrage risk, and distinct macroeconomic conditions. Overall, the results suggest that retail investors are not necessarily noise traders. Based on divergence-of-opinion theory (Miller, 1977), heterogeneous retail investors make trades due to their differences in sentiments. Meanwhile, retail short-sellers include professionals in profitmaking through short-selling by gauging the gambling propensity of general retail investors. Consequently, removing short-selling restrictions can increase the spillover effect of short-sellers' trading signals and help other retail investors move and adjust their investments in lottery-like stocks.

How noise trading affects informational efficiency: Evidence from an order-driven market

We use full order book data from the Australian Securities Exchange to investigate how noise trading affects informational efficiency of securities prices. In aggregate, noise trading harms price efficiency. However, this is driven mainly by higher levels of noise trading, indicating a non-linear effect. Further, behind the aggregate effects lies rich heterogeneity in how noise trading affects informational efficiency cross-sectionally. Noise trading harms informational efficiency of large and liquid stocks but can be beneficial in small and illiquid stocks, indicating that noise trading affects different stocks differently.

How market ecology explains market malfunction

Standard approaches to the theory of financial markets are based on equilibrium and efficiency. Here we develop an alternative based on concepts and methods developed by biologists, in which the wealth invested in a financial strategy is like the abundance of a species. We study a toy model of a market consisting of value investors, trend followers, and noise traders. We show that the average returns of strategies are strongly density dependent; that is, they depend on the wealth invested in each strategy at any given time. In the absence of noise, the market would slowly evolve toward an efficient equilibrium, but the statistical uncertainty in profitability (which is calibrated to match real markets) makes this noisy and uncertain. Even in the long term, the market spends extended periods of time away from perfect efficiency. We show how core concepts from ecology, such as the community matrix and food webs, give insight into market behavior. For example, at the efficient equilibrium, all three strategies have a mutualistic relationship, meaning that an increase in the wealth of one increases the returns of the others. The wealth dynamics of the market ecosystem explain how market inefficiencies spontaneously occur and gives insight into the origins of excess price volatility and deviations of prices from fundamental values.

U.S. TRADE POLICY UNCERTAINTY AND EXPECTED STOCK RETURNS OF CHINESE LISTED COMPANIES

Our study is the first to examine the pricing effect of U.S. trade policy uncertainty (TPU) on Chinese stocks. We estimate the U.S. TPU beta, which measures Chinese stock exposure to the U.S. TPU index. Both portfolio analyses and cross-sectional regressions suggest a significantly negative relation between the U.S. TPU beta and expected returns, which cannot be explained by other pricing factors. The stocks in the lowest U.S. TPU beta quintile can generate 3.48% higher annual returns compared to stocks in the highest U.S. TPU beta quintile. Furthermore, we provide two potential mechanisms that include a real economy channel and a behavioral finance channel using vector autoregression models. Our results indicate that the negative premium can be explained by both demanding more of high TPU beta stocks in hedging against adverse effects from TPU and selling more of low TPU beta stocks due to pessimistic beliefs of noise trader.