Neoclassical finance,behavioral finance and noise traders: A review and assessment of the literature

While mainstream neoclassical finance ignores the role played by noise traders, a significant amount of empirical evidence is available to show that noise traders are active market participants and that their participation gives rise to market anomalies. Unlike neoclassical finance, behavioral finance allows for market inefficiency on the grounds that market participants are subject to common human errors that arise from heuristics and biases. In this paper we review the literature on the behavior of noise traders and analyze the consequences of their presence in the market, starting with a distinction between neoclassical finance and behavioral finance. We identify the market anomalies that provide evidence for the tendency of markets to trade at irrational levels, demonstrate how noise trading is related to some market fundamentals, and describe the models used to quantify noise trader risk.     

Hidden Liquidity: Some New Light on Dark Trading

Using a laboratory market, we investigate how the ability to hide orders affects traders’ strategies and market outcomes in a limit order book environment. We find that order strategies are greatly affected by allowing hidden liquidity, with traders substituting nondisplayed for displayed shares and changing the aggressiveness of their trading. As traders adapt their behavior to the different opacity regimes, however, most aggregate market outcomes (such as liquidity and informational efficiency) are not affected as much. We also find that opacity appears to increase the profits of informed traders but only when their private information is very valuable.     

Noise and competition in strategic oligopoly

In this paper, we propose a model where N strategic informed traders who are endowed with heterogeneous noisy signals with different precisions compete in a market with a single risky asset. We explicitly describe the unique linear equilibrium that exists in this setup and derive its properties. Moreover, we focus on the effects of noise on the competition between traders. We show that noise softens the competition between traders. In particular, for N exceeding three and for certain sets of noise in traders' signals, each trader's individual profit is greater than the one obtained in the case of perfect information.     

Noise Trading in Small Markets

Considering noise traders as agents with unpredictable beliefs, we show that in an imperfectly competitive market with risk averse investors, noise traders may earn higher expected utility than rational investors. This happens when, by deviating from the Nash equilibrium strategy, noise traders hurt rational investors more than themselves. It follows that the willingness of arbitrageurs to exploit noise traders' misperceptions is lower relative to a perfectly competitive economy. This result reinforces the theory that noise trading may explain closed-end fund discounts and small firms' returns, since these markets are less competitive than the market for large firms' stock.     

Can earnings fixated investors survive in a competitive securities market? Implications for sustained price anomalies and mark-to-market accounting

We model the dynamic survival of earnings fixated investors in a competitive securities market that allows for learning and arbitrage and that is populated by heterogeneous investors. Our model is distinct from those based on aggressive trading by overconfident investors. We prove that in the absence of noise traders, rational investors will drive out earnings fixated investors from the market in the long-run. More interestingly, we show that in a market with noise traders, some proportion of earnings fixated investors survive in long-run equilibrium for all feasible model parameter values. Furthermore, under no circumstances can the earnings fixated investors be driven out of the market completely. On the contrary, for some parameter values, the earnings fixated investors drive out the rational investors entirely. These results rationalize the long-run sustainability of common pricing anomalies. They also highlight potential benefits to society of mark-to-market accounting.     

Chasing noise

We present a simple model in which rational but uninformed traders occasionally chase noise as if it were information, thereby amplifying sentiment shocks and moving prices away from fundamental values. In the model, noise traders can have an impact on market equilibrium disproportionate to their size in the market. The model offers a partial explanation for the surprisingly low market price of financial risk in the spring of 2007.     

Noise trading and the price formation process

This study proposes the dispersion in daily net initiated order flow across brokers as a proxy for the level of noise trading in a stock, and applies this proxy to test some basic implications of market microstructure theory. We use data from the Australian Stock Exchange, a computerized limit order market where price, quantity, and broker identity for each incoming order are shown on broker screens. We find daily movements in our noise measure are positively associated with trading volume and market depth, and negatively related to the bid-ask spread. We find monthly movements in our noise measure are negatively associated with the probability of informed trading, and positively correlated with the arrival rate of uninformed traders. We also find the sensitivity of stock prices to net initiated order flow decreases in the level of noise trading. In addition we find that, after controlling for noise trading, the sensitivity of stock prices to net initiated order flow is significantly greater on Mondays. These empirical results consistently support the implications of various models of market microstructure, suggesting that our proxy provides useful information as a daily measure of noise trading.     

Specialist participation and limit orders

We present a market microstructure model to examine specialist's strategic participation decisions in a security market where there are noise traders, limit order traders, an insider and a specialist. We argue that the specialist's participation rate depends on the depth of the limit book and its uncertainty. In particular, the specialist has incentives to trade against the market trend when the limit book depth is low and to trade with the market trend when the depth is high. Moreover, the specialist's participation rate is positively related to the limit book depth uncertainty and the asset price volatility, but is negative related to the average trading volume. We also discuss the specialist's participation strategies under the NYSE regulation that prohibits the specialist from trading with the market trend.     

INFORMATION ASYMMETRY AND OPTIONS TRADING

This paper demonstrates that options trading does not have a uniform impact on the volatility of underlying stocks. Although uninformed traders are able to hedge the risk of underlying stocks by maintaining opposite positions in the options market, informed traders hold outright options positions to capitalize on their information. This hedging behavior tends to reduce noise in the stock market, whereas the speculating behavior tends to generate noise in the stock market. As a result, stocks that were originally volatile, i.e., traded primarily by uninformed traders, will be stabilized by the introduction of options. Conversely, stocks that were more stable become destabilized by options trading.     

The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets

Cryptocurrencies have gained a lot of attention since Bitcoin was first proposed by Satoshi Nakamoto in 2008, highlighting the potential to play a significant role in e-commerce. However, relatively little is known about cryptocurrencies, their price behaviour, how quickly they incorporate new information and their corresponding market efficiency. To extend the current literature in this area, we develop four smart electronic Bitcoin markets populated with different types of traders using a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm. We apply the STGP technique to historical data of Bitcoin at the one-minute and five-minute frequencies to investigate the formation of Bitcoin market dynamics and market efficiency. Through a plethora of robust testing procedures, we find that both Bitcoin markets populated by high-frequency traders (HFTs) are efficient at the one-minute frequency but inefficient at the five-minute frequency. This finding supports the argument that at the one-minute frequency investors are able to incorporate new information in a fast and rationale manner and not suffer from the noise associated with the five-minute frequency. We also contribute to the e-commerce literature by demonstrating that zero-intelligence traders cannot reach market efficiency, therefore providing evidence against the hypothesis of Hayek (1945; 1968). One practical implication of this study is that we demonstrate that e-commerce practitioners can apply artificial intelligence tools such as STGP to conduct behaviour-based market profiling.     

DOES SPEED MATTER? THE ROLE OF HIGH-FREQUENCY TRADING FOR ORDER BOOK RESILIENCY

We analyze limit order book resiliency following liquidity shocks initiated by large market orders. Based on a unique data set, we investigate whether high-frequency traders are involved in replenishing the order book. Therefore, we relate the net liquidity provision of high-frequency traders, algorithmic traders, and human traders around these market impact events to order book resiliency. Although all groups of traders react, our results show that only high-frequency traders reduce the spread within the first seconds after the market impact event. Order book depth replenishment, however, takes significantly longer and is mainly accomplished by human traders’ liquidity provision.     

Informed traders and limit order markets

We consider a dynamic limit order market in which traders optimally choose whether to acquire information about the asset and the type of order to submit. We numerically solve for the equilibrium and demonstrate that the market is a “volatility multiplier”: prices are more volatile than the fundamental value of the asset. This effect increases when the fundamental value has high volatility and with asymmetric information across traders. Changes in the microstructure noise are negatively correlated with changes in the estimated fundamental value, implying that asset betas estimated from high-frequency data will be incorrect.     

Noise traders,exchange rate disconnect puzzle,and the Tobin tax

This paper proposes a framework to explain the “exchange rate disconnect puzzle”. Two types of foreign exchange traders, rational traders and noise traders are introduced into a sticky-price general-equilibrium model. The presence of noise traders creates deviations from the uncovered interest parity. Combined with local currency pricing and consumption-smoothing behavior, our model can help to explain the “disconnect puzzle”. The excess exchange rate volatility caused by noise traders can be reduced by the ‘Tobin tax’. However, the effect of the ‘Tobin tax’ depends on the market structure and the interaction between the Tobin tax and other trading costs.     

The role of hormones in financial markets

Steroid hormones, such as testosterone, have been shown to affect risk preferences in financial traders with high levels leading to excessive risk-taking. Hormone levels, in turn, are affected by trading outcomes as well as by gender - males are more sensitive to stimuli than females. We develop a model to investigate the effects of hormones on financial market stability and trader performance. An increase in the proportion of female traders does not necessarily make markets less volatile; however, it reduces the occurrence of market crashes. Male traders on average under-perform females, although the best performing individuals are more likely to be male.     

WHEN‐ISSUED SHARES,SMALL TRADES,AND THE VARIANCE OF RETURNS AROUND STOCK SPLITS

The increases in volatility after stock splits have long puzzled researchers. The usual suspects of discreteness and bid‐ask spread do not provide a complete explanation. We provide new clues to solve this mystery by examining the trading of when‐issued shares that are available before the split. When‐issued trading permits noise traders to compete with a more homogenous set of traders, decreasing the volatility of the stock before the split. Following the split, these noise traders reunite in one market and volatility increases. Thus, the higher volatility after the ex date of a stock split is a function of the introduction of when‐issued trading, the new lower price level after the split date, and the increased activity of small‐volume traders around a stock split.     

The effect of sequential information releases on trading volume and price behaviour

This paper examines a two-period setting in which each trader receives a private signal, possibly different, in each period before he trades. The principal objectives are threefold. First, we describe the risky asset demands and price reactions in a noisy rational expectations equilibrium where the time 1 average private signal is not revealed by the price sequence but the time 2 average private signal is. Secondly, we analyse how informed trading volume is affected by the revealed information and supply shocks when pure noise trading volume is uncorrected with observable market variables. Our result indicates that no trade occurs for informed traders when net supply remains fixed across rounds of trade. And, when supply shocks are random, trading volume is induced by the informed and the noise traders, but noise trading is not predictable. Finally, we investigate these properties in the case when pure noise trading volume is correlated with observable market variables. It is shown that no informed trading takes place when there is no supply shock. However, when net supply contains random shocks, trading volume consists of noise and informed trading, both of which can be estimated.     

Can representativeness heuristic traders survive in a competitive securities market?

The literature views aggressive trading behavior as the key for representativeness heuristic traders to survive in competition with rational traders. This paper provides another reason. That is, in this dynamic model of a competitive securities market, representativeness heuristic traders can derive more expected profit from the misvaluations (created by noise traders) than can rational traders. Consequently, the expected profit for heuristic traders can be bigger than that for rational traders. If traders' types replicate according to the profitability of the strategies, heuristic traders can survive or even drive out rational traders.     

Stock market dynamics with rational liquidity traders

In the Kyle (1985) finite horizon model of stock market dynamics with a trader who holds long-lived information, informed trading intensities rise with time, and the slopes of the equilibrium price schedules fall. This paper shows that this result depends crucially on the irrational liquidity trader assumption. We replace the irrational noise traders with a sequence of rational, risk averse, liquidity traders who receive endowment shocks to their holdings of the risky asset. We demonstrate that unless liquidity traders are sufficiently risk averse, the slope of equilibrium price schedule rises over time, while informed trading intensities fall. In particular, Kyle's result holds only when liquidity traders are so risk averse that they ‘over-rebalance’ their portfolio's holdings of the risky asset, so that their final holdings of the risky asset have the opposite sign of their initial position.     

Investor Sentiment,Trading Behavior and Informational Efficiency in Index Futures Markets

This paper shows that traders in index futures markets are positive feedback traders—they buy when prices increase and sell when prices decline. Positive feedback trading appears to be more active in periods of high investor sentiment. This finding is consistent with the notion that feedback trading is driven by expectations of noise traders. Consistent with the noise trading hypothesis, order flow in index futures markets is less informative when investors are optimistic. Transitory volatility measured at high frequencies also appears to decline in periods of bullish sentiment, suggesting that sentiment‐driven trading increases market liquidity.     

Do Proprietary Algorithmic Traders Withdraw Liquidity during Market Stress?

We investigate the role of proprietary algorithmic traders in facilitating liquidity in a limit order market. Using order‐level data from the National Stock Exchange of India, we find that proprietary algorithmic traders increase limit order supply following periods of both high short‐term stock‐specific volatility and extreme stock price movement. Even following periods of high marketwide volatility, they do not decrease their supply of liquidity. We define orders from high‐frequency traders as a subclass of orders from proprietary algorithmic traders that are revised in less than three milliseconds. The behavior of high‐frequency trading mimics the behavior of its parent class. This is inconsistent with the theory that fast traders leave the market when stress situations arise, although their limit‐order‐supplying behavior becomes weaker when the increase in short‐term volatility is more informational than transitory. Agency algorithmic traders and nonalgorithmic traders behave opposite to proprietary algorithmic traders by reducing the supply of liquidity during stress situations. The presence of faster traders in the market possibly instills the fear of adverse selection in them. We document that the order imbalance of agency algorithmic traders is positively related to future short‐term returns, whereas the order imbalance of proprietary algorithmic traders is negatively related to future short‐term returns.