Overconfident individual day traders: Evidence from the Taiwan futures market

A specific day-trading policy in Taiwan futures market allows an investigation of the performance of day traders. Since October 2007, investors who characterize themselves as “day traders” by closing their day-trade positions on the same day enjoy a 50% reduction in the initial margin. Because we can identify day traders ex ante, we have a laboratory to explore trading behavior without the contamination of potential behavioral biases. Our results show that the 3470 individual day traders in the sample incur on average a significant loss of 61,500 (26,700) New Taiwan dollars after (before) transaction costs over October 2007–September 2008. This implies that day traders are not only overconfident about the accuracy of their information but also biased in their interpretations of information. We also find that excessive trading is hazardous only to the overconfident losers, but not to the winners. Last, we provide evidence that more experienced individual investors exhibit more aggressive day trading behavior, although they do not learn their types or gain superior trading skills that could mitigate their losses.     

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.     

Do Behavioral Biases Affect Prices?

This paper documents strong evidence for behavioral biases among Chicago Board of Trade proprietary traders and investigates the effect these biases have on prices. Our traders appear highly loss‐averse, regularly assuming above‐average afternoon risk to recover from morning losses. This behavior has important short‐term consequences for afternoon prices, as losing traders actively purchase contracts at higher prices and sell contracts at lower prices than those that prevailed previously. However, the market appears to distinguish these risk‐seeking trades from informed trading. Prices set by loss‐averse traders are reversed significantly more quickly than those set by unbiased traders.     

Expiration day effects and market manipulation: evidence from Taiwan

In this study, we analyze the expiration day effects of index futures on the cash market in Taiwan, and find that both volatility and trading volume are higher on the final settlement days than on other trading days. We also calculate the volume of open interest for the final settlement of index futures contracts relating to different classes of traders, as well as the profits they earn from their open interest positions. We find that proprietary traders exhibit superior performance whereas foreign investors achieve the worst returns. Our empirical results support the view that the expiration day effects in the Taiwan futures market are at least partially attributable to attempts at ‘marking the close’.     

A test of the different implications of the overconfidence and disposition hypotheses

Under both the overconfidence and disposition biases, a positive relationship is predicted between prior returns and subsequent trading volume. However, theoretically the overconfidence and disposition effects have different implications on the relationships between the long- and short-position gains of traders and their subsequent buying and selling activities. We examine a unique dataset obtained from the Taiwan Futures Exchange which records all account-level trades and orders. Our data and methodology have the advantage of being able to empirically differentiate these two effects and we demonstrate that different types of traders exhibit different types and levels of behavioral biases.     

What Makes a Good Trader? On the Role of Intuition and Reflection on Trader Performance

Using laboratory experiments, we provide evidence on three factors influencing trader performance: fluid intelligence, cognitive reflection, and theory of mind (ToM). Fluid intelligence provides traders with computational skills necessary to draw a statistical inference. Cognitive reflection helps traders avoid behavioral biases and thereby extract signals from market orders and update their prior beliefs accordingly. ToM describes the degree to which traders correctly assess the informational content of orders. We show that cognitive reflection and ToM are complementary because traders benefit from understanding signals’ quality only if they are capable of processing these signals.     

Professional trader discipline and trade disposition

Recent evidence indicates irrational behavior among retail investors. They hold onto losses and sell winners in a manner consistent with the disposition effect. Market professionals often use the term “discipline” to indicate trading strategies that minimize potential behavioral influences. We investigate the nature of trading discipline and whether professional traders are able to avoid the costly irrational behaviors found in retail populations. The full-time traders in our sample hold onto losses significantly longer than gains, but we find no evidence of costs associated with this behavior. The successful floor futures traders in our sample exhibit trading behavior characterized as rational and disciplined. Moreover, measures of relative trading discipline have predictive power for subsequent trading success.     

Whose trades convey information? Evidence from a cross-section of traders

This paper contributes empirically to our understanding of informed traders. It analyzes traders’ characteristics in a foreign exchange electronic limit order market via anonymous trader identities. We use six indicators of informed trading in a cross-sectional multivariate approach to identify traders with high price impact. More information is conveyed by those traders’ trades which—simultaneously—use medium-sized orders (practice stealth trading), have large trading volume, are located in a financial center, trade early in the trading session, at times of wide spreads and when the order book is thin.     

Technical Analysis,Investment Psychology,and Liquidity Provision: Evidence from the Taiwan Stock Market

We set out to empirically identify the effects on technical signals attributable to psychological biases, adopting a set of specific liquidity provision proxies for a sample of firms listed on the Taiwan Stock Exchange. The main findings of our empirical analysis are that the "disposition," "information cascade," and "anchoring" effects each have significant impacts on trading signals. Our results should help to shed further light on the asymmetric market responses to technical buy and sell signals, while also providing some potential clarification of the different attitudes of traders toward big-cap and small-cap firms.     

Informed and uninformed trading on the Australian dollar

Using a high-frequency data set of the spot Australian/US dollar, this study examines the distribution of quotes, spreads, and returns across the trading day. By identifying the direction of trade and the subsequent quote returns from contributing banks, the segmented nature of the market into market-makers and informed and uninformed traders is investigated. The results suggest that the economic gain possible from private information is maximised over 2 to 5 quotes and is rapidly eroded by 20 quotes (about 2 min later during busy trading times) as other new information enters the market. Also, the analysis is revealing of discontinuities in trading and the volatility of pricing across the trading day.     

What do retail FX traders learn?

What is the benefit of experience? Using data from a leading trading platform we find no evidence that retail FX traders learn to trade better, but they do appear to learn about their innate abilities as traders and respond appropriately. In particular, following an unsuccessful trading day, they are more likely to cease trading, to trade smaller amounts and to trade less frequently. These effects are stronger for younger and less experienced traders who might be expected to have more to learn than older, more experienced traders. As regards learning through experience, surprisingly we find that more seasoned traders demonstrate a slight decline in performance once we account for the endogenous decision to cease trading, and even very experienced traders consistently lose money.     

An intertemporal capital asset pricing model with heterogeneous expectations

This paper extends the intertemporal capital asset pricing model (ICAPM) to integrate the heterogeneous trading behavior of three groups of investors; rational utility maximizers, positive feedback, or momentum, traders, and fundamental traders. Using several contemporary fundamental factors to proxy for the latter of these investors’ trading patterns, the interaction of these three groups of investors is explored in the G-7 markets using monthly stock market prices. There is no evidence that positive feedback traders are present in the sample data. Fundamental traders are however observable. This finding suggests that although positive feedback traders may drive stock prices in the short-run, as is typically observed in higher frequency data, fundamental traders likely play a role in pushing prices back to their fundamental value in the longer-run.     

High-frequency trading: a literature review

The relatively recent phenomenon of high-frequency trading has had a profound impact on the micro-structure of financial markets. Several authors hailed it as a provider of liquidity and a mechanism for controlling volatility, two highly welcome features, especially beneficial to retail traders, whereas other authors view the situation generated by algorithmic trading as damaging for both small and institutional traders, and the orderly functioning of the markets. This paper analyzes the impact of high-frequency trading in respect of the main parameters affecting market quality: volatility, transaction costs, liquidity, price discovery, penalization of slower traders, and impact on sudden financial crises, the notorious flash crashes. As often happens within the financial community, different views stand to each other and no conclusive agreement on the value of most parameters has been reached as yet. A section on the apparently falling profits of high-frequency traders, as denounced in recent times, completes the review.

     

Behavioral biases of mutual fund investors

We examine the effect of behavioral biases on the mutual fund choices of a large sample of US discount brokerage investors using new measures of attention to news, tax awareness, and fund-level familiarity bias, in addition to behavioral and demographic characteristics of earlier studies. Behaviorally biased investors typically make poor decisions about fund style and expenses, trading frequency, and timing, resulting in poor performance. Furthermore, trend chasing appears related to behavioral biases, rather than to rationally inferring managerial skill from past performance. Factor analysis suggests that biased investors often conform to stereotypes that can be characterized as Gambler, Smart, Overconfident, Narrow Framer, and Mature.     

A dynamic limit order market with fast and slow traders

This paper considers the role of high-frequency trading in a dynamic limit order market. Fast traders? ability to revise their quotes quickly after news arrivals helps to reduce the inefficiency that is rooted in the risk of being picked off, which increases trade. However, their presence induces slow traders to strategically submit limit orders with a lower execution probability, thereby reducing trade. Because speed is a source of market power, it enables fast traders to extract rents from other market participants and triggers a costly arms race that reduces social welfare. The model generates a number of testable implications concerning the effects of high-frequency trading in limit order markets.     

Trader Leverage and Liquidity

Does trader leverage drive equity market liquidity? We use the unique features of the margin trading system in India to identify a causal relationship between traders’ ability to borrow and a stock's market liquidity. To quantify the impact of trader leverage, we employ a regression discontinuity design that exploits threshold rules that determine a stock's margin trading eligibility. We find that liquidity is higher when stocks become eligible for margin trading and that this liquidity enhancement is driven by margin traders’ contrarian strategies. Consistent with downward liquidity spirals due to deleveraging, we also find that this effect reverses during crises.     

High-frequency trading and stock liquidity: An intraday analysis

This paper studies the impact of high-frequency trading (HFT) on intraday liquidity of CAC40 stocks listed on Euronext. Spreads display an intraday L-shaped pattern, while quoted depth follows an inverse pattern: low at the open and increasing towards the end of the trading day. When liquidity demand is particularly high, there is a high rate of order cancellations attributable to high-frequency traders who use frequent order cancellations to strategically manage their limit orders and close positions near the market close. Using the generalized method of moments estimator, we generate strong evidence that greater intensity of HFT is associated with lower spreads and higher depth. The positive effect of HFT on liquidity is due mainly to decreased adverse selection costs arising from asymmetric information among market participants.     

Non-technical skills for managing risk and performance in financial trading

Recent large-scale failures in financial institutions have been found to be caused, in-part, by human factors-related issues in financial trading. In other environments where risk management and performance are intertwined, a human factors approach is often adopted to understand how the ‘non-technical skills (NTS)’ (leadership (LD), decision-making (DM), situation awareness (SA), teamwork) of organisational actors influence outcomes. Yet, to date, there has been minimal application of human factors research in financial trading. This study (i) identifies ‘real-world’ (i.e. non-laboratory) research studies investigating the NTS important for performance in financial trading, (ii) examines and synthesises data on the NTS found to underpin good or poor performance and (iii) considers the quality and coverage of research investigating NTS in financial trading, and identifies potential areas for future research. Nineteen studies were identified through a systematic literature search and then content-analysed for associations between NTS and performance in financial trading. The review found a range of decision-making (e.g. heuristics and biases, intuitive DM, emotional regulation) and LD skills (e.g. setting standards, monitoring behaviour, encouraging speaking-up) to have been identified as important for managing risk and performance in financial trading environments. Furthermore, SA (e.g. information search and assessment strategies, vigilance, identifying ‘noise’ data) and teamwork (e.g. avoiding ‘role’ conflict, communication between traders) were found to be important, yet remain less explored within the literature, and should be the focus of future research. NTS appear essential for effective risk management within the financial sector, yet further field research is required to examine the context-relevant behaviours that underpin safe activity. This will facilitate the development of evidence-based systems for assessing and training NTS competencies.     

Clustering of intraday order-sizes by uninformed versus informed traders

We examine quantity choice patterns by equity traders across trading hours in the U.S. Controlling for intraday variations in trading activity, we find that traders submit more non-rounded order sizes and more order sizes overall leading up to a day’s market close. Traders who submit more distinct order sizes pay a higher cost to trade, and they are also less informed about future prices. Our results suggest that the goal to satisfy specific quantity demands rises across the day. This differs from total trading demand, which resembles a U-shape pattern intraday, but is consistent with less trade-size clustering at the ends of fiscal quarters.     

Financial Speculators' Underperformance: Learning, Self-Selection, and Endogenous Liquidity

We develop an equilibrium model of learning by rational traders to reconcile several empirical regularities: Cross sectionally, most individual speculators lose money; large speculators outperform small speculators; past performance positively affects subsequent trade intensity; most new traders lose money and cease speculation; and performance shows persistence. Learning from trading generates substantial endogenous liquidity, reducing bid–ask spreads and the impact of exogenous liquidity shocks on asset prices, but amplifying the effects of real shocks. Introducing slightly overconfident traders increases bid–ask spreads, hurting all traders. Finally, behavioral theories cannot reconcile all of these empirical regularities.