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.     

Microstructure and Ambiguity

A goal for stock exchanges is to increase participation by firms and investors. We show how specific features of the microstructure can reduce perceived ambiguity, and induce participation by both investors and issuers. We develop a model with sophisticated traders, who we view as expected utility maximizers with rational expectations, and unsophisticated traders, who we view as rational traders facing ambiguity about the payoffs to participating in the market. We show how designing markets to reduce ambiguity can benefit investors through greater liquidity, exchanges through greater volume, and issuing firms through a lower cost of capital.     

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.     

Learning from others,reacting, and market quality

Traders pay attention to one another but are unable to perfectly deduce each others’ beliefs from transactions alone. This explains why markets are hard to beat and also why trading occurs at all. Even when traders react rationally to the actions of others, they cannot arrive easily at a common posterior assessment of value. We model a realistic market composed of traders who combine their own private information with rational learning about the information possessed by others. We compare phenomena in this market with an otherwise identical market populated by traders who receive the same private information but ignore other traders. Using simulation to engender greater realism, we find that learning usually reduces volatility, increases the accuracy of the market price as a forecast of value, reduces trading volume, and decreases the prevalence of bubbles. However, for some combinations of market conditions, learning can have the opposite effect. The marginal influences of eight different market conditions, ranging from information heterogeneity through resource diversity, are estimated. Prices, volatility, volume, and bubbles exhibit subtle and complex responses to market conditions.     

Option Prices Leading Equity Prices: Do Option Traders Have an Information Advantage?

Recent evidence shows that option volatility skews and volatility spreads between call and put options predict equity returns. This study investigates whether such predictive ability is driven by option traders’ information advantage. We examine the predictive ability of volatility skews and volatility spreads around significant information events including earnings announcements, other firm‐specific information events, and events that trigger significant market reactions. Consistent with option traders having an information advantage relative to equity traders before information events, we find that the option measures immediately before these events have higher predictive ability for short‐term event returns than they do in a more dated window or before a randomly selected pseudo‐event. We also find that option measures have predictive ability after information events. However, this predictive ability holds only for unscheduled corporate announcements, which suggests that, relative to equity traders, option traders have superior ability to process less anticipated information.     

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’.     

THE IMPLICATIONS OF TRADER COGNITIVE ABILITIES ON STOCK MARKET PROPERTIES

Market structure and individual rationality remain at the centre of a debate as to which is the main driving force in market performance. We examine the role of individual rationality, comparing zero‐intelligence traders with traders with different levels of intelligence using a special adaptive form of strongly typed genetic programming‐based learning algorithm. We use this approach with real data: historical quotes of the S&P 500 and Coca‐Cola stock prices. We find a mixture of positive and negative impacts from intelligence on market performance. Because the concept of market structure as a driving force has been significantly challenged in the literature, we suggest that the inclusion of both intelligence and market structures is important when examining the driving forces of market performance. This inclusion is consistent with the research of Todd and Gigerenzer (Journal of Economic Psychology, 24 (2003) 143–165), which asserts that both environment (market structure) and agents’ cognition play important roles. Copyright © 2013 John Wiley & Sons, Ltd.     

Trade Disclosure,Information Learning and Securities Market Performance

In this paper we examine the effect of information disclosure on securities market performance when liquidity traders are able to acquire information about inside trading. We show that the bid-ask spread increases with the liquidity trader's learning efficiency, which is greater when trade information is disclosed. The bid-ask spread is always higher when trade information is not disclosed. However, the discrepancy between the bid-ask spreads with and without information disclosure narrows when the learning efficiency increases. We also show that the gains of the informed traders in a market without trade information disclosure are reduced in the presence of the liquidity trader's learning. Nevertheless, liquidity traders do not necessarily benefit from increased transparency. In particular, liquidity traders may face higher trading costs.     

Who trades bitcoin futures and why?

Using regulatory data with identifiers, we analyze the traders active in the Bit- coin futures (BTC) contracts traded on the Chicago Mercantile Exchange (CME). We find two primary trader types, those who hold almost exclusively BTC (con- centrated traders) and those who hold BTC to diversify a broader futures portfolio (diversified traders). The prevalence of these two types changes over time. We also study how BTC markets are connected to other futures markets through common holdings of BTC traders. Finally, we analyze the micro BTC contract and find that the trader composition is different than that of the full-size contract.     

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.     

Information, Trading Demand, and Futures Price Volatility

We examine the relation between futures price volatility and trading demand by type of trader in the Standard & Poor's (S&P) 500-stock index futures market. We find that volatility covaries negatively with signed speculative demand shocks but is positively related to signed hedging demand shocks. No significant relation between volatility and demand shocks for small traders is found. Our results suggest that changes in positions of large hedgers destabilize the market, whereas changes in positions of large speculators stabilize volatility. Consistent with models with asymmetrically informed traders, we find that large speculators are likely to possess superior forecasting ability, large hedgers behave like positive feedback traders, and small traders are liquidity traders.     

Does Trading Improve Individual Investor Performance?

From 52,649 accounts and 10,615,117 transaction records obtained from a renowned brokerage house in Taiwan we find that individual investors purchase 73.4% and sell 64.5% of their stock portfolios each month. This is more than ten times the statistics for their U.S. counterparts. In general, individual investors have positive abnormal returns from factor-based models. However, they would have earned higher returns from following a buy-and-hold strategy. We find a U-shaped rather than a monotonic turnover and performance relation. The results do not support the overconfidence argument proposed by Barber and Odean (2000, 2001) nor does the rational model of Grossman and Stiglitz (1980). We find that investors with large portfolio values tend to be informed traders whose excess trading does create performance value. We also investigate whether men are more overconfident than women and find that even though men trade more excessively than women, men's performance measures are not dramatically lower than women's. Specifically, the own-benchmark adjusted gross return for men is higher than that for women. The regression results indicate that electronic traders rather than men are overconfident.     

Do Japanese candlesticks help solve the trader’s dilemma?

In this paper, we investigate whether Japanese candlesticks can help traders to find the best trade-off between market timing and market impact costs. Based on fixed-effect panel regressions on a sample of 81 European stocks, we show that implicit transaction costs are better characterized by using specific Japanese candlesticks patterns. Although market timing costs are not lower when Hammer-like and Doji configurations occur, market impact costs are significantly lower when and after a Doji structure occurs. We further check the potential gains through order submission simulations and find that submission strategies based on the occurrence of Doji result in significantly lower market impact cost than random submission strategies. These findings are of great interest for investors who look for occasional liquidity pools to execute their orders inexpensively such as institutional traders or hedgers.     

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.     

Learning to be overconfident

We develop a multiperiod market model describing both the processby which traders learn about their ability and how a bias inthis learning can create overconfident traders. A trader inour model initially does not know his own ability. He infersthis ability from his successes and failures. In assessing hisability the trader takes too much credit for his successes.This leads him to become overconfident. A trader's expectedlevel of overconfidence increases in the early stages of hiscareer. Then, with more experience, he comes to better recognizehis own ability. The patterns in trading volume, expected profits,price volatility, and expected prices resulting from this endogenousoverconfidence are analyzed.     

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.     

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.     

The perceived fairness of performance evaluation: The role of uncertainty

This paper investigates the relationship between performance evaluation procedures and managerial perceptions of procedural justice. We examine two metric and two process characteristics. Metric characteristics are the diversity of metrics used by superiors and their reliance on outcome vs. effort metrics. Regarding process characteristics, we analyse the amount of subordinate's voice in the performance evaluation process, and the extent of formalization of the process. Using uncertainty management theory, we argue that justice effects of performance evaluation procedures may not be direct but are instead conditional on the amount of task uncertainty managers face in the their job context as well as on managers’ tolerance for ambiguity. Using a sample of 178 managers from the banking industry, we find that all four performance evaluation characteristics are related to justice perceptions, yet their effect depends on the level of task uncertainty and tolerance for ambiguity. These findings explain some inconsistencies in extant studies on fairness of performance evaluation procedures.     

Daily income target effects: Evidence from a large sample of professional commodities traders

We provide evidence of rational reference-dependent preferences in the proprietary trading of professional traders. We find increased trading effort and risk taking by traders following morning losses. Further analysis provides no evidence of a deterioration in trading performance subsequent to losses, as neither risk-adjusted performance nor trade execution appear to be negatively affected by prior losses. The evidence supports the existence of rational reference-dependent preferences in the form of trader daily income targets: these professional traders exhibit increased work effort subsequent to abnormal morning losses. The evidence is inconsistent with the alternative explanation of costly loss aversion.     

Dancing in the dark: post-trade anonymity,liquidity and informed trading

We analyze the impact of post-trade anonymity on liquidity and informed trading in an order driven stock market. The German stock market introduced the Central Counterparty (CCP) in March 2003 for German equities traded on its anonymous electronic trading platform Xetra leading to a major change in its existing transparency regime. Before the introduction trader IDs were revealed to the counterparties of a trade, with the introduction of the CCP even after the transaction the traders remain anonymous. Previous theoretical and empirical research documents that pre-trade anonymity results in increased liquidity, while results on post-trade anonymity are mixed. We find a significant increase in liquidity measured through a reduction of 25% in implicit transaction costs. We also document that the arrival rate of informed traders is reduced in the anonymous setting. Following recent findings of Bloomfield et al. (J Finan Econ 75:165–199, 2005) that informed traders take on the role of liquidity providers we interpret our findings as indication that informed traders change their behavior in providing liquidity more aggressively in an anonymous environment.