Investor type trading behavior and trade performance: Evidence from the Thai stock market

This paper examines the trading behavior and decomposes the trading performance of foreign, individual and institutional investors as well as proprietary traders in a dynamic emerging stock market, the Stock Exchange of Thailand. Foreign investors follow a positive feedback, momentum strategy and are good short term market timers but have poor security selection performance in poor markets, thus suggesting that they have a macro (market timing) but not a micro (security selection) informational advantage relative to local investors. Institutions and proprietary traders have poor security selection trading performance. Individuals display herding behavior and have fairly good security selection performance, but individual investors appear to compensate proprietary traders for the provision of short term liquidity by proprietary traders, so individuals' security selection gains are canceled out by market timing losses.     

An improved estimation method and empirical properties of the probability of informed trading

We report evidence that boundary solutions can cause a bias in the estimate of the probability of informed trading (PIN). We develop an algorithm to overcome this bias and use it to estimate PIN for nearly 80,000 stock-quarters between 1993 and 2004. We obtain two sets of PIN estimates by using the factorized likelihood functions in both [Easley et al., 2010] and [Lin and Ke, 2011], respectively. We find that the estimate based on the EHO factorization is systematically smaller than the estimate based on the LK factorization, meaning that there is a downward bias associated with the EHO factorization. In addition, we find that boundary solutions appear with a very high frequency when the LK factorization is used. Thus it is necessary to use the LK factorization together with the algorithm in this paper. At last, we document several interesting empirical properties of PIN.     

Detecting the presence of insider trading via structural break tests

The occurrence of abnormal returns before the unscheduled announcement of price sensitive information is a potential indicator of insider trading. We identify insider trading with a structural change in the intercept of an extended capital asset pricing model. To detect such a change we introduce a consistent timing structural break test (CTSB) based upon a U-statistic type process. Unlike the traditional CUSUM test, the CTSB test provides a consistent estimator of the timing of a break in the intercept that occurs across the whole evaluation period. We apply our test to a rich data set covering 370 price sensitive announcements relating to FTSE 350 companies. Our test is able to detect potential insider trading far more reliably than the standard CUSUM test. We also show that the majority of suspected insider trading takes place in the 25 days prior to the release of market sensitive information.     

Informed momentum trading versus uninformed “naive” investors strategies

We construct a zero net-worth uninformed “naive investor” who uses a random portfolio allocation strategy. We then compare the returns of the momentum strategist to the return distribution of naive investors. For this purpose we reward momentum profits relative to the return percentiles of the naive investors with scores that are symmetric around the median. The score function thus constructed is invariant and robust to risk factor models. We find that the average scores of the momentum strategies are close to zero (the score of the median) and statistically insignificant over the sample period between 1926 and 2005, various sub-sample periods including the periods examined in [Jegadeesh and Titman, 1993] and [Jegadeesh and Titman, 2001]. The findings are robust with respect to sampling or period-specific effects, tightened score intervals, and the imposition of maximum-weight restrictions on the naive strategies to mitigate market friction considerations.     

The role of trading intensity estimating the implicit bid–ask spread and determining transitory effects

In this paper, we investigate the information content of trading intensity applying the Madhavan, Richardson and Roomans (1997) structural model to express trading intensity as trading momentum in duration and volume. Using both transactions and intraday data from the Helsinki Stock Exchange Limit Order Bookmarket, we find that momentum in duration and volume enhances the information effect. We reach this conclusion based on the parametric effect determined by the sign and the magnitude of the coefficients associated with the trading intensity variables, the trading effect determined by the ratio of transitory effects to permanent effects, and the economic effect determined by the size of the implicit bid–ask spread. While we find that the implicit bid–ask spread and transitory effects are decreasing toward the end of the trading day in consistency with information models in the literature, there is a surge of trades at the market close, most probably due to information uncertainty at market opening in New York.     

Stock market trading activity and returns around milestones

We study the relation between daily stock market trading activity and the Dow Jones Industrial Average's (DJIA) movement around millenary milestones—numbers that end in three zeros. We find aggregate turnover to be 5% lower when the DJIA level is less than 1% away from the nearest milestone. The effect emerges as the DJIA approaches a milestone from below, and is stronger for first-time milestones compared to subsequent passages. The aggregate price impact is large, such that daily stock returns show a negative abnormal performance of − 10 basis points. Our findings suggest that millenary milestones of the DJIA play a role in some investors' decision making.     

Strategic trading by index funds and liquidity provision around S&P 500 index additions

We examine the trades of index funds and other institutions around S&P 500 index additions. We find index funds begin rebalancing their portfolios with the announcement of composition changes and do not fully establish their positions until weeks after the effective date. Trading away from the effective date is more prevalent for stocks with lower levels of liquidity and among large index funds, which is consistent with index funds accepting higher tracking error in order to reduce the price impact of their trades. Small and mid-cap funds provide liquidity to index funds around additions, and added stocks with a greater proportion of these natural liquidity providers experience lower inclusion returns.     

Futures trading and supply contracting in the oil refining industry

This paper examines the relation between commodity futures trading and the real side contracting behavior of firms dealing in the commodity. I argue that futures serve as a flexible form of physical contracting and should be examined in the context of the firm's contracting activities, and not strictly in the context of its financial activities. Data from an oil refining company are used to empirically study this relation. The results are consistent with a contracting view of futures use and appear inconsistent with implications of hedging theories.     

Volatility and covariation of financial assets: A high-frequency analysis

Using high frequency data for the price dynamics of equities we measure the impact that market microstructure noise has on estimates of the: (i) volatility of returns; and (ii) variance–covariance matrix of n assets. We propose a Kalman-filter-based methodology that allows us to deconstruct price series into the true efficient price and the microstructure noise. This approach allows us to employ volatility estimators that achieve very low Root Mean Squared Errors (RMSEs) compared to other estimators that have been proposed to deal with market microstructure noise at high frequencies. Furthermore, this price series decomposition allows us to estimate the variance covariance matrix of n assets in a more efficient way than the methods so far proposed in the literature. We illustrate our results by calculating how microstructure noise affects portfolio decisions and calculations of the equity beta in a CAPM setting.     

Nonparametric estimation and testing of stochastic discount factor

This paper attempts to estimate stochastic discount factor (SDF) proxies nonparametrically using the conditional Hansen–Jagannathan distance. Nonparametric estimation can not only avoid misspecification when dealing with nonlinearity in the model but also provide more precise information about the local properties of the estimators. Empirical studies show that our method performs better than the alternative parametric polynomial models, and furthermore, we find that the return on aggregate wealth can sufficiently explain the SDF proxies when one deals with nonlinearity appropriately.     

Conditional beta pricing models: A nonparametric approach

We propose a two-stage procedure to estimate conditional beta pricing models that allows for flexibility in the dynamics of asset betas and market prices of risk (MPR). First, conditional betas are estimated nonparametrically for each asset and period using the time-series of previous data. Then, time-varying MPR are estimated from the cross-section of returns and betas. We prove the consistency and asymptotic normality of the estimators. We also perform Monte Carlo simulations for the conditional version of the three-factor model of Fama and French (1993) and show that nonparametrically estimated betas outperform rolling betas under different specifications of beta dynamics. Using return data on the 25 size and book-to-market sorted portfolios, we find that the nonparametric procedure produces a better fit of the three-factor model to the data, less biased estimates of MPR and lower pricing errors than the Fama–MacBeth procedure with betas estimated under several alternative parametric specifications.     

A note on the returns from minimum variance investing

Disappointed with the performance of market weighted benchmark portfolios yet skeptical about the merits of active portfolio management, investors in recent years turned to alternative index definitions. Minimum variance investing is one of these popular concepts. I show in this paper that the portfolio construction process behind minimum variance investing implicitly picks up risk-based pricing anomalies. In other words the minimum variance tends to hold low beta and low residual risk stocks. Long/short portfolios based on these characteristics have been associated in the empirical literature with risk adjusted outperformance. This paper shows that 83% of the variation of the minimum variance portfolio excess returns (relative to a capitalization weighted alternative) can be attributed to the FAMA/FRENCH factors as well as to the returns on two characteristic anomaly portfolios. All regression coefficients (factor exposures) are highly significant, stable over the estimation period and correspond remarkably well with our economic intuition. The paper also shows that a direct combination of market weighted benchmark portfolio and risk based characteristic portfolios will provide a statistically significant improvement over the indirect pickup via the minimum variance portfolio.     

Investor sentiment and feedback trading: Evidence from the exchange-traded fund markets

This paper extends the standard feedback trading model of Sentana and Wadhwani (1992) by allowing the demand for shares by feedback traders to depend on sentiment. Our empirical analysis of three largest Exchange-Traded Fund (ETF) contracts in the U.S. suggests that there is a significant positive feedback trading in these markets and the intensity of which is generally linked to investor sentiment. Specifically, the level of feedback trading tends to increase when investors are optimistic. In addition, we find that the influence of sentiment on feedback trading varies across market regimes. These results are consistent with the view that feedback trading activity is largely caused by the presence of sentiment-driven noise trading. Overall, the findings are important in understanding the role of sentiment in investment behaviour and market dynamics and are of direct relevance to the regulators and investors in ETF markets.     

Automation, speed, and stock market quality: The NYSE's Hybrid

Automation and trading speed are increasingly important aspects of competition among financial markets. Yet we know little about how changing a market's automation and speed affects the cost of immediacy and price discovery, two key dimensions of market quality. At the end of 2006 the New York Stock Exchange introduced its Hybrid Market, increasing automation and reducing the execution time for market orders from 10 seconds to less than one second. We find that the change raises the cost of immediacy (bid-ask spreads) because of increased adverse selection and reduces the noise in prices, making prices more efficient.     

Common asset pricing factors in volatilities and returns in futures markets

Factor-based asset pricing models have been used to explain the common predictable variation in excess asset returns. This paper combines means with volatilities of returns in several futures markets to explain their common predictable variation. Using a latent variables methodology, tests do not reject a single factor model with a common time-varying factor loading. The single common factor accounts for up to 53% of the predictable variation in the volatilities and up to 14% of the predictable variation in the means. S&P500 futures volatility predicted by the factor model is highly correlated with volatility implied in S&P500 futures options. But both the factor and implied volatilities are significant in predicting future volatility. In derivatives pricing, both implied volatility from options and factors extracted from asset pricing models should be employed.     

Measuring price discovery: The variance ratio, the R, and the weighted price contribution

We analyze the statistical properties of three price discovery measures: The variance ratio, the weighted price contribution (WPC), and the R² of unbiasedness regressions. We find that, if the price process is a driftless martingale, only the WPC is an unbiased estimator for the return variance explained during a time interval. For autocorrelated processes with a drift, only the R² of the unbiasedness regression is consistent, but it is biased for small samples.     

Possible solutions to the forward bias paradox

This note outlines the economic theory behind the theory of uncovered interest parity and some of the econometric issues involved in testing and interpretation. I illustrate some of the issues involved by estimating a rolling regression of the forward premium regression from 22 years of eight major currencies. I also conclude that Pippenger's model is not consistent with the theory of UIP and that furthermore there are severe econometric problems in estimating his model. The forward premium anomaly remains a paradox in international finance that is important and worthwhile to understand more fully.     

The incremental volatility information in one million foreign exchange quotations

The volatility information found in high-frequency exchange rate quotations and in implied volatilities is compared by estimating ARCH models for DM/$ returns. Reuters quotations are used to calculate five-minute returns and hence hourly and daily estimates of realised volatility that can be included in equations for the conditional variances of hourly and daily returns. The ARCH results show that there is a significant amount of information in five-minute returns that is incremental to options information when estimating hourly variances. The same conclusion is obtained by an out-of-sample comparison of forecasts of hourly realised volatility.     

Dividend signaling under economic adversity: Evidence from the London Stock Exchange

The signaling or information content hypothesis is amongst the most prominent theories attempting to explain dividend policy decisions. However, no research has, to date, examined the information content of dividends in conjunction with generalized economic adversity. With the majority of the western economies facing the tough reality of the economic recession since late 2007–early 2008, we focus on the possibility of asymmetrical dividend signaling effects between periods of stability and economic adversity. Using data from the London Stock Exchange (LSE), where earnings and dividend news are released simultaneously, we test the dividend signaling hypothesis and the interaction of earnings and dividends under both steady and adverse economic conditions. We document positive and significant average abnormal stock price returns around the dividend/earnings announcements. We also find a significant interaction between economic conditions and the information content of dividends. After testing the dividend signaling hypothesis under both stable and recessionary economic conditions we find that dividends have less information content than earnings in periods of growth and stability, but more in periods of economic adversity.     

Trading frequency and asset pricing on the London Stock Exchange: Evidence from a new price impact ratio

This study proposes a new price impact ratio as an alternative to the widely used Amihud’s (2002) Return-to-Volume ratio. We demonstrate that the new price impact ratio, which is deemed Return-to-Turnover ratio, has a number of appealing features. Using daily data from all stocks listed on the London Stock Exchange over the period 1991–2008, we provide overwhelming evidence that this ratio, while being unequivocal to construct and interpret, is also free of a size bias. More importantly, it encapsulates the stocks’ cross-sectional variability in trading frequency, a relatively neglected but possibly important determinant of stock returns given the recently observed trends in financial markets. Overall, our findings argue against the conventional wisdom that there is a simple direct link between trading costs and stock returns by strongly suggesting that it is the compound effect of trading frequency and transaction costs that matters for asset pricing, not each aspect in isolation.