Latency,liquidity and price discovery

The speed of trading is an important factor in modern security markets, although relatively little is known about the effect of speed on liquidity and price discovery, two important aspects of market quality. On April 23, 2007, Deutsche Boerse made an important upgrade to their trading system. With the 8.0 release of Xetra, system latency was reduced from 50 ms to 10 ms. Subsequently, both quoted and effective spreads decreased, which are mainly concentrated in small- and medium-sized stocks. This increase in liquidity is due to dramatically lower adverse selection costs that were only partially translated into higher liquidity. We interpret this as a decrease in the competition between liquidity suppliers who are able to increase their revenues by more than 90 million euros. The contribution of quotes to price discovery doubles to 90% post upgrade, indicating that prices are more efficient.     

Front‐Running Scalping Strategies and Market Manipulation: Why Does High‐Frequency Trading Need Stricter Regulation?

Regulators continue to debate whether high‐frequency trading (HFT) is beneficial to market quality. Using Strongly Typed Genetic Programming (STGP) trading algorithm, we develop several artificial stock markets populated with HFT scalpers and strategic informed traders. We simulate real‐life trading in the millisecond time frame by applying STGP to real‐time and historical data from Apple, Exxon Mobil, and Google. We observe that HFT scalpers front‐run the order flow, resulting in damage to market quality and long‐term investors. To mitigate these negative implications, we propose batch auctions every 30 milliseconds of trading.     

Speed of convergence to market efficiency: The role of ECNs

Chordia, Roll and Subrahmanyam (2005, CRS) estimate the speed of convergence to market efficiency based on short-horizon return predictability of the 150 largest NYSE firms. We extend CRS to a broad panel of NYSE stocks and are the first to examine the relation between electronic communication networks (ECNs) and the corresponding informational efficiency of prices. Overall, we confirm CRS's result that price adjustments to new information occur on average within 5–15 min for large NYSE stocks. We further show that it takes about 20 min longer for smaller firms to incorporate information into prices. Most importantly, we demonstrate that the speed of convergence to market efficiency is significantly related to the type of trading platform where orders are executed, even after controlling for relative order flows, trading costs, volatility, informational effects, trading conditions, market quality, institutional trading activity, and other firm-specific characteristics. Our findings provide direct answers and insights to issues raised in a recent SEC concept release document.     

Market microstructure: A survey

Market microstructure studies the process by which investors’ latent demands are ultimately translated into prices and volumes. This paper reviews the theoretical, empirical and experimental literature on market microstructure relating to: (1) price formation, including the dynamic process by which prices come to impound information, (2) market structure and design, including the relation between price formation and trading protocols, (3) Transparency, the ability of market participants to observe information about the trading process, and (4) Applications to other areas of finance including asset pricing, international finance, and corporate finance.     

Very fast money: High-frequency trading on the NASDAQ

This paper provides evidence regarding high-frequency trader (HFT) trading performance, trading costs, and effects on market efficiency using a sample of NASDAQ trades and quotes that directly identifies HFT participation. I find that HFTs engage in successful intra-day market timing, spreads are wider when HFTs provide liquidity and tighter when HFTs take liquidity, and prices incorporate information from order flow and market-wide returns more efficiently on days when HFT participation is high.     

Post earnings announcement drift and the roles of drift-enhanced factors in New Zealand

This study examines the profitability of trading on analyst forecast-based earnings surprises during the post announcement period in the New Zealand stock market over the period 1994 to 2008. The results show that a post earnings announcement drift (PEAD) anomaly exists in the New Zealand equity market. A hedge strategy of going long the top quintile of earnings surprise stocks and short the bottom quintile of earnings surprise stocks can generate more than 6% excess return in the 60 days following the earnings announcement. I further test the association between PEAD and several control variables and find that PEAD is increasing in 1) earnings surprise defined relative to past earnings, and 2) the level of arbitrage risk. Interestingly, I do not find evidence of a positive relation between PEAD and revenue surprise after controlling for earnings surprise as documented in the United States (Jegadeesh and Livnat, 2006). There is also no evidence that the 2002 Disclosure Reform in the New Zealand Stock Exchange reduced the magnitude of PEAD.     

Individual and institutional herding and the impact on stock returns: Evidence from Taiwan stock market

Using high frequency intraday data, this paper investigates the herding behavior of institutional and individual investors in the Taiwan stock market. The study finds evidence of herding by both investors but a stronger herding tendency among institutional than among individual investors. Institutional investors herd more on firms with small capitalizations and lower turnovers and they follow positive feedback strategies. The portfolios that institutional investors herd buy outperform those they sell by an average of 1.009% during the 20 days after intense trading episodes. By contrast, individual investors herd more on firms with small sizes and higher turnovers, and they crowd to buy (sell) stocks with negative (positive) past returns. The portfolios that individual investors herd buy underperform those they sell by an average of − 0.829% during the following 20 days. Moreover, these return differences of both investors are more pronounced under a market with higher pressure and among small stocks. These findings suggest that the herding of institutional investors speeds up the price-adjustment process and is more likely to be driven by correlated private information, while individual herding is most likely to be driven by behavior and emotions.     

Do DOW returns really influence the intraday Spanish stock market behavior?

Although the behavior of the Spanish stock market has been studied from many different points of view, none of the previous research has ever analyzed the influence of previous daytime, overnight and daily returns from the DOW and IBEX upon 5-min intraday returns of the IBEX throughout the complete trading session. Clear evidence is provided relative to the influence of the DOW. The main finding that it underreacts to the DOW returns in the first hours of trading but overreacts during the last 2 h (after the opening of the US markets) would help to develop a profitable trading strategy.     

The diversity of high-frequency traders

The regulatory debate concerning high-frequency trading (HFT) emphasizes the importance of distinguishing different HFT strategies and their influence on market quality. Using data from NASDAQ-OMX Stockholm, we compare market-making HFTs to opportunistic HFTs. We find that market makers constitute the lion's share of HFT trading volume (63–72%) and limit order traffic (81–86%). Furthermore, market makers have higher order-to-trade ratios and lower latency than opportunistic HFTs. In a natural experiment based on tick size changes, we find that the activity of market-making HFTs mitigates intraday price volatility.     

Noise and efficient variance in the Indonesia Stock Exchange

We separate noise from information related variance for stocks traded on the Indonesian Stock Exchange with a realized variance estimator. We find that the average optimal sampling frequency to estimate the realized variance is 9 min and that market quality has improved after 2004. The positive relation between the standard deviation of the noise variance and the square root of the efficient realized variance implies that as uncertainty about asset values increases the risk of transacting with traders with superior information increases as well. Furthermore, variance ratio comparisons reveal that private information is a significant trading component on the IDX.     

Modeling and forecasting stock return volatility using a random level shift model

We consider the estimation of a random level shift model for which the series of interest is the sum of a short-memory process and a jump or level shift component. For the latter component, we specify the commonly used simple mixture model such that the component is the cumulative sum of a process which is 0 with some probability (1 ? α) and is a random variable with probability α. Our estimation method transforms such a model into a linear state space with mixture of normal innovations, so that an extension of Kalman filter algorithm can be applied. We apply this random level shift model to the logarithm of daily absolute returns for the S&P 500, AMEX, Dow Jones and NASDAQ stock market return indices. Our point estimates imply few level shifts for all series. But once these are taken into account, there is little evidence of serial correlation in the remaining noise and, hence, no evidence of long-memory. Once the estimated shifts are introduced to a standard GARCH model applied to the returns series, any evidence of GARCH effects disappears. We also produce rolling out-of-sample forecasts of squared returns. In most cases, our simple random level shift model clearly outperforms a standard GARCH(1,1) model and, in many cases, it also provides better forecasts than a fractionally integrated GARCH model.     

The effect of Sarbanes-Oxley on the timely disclosure of restricted stock trading

Our paper investigates the effect of the Sarbanes-Oxley Act (SOX) on the disclosure timeliness of restricted stock trading. Insiders selling restricted stock are required to file a Form 144 because the stock is restricted and also a Form 4 because they are an insider. We confirm that mandatory filing requirements under Section 403 of SOX reduced the Form 4 disclosure delay for restricted stock transactions from 24 days in the pre-SOX period to the mandated 2 days in the post-SOX period. Although SOX did not mandate changes to Form 144 filings, we expect that disclosure timeliness of Form 144 filings is likely impacted by SOX. We find that Form 144 filings of restricted stock sales have become less timely. In the post-SOX period, Form 144, the intent to sell restricted stock, is almost always reported after the Form 4 disclosure of the executed trade. Thus, an unintended consequence of SOX is that by making the Form 4 filing more timely than the Form 144, market participants will know about a trade sooner, but have less information about the type of equity traded. An implication of this finding is that Section 403 of SOX may not have unambiguously improved investor protection as intended.     

Intraday stock price effects of ad hoc disclosures: the German case

This paper examines intraday stock price and trading volume effects caused by ad hoc disclosures in Germany. The evidence suggests that the stock prices react within 30 min after the ad hoc disclosures. The adjustment of the trading volume needs even more time. We find no evidence for abnormal high price nor trading volume reactions in the five transactions before ad hoc disclosures. The bigger the company, which announces an ad hoc disclosure, the less severe the abnormal price effect, following the announcement, is. The higher the trading volume at the last trading day before the announcement, the higher the price and trading volume effects, after the ad hoc disclosures, are.     

Risk management implications of time-inconsistency: Model updating and recalibration of no-arbitrage models

A widespread approach in the implementation of asset pricing models is based on the periodic recalibration of its parameters and initial conditions to eliminate any conflict between model-implied and market prices. Modern no-arbitrage market models facilitate this procedure since their solution can usually be written in terms of the entire initial yield curve. As a result, the model fits (by construction) the interest rate term structure. This procedure is, however, generally time inconsistent since the model at time t = 0 completely specifies the set of possible term structures for any t > 0. In this paper, we analyze the pros and cons of this widespread approach in pricing and hedging, both theoretically and empirically. The theoretical section of the paper shows (a) under which conditions recalibration improves the hedging errors by limiting the propagation of an initial error, (b) that recalibration introduces time-inconsistent errors that violate the self-financing argument of the standard replication strategy. The empirical section of the paper quantifies the trade-off between (a) and (b) under several scenarios. First, we compare this trade-off for two economies with and without model specification error. Then, we discuss the trade-off when the underlying economy is not Markovian.     

Predictive regression with order-p autoregressive predictors

Studies of predictive regressions analyze the case where y_t is predicted by x_t ? 1 with x_t being first-order autoregressive, AR(1). Under some conditions, the OLS-estimated predictive coefficient is known to be biased. We analyze a predictive model where y_t is predicted by x_t ? 1, x_t ? 2,… x_t ? p with x_t being autoregressive of order p, AR(p) with p > 1. We develop a generalized augmented regression method that produces a reduced-bias point estimate of the predictive coefficients and derive an appropriate hypothesis testing procedure. We apply our method to the prediction of quarterly stock returns by dividend yield, which is apparently AR(2). Using our method results in the AR(2) predictor series having insignificant effect, although under OLS, or the commonly assumed AR(1) structure, the predictive model is significant. We also generalize our method to the case of multiple AR(p) predictors.     

Momentum is really short-term momentum

We demonstrate the estimation biases that arise when stock returns from 12 month prior and 2 month prior are included within intermediate and recent past momentum profits. These biases lead to an overestimation of intermediate past momentum but an underestimation of recent past momentum in the US market. There is no significant difference between the predictability of stock performance in the intermediate past and the recent past once we exclude these two months from the construction of momentum strategies in the US and each of the 26 major international markets.     

Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets

Using two newly available ultrahigh-frequency datasets, we investigate empirically how frequently one can sample certain foreign exchange and U.S. Treasury security returns without contaminating estimates of their integrated volatility with market microstructure noise. Using the standard realized volatility estimator, we find that one can sample dollar/euro returns as frequently as once every 15 to 20 s without contaminating estimates of integrated volatility; 10-year Treasury note returns may be sampled as frequently as once every 2 to 3 min on days without U.S. macroeconomic announcements, and as frequently as once every 40 s on announcement days. Using a simple realized kernel estimator, this sampling frequency can be increased to once every 2 to 5 s for dollar/euro returns and to about once every 30 to 40 s for T-note returns. These sampling frequencies, especially in the case of dollar/euro returns, are much higher than those that are generally recommended in the empirical literature on realized volatility in equity markets. The higher sampling frequencies for dollar/euro and T-note returns likely reflect the superior depth and liquidity of these markets.     

High frequency trading and end-of-day price dislocation

We show that the presence of high frequency trading (HFT) has significantly mitigated the frequency and severity of end-of-day price dislocation. The effect of HFT is more pronounced on days when end of day price dislocation is more likely to be the result of market manipulation. Moreover, the effect of HFT is more pronounced than the role of trading rules, surveillance, enforcement and legal conditions in curtailing the frequency and severity of end-of-day price dislocation. We show our findings are robust to different proxies of the start of HFT by trade size, cancellation of orders, and co-location.     

The nexus between financial integration and real economy: Solow-growth model concept

This paper aims to investigate the nexus between financial integration and the real economy in ASEAN + 3 economies based on the concept of Solow-Growth Model. The equity indices as a proxy for financial markets are collected from each ASEAN + 3 members and are segmented between two periods; before and after the financial cooperation agreement period. The finding presents several outcomes; 1) no cointegration nexus is found in the system during the pre-agreement periods; 2) the markets are found cointegrated during the post-agreement period, 3) financial integration is found to influence the real sectors of ASEAN + 3 economies. Finally, this study offers policy implications to improve financial integration for stabilizing the real economy.     

Gaining a competitive edge through acquisitions: Evidence from the telecommunications industry

I study the announcement effects of all acquisitions in the recent telecom wave on both the acquirers and their industry competitors. I find evidence of negative rival returns (? 0.55%, t-stat = 2.47) by focusing on non-horizontal acquisitions where rivals are less susceptible to experience positive returns due to increased market power or expectation that some will become future targets themselves. I find that this effect is worse for closer rivals defined as having similar size and being in the same primary service area as the acquirer. Competitor returns are positively correlated with those of the acquirers suggesting that the negative impact experienced by competitors is driven by acquisitions in which the acquirer itself is earning negative abnormal returns. Results are broadly consistent with the Competitive Advantage Hypothesis that posits acquisitions are a means of corporate restructuring in a changing environment, awarding the acquirer a competitive edge and thereby making these acquisitions costly for their non-merging competitors.