Nontrading Periods Research Articles

Tweets versus broadsheets: Sentiment impact on stock markets around the world

AbstractWe contrast sentiment derived from social and news media to investigate its impact across 14 international markets. We find that heightened media sentiment during nontrading periods significantly affects the next day's opening returns even after accounting for the previous‐day activity. Markedly, only the US market exhibits strong reactions to social media, whereas other markets are more responsive to the news. We find that most variability in overnight returns is explained by sentiment aggregated 3 h before markets open. Our findings suggest that the overnight sentiment does not simply subsume previous‐day market activity but contains additional information that helps improve predictability in return forecasting models.

Betting against beta with intraday and overnight signals

The abnormal returns of the Betting Against Beta (BAB) strategy have attracted much interest among researchers and practitioners. Based on a market anomaly related to the Capital Asset Pricing Model, this strategy uses daily beta as a signal for portfolio construction. However, recent literature shows how some financial quantities, including beta, change between trading and non-trading periods. For this reason, we decided to compare the performance of the original BAB strategy with two BAB variants, where the signal for portfolio construction is given by intraday and overnight beta, respectively. Despite all strategies exhibiting positive cumulative returns, using the intraday beta signal leads to significantly higher performances. Further analyses show that the abnormal intraday BAB returns are mainly due to nano and micro-cap stocks which tend to outperform large-cap stocks, as well known from the literature.

The Influence of Hedge, Arbitrage, and After-Hours Trading on the Holding Returns of TAIEX Futures

This study points out a new explanation of the non-trading effect of financial derivatives from the perspective of hedging demand. We examine the influence of hedging demand on the non-trading effect of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) Futures. By dividing the sample period into trading period and non-trading period and testing the difference between the risk premiums in these two intervals, we find that there is a non-trading effect in TAIEX Futures, which means that the holding returns of TAIEX Futures in the non-trading period are higher than those in the trading period. By estimating a dummy-regression model, the evidence shows that when the VIX (Taiwan Index Option Volatility Index) indicator is relatively high, the non-trading effect will be more significant, indicating that the non-trading effect may come from investors’ hedging needs. In addition, it is found that when the futures index is higher than the spot index, the non-trading effect becomes less obvious. The possible reason is that when there is a positive spread in index futures, investors will expect a bull market, thus reducing the hedging demand of index futures. In the end, we find that the liquidity in the after-hours trading session is poor, resulting in high hedging costs, and forcing investors to hedge during the regular trading period. Therefore, the after-hours trading of TAIEX Futures fails to reduce the non-trading effect.

Does social media distort price discovery? Evidence from rumor clarifications

Using a sample of clarification announcements for all A-share listed firms from 2008 to 2020, we reveal the influence of social media activities on price discovery after rumor clarifications in China. As big information events, rumors significantly impact return performance before and after the official release. For the influences of social media activities, these activities intensify the short-term market’s response to clarification announcements, while in the long-term period, the influences of social media activity are variant: it speeds return reversal for the whole sample, insignificantly influences return reversal following positive rumor clarifications, and has a varied impact on price persistence following negative rumor clarifications. Our findings are robust when controlling return-related determinants and using new proxies for social media activities and CARs. The results based on rumor clarifications announced during the non-trading period further support the causality. Therefore, we determine the impact of social media activities on price discovery following rumor clarifications.

Does systematic risk change when markets close? An analysis using stocks’ beta

Investors' behavior and news or events occurring during the market closure affect open price variation. For these reasons, daytime and overnight returns and volatilities move differently. In light of these effects, it is natural to ask whether systematic risk varies between trading and non-trading periods. We answer this question by evaluating the US stocks’ beta from 1992 to 2020. Computing daily, daytime, and overnight betas using a proper market index for each trading period, we show how market risk varies over time and is influenced by market shocks. We observe a high overnight risk concentrated within small and large stocks. We find that daytime systematic risk is generally higher, especially between 2001 and 2019. Furthermore, we show how the outbreak of the Covid-19 pandemic led to an increase in overnight risk implying that US stocks became more sensitive to the market closure.

Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?

We study the potential merits of using trading and non-trading period market volatilities to model and forecast the stock volatility over the next one to 22 days. We demonstrate the role of overnight volatility information by estimating heterogeneous autoregressive (HAR) model specifications with and without a trading period market risk factor using ten years of high-frequency data for the 431 constituents of the S&P 500 index. The stocks’ own overnight squared returns perform poorly across stocks and forecast horizons, as well as in the asset allocation exercise. In contrast, we find overwhelming evidence that the market-level volatility, proxied by S&P Mini futures, matters significantly for improving the model fit and volatility forecasting accuracy. The greatest model fit and forecast improvements are found for short-term forecast horizons of up to five trading days, and for the non-trading period market-level volatility. The documented increase in forecast accuracy is found to be associated with the stocks’ sensitivity to the market risk factor. Finally, we show that both the trading and non-trading period market realized volatilities are relevant in an asset allocation context, as they increase the average returns, Sharpe ratios and certainty equivalent returns of a mean–variance investor.

Holidays, weekends and range-based volatility

This study analyses the effect of non-trading periods on the forecasting ability of S&P500 index range-based volatility models. We find that volatility significantly diminishes on the first trading day after holidays and weekends, but not after long weekends. Our findings indicate that models that include autoregressive terms that interact with dummies that allow us to capture changes in volatility levels after interrupting periods provide greater explanatory power than simple autoregressive models. Therefore, the shorter the length of the non-trading periods between two trading days, the higher the overestimation of the volatility if this effect is not considered in volatility forecasting.

Option Mispricing around Nontrading Periods

ABSTRACTWe find that option returns are significantly lower over nontrading periods, the vast majority of which are weekends. Our evidence suggests that nontrading returns cannot be explained by risk, but rather are the result of widespread and highly persistent option mispricing driven by the incorrect treatment of stock return variance during periods of market closure. The size of the effect implies that the broad spectrum of finance research involving option prices should account for nontrading effects. Our study further suggests how alternative industry practices could improve the efficiency of option markets in a meaningful way.

Institutional Investors and Equity Prices: Information, Behavioral Bias, and Arbitrage

This paper investigates how institutional investors matter for asset pricing by using daily institutional trading data and a natural experiment, the split–share structure reform in China. This reform required all listed companies to convert their non-tradable shares to tradable shares after paying a negotiated compensation. We find that: 1). Only passive institutions are privy to company-specific details: they buy (avoid) shares of companies that end up with high (low) compensation ratios on announcement day T0. 2). Trading halts after T0. On the first subsequent trading day T1, both passive domestic institutions and individuals are prone to the disposition effect: they sell a large and abnormal amount of shares in the reform companies with high unrealized gains accumulated during the non-trading period, and 3). The selling pressure drives stock prices significantly below fair values. Both active and foreign institutions take advantage of the stock undervaluation and earn significant abnormal return, which eliminate stock mispricing.

Analisis Pergerakan Harga Saham untuk Mendeteksi Adanya Noise Atau Kedatangan Informasi di Bursa Efek Indonesia

This study aimed to test the presence of noise or the arrival of information between non-trading period returns and trading period with prior periods. The data used are secondary data from the companies listed in LQ45for 2009 and 2011. The sample in this study wasl03 companies. Analysis tools used to test the hypothesis is auto correlation. Results of this study indicate that the price movement of shares between non-trading period and the period of trading in the stock exchanges in Indonesia caused by noise and arrival information. Correction caused by the noise made during the trading period. Based on company size, sales volume up and down market conditions, the price correction caused by the noise made during the trading period. Unless quintile based on trading volume, because the price correction caused by the noise made during non-trading periods.

24-Hour realized volatilities and transatlantic volatility interdependence

This paper proposes an innovative econometric approach for the computation of 24-h realized volatilities across stock markets in Europe and the US. In particular, we deal with the problem of non-synchronous trading hours and intermittent high-frequency data during overnight non-trading periods. Using high-frequency data for the Euro Stoxx 50 and the S&P 500 Index between 2003 and 2011, we combine squared overnight returns and realized daytime variances to obtain synchronous 24-h realized volatilities for both markets. Specifically, we use a piece-wise weighting procedure for daytime and overnight information to take into account structural breaks in the relation between the two. To demonstrate the new possibilities that our approach opens up, we use the new 24-h volatilities to estimate a bivariate extension of Corsi et al.’s [Econom. Rev., 2008, 27(1–3), 46–78] HAR-GARCH model. The results suggest that the contemporaneous transatlantic volatility interdependence is remarkably stable over the sample period.

Trading and non-trading period Internet information flow and intraday return volatility

This paper employs the news appeared in Baidu News as the proxy for Internet information flow, separates them into trading period and non-trading period information and provides alternative evidence for the Mixture of Distribution Hypothesis (MDH). The empirical results show that the contemporary information can effectively reduce the volatility persistence; meanwhile, the lead information and the aggregate information also show some explanatory power. Some future directions are pointed out in the concluding remarks.

Portfolio Choice with Market Closure and Implications for Liquidity Premia

Most existing portfolio choice models ignore the prevalent periodic market closure and the fact that market volatility is significantly higher during trading periods. We find that market closure and the volatility difference across trading and nontrading periods significantly change optimal trading strategies. In addition, we demonstrate numerically that transaction costs can have a first-order effect on liquidity premia that is largely comparable to empirical findings. Moreover, this effect on liquidity premia increases in the volatility difference, which is supported by our empirical analysis. This paper was accepted by Jerome Detemple, finance.

Is risk higher during non-trading periods? The risk trade-off for intraday versus overnight market returns

We study the magnitude of tail risk – particularly lower tail downside risk – that is present in intraday versus overnight market returns and thereby examine the nature of the respective market risk borne by market participants. Using the Generalized Pareto Distribution for the return innovations, we use a GARCH model for the conditional market return components of major stock markets covering the U.S., France, Germany and Japan. Testing for fat-tails and tail index equality, we find that overnight return innovations exhibit significant tail risk, while intraday innovations do not. We illustrate this volatility versus tail risk trade-off based on conditional Value-at-Risk calculations. Our results show that overnight downside market risk is composed of a moderate volatility risk component and a significant tail risk component. We conclude that market participants face different intraday versus overnight risk profiles and that a risk assessment based on volatility only will severely underestimate overnight downside risk.

Is Risk Higher During Non-Trading Periods? Tail Risk Evidence from Overnight Market Returns

We study the magnitude of tail risk --- particularly lower tail downside risk --- that is present in intraday versus overnight market returns and thereby examine the nature of the respective market risk borne by market participants. Using the Generalized Pareto Distribution for the return innovations, we use a GARCH model for the conditional market return components of major stock markets covering the U.S., France, Germany and Japan. Testing for fat-tails and tail index equality, we find that overnight return innovations exhibit significant tail risk, while intraday innovations do not. We illustrate this volatility versus tail risk trade-off based on conditional Value-at-Risk calculations. Our results show that overnight downside market risk is composed of a moderate volatility risk component and a significant tail risk component. We conclude that market participants face different intraday versus overnight risk profiles and that a risk assessment based on volatility only will severely underestimate overnight downside risk.

NOISE AS THE IMPACT OF TRADING MECHANISM AND PERIODIC CLEARING PROCEDURES: SUBSTANTIVE EVIDENCE FROM INDONESIA STOCK EXCHANGE

Sumiyana (2007b) investigates that noises occurred in Indonesia Stock Exchange. Especially, Sumiyanas’ research concludes that noise over trading and nontrading period, along with overnight and lunch break nontrading session, and the first and second trading session, had occurred. However, Sumiyana’s research does not identify who are responsible for the noise. This research used intraday data in Indonesia Stock Exchange. Samples of the data are the firms consecutively listed in LQ 45 indexes for the year of 2004-2006. This research attempts to identify who are responsible for the noise. This research studies effects of trading mechanism and clearing mechanism on the stock return behaviour. Finally, this research is enabling to differentiate the effect of trading mechanism from the effect of clearing mechanism. This research concludes that periodic clearing procedure at the beginning of the trading period is noisy. Therefore, this research suggests that trading mechanism in Indonesia Stock Exchange is inefficient. This research also hopefully recommends shifting from the periodic to continuous trading mechanism. Keywords: Intraday data, trading mechanism, trading and nontrading periods, stock return behaviour, noise, negative autocorrelation, clearing mechanism, periodic clearing procedure.

News, volatility and jumps: the case of natural gas futures

We investigate the impact of news sentiment on the price dynamics of natural gas futures. We propose a Local News Sentiment Level model, based on the Local Level model of Durbin and Koopman [Time Series Analysis by State Space Methods, 2001], to construct a running series of news sentiment from irregularly observed news items’ sentiments. We construct several return and variation measures to proxy for the fine dynamics of the natural gas futures prices. We employ event studies, Granger causality tests and several state-of-the-art volatility models to assess the effect of news on the returns, price jumps and the volatility. We find significant relationships between news sentiment and the dynamic characteristics of natural gas futures prices. Our findings are, among others, that the arrival of news in non-trading periods causes overnight returns, that news sentiment is Granger caused by negative semi-volatility and that news sentiment is more sensitive to negative than to positive jumps. In addition to that we find strong evidence that news sentiment severely Granger causes price jumps and conclude that market participants trade futures as some function of aggregated news. We augment volatility models with news sentiment measures and conduct an out-of-sample volatility forecasting study. The first class of models is the generalized autoregressive conditional heteroskedasticity models the second class is the high-frequency-based volatility models of Shephard and Shephard [J. Appl. Econ., 2010, 25, 197–231] and Noureldin et al. [J. Appl. Econ., 2012, 27(6), 907–933]. We adapt both models to account for asymmetric volatility, leverage and time to maturity effects. By augmenting all models with news sentiment variables, we find that including news sentiment in volatility models significantly improves volatility forecasts.

24-Hour Realized Volatilities and Transatlantic Volatility Interdependence

This paper proposes an innovative econometric approach for the computation of 24-hour realized volatilities across stock markets in Europe and the US. In particular, we deal with the problem of non-synchronous trading hours and intermittent high-frequency data during overnight non-trading periods. Using high-frequency data for the Euro Stoxx 50 and the S&P 500 Index between 2003 and 2011, we combine squared overnight returns and realized daytime variances to obtain synchronous 24-hour realized volatilities for both markets. Specifically, we use a piece-wise weighting procedure for daytime and overnight information to take structural breaks in the relation between the two into account. To demonstrate the new possibilities that our approach opens up, we use the new 24-hour volatilities to estimate a bivariate extension of Corsi et al. (2008)’s HAR-GARCH model. The results suggest that the contemporaneous transatlantic volatility interdependence is remarkably stable over the sample period.

Risk contributions of trading and non-trading hours: Evidence from Chinese commodity futures markets

This paper examines the overall risks in Chinese copper, rubber, and soybean futures markets using a copula-VaR (value at risk) and copula-ES (expected shortfall) framework that explicitly accounts for both trading and non-trading information. Our results show that information accumulating during non-trading hours contributes substantially to overall risks, with non-trading VaR weights exceeding 40% in all these markets. In particular, the information during non-trading hours is more important than the information during trading hours in explaining the total risk of all three futures as measured by ESs and volatility weights. Moreover, the risk due to non-trading information increases with the length of non-trading periods, reflecting the fact that information accumulates continuously over time.

LIMIT LAWS IN TRANSACTION-LEVEL ASSET PRICE MODELS

We consider pure-jump transaction-level models for asset prices in continuous time, driven by point processes. In a bivariate model that admits cointegration, we allow for time deformations to account for such effects as intraday seasonal patterns in volatility and nontrading periods that may be different for the two assets. We also allow for asymmetries (leverage effects). We obtain the asymptotic distribution of the log-price process. For the weak fractional cointegration case, we obtain the asymptotic distribution of the ordinary least squares estimator of the cointegrating parameter based on data sampled from an equally spaced discretization of calendar time, and we justify a feasible method of hypothesis testing for the cointegrating parameter based on the correspondingt-statistic. In the strong fractional cointegration case, we obtain the limiting distribution of a continuously averaged tapered estimator as well as other estimators of the cointegrating parameter, and we find that the rate of convergence can be affected by properties of intertrade durations. In particular, the persistence of durations (hence of volatility) can affect the degree of cointegration. We also obtain the rate of convergence of several estimators of the cointegrating parameter in the standard cointegration case. Finally, we consider the properties of the ordinary least squares estimator of the regression parameter in a spurious regression, i.e., in the absence of cointegration.