Non-synchronous Trading Research Articles

Portfolio Cross-Autocorrelation Puzzles

This paper investigates the driving forces underlying lead-lag cross-autocorrelations in daily portfolio returns. By contrasting autocorrelation patterns using portfolio returns based on trade prices before an arbitrary point in the trading day with those using returns based on prices after, we isolate the impact of nonsynchronous trading and conclude decisively that its impact on portfolio correlation patterns is negligible. We also reject the possibility that time-varying returns, contemporaneous correlations or discreteness in pricing underlies the portfolio autocorrelation patterns. Rather, the intradaily evolution of portfolio autocorrelations reflect inefficient pricing of infrequentlytraded stocks—prices of less active stocks appear not to incorporate some of the recent information that is already contained in the prices of more active stocks. Portfolio autocorrelations rise systematically when we calculate returns using later times in the day, indicating that prices of infrequently-traded stocks grow increasingly stale over the trading day. JEL Classification: G12, G14.

Information Diffusion, Transaction Costs, and the Performance of Technical Trading Rules in the Emerging Stock Markets of China

We examine the performance of technical trading rules in the emerging Chinese stock markets. After controlling for non-synchronous trading and transaction costs, we find significant evidence to support the predictability and profitability of technical rules for Chinese foreign B-shares but not for domestic A-shares. The index returns of B-shares can be explained by one-day-lagged own market trading signals, but not by the trading signals emitted from the corresponding A-share market or from the U.S. market (a proxy for the international market). However, after February 19, 2001, when domestic investors were allowed to trade B-shares, the predictive power of the trading rules in B-share markets disappeared. We conclude that the predictability of technical trading rules in B-share markets can be attributed to the gradual diffusion of information among foreign investors under the foreign share ownership restriction, and, partly, to positive autocorrelations induced by thin trading.

Validity of discrete-time stochastic volatility models in non-synchronous equity markets

The paper investigates the validity of versions of discrete-time stochastic volatility models for index series known to contain component stocks exhibiting non-synchronous trading. The efficient method of moments (EMM) is used to fit versions of the discrete-time stochastic volatility (SV) model. The EMM methodology confronts moment conditions generated by a score generator (SNP) that are valid by construction. The moment generator suggests non-linearity in the index series. The EMM construction shows that a classical discrete time stochastic volatility model is rejected. An extended model incorporating an asymmetric volatility specification validates all the moment scores. Option values from Black and Scholes (BS) and Monte Carlo simulations (MC) seem significantly different. The results suggest that BS does not price asymmetry adequately. Asymmetry suggests increased market risk inducing higher BS call prices and lower (higher) BS put pricing for ATM and OTM options (ITM) relative to MC.

Stock Return Cross-Autocorrelations and Market Conditions in Japan

In this paper, we show that changes in market conditions significantly affect cross-autocorrelations in short-horizon stock returns in Japan. We find strong (weak) cross-autocorrelations between returns on small firms and lagged returns on large firms following periods of aggregate market losses (gains). This asymmetric effect of lagged market state on cross-autocorrelations cannot be explained by market micro-structure biases such as non-synchronous trading and thin trading. Further analyses suggest that lack of aggregate trading activity following periods of market decline contribute to the delays in registering negative market-wide information in stock prices, particularly for small stocks.

Is there a Symmetric Nonlinear Causal Relationship between Large and Small Firms?

This paper uses both linear and nonlinear causality tests to reexamine the cross-autocorrelation between the returns on large and small firms. Consistent with previous results, we find that large firms lead small firms, but small firm autocorrelation, nonsynchronous trading, or a differential response to a large set of economy-wide information cannot explain this finding. More important, in contrast to the received literature, we find significant nonlinear causality that is bidirectional and of the same duration in either direction. After controlling for possible sources of nonlinearities, such as information flow as proxied by volatility, macroeconomic information, business cycle effects, and nonsynchronous trading, we continue to find strong evidence of equal duration, bi-directional nonlinear causality.

Analisis Kandungan Informasi Stock Split dan Likuiditas Saham: Studi Empiris Pada Non-synchronous Trading

Stock Split has been a debatable and puzzling phenomenon for financial theoritist, for there is inconsistency between the theory and practice. Theoretically, Stock Split will only increase the amount of shared stocks, without increasing the profit for investors, nor adding any economic value to the firms. While in practice, some empirical evidences show that market tends to react to the announcement of Stock Split. The objective of this research is to empirically examine about the information content of Stock Split and its influence to stock liquidity by firstly corecting the beta bias, since the trade condition in Jakarta Stock Exchange is still a non-synchronous trading. Sample consists of 61 stocks performing the Stock Split during the period of June 1994 to June 1997. The examination of information content of Stock Split made use of Single Index Model (William Sharpe, 1963) and correcting the beta bias made use of Fowler and Rorke Method (1983) with four lags and four leads (Hartono & Surianto,1999) Comparison of stock liquidity before and after performing the Stock Split made use of paired sample test. The result of this research shows that Stock Split has information content which is negatively responded statistically but significantly responded by the market around the date of Stock Split announcement. The difference between stock liquidity before and after the Stock Split is insignificant.

Testing the univariate conditional CAPM in thinly traded markets

Traditional tests of asset pricing undertaken within the CAPM framework have to control for nonsynchronous trading and non-trading as well as volatility clustering in especially thinly traded financial markets. This investigation therefore set out to control for nonsynchronous trading and non-trading effects and volatility clustering in the Norwegian equity market. The problem is approached by applying a linear ARMA-GARCH-in-mean lag specification. The ARMA lag specification controls for nonsynchronous trading and non-trading effects in the mean equation. The GARCH lag specification controls for conditional heteroscedasticity and volatility clustering in the latent conditional volatility equation. All lags are Schwarz efficient. The results suggest that the conditional CAPM cannot be rejected but the in-mean parameter in ARMA-GARCH-in-mean specifications show very low statistical significance except for daily data. The result therefore suggests a compensation for risk only for short time-horizons and the in-mean parameter in ARMA-GARCH-in-mean lag specifications is a poor proxy for risk in the conditional CAPM sense. Conditional heteroscedasticity and volatility clustering need to be controlled for in daily and weekly time intervals while nonsynchronous trading needs to be controlled for in daily, weekly and monthly time intervals.

Contrarian and momentum strategies in the China stock market: 1993–2000

Using data on “A” shares, accessible only to local investors in China, we find statistically significant abnormal profits for some short-horizon contrarian and intermediate-horizon momentum strategies. Further analysis indicates that: (1) overreaction to firm-specific information is the single most important source of short-term contrarian profits; (2) the intermediate-term momentum profits are not, however, distinct due to the dominance of overreaction effect; and (3) the negative cross-serial correlation contributes to momentum profits. The lead–lag structure in China is unique in that (i) lag firms follow lead firms in the opposite direction and (ii) large firms lead small firms in holding periods from 1 to 8 weeks, while small firms lead large firms in holding periods from 12 to 26 weeks. These findings are robust to bid–ask spread and nonsynchronous trading, time-varying market risk and firm-size effect.

The predictability of asset returns: an approach combining technical analysis and time series forecasts

We investigate predictability of asset returns by developing an approach that combines technical analysis and conventional time series forecasts. While exploiting predictable components as functions of past prices or returns, technical trading rules and time series forecasts capture different aspects of market predictability: the former tends to identify periods to be in the market when returns are positive and the latter is capable of identifying periods to be out when returns are negative. Applied to daily Dow Jones Averages over the first 100 years, the combined strategies outperform both technical trading rules and time series forecasts. The predictability can be explained largely by non-trivial low-order serial correlations in returns and is not mainly attributable to measurement errors arising from non-synchronous trading.

On the Perils of Financial Intermediaries Setting Security Prices: The Mutual Fund Wild Card Option

ABSTRACTEconomic distortions can arise when financial claims trade at prices set by an intermediary rather than direct negotiation between principals. We demonstrate the problem in a specific context, the exchange of open‐end mutual fund shares. Mutual funds typically set fund share price (NAV) using an algorithm that fails to account for nonsynchronous trading in the fund's underlying securities. This results in predictable changes in NAV, which lead to exploitable trading opportunities. A modification to the pricing algorithm that corrects for nonsynchronous trading eliminates much of the predictability. However, there are many other potential sources of distortion when intermediaries set prices.

Efficiently ARMA–GARCH estimated trading volume characteristics in thinly traded markets

ARMA-GARCH lag specification is employed to fit a model exhibiting nonsynchronous trading and volatility clustering for the Norwegian thinly traded equity market. In particular, characteristics of the conditional mean and conditional volatility inhibited in thinly traded equity markets are investigated. Trading volume is employed as a proxy measure for trading frequency. Low to no trading volume induces thin trading and non-trading effects while a relative higher trading frequency induces continuous trading. The main objective is to investigate trading frequency differences in serial correlation and cross-autocorrelation in the mean equation and volatility clustering in the volatility equation as well as any symptoms of data dependencies in the model residuals, which imply ARMA-GARCH model misspecification. BIC efficient ARMA-GARCH lag specifications are employed for the conditional mean and volatility and relevant mean and volatility parameter measures introduced that are well known from the changing volatility literature. The empirical results report consistent mean and volatility patterns over the increasing trading frequency series. Nonsynchronous trading and non-trading effects show a consistent pattern in serial correlation and cross-autocorrelation for the conditional mean and the latent volatility exhibits a consistent pattern in past shocks, past conditional volatility, persistence and weight to long-run average volatility. In contrast to the more relatively frequently traded asset series the most thinly traded series report insignificant asymmetric volatility. Moreover, for the most thinly traded series, specification tests suggest data dependence, which seems to be prolonged into the equal-weighted index series. Hence, due to serial correlation and data dependence in the model residuals the ARMA–GARCH lag specifications seem only appropriate for relatively frequently traded return series.

A stochastic volatility model specification with diagnostics for thinly traded equity markets

The majority of world equity markets exhibit non-synchronous- and non-trading for some quoted asset series. This investigation sets out to determine the complexity of illiquid markets applying versions of stochastic differential equation (SDE) specifications. Efficient method of moments (EMM) is used to estimate and evaluate the diffusion models. EMM estimation on SNP scores reveals that a standard SDE with first and second order drift, non-synchronous trading and constant diffusion are rejected. However, a simple two-factor stochastic volatility specification with non-synchronous trading and conditional heteroscedasticity, reports success for illiquid equity markets. Tracking portfolios may therefore not track derivative products perfectly and importantly; the stochastic volatility model seems to be the preferred volatility specification.

Elusive anomalies in the Brazilian stock market

We study the twist-of-the-Monday effect in the Brazilian stock market and provide evidence that it is due to index construction problems, such as the nonsynchronous trading of stocks. The effect is present for indices but absent for most individual stocks and in the most recent subperiods of the 1986–1998 period. When present, it was due to negative weekend returns while Monday intraday returns were significantly positive. When absent, Monday returns remain positively correlated with the previous week return although Monday returns are no longer significantly negative. Monday trading strategies based on the previous week return were profitable in and out of the sample.

Unconditional First Moment and Conditional Second Moment Effects: Intra-Day Stock Price, Market Index and Futures Price Processes

A theoretical framework is developed in order to consider effects of mis-specification in either first and/or second moment equations on resultant conditional volatility parameter estimates. The conditional volatility model is considered as a special case of a general stochastic volatility structure. Conditions necessary for the behaviour of the underlying asset price processes to approximate a diffusion limit are considered as the observation interval approaches 0 (d~0). The asymptotic distribution of the measurement error process may not be obtainable as d~0 if these conditions are violated. The relative impact of mis-specification of drift in mean and drift in conditional volatility is the focus. Market features such as bid/ask bounce effects in futures and stock price processes and non-synchronous trading effects in cash index processes are explored within this framework. Other mis-specifications in mean equations such as over-differencing are jointly explored. The most important effect is mis-specification of conditional volatility equations by failing to account for contemporaneous market trading and volume of trade effects. Empirical examples are provided employing Australian, U.S. and U.K. cash index, stock price and futures price data sampled from transactions records. These estimates help quantify the relative effects on conditional volatility estimates from mis-specifying the dynamics and sampling interval for these asset price mean equations. The relative importance of mis-specification of conditional volatility equations from incorrect exclusion of variables is seen to be crucial. Volume of trade, market opening/closing and other contemporaneous volatility effects in parallel processes are allowed to enter conditional volatility equations to highlight this issue.

Causality and cointegration of stock markets among the United States, Japan and the South China Growth Triangle

This paper explores the causality and cointegration relationships among the stock markets of the United States, Japan and the South China Growth Triangle (SCGT) region. Applying the recently advanced unit root and cointegration techniques that allow for structural breaks over the sample period (October 2, 1992 to June 30, 1997), we find that there exists no cointegration among these markets except for that between Shanghai and Shenzhen. By invoking the Granger causality test and considering the non-synchronous trading problem, we will show that stock price changes in the US have more impact on SCGT markets than do those of Japan. More specifically, price changes in the US can be used to predict those of the Hong Kong and Taiwan markets on next day. Similarly, price changes on the Hong Kong stock market lead the Taiwan market by 1 day. Furthermore, the stock returns of the US and Hong Kong markets are found to be contemporaneous. Finally, there is a significant feedback relationship between the Shanghai and the Shenzhen Stock Exchanges.

Trading volume and autocorrelation: Empirical evidence from the Stockholm Stock Exchange

In accordance with studies for other markets, Swedish index returns exhibit high autocorrelation, (a) after days of above average performance of the stock market, (b) after low absolute returns, (c) when trading volume is low, and (d) following Fridays. Contrary to the non-synchronous trading and the transaction cost hypotheses, all results extend to individual stock returns. It is concluded that autocorrelation patterns are related to the trading patterns of individual investors, and not the cross-security information processing of the market. In particular, the observed autocorrelation structure corresponds to feedback trading.

Stock return volatility in thinly traded markets. An empirical analysis of trading and non-trading processes for individual stocks in the Norwegian thinly traded equity market

This paper reports studies of the volatility of prices for individual stocks in the thinly traded Norwegian equity market during periods of trading and non-trading when the market is open for trading and closed. Building a model using Brownian motions, returns and variance ratios in trading and non-trading periods can be hypothesized. The model presents results that show an identical volatility in periods in which the market is open but no trades occur, and in periods of frequent trading. Furthermore, when the market is closed (weekends and holidays), the volatility is almost identical to consecutive days of trading. That is, the observed that on correspondence between return variance and transaction arrival is dependent on whether the market is open, and not simply on whether the stock is trading. This finding prevails after adjusting for non-synchronous trading using Poisson distributed trade arrivals.

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.

Interdependence between the US and major European equity markets: evidence from spectral analysis

This paper uses spectral analysis to examine interrelationships between the daily returns generated by one US (S&P 500) and three major European (FTSE 100, DAX 30, CAC 40) share price indices. Evidence is found of strong interdependence between the European returns series, as well as a lead-lag relationship between the US and each of the European series, explained by non-synchronous trading. The spectra also reveal some evidence of cyclical fluctuation in the return series. The patterns are similar among the European series for cycles of all frequencies, while similarities between the US and the European series are evident at low, but not at high frequencies.

The deal of the century in Chile Endesa España's takeover of Enersis

We analyze the effect of the announcement of the tender offer made by Endesa España to control Enersis Chile through the Chispas holding structure. In this clinical study of the case, we summarize the salient points of the Chispa case—one of the most controversial in Chile—and obtain the abnormal returns to the shareholders of the target firm using three alternative models for robustness of results: the constant mean model, the market model, and the market model adjusted for non-synchronous trading problems. Contrary to expectations based on prior empirical research in the United States and United Kingdom, our results demonstrate the absence of positive cumulative abnormal returns (CARs) for Chispa stockholders during the takeover bidding event window. We present evidence in this case that the governance structure of the target firm led to this seeming anomaly and conclude that the generally accepted theories of corporate control and governance in the United States and United Kingdom do not automatically apply to developing countries with differing legal environments.