Tick size and market quality: Simulations based on agent‐based artificial stock markets

This paper investigates the way that minimum tick size affects market quality based on an agent‐based artificial stock market. Our results indicate that stepwise and combination systems can promote market quality in certain aspects, compared with a uniform system. A minimal combination system performed the best to improve market quality. This is the first study to analyse tick size systems that remain at the theory stage and compare four types of system under the same experimental environment. The results suggests that a minimal combination system could be considered a new direction for market policy reform to improve market quality.     

THE EFFECT OF BOUNDARY CONDITIONS ON EFFICIENCY AND PRICING IN DOUBLE‐AUCTION MARKETS WITH ZERO‐INTELLIGENCE AGENTS

The computational tractability of many markets with computerized agents requires they be subject to boundary conditions that are both unnecessary and undesirable in markets with humans. In particular, double‐auction markets with zero‐intelligence agents, which are a standard baseline in computational economics, constrain all bids, offers and trade prices to lie between an upper and a lower bound. This paper shows how changes in these boundary conditions influence pricing and efficiency. It is found that boundary conditions consistent with competitive pricing are generally inconsistent with the most efficient operation of those markets. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

Predictability of the simple technical trading rules: An out‐of‐sample test

In a true out-of-sample test based on fresh data we find no evidence that several well-known technical trading strategies predict stock markets over the period of 1987 to 2011. Our test safeguards against sample selection bias, data mining, hindsight bias, and other usual biases that may affect results in our field. We use the exact same technical trading rules that Brock, Lakonishok, and LeBaron (1992) showed to work best in their historical sample. Further analysis shows that this poor out-of-sample performance most likely is not due to the market becoming more efficient – instantaneously or gradually over time – but probably a result of bias.     

An analysis of trading strategies in eleven European stock markets

Abstract

In recent years, the validity of the weak form efficient market hypothesis (EMH) has been called into question as several studies have uncovered evidence that technical trading rules have predictive ability with respect to both developed and emerging stock market indices. This study analyses the forecasting power of 2 of the most popular trading rules using index data for a selection of 11 European stock markets over the January 1991 to December 2000 period. The findings indicate that the emerging markets included in this paper are informationally inefficient; these markets displayed some degree of predictability in their share returns, although the developed markets did not. Furthermore, the results point to large differences in the performance of the rules examined; while small size filters consistently outperformed the buy-and-hold strategy in the emerging markets examined even after the consideration of transaction costs, the performance of the moving average rules was erratic and varied dramatically from market to market.     

How information and communication technology affects decision‐making on innovation diffusion: An agent‐based modelling approach

We introduce a computational agent‐based model of innovation diffusion that allows us to analyse the influence of information and communication technology (ICT) development on decision‐making. Model dynamics are based on local emulation between pairs of individuals that generate an evolving social network on which an innovation is virally spread (by word of mouth). Results suggest that ICT development affects the data usefulness for decision‐making by changing the topology of the social network (the means whereby the innovation is propagated). Paradoxically, a higher level of ICT development (providing a larger volume of data) narrows the differences between better and worse launch strategies, thus reducing data‐driven decision‐making usefulness, which then shows diminishing returns on the ICT level.     

Pareto‐archived evolutionary wavelet network for financial constrained portfolio optimization

The multi‐objective portfolio optimization problem is too complex to find direct solutions by traditional methods when constraints reflecting investor's preferences and/or market frictions are included in the mathematical model and hence heuristic approaches are sought for their solution. In this paper we propose the solution of a multi‐criterion (bi‐objective) portfolio optimization problem of minimizing risk and maximizing expected return of the portfolio which includes basic, bounding, cardinality, class and short sales constraints using a Pareto‐archived evolutionary wavelet network (PEWN) solution strategy. Initially, the empirical covariance matrix is denoised by employing a wavelet shrinkage denoising technique. Second, the cardinality constraint is eliminated by the application of k‐means cluster analysis. Finally, a PEWN heuristic strategy with weight standardization procedures is employed to obtain Pareto‐optimal solutions satisfying all the constraints. The closeness and diversity of Pareto‐optimal solutions obtained using PEWN is evaluated using different measures and the results are compared with existing only solution strategies (evolution‐based wavelet Hopfield neural network and evolution‐based Hopfield neural network) to prove its dominance. Eventually, data envelopment analysis is also used to test the efficiency of the non‐dominated solutions obtained using PEWN. Experimental results are demonstrated on the Bombay Stock Exchange, India (BSE200 index: period July 2001–July 2006), and the Tokyo Stock Exchange, Japan (Nikkei225 index: period March 2002–March 2007), data sets. Copyright © 2010 John Wiley & Sons, Ltd.     

The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

In financial trading, technical and quantitative analysis tools are used for the development of decision support systems. Although these traditional tools are useful, new techniques in the field of machine learning have been developed for time‐series forecasting. This paper analyses the role of attribute selection on the development of a simple deep‐learning ANN (D‐ANN) multi‐agent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluates the performance of the D‐ANN multi‐agent framework over different time spans of high‐frequency (HF) intraday asset time‐series data and determines how a set of the framework attributes produces effective forecasting for profitable trading. The paper shows the existence of predictable short‐term price trends in the market time series, and an understanding of the probability of price movements may be useful to HF traders. The results of this paper can be used to further develop financial decision‐support systems and autonomous trading strategies for the financial market.     

Applying developed‐country regulation in emerging markets: an analysis of Thai insider trading

This paper undertakes an out‐of‐sample test of developed‐country insider trading regulation in an emerging market environment (Thailand), where severe information asymmetry, lax enforcement and poor pricing efficiency are endemic. Thai insider trading regulation, which mimics developed market rules, fails on all three measures of success. Insiders trade with impunity during a regulated trading ban. Their trading performance outperforms other investors at all times, and they continue to exploit their privileged position with respect to information flow. Our study suggests it is inappropriate for emerging market regulators to adopt developed market regulation without first considering the unique characteristics of their own environment.     

FORECASTING FIRM RISK IN THE EMERGING MARKET OF CHINA WITH SEQUENTIAL OPTIMIZATION OF INFLUENCE FACTORS ON PERFORMANCE OF CASE‐BASED REASONING: AN EMPIRICAL STUDY WITH IMBALANCED SAMPLES

With the development of the Chinese economy, how to make the right decision regarding firms’ risk is becoming more and more important. Case‐based reasoning (CBR) is a potential method that can help forecast business risk status in advance; it is easy to apply and is able to provide good explanations of output. In order to obtain more accurate prediction with CBR, it is essential to investigate factors that influence CBR's performance, and to optimize these factors sequentially for the improvement of CBR's performance in firm risk prediction in emerging markets under a more practicable assumption. We verified that sequential optimization of feature selection, feature weighting, instance selection and the number of nearest neighbours is a possible alternative for improving predictive performance of CBR forecasting under the assumption that the number of failed samples is smaller than that of nonfailed samples. The detailed implementation includes: (1) selecting significant features through a correlation matrix and reducing feature dimensions with factor analysis; (2) using variance contribution ratios of features from factor analysis as feature weights; (3) eliminating noisy cases via a state matrix; and (4) obtaining the optimal number of nearest neighbours from empirical results among different numbers of nearest neighbours. To validate the usefulness of the sequential optimization approach, we applied it to a real‐world case: firm risk prediction with imbalanced data from the emerging market of China. Experimental results show that predictive accuracy of CBR applied in the emerging market was improved with the sequential optimization approach. Insightful thoughts from the results of the sequential optimization of the CBR forecasting system on modelling social tasks are also provided. Copyright © 2013 John Wiley & Sons, Ltd.     

Capital gains tax,supply‐driven trading and ownership structure: direct evidence of the lock‐in effect

This study investigates the effect of differential capital gains tax rates on investor trading and share prices in a unique market setting that facilitates the resolution of conflicting prior evidence of holding period tax incentives. In particular, we examine whether the concessionary tax treatment of long‐term capital gains increases the supply of shares that qualify for long‐term status, thereby causing downward price pressure. We find evidence of abnormal seller‐initiated trading following the 12‐month anniversary of listing for IPO firms that appreciate in price (‘winners’) and report no such evidence for firms that decline in price (‘losers’). Consistent with the tax concessions being greater for individual than institutional investors, we report that abnormal seller‐initiated trading is mitigated by higher levels of ownership by institutional investors. We also report limited evidence, for winners, of declining share prices upon qualifying for long‐term tax status.     

Time‐Varying Risk–Return Trade‐off in the Stock Market

We uncover a strong comovement of the stock market risk–return trade‐off with the consumption–wealth ratio (CAY). The finding reflects time‐varying investment opportunities rather than countercyclical aggregate relative risk aversion. Specifically, the partial risk–return trade‐off is positive and constant when we control for CAY as a proxy for investment opportunities. Moreover, conditional market variance scaled by CAY is negatively priced in the cross‐section of stock returns. Our results are consistent with a limited stock market participation model, in which shareholders require an illiquidity premium that increases with CAY, in addition to the risk premium that is proportional to conditional market variance.     

The Performance of Characteristics‐based Indices1

This paper analyses a set of characteristics‐based indices that, it has been argued, outperform market cap‐weighted indices. We analyse the performance of an exhaustive list of these indices and show that i) the outperformance over value‐weighted indices may be negative over long time periods, and ii) there is no significant outperformance over equal‐weighted indices. An analysis of the style and sector exposures of characteristics‐based indices reveals a significant value tilt. When this tilt is properly adjusted for, the abnormal returns of these indices decrease considerably. Moreover, it is straightforward to construct portfolios with higher Sharpe ratios than characteristics‐based indices through factor or sector tilts.     

Stock‐Based Compensation in a Concentrated Ownership Setting: An Empirical Investigation

Stock‐based compensation has been viewed as an important mechanism for tying managers’ wealth to firm performance, and thus alleviating the agency conflict between the shareholders and the managers when ownership is diffused. However, in a concentrated ownership structure, controlling owners are usually the management of the firm; they can engage in self‐dealing activities to the detriment of minority shareholders’ interests. Yet, outside investors may anticipate the problem and discount the share price for the entrenchment behaviors they observe. In this study, we investigate how controlling owners trade off the benefits and the costs of using stock‐based compensation. Based on a sample of Taiwanese firms, our evidence shows that stock‐based compensation is negatively related to the agency problem embedded in a concentrated ownership structure. This relationship is evident among firms with more frequent equity offerings. Overall, our empirical evidence suggests that controlling owners consider the negative price effects of stock‐based compensation and trade off these costs with the benefits of expropriating minority shareholders’ interests, particularly when firms seek more external equity capital. Our results hold after controlling for selection bias and share collateral by controlling owners.     

Women in leadership: an analysis of the gender pay gap in ASX‐listed firms

This study provides evidence that after several decades of fighting for equal pay for equal work, an unexplained gender pay gap remains amongst senior executives in ASX‐listed firms. After controlling for a large suite of personal, occupational and firm observables, we find female senior executives receive, on average, 22.58 percent less in base salary for the period 2002–2013. When executives are awarded performance‐based pay, females receive on average 16.47 percent less in cash bonus and 18.21 percent less in long‐term incentives than males. The results are robust to using firm fixed effects and propensity‐score matching. Blinder–Oaxaca decomposition results show that the mean pay gap cannot be attributed to gender differences in attributes, including job titles. Instead, the results point to differences in returns on firm‐specific variables, in particular firm risk.     

The determinants of high‐interest entrusted loans in China

We examine the determinants of high‐interest entrusted loans in China from the perspective of corporate risk‐taking. The results of a baseline model illustrate that the propensity to offer high‐interest entrusted loans increases with loose monetary policies, corporate cash holdings and firm age, and it decreases with firm size and growth opportunity. These findings support the claim that firms offer high‐interest entrusted loans mainly for short‐term profits. Other determining factors include CEO behavior traits, market imperfections and the intensity of corporate governance. Specifically, market imperfections create an opportunity for risk‐taking while CEO behavior and the intensity of corporate governance affect a firm's tendency to take risk and engage in high‐interest entrusted loans.     

Interaction of size,book‐to‐market and momentum effects in Australia

This study seeks to disentangle the effects of size, book‐to‐market and momentum on returns. Initial results show that each characteristic has a role in explaining returns, but that there is interaction between size and momentum, as well as between size and book‐to‐market. Three key findings emerge. First, the size premium is the strongest, particularly in the loser portfolios. Second, the value premium is generally limited to the smallest portfolios. Third, the momentum premium is evident for the large‐ and middle‐sized portfolios, but loser stocks significantly outperform winner stocks in the smallest size portfolio. When these interactions are controlled with multivariate regression, we find a significant negative average relation between size and returns, a significant positive average relation between book‐to‐market and returns, and a significant positive average relation between momentum and returns.