NYSE Stocks Research Articles

Accentuated Intraday Stock Price Volatility: What is the Cause?

In equity markets, the opening and closing of trading are particularly stressful periods. Ozenbas, Pagano, and Schwartz investigate the quality of price determination at these times (compared to midday periods) for large-, mid-, and smallcapitalization stocks on the NYSE, NASDAQ, and London Stock Exchange. Using three different metrics, they consistently find lower quality at both the open and the close.The deterioration of market quality at openings is greatest for large-cap stocks, but no systematic association with cap size is observed at the close. Large-cap stocks evidently lead smaller-cap stocks in finding new equilibrium values, and accentuated volatility at the open is in large part attributable to the complexities of price discovery.

Do Common Stocks Have Perfect Substitutes?

Though common stocks are one of the most important assets in an economy, little is known about their demand curves. I estimate demand curves for 144 NYSE stocks using a unique dataset of all orders, including off-equilibrium orders, during three months in 1990-1991. Connecting asset pricing with industrial organization, I find that stocks of firms in less competitive industries are more elastic because they have closer substitutes than stocks in more competitive industries. Tests that exploit the 1991 Gulf War shock and S&P 500 Index additions confirm these results.

Naked Short Selling: The Emperor’s New Clothes?

We investigate the aggregate market quality impact of equity shares that fail to deliver (hereafter “FTDs”). For a sample of 1,492 NYSE stocks over a 42-month period from 2005 to 2008, greater FTDs lead to higher liquidity and pricing efficiency, and their impact is similar to our estimate of delivered short sales. Furthermore, during the operative period of a Security and Exchange Commission (SEC) order mandating stock borrowing prior to short sales, the securities affected display relatively lower liquidity and higher pricing errors. Finally, we do not find any evidence that FTDs caused price distortions or the failure of financial firms during the 2008 financial crisis.

Tick size, market structure, and market quality

Large tick sizes imposed on high-priced stocks on the Korea Stock Exchange (KSE) are significant binding constraints on bid-ask spreads. Nearly 60% of quoted spreads are equal to the tick size for stocks with the largest tick size and more than 87% of quoted spreads are equal to the tick size for stocks in the largest size portfolio. We also show that the average spread of KSE stocks with large tick sizes is greater than that of matched NYSE stocks, whereas the average spread of KSE stocks with the smallest tick size is smaller than the corresponding figure for the matched NYSE stocks. We interpret these results as evidence that traders on the KSE are paying large trading costs because of the artificially imposed large tick sizes.

How to Construct Fundamental Risk Factors?

Our paper reexamines the methodology of Fama and French (1993) for creating US empirical risk factors, and proposes an extension on the way to compute the mimicking portfolios. Our objective is to develop a modified Fama and French (F&F) methodology that could be easily implemented on other markets, and that could also easily price other risk fundamentals. We raise three main problems in the F&F methodology. First, their annual rebalancing is consumptive in long time-series which sometimes simply do not exist for small exchange markets. Moreover, this does not match with the investment horizon of the investors. Second, the independent sorting procedure underlying the formation of the 6 F&F two-dimensional portfolios causes moderate level of correlation between premiums. Finally, sorting the stocks into portfolios according to NYSE stock returns tend to over-represent the proportion of small stocks in small and value portfolios. We estimate, along our technology, alternative premiums for the size, book-to-market and momentum risk fundamentals. We compare these three risk premiums to the Fama and French and Carhart benchmarks that Kenneth French make available on his website. In an analysis framework without data snooping bias, we show evidence that although they are correlated, the original F&F premiums and our versions of the F&F premiums bring complementary information. Furthermore, we find that our empirical model better complements the market model for explaining cross-sectional dispersion in returns than the F&F premiums.

Long Term Mean Reversion of Stock Prices Based on Fractional Integration

In this study we examine the long term behavior of stock returns. The analysis reveals that negative autocorrelations of the returns exist for a super-long horizon as long as 10 years. This pattern, however, contrasts to predictions of previous stock price models which include random walks. We suggest the introduction of a fractionally integrated process into a nonstationary component of stock prices, and demonstrate empirically the existence of the process in NYSE stock returns. The predicted values of autocorrelation from our stock price model confirm the super-long term behavior of the returns observed in regression, indicating that inefficiency in the stock market could remain for a long time.

Removing biases in computed returns

This paper presents a straightforward method for asymptotically removing the well-known upward bias in observed returns of equally-weighted portfolios. Our method removes all of the bias due to any random transient errors such as bid-ask bounce and allows for the estimation of short horizon returns. We apply our method to the CRSP equally-weighted monthly return indexes for the NYSE, Amex, and NASDAQ and show that the bias is cumulative. In particular, a NASDAQ index (with a base of 100 in 1973) grows to the level of 17,975 by 2006, but nearly half of the increase is due to cumulative bias. We also conduct a simulation in which we simulate true prices and set spreads according to a discrete pricing grid. True prices are then not necessarily at the midpoint of the spread. In the simulation we compare our method to calculating returns based on observed closing quote midpoints and find that the returns from our method are statistically indistinguishable from the (simulated) true returns. While the mid-quote method results in an improvement over using closing transaction prices, it still results in a statistically significant amount of upward bias. We demonstrate that applying our methodology results in a reversal of the relative performance of NASDAQ stocks versus NYSE stocks over a 25 year window.

Short Term Reversals, Returns to Liquidity Provision and the Costs of Immediacy

In this paper, we study short-term return reversals using a sample of all but the smallest of NYSE and Amex stocks. We perform for each day a cross-sectional regression, where we regress the stocks’ next 5-days’ abnormal returns on each of their past five days’ abnormal returns. Our daily measure of liquidity is the sum of the coefficients from this regression. We show that 1) changes in quarterly averages of liquidity explain 10% of the quarterly changes in the average turnover in the US stock market and 2) changes in quarterly averages of liquidity explain 12% of the quarterly returns to CRSP equallyweighted market index. We then look at the returns to providing liquidity, i.e., the returns to a trading strategy that buys stocks in the decile (quartile) with the highest expected weekly returns, evaluated using historical estimates of mean reversion, and sells stocks in the decile (quartile) with the lowest expected returns. During our sample period, 1926-2008, this strategy has generated on average 0.3% (0.2%) daily returns. These returns have been declining over time and were only 0.07% (0.05%) during the last decade. The market makers’ returns to providing liquidity equal the costs of immediacy to other investors. Using the daily returns to liquidity provision as a measure of the costs of immediacy, we find that mutual funds have on average lost 3.0% per year during the last decade to trading costs that arise from the price pressure from requiring immediacy. Without these costs, according to our results, the average mutual fund would have generated returns roughly equal to those of the value-weighted CRSP index.

Estimating a Structural Model of Herd Behavior in Financial Markets

We develop a new methodology to estimate the importance of herd behavior in financial markets: we build a structural model of informational herding that can be estimated with financial transaction data. In the model, rational herding arises because of information-event uncertainty. We estimate the model using data on a NYSE stock (Ashland Inc.) during 1995. Herding often arises and is particularly pervasive on some days. The proportion of herd buyers (sellers) is 2 percent (4 percent) and is greater than 10 percent in 7 percent (11 percent) of information-event days. Herding causes important informational inefficiencies, amounting, on average, to 4 percent of the expected asset value.

Estimating a Structural Model of Herd Behavior in Financial Markets

We develop a new methodology to estimate the importance of herd behavior in financial markets: we build a structural model of informational herding that can be estimated with financial transaction data. In the model, rational herding arises because of information-event uncertainty. We estimate the model using data on a NYSE stock (Ashland Inc.) during 1995. Herding often arises and is particularly pervasive on some days. The proportion of herd buyers (sellers) is 2 percent (4 percent) and is greater than 10 percent in 7 percent (11 percent) of information-event days. Herding causes important informational inefficiencies, amounting, on average, to 4 percent of the expected asset value.

Individual Investor Trading and Return Patterns Around Earnings Announcements

This paper documents evidence consistent with informed trading by individual investors around earnings announcements using a unique dataset of NYSE stocks. We show that intense aggregate individual investor buying (selling) predicts large positive (negative) abnormal returns on and after earnings announcement dates. We decompose the abnormal returns into a component that is attributed to risk-averse liquidity provision and a component that is attributed to private information or skill, and show that about half of the abnormal returns in the three months following the event can be attributed to private information. We also examine the behavior of individuals after the earnings announcement and find that they trade in the opposite direction to both pre-event returns (i.e., exhibit contrarian behavior) and the earnings surprise (i.e., exhibit news-contrarian behavior). The latter behavior, which could be consistent with profit-taking, has the potential to slow down the adjustment of prices to earnings news and contribute to the post-earnings announcement drift.

Return dispersion and expected returns

This paper proposes a two-factor asset-pricing model that incorporates market return and return dispersion. Consistent with this model, we find that stocks with higher sensitivities to return dispersion have higher average returns, and that return dispersion carries a significant positive price of risk. In particular, the return dispersion factor dominates the book-to-market factor in explaining cross-sectional expected returns. The return dispersion model outperforms the CAPM, MVM, IVM, and FF-3M when using a set of 5×5 test portfolios constructed from NYSE and AMEX stock returns from August 1963 to December 2005. Return dispersion continues to play an important role in explaining the cross-sectional variation of expected returns, even when market volatility, idiosyncratic volatility, size, book-to-market factors, and a momentum factor are included. This study sheds some light on the ability of return dispersion to explain expected returns beyond the standard asset-pricing factors. Our finding suggests that return dispersion captures two dimensions of systematic risk: the business cycle and fundamental economic restructuring.

Return Dispersion and Expected Returns

This paper proposes a two-factor asset-pricing model that incorporates market return and return dispersion. Consistent with this model, we find that stocks with higher sensitivities to return dispersion have higher average returns, and that return dispersion carries a significant positive price of risk. In particular, the return dispersion factor dominates the book-to-market factor in explaining cross-sectional expected returns. The return dispersion model outperforms the CAPM, MVM, IVM, and FF-3M when using a set of 5×5 test portfolios constructed from NYSE and AMEX stock returns from August 1963 to December 2005. Return dispersion continues to play an important role in explaining the cross-sectional variation of expected returns, even when market volatility, idiosyncratic volatility, size, book-to-market factors, and a momentum factor are included. This study sheds some light on the ability of return dispersion to explain expected returns beyond the standard asset-pricing factors. Our finding suggests that return dispersion captures two dimensions of systematic risk: the business cycle and fundamental economic restructuring.

Stealth Trading by Corporate Insiders

This paper analyzes stealth trading by corporate insiders in US equity markets. Stealth trading is the practice to break up trades into sequences of smaller trades. We find that stealth trading is pervasive and distinguish two explanations. The first argues that insiders break up trades in order to conceal private information about the fundamental value of the stock, whereas the second holds that insiders act like discretionary liquidity traders who want to reduce the tem-porary price impact from trading large stakes. We find some, but inconsistent evidence for information-based explanations, but strong and unambiguous evidence for liquidity-based ex-planations. These conclusions hold across subsamples for transactions before and after the Sarbanes-Oxley act and for NASDAQ as well as NYSE stocks.

Removing Biases in Computed Returns

This paper presents a straightforward method for asymptotically removing the well-known upward bias in observed returns of equally-weighted portfolios. Our method removes all of the bias due to any random transient errors such as bid-ask bounce and allows for the estimation of short horizon returns. We apply our method to the CRSP equally-weighted monthly return indexes for the NYSE, Amex, and NASDAQ and show that the bias is cumulative. In particular, a NASDAQ index (with a base of 100 in 1973) grows to the level of 17,975 by 2006, but nearly half of the increase is due to cumulative bias. We also conduct a simulation in which we simulate true prices and set spreads according to a discrete pricing grid. True prices are then not necessarily at the midpoint of the spread. In the simulation we compare our method to calculating returns based on observed closing quote midpoints and find that the returns from our method are statistically indistinguishable from the (simulated) true returns. While the mid-quote method results in an improvement over using closing transaction prices, it still results in a statistically significant amount of upward bias. We demonstrate that applying our methodology results in a reversal of the relative performance of NASDAQ stocks versus NYSE stocks over a 25 year window.

Is information risk priced for NASDAQ-listed stocks?

Easley et al. (J Finance 57:2185–2221, 2002), building upon the asset pricing model of Fama and French (J Finance 47:427–465, 1992), show that the probability of informed trading (PIN) is a determinant of asset returns for NYSE-listed securities. We extend this work by examining whether the PIN is a predictive factor for NASDAQ stocks, as many studies document significant differences between NYSE and NASDAQ listed securities. In the process we examine whether the use of PIN is appropriate for NASDAQ-listed securities. We find that PIN and certain stock characteristics correlate differently for our sample of NASDAQ stocks than that of Easley et al. sample of NYSE stocks. We also determine that the risk of informed trading is only weakly priced for NASDAQ stocks. Contrary to Easley et al. we do not find evidence that excess returns increases as PIN increases.

Sell-Order Illiquidity and the Cross-Section of Expected Stock Returns

We estimate buy- and sell-order illiquidity measures (lambdas) for a comprehensive sample of NYSE stocks. We show that sell-order liquidity is priced more strongly than buy-order liquidity in the cross-section of equity returns. Indeed, our analysis indicates that the liquidity premium in equities emanates predominantly from the sell-order side. We also find that the average difference between sell and buy lambdas is generally positive throughout our sample period. Both buy and sell lambdas are significantly positively correlated with measures of funding liquidity such as the TED spread as well option implied volatility. .

Information in Short Selling: Comparing NASDAQ and the NYSE

This study directly compares the level and return predictability of short selling for NYSE stocks to a matched sample of NASDAQ stocks. When considering trading that executes on all exchanges, we document that the NASDAQ has greater levels of short selling, relative to total trading activity, than the NYSE. However, NASDAQ has less relative short activity than the NYSE when considering short selling that executes on the primary exchange. When comparing the contrarian trading behavior and the return predictability of short sellers, we show that NASDAQ short sellers are more contrarian in contemporaneous and past returns and better at predicting negative returns than NYSE short sellers. These results are robust in each trade-size category.

Adverse selection costs for NASDAQ and NYSE after decimalization

NYSE and NASDAQ completed their decimalization on January 29, 2001 and on April 9, 2001 respectively. In this paper, we compare adverse selection component of the bid–ask spread for NASDAQ and NYSE stocks after decimalization using the data from May 2001 and July 2001. We find that the adverse selection component of the bid–ask spread is significantly lower on NASDAQ than on NYSE, and these differences cannot be attributed to the differences in the characteristics of the stocks traded in the two markets. In addition, we find that the adverse selection costs increase with trade size on NYSE, however there is no monotonic pattern observed for NASDAQ stocks. Lastly, we report that although the order flows arrived in the two markets are significantly different, they can at best explain a small portion of the observed differences in adverse selection costs.

Dealer attention, the speed of quote adjustment to information, and net dealer revenue

Using trade and quote data from the NYSE, we examine the relation between dealer attention, dealer revenue, and the probability of informed trade. We find that dealer revenue net of losses to better-informed traders in NYSE stocks is positively related to the speed at which quotes adjust to full information levels. The speed of quote adjustment is faster for stocks with greater dealer attention, as measured by a stock’s relative prominence at its post and panel location on the NYSE floor. The level of dealer attention in turn is positively related to a stock’s probability of information-based trading. The results are consistent with a theoretical model we derive in which dealers trade multiple securities and must optimally allocate their limited attention to monitoring order flow to minimize losses to better-informed traders.