CHANGES IN THE STOCK PRICE REACTION OF SMALL FIRMS TO COMMON INFORMATION

We hypothesize that changes in the technological and regulatory environment result in a more rapid response to marketwide information by small firms. We find that the correlations between small-firm returns and lagged large-firm returns decline over time, which suggests an increase in the efficiency of capital markets. Similar lead-lag patterns are found in the returns of portfolios sorted by dollar trading volume. The price response of low-volume stocks improves over time in much the same way as that of small-capitalization stocks.     

Cleaning house: Stock reassignments on the NYSE

A frequently occurring, yet unexplored, phenomenon of the New York Stock Exchange specialist system is that of reassignments of stocks by specialist firms on the floor of the Exchange. These events change the portfolios at the individual specialist level by reassigning one or more stocks from one individual specialist portfolio to another. We find that reassigned stocks have unusually wide spreads before reassignments and experience a decline in spreads to levels comparable to matched stocks after the reassignment. This improvement in liquidity is associated with a reduced cost of capital for the reassigned firms. We find that portfolio size, and industry and size concentration of the individual specialist portfolios are associated with the decision of specialist firms to reassign stocks.     

Does the Specialist Matter? Differential Execution Costs and Intersecurity Subsidization on the New York Stock Exchange

This article tests for differences in execution costs among specialist firms for New York Stock Exchange listed securities. Execution cost differences provide a measure of the relative performance of specialist firms. We find a substantial difference in effective spreads and order processing costs across specialist firms, controlling for stock characteristics. While economically significant, the differences in execution costs between specialist firms are much smaller than the cross-market differences reported by Huang and Stoll (1996). Within a specialist firm, there is a positive relation between order processing costs and trading activity that is consistent with the hypothesis that active stocks subsidize inactive stocks.     

Short-term predictability of equity returns along two style dimensions

This study uses daily return data on 20 portfolios split along two dimensions, growth/value and market size, over the period of four decades and employs over 12,000 trading rules to investigate the short-term predictability of portfolio returns. It shows that, historically, portfolios of small stocks and value stocks have been more suitable for active trading strategies since returns on value portfolios exhibit more predictability than returns on growth portfolios and returns on portfolios of large stocks appear to be less predictive than returns on portfolios of small stocks. The predictive ability of trading rules is all but gone during the 2000s. Popularization of exchange-traded funds and the introduction of quote decimalization on the exchanges are the most likely reasons behind the lack of predictability.     

Non-linear Dependence in Stock Returns: Does Trading Frequency Matter?

The UK has a quote-driven pure dealer market structure that is very different from order driven markets such as the NYSE and Japanese markets. This paper investigates non-linear dependence in stock returns for an exhaustive sample of UK stocks for a 21 year period. The results are analysed on the basis of trading frequency. It is found that non-linear dependence is highly significant in all cases for both individual stocks and stock portfolios formed on the basis of trading frequency. The non-linear dependence is primarily over a one day interval, although statistically significant non-linear dependence exists consistently even up to five trading days. Most of the non-linear dependence is in the form of ARCH-type conditional heteroskedasticity. However, statistically significant non-linearity in addition to an EGARCH(1,1) dependence also appears to be present. This additional non-linearity is greater for individual stocks than for portfolios and greater for smaller, less-liquid portfolios. Non-linear dependence does not appear to be caused by non-stationarity in underlying economic fundamentals or by non-linearity in the conditional mean. However, low dimensional chaos is not generally supported. The limited evidence on chaotic behaviour is stronger for portfolios with long price adjustment delays across component stocks. The main results are consistent with US studies on stock indices, suggesting that the process generating non-linear dependence is not dependent on market microstructure characteristics.     

Institutional investor’ proportions and inactive trading

We focus on a typical market anomaly-inactive trading: trading volume shrinks while stock price abnormally jumps. We calibrate a theoretical model with variance ambiguity heterogeneous among investors and illustrate that ambiguity averse investors' proportions enhance trading volume shrinkage and abnormal price jumps. We provide a cross-section analysis of stocks' inactive trading by introducing institutional investors' proportions to measure investor structures' differences among stocks. We also empirically measure relative inactive trading for constituent stocks in S&P 500 from 2014 to 2019 and demonstrate that institutional investors' proportion is negatively related to inactive trading. Finally, we demonstrate that higher proportions of institutional investors lead to less inactive trading anomalies.     

Return and Volatility Spillovers Between Large and Small Stocks in the UK

Abstract:  This paper investigates return and volatility spillover effects between the FTSE 100, FTSE 250 and FTSE Small Cap equity indices using the multivariate GARCH framework. We find that return and volatility transmission mechanisms between large and small stocks in the UK are asymmetric. In particular, there are significant spillover effects in both returns and volatility from the portfolios of larger stocks to the portfolios of smaller stocks. For volatility, there is also evidence of limited feedback from the portfolios of smaller stocks to the portfolios of larger stocks, although sub-period analysis suggests that this is to some extent period-specific. Simulation evidence shows that non-synchronous trading potentially explains some, but not all, of the spillover effects in returns, and that it explains none of the spillover effects in volatility. These results are consistent with a market in which information is first incorporated into the prices of large stocks before being impounded into the prices of small stocks.     

Is Proprietary Trading Detrimental to Retail Investors?

We study the conflict of interest that arises when a universal bank conducts proprietary trading alongside its retail banking services. Our data set contains the stock holdings of every German bank and those of their corresponding retail clients. We investigate (i) whether banks sell stocks from their proprietary portfolios to their retail customers, (ii) whether those stocks subsequently underperform, and (iii) whether retail customers of banks engaging in proprietary trading earn lower portfolio returns than their peers. We present affirmative evidence for all three questions and conclude that proprietary trading can, in fact, be detrimental to retail investors.     

Time Variation in Liquidity: The Role of Market-Maker Inventories and Revenues

We show that market-maker balance sheet and income statement variables explain time variation in liquidity, suggesting liquidity-supplier financing constraints matter. Using 11 years of NYSE specialist inventory positions and trading revenues, we find that aggregate market-level and specialist firm-level spreads widen when specialists have large positions or lose money. The effects are nonlinear and most prominent when inventories are big or trading results have been particularly poor. These sensitivities are smaller after specialist firm mergers, consistent with deep pockets easing financing constraints. Finally, compared to low volatility stocks, the liquidity of high volatility stocks is more sensitive to inventories and losses.     

Institutional trading and information processing: Evidence from complicated firms and easy-to-analyze firms

In this article, I examine institutional trading within two groups of firms with different demands on investor information processing: conglomerate firms and stand-alone firms. On average, institutional trading in conglomerate firm stocks yields significantly lower returns than institutional trading in stand-alone firm stocks. Inferior returns following institutional trading in conglomerate firm stocks persist across small and large firms. Moreover, financial institutions with a low concentration of conglomerate firms in their portfolios are more profitable in their trading. This study provides evidence that skilled institutional investors intentionally focus their information-processing efforts on easy-to-analyze firms.     

Information,trading and stock returns: Lessons from dually-listed securities

This paper compares the intra-day patterns on the NYSE and AMEX of volatility, trading volume and bid-ask spreads for European and Japanese dually-listed stocks with American stocks of comparable average trading volume and volatility. It is shown that the intra-day patterns for these stocks are remarkably similar even though public information flows differ markedly across these stocks during the trading day. In the early morning, all stocks have higher volatility than later in the day, but this phenomenon is most pronounced for Japanese stocks and affects American stocks the least. We argue that these patterns are consistent with markets reacting to the overnight accumulation of public information but are inconsistent with the view that early morning volatility can be attributed to monopolistic specialist behavior.     

Liquidity in auction and specialist market structures: Evidence from the Italian bourse

Several studies find that bid-ask spreads for stocks listed on the NYSE are lower than for stocks listed on NASDAQ. While this suggests that specialist market structures provide greater liquidity than competing dealer markets, the nature of trading on the NYSE, which comprises a specialist competing with limit order flow, obfuscates the comparison. In 2001, a structural change was implemented on the Italian Bourse. Many stocks that traded in an auction market switched to a specialist market, where the specialist controls order flow. Results confirm that liquidity is significantly improved when stocks commence trading in the specialist market. Analysis of the components of the bid-ask spread reveal that the adverse selection component of the spread is significantly reduced. This evidence suggests that specialist market structures provide greater liquidity to market participants.     

Stochastic portfolio theory and the low beta anomaly

Many studies have found that portfolios of low beta stocks have higher growth rates than portfolios of high beta stocks and have concluded that low beta stocks have higher growth rates than high beta stocks. Since rational investor behavior is thought to imply that additional risk is rewarded with additional return, the alleged higher growth rates of low beta versus high beta stocks has been termed a ‘Low Beta Anomaly’ (LBA). However, it is premature to conclude that these observed LBAs are due to stocks’ differential growth rates, because the tested portfolios are traded. Stochastic Portfolio Theory (SPT) shows that traded portfolios’ growth rates can exceed the growth rates of their stocks. This paper presents several SPT models of an LBA that do not require investment constraints, irrational investor behavior, or that low beta stocks have higher growth rates than high beta stocks. These LBAs are due to reconstitution relative volatility capture that favors portfolios of low vs. high beta stocks. They result from trading profit, not differential growth rates between low and high beta stocks. Monte Carlo simulations demonstrate a reconstitution relative volatility capture LBA that is consistent with the models and the literature.     

Volatility timing,sentiment, and the short-term profitability of VIX-based cross-sectional trading strategies

This paper explores the profitability of simple short-term cross-sectional trading strategies based on the implied volatility index (VIX), often referred to as an “investor fear gauge” in the stock market. These strategies involve holding sentiment-prone stocks when VIX is low and sentiment-immune stocks when VIX is high and generate significantly higher excess returns than the benchmark long–short portfolios that do not condition on VIX. We show that the profitability of our trading strategies is not subsumed by the well-known risk factors or transaction cost adjustments. Our findings are consistent with the theory of delayed arbitrage and the synchronization problem of Abreu and Brunnermeier (2002).     

The Variation of Economic Risk Premiums in Real Estate Returns

We examine the predictable components of returns on stocks, bonds, and real estate investment trusts (REITs). We employ a multiple-beta asset pricing model and find that there are varying degrees of predictability among stocks, bonds, and REITs. Furthermore, we find that most of the predictability of returns is associated with the economic variables employed in the asset pricing model. The stock market risk premium is highly important in capturing the predictable variation in stock portfolios, and the bond market risk premiums (term and risk structure of interest rates) are important in capturing the predictable variation in bond portfolios. For REITs, however, both the stock and bond market risk premiums capture the predictable variation in returns. REITs have comparable return predictability to stock portfolios. We conclude that there is an important economic risk premium for REITs that are not captured by traditional multiple-beta asset pricing models.     

A transactions data analysis of nonsynchronous trading

Weekly returns of stock portfolios exhibit substantial autocorrelation.Analytical studies suggest that nonsynchronous trading is capableof explaining from 5% to 65% of the autocorrelation. The varyingimportance of nonsynchronous trading in these studies arisesprimarily from differing assumptions regarding nontrading periodsof stocks. We simulate the effects of nonsynchronous tradingby sampling stock returns from a return generating process usingtransactions data to obtain the precise time of each stock'slast trade. We find that simulated weekly portfolio returnsexhibit autocorrelations that are roughly 25% that of theirobserved (CRSP) weekly returns.     

Trading Volume and Cross-Autocorrelations in Stock Returns

This paper finds that trading volume is a significant determinant of the lead-lag patterns observed in stock returns. Daily and weekly returns on high volume portfolios lead returns on low volume portfolios, controlling for firm size. Nonsynchronous trading or low volume portfolio autocorrelations cannot explain these findings. These patterns arise because returns on low volume portfolios respond more slowly to information in market returns. The speed of adjustment of individual stocks confirms these findings. Overall, the results indicate that differential speed of adjustment to information is a significant source of the cross-autocorrelation patterns in short-horizon stock returns.     

The Impact of Options Trading on the Market Quality of the Underlying Security: An Empirical Analysis

We find that option listings are associated with a decrease in the variance of the pricing error, a decrease in the adverse selection component of the spread, and an increase in the relative weight placed by the specialist on public information in revising prices for the underlying stocks. We also find that there is a decrease in the spread and increases in quoted depth, trading volume, trading frequency, and transaction size after option listings. Overall, our results suggest that option listings improve the market quality of the underlying stocks.     

Risk‐Adjusted Long‐Term Contrarian Profits: Evidence from Non‐S&P 500 High‐Volume Stocks

Can trading volume help unravel the long‐term overreaction puzzle? With portfolios of non‐S&P 500 NYSE stocks, we show that (1) both the high‐ and low‐volume (abnormal volume) contrarian portfolios earn a much higher market‐adjusted excess return than the normal‐volume contrarian portfolio, (2) however, when leverage‐induced risk is factored in, excess returns from contrarian portfolios with normal‐ and low‐volume stocks are insignificant, (3) only excess returns from high‐volume contrarian stocks are significant and cannot be explained by the time‐varying risk and return framework, and (4) such high‐volume, risk‐adjusted excess returns arise mainly from winner (glamour) stocks.     

UK stock returns and robust tests of mean variance efficiency

We test both the unconditional and conditional Mean Variance Efficiency of the UK stockmarket, paying particular attention to choosing a suitable set of instruments for the conditional version of the model. By considering more carefully than previous authors the pricing of economic risk within the mean-variance framework we show that certain instruments can enhance the basic model structure. Given the tendency for financial market data to display non-constancy in variance and non-normality we employ the GMM procedure described in Hansen (1982), which requires much weaker distributional assumptions than the more traditional OLS techniques. We discuss forming portfolios of stocks using both size and dividend yield as a criterion to achieve a suitable spread of risk and return, and find that our conclusions are sensitive both to the method of portfolio formation and to the choice of estimator. This is an important finding given the problem of thin trading associated with the size ordering of UK stocks. We find some support for both the unconditional and conditional version of the CAPM, though we are cautious about our conclusions given the instability of the parameter estimates.