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Abstract:
The existence and the enforcement of insider trading laws in stock markets is a phenomenon of the 1990s. A study of the 103 countries that have stock markets reveals that insider trading laws exist in 87 of them, but enforcement - as evidenced by prosecutions - has taken place in only 38 of them. Before 1990, the respective numbers were 34 and 9.

Does this matter? We find that the cost of equity in a country, after controlling for a number of other variables, does not change after the introduction of insider trading laws, but decreases significantly after the first prosecution.

Insider trading, cost of equity, international finance

Abstract:
Shares trading in the Bolsa Mexicana de Valores do not seem to react to company news. Using a sample of Mexican corporate news announcements from the period July 1994 to June 1997, this paper finds that there is nothing unusual about returns, volatility of returns, volume of trade or bid-ask spreads in the event window. This suggests one of five possibilities: our sample size is small; or markets are inefficient; or markets are efficient but the corporate news announcements are not value-relevant; or markets are efficient and corporate news announcements are value-relevant, but they have been fully anticipated; or markets are efficient and corporate news announcements are value-relevant, but unrestricted insider trading has caused prices to fully incorporate the information. A classification into A-shares (which only citizens may hold) and B-shares (which foreigners can hold) reveals that this lack of reaction is mostly concentrated in the A-shares, suggesting that foreigners are more surprised than the locals. This, and the result that the return volatility of A-shares leads return volatility of B-shares (but not strongly enough for there to exist trading rules to arbitrage it away), insinuate that it is insider trading that is responsible for a Mexican corporate news announcement to be a non-event.

The paper thus points toward a methodology for ranking emerging stock markets in terms of their "market integrity," an approach that can be used with the limited data available in such markets.

Abstract:
Shares trading in the Bolsa Mexicana de Valores do not seem to react to company news. Using a sample of Mexican corporate news announcements from the period July 1994 through June 1997, this paper finds that there is nothing unusual about returns, volatility of returns, trading volume, or bid-ask spreads in the event window. We provide evidence that suggests that unrestricted insider trading causes prices to fully incorporate the information before its public release. The paper thus points toward a methodology for ranking emerging stock markets in terms of their market integrity, an approach that can be used with the limited data available in such markets.

Insider trading; Event studies; Emerging markets

Abstract:
In the last decade, neural networks have drawn noticeable attention from many computer and operations researchers. While some previous studies have found encouraging results with using this artificial intelligence technique to predict the movements of established financial markets, it is interesting to verify the persistence of this performance in the emerging markets. These rapid growing financial markets are usually characterized by high volatility, relatively smaller capitalization, and less price efficiency, features which may hinder the effectiveness of those forecasting models developed for established markets. In this study, we attempt to model and predict the direction of return on the Taiwan Stock Exchange Index, one of the fastest growing financial exchanges in developing Asian countries. Our approach is based on the notion that trading strategies guided by forecasts of the direction of price movement may be more effective and lead to higher profits. The Probabilistic Neural Network (PNN) is used to forecast the direction of index return after it is trained by historical data. The forecasts are applied to various index trading strategies, of which the performances are compared with those generated by the buy and hold strategy, and the investment strategies guided by the forecasts estimated by the random walk model and the parametric Generalized Methods of Moments (GMM) with Kalman filter. Empirical results show that the PNN-based investment strategies obtain higher returns than other investment strategies examined in this study. The influences of the length of investment horizon and the commission rate are also considered.

Emerging economy, forecasting, trading strategy, Neural Networks, Generalized Methods of Moments (GMM)

Abstract:
We analyze financial statements from 34 countries for the period 1985-1998 to construct a panel data set measuring three dimensions of reported accounting earnings for each country - earnings aggressiveness, loss avoidance, and earnings smoothing. We hypothesize that these three dimensions are associated with uninformative or opaque earnings, and so we combine these three measures to obtain an overall earnings opacity time-series measure per country. We then explore whether our three measures of earnings opacity affect two characteristics of an equity market in a country - the return the shareholders demand and how much they trade. While not all results are consistent for our three individual earnings opacity measures, our panel data tests document that, after controlling for other influences, an increase in overall earnings opacity in a country is linked to an economically significant increase in the cost of equity and an economically significant decrease in trading in the stock market of that country.

earnings opacity, earnings aggressiveness, loss avoidance, earnings smoothing, cost of equity, trading volume

Abstract:
We analyze the financial statements of 58,653 firm-years from 34 countries for the period 1985-1998 to construct a panel data set measuring three dimensions of earnings opacity for each country - earnings aggressiveness, loss avoidance, and earnings smoothing. We combine these three dimensions to obtain an overall earnings opacity time-series measure per country. We then explore whether earnings opacity affects two dimensions of an equity market in a country - the return the shareholders demand and how much they trade. While not all results are consistent for our three individual earnings opacity dimensions, our panel data tests document that, after controlling for other influences, an increase in overall earnings opacity in a country is linked to an increase in the cost of equity and a decrease in trading in the stock market of that country.

Earnings Opacity; Cost of Equity; Turnover

Abstract:
This paper contributes empirical evidence to the debate on short sales. Our examination of how market-wide short-sale restrictions affect aggregate market returns focuses on two main questions: What is the effect of short-sale restrictions on skewness, volatility, the probability of market crashes, and liquidity? What is the effect on the market expected return or cost of capital? We report new data on the history of short-selling and put option trading regulations and practices from 111 countries, and create a short-selling feasibility indicator for empirical of stock market indices around the world. We find that when short-selling is possible, aggregate stock returns are less volatile and there is greater liquidity. When countries start to permit short-selling, aggregate stock price increases, implying a cost of capital. There is no evidence that short-sale restrictions affect either the level of skewness of returns or the probability of a market crash. Collectively, our empirical evidence shows that allowing short sales improves market quality.

Short-sale constraints, Stock returns, Cost of capital, International finance

Abstract:
This paper argues, both theoretically and empirically, that sometimes no securities law may be better than a good securities law that is not enforced. The first part of the paper formalizes the sufficient conditions under which this happens for any law. The second part of the paper shows that a specific securities law - the law prohibiting insider trading - may satisfy these conditions, which implies that our theory predicts that it is sometimes better not to have an insider trading law than to have an insider trading law but not enforce it. The third part of the paper takes this prediction to the data. We revisit the panel data set assembled by Bhattacharya and Daouk (2002), who showed that enforcement, not the mere existence, of insider trading laws reduced the cost of equity in a country. We find that the cost of equity actually rises when some countries enact an insider trading law, but do not enforce it.

insider trading, cost of capital, emerging markets, securities law, enforcement

Abstract:
This paper argues, both theoretically and empirically, that sometimes no securities law may be better than a good securities law that is not enforced. The first part of the paper formalizes the sufficient conditions under which this happens for any law. The second part of the paper shows that a specific securities law – the law prohibiting insider trading – may satisfy these conditions, which implies that our theory predicts that it is sometimes better not to have an insider trading law than to have an insider trading law but not enforce it. The third part of the paper takes this prediction to the data. We revisit the panel data set assembled by Bhattacharya and Daouk (2002), who showed that enforcement, not the mere existence, of insider trading laws reduced the cost of equity in a country. We find that the cost of equity actually rises when some countries enact an insider trading law, but do not enforce it.

insider trading, cost of capital, emerging markets, securities law, enforcement

Abstract:
In recent years, there has been a growing trend of using multiobjective techniques. The primary advantage of using multiobjective techniques in decision making is, as stated in Spronk (1981), "that most of these (single objective) models and methods are unsuitable for decision situations in which multiple and possibly conflicting objectives play a role, because they are concentrated on the optimal fulfilment of only one objective." Given this notion, we attempt to explore the possibility of taking the multiobjective approach to solve a typical problem encountered by many financial and investment managers, namely, making investment trading decisions based on a set of potentially incompatible forecasts supplied by different analysts. In our experiment, several series of financial analysts' forecasts are generated by different forecasting techniques. The approach examines the historical performance of the various series of forecasts and combines them based on the average, variance, and skewness of investment returns. Through the use of a goal programming model, an investor can construct a portfolio which matches his or her preference. This portfolio-based approach also adds the benefits of diversification in trading. We test our proposed approach with three widely traded broad market indices, S&P 500, FTSE 100, and Nikkei 225. Improved performance of the multiobjective portfolio approach relative to those of individual forecasting techniques and some previously suggested forecast-combining models is measured The empirical results indicates that the performance of the proposed approach statistically outperforms the others at a significance level of 0.05. Moreover, we find that the benefits of our approach becomes more apparent when the market exhibits higher volatility and instability.

Investment analysis, goal programming, combining forecasts, multiobjective decision analysis, trading strategies

Abstract:
This study investigates the usefulness and efficacy of a multiobjective decision method for financial trading guided by a set of seemingly diverse analysts' forecasts. The paper proposes a goal programming (GP) approach which combines various forecasts based on the performance of their previous investment returns. In our experiment, several series of financial analysts' forecasts are generated by different forecasting techniques. Investment returns on each series of forecasts are measured and then evaluated by three performance criteria, namely, mean, variance, and skewness. Subsequently, these distributional properties of the returns are used to construct a GP model. Results of the GP model provide a set of weights to compose an investment portfolio using various forecasts. To examine its practicality, the approach is tested on several major stock market indices. The performance of the proposed GP approach is compared with those of individual forecasting techniques and a number of forecast combination models suggested by previous studies. This comparison is conducted with respect to different levels of investor preference over return, variance, and skewness. Statistical significance of the results are accessed by bootstrap re-sampling. Empirical results indicate that, for all examined investor preference functions and market indices, the GP approach is significantly better than all other models tested in this study.

Investment analysis, neural networks, econometric forecasting, goal programming, combining forecasts, multiobjective decision analysis

Abstract:
In this study, we examine the forecastability of a specific neural network architecture called General Regression Neural Network (GRNN) and compare its performance with a variety of forecasting techniques, including Multi-Layered Feedforward Network (MLFN), multivariate transfer function, and random walk models. The comparison with MLFN provides a measure of GRNN's performance relative to the more conventional type of neural networks while the comparison with transfer function models examines the difference in predictive strength between the non-parametric and parametric techniques. The random walk model is used for benchmark comparison. Our findings show that GRNN not only has a higher degree of forecasting accuracy but also performs statistically better than other evaluated models for different currencies.

general regression neural networks, currency exchange rate, forecasting

Abstract:
Predicting currency movements has always been a problematic task as most conventional econometric models are not able to forecast exchange rates with significantly higher accuracy than a naive random walk model. For large multinational firms which conduct substantial currency transfers in the course of business, being able to accurately forecast the movements of exchange rates can result in considerable improvement in the overall profitability of the firm. In this study, we apply the General Regression Neural Network (GRNN) to predict the monthly exchange rates of three currencies, British pound, Canadian dollar, and Japanese yen. Our empirical experiment shows that the performance of GRNN is better than other neural network and econometric techniques included in this study. The results demonstrate the predictive strength of GRNN and its potential for solving financial forecasting problems.

General regression neural networks, currency exchange rate, forecasting

Abstract:
Few proposed types of derivative securities have attracted as much attention as option contracts on volatility. Grunbichler and Longstaff (1996) proposes a model to value options written on a volatility index. Their model does not take into account the switching regime and asymmetry properties of volatility. We show that the Grunbichler and Longstaff (1996) model underprice a 3-month option by about 10%. A Switching Regime Asymmetric GARCH is used to model the generating process of security returns. The comparison between the switching regime model and the traditional uni-regime GARCH used by Grunbichler and Longstaff (1996) demonstrates that the switching regime EGARCH model fits the data best. When comparing the values of the option based on the Switching Regime Asymmetric GARCH model and the traditional GARCH specification, it is found that the option values obtained from the different processes are very different. This clearly shows that the Grunbichler-Longstaff model is too stylized to be used in pricing derivatives on a volatility index.

Option pricing, volatility index, switching regime, GARCH

Abstract:
Few proposed types of derivative securities have attracted as much attention and interest as option contracts on volatility. Grunbichler and Longstaff (1996) is the only study that proposes a model to value options written on a volatility index. Their model, which is based on modeling volatility as a GARCH process, does not take into account the switching regime and asymmetry properties of volatility. We show that the Grunbichler and Longstaff (1996) model underprice a 3-month option by about 10%. A Switching Regime Asymmetric GARCH is used to model the generating process of security returns. The comparison between the switching regime model and the traditional uni-regime model among GARCH, EGARCH, and GJR-GARCH demonstrates that a switching regime EGARCH model fits the data best. Next, the values of European call options written on a volatility index are computed using Monte Carlo integration. When comparing the values of the option based on the Switching Regime Asymmetric GARCH model and the traditional GARCH specification, it is found that the option values obtained from the different processes are very different. This clearly shows that the Grunbichler-Longstaff model is too stylized to be used in pricing derivatives on a volatility index.

Option pricing, volatility index, switching regime, GARCH

Abstract:
Although there exists a vast number of articles addressing the predictability of stock market return, most of the proposed models rely on accurate forecasting of the level (i.e., value) of the underlying stock index or its return. In most cases, the degree of accuracy and the acceptability of certain forecasts are measured by the estimates' deviations from the observed values. Depending on the trading strategies adopted by investors, forecasting methods based on minimizing forecast error may not be adequate to meet their objectives. In other words, trading driven by a certain forecast with a small forecast error may not be as profitable as trading guided by an accurate prediction of the direction of movement (or sign of return.) Given that, we evaluate the efficacy of several multivariate classification techniques relative to a group of level estimation approaches. Specifically, we conduct time series comparisons between the two types of models on the basis of forecast performance and investment return. The tested classification models, which predict direction based on probability, include linear discriminant analysis, logit, probit, and probabilistic neural network. On the other hand, the level estimation counterparts, which forecast the level, are exponential smoothing, multivariate transfer function, vector autoregression with Kalman filter, and multilayered feedforward neural network. Our comparative study also measures the relative strength of these models with respect to the trading profit generated by their forecasts. To facilitate more effective trading, we develop a set of threshold trading rules driven by the probabilities estimated by the classification models. Empirical experimentation suggests that the classification models outperform the level estimation models in terms of predicting the direction of the stock market movement and maximizing returns from investment trading. Further, investment returns are enhanced by the adoption of the threshold trading rules.

Forecasting, multivariate classification, neural networks, econometric time series analysis, stock index and return, trading strategy

Abstract:
Despite abundant research which focuses on estimating the level of return on stock market index, there is a lack of studies examining the predictability of the direction/sign of stock index movement. Given the notion that a prediction with little forecast error does not necessarily translate into capital gain, we evaluate the efficacy of several multivariate classification techniques relative to a group of level estimation approaches. Specifically, we conduct time series comparisons between the two types of models on the basis of forecast performance and investment return. The tested classification models, which predict direction based on probability, include linear discriminant analysis, logit, probit, and probabilistic neural network. On the other hand, the level estimation counterparts, which forecast the level, are exponential smoothing, multivariate transfer function, vector autoregression with Kalman filter, and multilayered feedforward neural network. Our comparative study also measures the relative strength of these models with respect to the trading profit generated by their forecasts. To facilitate more effective trading, we develop a set of threshold trading rules driven by the probabilities estimated by the classification models. Empirical experimentation suggests that the classification models outperform the level estimation models in terms of predicting the direction of the stock market movement and maximizing returns from investment trading. Further, investment returns are enhanced by the adoption of the threshold trading rules.

Forecasting, Multivariate classification, Stock index, Trading strategy

Abstract:
This paper explores the link between capital market governance and several key characteristics of equity markets. Using detailed data glean from individual stock exchanges, we develop a composite capital market governance measure (CMG index) that captures three dimensions of market regulation and enforcement: earning opacity, enforcement of insider trading laws, and short-selling restrictions. Our analysis shows that after, controlling for other factors, an increase in a country's CMG index is associated with significant decreases in the cost-of-equity capital, and price synchronicity (Morck et al. (2000)); as well as significant increases in trading volume and US foreign stockholdings. We conclude that changes in these laws can significantly affect market performance.

capital market governance, insider trading, earnings opacity, short-selling constraints, market performance

Abstract:
This paper examines the link between capital market governance (CMG) and several key measures of market performance. Using detailed data from individual stock exchanges, we develop a composite CMG index that captures three dimensions of security laws: the degree of earnings opacity, the enforcement of insider laws, and the effect of removing short-selling restrictions. We find that improvements in the CMG index are associated with decreases in the cost-of-equity capital (both implied and realized), increases in market liquidity (trading volume and market depth), and increases in market pricing efficiency (reduced price synchronicity and IPO underpricing). The results are quite consistent across individual components of CMG and over alternative market performance measures.

capital market governance, insider trading, earnings opacity, market performance

Abstract:
We develop a new, unlevering approach to document how well financial and operating leverage explain volatility asymmetry on a firm-by-firm basis. Volatility asymmetry means that when stock price drops (rises), the volatility of the returns typically increases (decreases). Our evidence, using a large sample of U.S. firms, shows that almost all of the firm-level asymmetry can be explained by financial leverage and, to a smaller extent, operating leverage. This result is robust even when we allow for risky debt. On the index-level, however, even after removing financial and operating leverage from each component firm, a large portion of volatility asymmetry persists. When the market goes down, unlevered index-level returns have higher volatility because the unlevered component stock returns have higher covariance rather than higher volatility. Covariance asymmetry explains why financial leverage causes the firm-level asymmetry but not the index-level asymmetry.

Volatility asymmetry, Financial leverage, Operating leverage, Covariance asymmetry, Merton model, Stock returns

Abstract:
The characteristics of the distribution of security returns, such as skewness, play a significant role in financial theory and practice. This paper examines whether conditional skewness of daily aggregate market returns is predictable and investigates the economic mechanisms underlying this predictability. In both developed and emerging markets, there is strong evidence that lagged returns predict skewness; returns are more negatively skewed following an increase in stock prices, and returns are more positively skewed following a decrease in stock prices. The empirical evidence shows that the traditional explanations, such as the leverage effect, the volatility feedback effect, the stock bubble model (Blanchard and Watson, 1982), and the fluctuating uncertainty theory (Veronesi, 1999), are not driving the predictability of conditional skewness at the market level. The relation between skewness and lagged returns is more consistent with the Cao, Coval, and Hirshleifer (2002) model. Hong and Stein (2003) model predict a relation between turnover and skewness. We find some weak evidence that in developed countries, high trend-adjusted turnover predicts more negative skewness in returns. Our findings have implications for future theoretical and empirical models of time-varying market returns.

Conditional skewness, conditional volatility, predicting skewness and volatility, aggregate market returns, international finance

Abstract:
Previous work on crude oil price modeling has generally focused on two theoretical approaches, either the optimal control analysis of pricing of a depletable resource, or OPEC as a partial monopolist setting oil prices to maximize net present value. Neither has been wholly satisfactory. We consider a different perspective, a game theory based framework in which political and military factors interacted with economic considerations for oil exporters and importers to define a target price zone (TPZ). We analyze several issues in this context: monthly vs. annual average prices, beginning and ending dates for TPZs, degree of stability in several price series (WTI, Brent, etc.), FOB and landed prices, real or nominal prices, OPEC behavior, and effect of the Euro exchange rate on dollar denominated oil prices. We conclude that a TPZ system was in operation from 1986 through 2003. The TPZ worked imperfectly but with a substantial degree of predictability for 18 years. In 2004 the TPZ system deteriorated for several reasons, and has not been re-established.

Abstract:
We study the impact of analyst forecasts on prices to determine whether investors learn about analyst accuracy. Our test market is the crude oil futures market. Prices rise when analysts forecast a decrease (increase) in crude supplies. In the 15 minutes following supply realizations, prices rise (fall) when forecasts have been too high (low). In both the initial price action relative to forecasts and in the subsequent reaction relative to realized forecast errors, the price response is stronger for more accurate analysts. These price reactions imply that investors learn about analyst accuracy and trade accordingly.

learning, financial analyst, forecast, accuracy

Abstract:
Merger and Acquisition (M&A) activities are not well-anticipated corporate events in the equity market. Do institutional investors have material non-public information before M&A announcements, and front-run other investors? Using a high frequency institutional trading dataset that combines publicly available NYSE Trades and Quotes (TAQ) data with the institutional ownership report (13F), this paper investigates the daily trading behavior of institutional investors on target firms before and after M&A announcements in the US equity market from 1993 to 2004. I find that all institutional investors start to accumulate net buying positions on target firms at far ahead of time as 30 days before an announcement date. Institutional investors are not a homogeneous group in terms of trading strategies, regulations or information venues, but, surprisingly, they exhibit similar trading patterns prior to the event. Controlling for other factors, this significant trading pattern indicates that institutional investors may possess material non-public information and use it to exploit profits. On and after announcement day, investment advisors tend to be merger arbitragers and buy more shares of target firm stocks to speculate on final deal consummation; while banks, insurance companies, and mutual funds immediately reverse their positions to cash in, a behavior consistent with the early informed traders acting as 'short-term profit takers.' Using probability of informed trading (PIN) as a proxy to measure the cross-sectional degree of information asymmetry, I confirm that the significant front-running pattern of institutional investors is associated with a high probability of informed trading. Further, institutional net selling pattern on rival firm of targets before the announcements shows that institutional investors have better information on actual targets rather than have better models to predict possible takeovers.

institutional trading, informed trading, merger and acquisitions

Abstract:
The skewness of the conditional return distribution plays a significant role in financial theory and practice. This paper examines whether conditional skewness of daily aggregate market returns is predictable and investigates the economic mechanisms underlying this predictability. In both developed and emerging markets, there is strong evidence that lagged returns predict skewness; returns are more negatively skewed following an increase in stock prices and returns are more positively skewed following a decrease in stock prices. The empirical evidence shows that the traditional explanations such as the leverage effect, the volatility feedback effect, the stock bubble model (Blanchard and Watson, 1982), and the fluctuating uncertainty theory (Veronesi, 1999) are not driving the predictability of conditional skewness at the market level. The relation between skewness and lagged returns is more consistent with the Cao, Coval, and Hirshleifer (2002) model. Our findings have implications for future theoretical and empirical models of time-varying market return distributions, optimal asset allocation, and risk management.

Conditional skewness, Conditional Volatility, Predicting Skewness, Aggregate market returns, International finance

Abstract:
Although there exists some studies which deal with the issues of forecasting stock market index and development of trading strategies, most of the empirical findings are associated with the developed financial markets (e.g., U.S., U.K., and Japan). Currently, many international investment bankers and brokerage firms have major stakes in overseas markets. Given the economic success of Taiwan in the last two decades, the financial markets in this Asian country have attracted considerable global investments. Our study models and predicts the TSE Index using neural networks. Their performance is compared with that of parametric forecasting approaches, namely the Generalized Methods of Moments (GMM) and random walk. These rapidly growing financial markets are usually characterized by high volatility, relatively smaller capitalization, and less price efficiency, features which may hinder the effectiveness of those forecasting models developed for established markets. The good performance of the PNN suggests that the neural network models are useful in predicting the direction of index returns. Furthermore, PNN has demonstrated a stronger predictive power than both the GMM-Kalman filter and the random walk forecasting models. This superiority is partially attributed to PNN's ability to identify outliers and erroneous data. Compared to the other two parametric techniques examined in this study, PNN does not require any assumption of the underlying probability density functions of the class populations. The trading experiment shows that the PNN-guided trading strategies obtain higher profits than the other investment strategies utilizing the market direction generated by the parametric forecasting methods. In addition, the PNN-guided trading with multiple triggering thresholds is generally better than the one with single triggering thresholds. The multiple threshold version is able to consider the degree of certainty of a particular PNN classification and thereby reduce potential loss in the market.

emerging economy, index forecasting, trading strategy, neural networks, generalized gethods of moments (GMM)