Non-trading Hours Research Articles

The Structural Shifts in Bitcoin’s Role as a Predictor of the KOSPI Index

This paper explores the efficiency of the KOSPI market by examining its predictability, with a focus on the influence of Bitcoin returns during non-trading hours. Utilizing predictive models incorporating many financial assets, we find a significant predictive value in the Bitcoin return for the KOSPI return. We also identify a gradual structural break in the KOSPI-Bitcoin relationship, a shift from traditional market efficiency paradigms. This gradual transition, influenced by recent economic and regulatory changes, underscores potential arbitrage opportunities.

Stock Prices Reactions to Earnings Announcement Released During Trading versus Non-Trading Hours

The study has examined the stock prices reactions to quarterly earnings announcements released during trading versus non-trading hours. An event study method is applied to test whether the Indian stock market is semi-efficient. The NSE Nifty 500 companies are included in our sample. The sales, profit, and EPS figures from the current year are compared to similar quarters from the prior year to determine earnings changes. According to the study, the market's reaction to the announcements both during and non- trading hours was similar. We found that Firms generally released positive earnings changes during trading hours and negative earnings changes outside of regular trading hours. An empirical examination of CARs for multi-day event windows indicates there is market inefficiency and that there is a delayed price reactions.

Variable trading hours and market reactions to earnings announcements

This study examines the relationship between stock market trading hours and market reactions to earnings announcements. The analysis demonstrates that post-announcement non-trading hours diminish trading volume reactions associated with differential interpretations. Moreover, post-announcement non-trading hours facilitate timely price reactions.

Renewable energy stocks forecast using Twitter investor sentiment and deep learning

This paper examines the impact of investor sentiment on forecasting returns and volatility for renewable energy stocks. We apply a natural language processing technique to extract investor sentiment from Twitter during both trading and non-trading hours. Forecasting analyses are conducted using a state-of-the-art hybrid deep learning technique and benchmark models. Results show that the sentiment variables hold significant add-on information not captured by standard financial market variables. Twitter investor sentiment considerably improves return and volatility forecasts of renewable energy stocks, especially when the deep learning method is employed. Our results are statistically significant and robust under different settings.

Barrier Option Pricing with Trading and Non-Trading Hours

We introduce a semi-analytical pricing approach, which takes non-trading hours into account.

Bayesian estimation of realized GARCH-type models with application to financial tail risk management

Advances in the various realized GARCH models have proven effective in taking account of the bias in realized volatility (RV) introduced by microstructure noise and non-trading hours. They have been extended into nonlinear or long-memory patterns, including the realized exponential GARCH (EGARCH), realized heterogeneous autoregressive GARCH (HAR-GARCH), and realized threshold GARCH (TGARCH) models. These models with skew Student’s t-distribution are applied to quantile forecasts such as Value-at-Risk and expected shortfall of financial returns as well as volatility forecasting. Parameter estimation and quantile forecasting are built on Bayesian Markov chain Monte Carlo sampling methods. Backtesting measures are presented for both Value-at-Risk and expected shortfall forecasts and employ two loss functions to assess volatility forecasts. Results taken from the S&P500 in the U.S. market with approximately 5-year out-of-sample periods covering the COVID-19 pandemic period are reported as follows: (1) The realized HAR-GARCH model performs best in respect of violation rates and expected shortfall at the 1% and 5% significance levels. (2) The realized EGARCH model performs best with regard to volatility forecasts.

Investor Attention and Corporate Disclosure Timing: Evidence from Korea

This paper investigates the association between investor attention and the timing of the release of corporate information in the Korean stock market. The association provides the evidence on whether the investor demand for earnings information affects the managerial disclosure decision. Similar to a previous study that uses the Google Search Volume Index to measure investor attention, this paper re-measures investor attention using the Search Volume Index provided by Naver, which has the largest market share of all internet search portals in Korea. Using 9,526 firm-quarter observations, the paper regresses disclosure timing on investor attention. Empirical results show that managers facing greater investor attention announce quarterly earnings information on an earlier date than in the previous year, and they release earnings information during trading hours rather than non-trading hours. The findings indicate that managers consider timely disclosure to be an important factor in meeting investor interests. In addition, this paper sheds light on the role of individual/retail investors in managerial disclosure decisions, considering that the measure of investor attention primarily captures individual/retail investor interest.

The impacts of public news announcements on intraday implied volatility dynamics

AbstractWe examine the responses of intraday option‐implied volatilities to scheduled announcements of macroeconomic indicators. The increase in implied volatility around macroeconomic news announcements is more pronounced for puts than for calls and is stronger for announcements made during trading hours than for those made during nontrading hours. These effects are also more pronounced in the crisis and postcrisis periods than in the precrisis period. Monetary policy announcements have a more substantial impact on volatility than other announcements have, even after controlling for news surprise components. The impact appears to be greater for policy rate hikes than for policy rate cuts.

Liquidity and the convergence to market efficiency

ABSTRACTThe aim of this study is to investigate the relationship between market liquidity changes on the Johannesburg Stock Exchange (JSE) and the market’s degree of efficiency. Market efficiency is characterised in terms of two philosophies: Fama’s (1970) Efficient Markets Hypothesis (EMH), and Shiller’s (1981; 2003) informational efficiency designation. Efficiency was tested using measures of return predictability, a random walk benchmark and price volatility; liquidity was measured using market turnover. The tests were conducted on JSE Top 40 shares across three regimes, spanning January 2012–June 2016. The regimes are demarcated by two structural breaks in the JSE’s microstructure: the 2012 trading platform upgrade and the 2014 colocation centre launch. The results show that past order imbalances are a significant predictor of daily returns, although the significance of this predictability has dissipated over time. Return predictability is not influenced by liquidity. In fact, there is evidence that illiquidity weakens return predictability. Prices were closer to random walk benchmarks during the third regime. In consideration of informational efficiency, during the latter two regimes price volatility is greater during trading versus non-trading hours. This is coupled with an emergence of nonlinear return dependence, which is indicative of greater mispricing. Thus, over the three regimes, market efficiency improved in the sense of the EMH, but informational efficiency deteriorated. This study contributes to the field by: introducing an inverse measure of market efficiency; providing insight into the measure’s time variation and relation to liquidity; and demonstrating that market efficiency tests should incorporate the dual meanings of the term, enabling richer understanding of how such meanings intersect.

Intraday market effects in electronic soybean futures market during non-trading and trading hour announcements

ABSTRACTThis article investigates market reactions to major United States Department of Agriculture announcements during non-trading and trading hours in the soybean futures market using microstructure data. Following report release, volume increases and remains elevated for up to 15 to 20 minutes. The volume spikes for the non-trading releases relative to the trading releases, but are identical after the first reaction. Report releases during non-trading hours cause a large spike in volatility at the onset of trading which subsides quickly. In contrast, releases during trading hours result in a smaller volatility spike, which extends for 5–6 min at a higher magnitude. Adjusting volatility by normal trading volatility indicates that volatility in trading hour release is higher in both immediate response and persistence. Return correlations provide little evidence to support systematic under- or overreaction in prices regardless of when the report is released reflecting the efficiency of the market.

Overnight returns of stock indexes: Evidence from ETFs and futures

During the period 1999–2014, overnight returns of US exchange-traded index funds and most international index futures are significantly positive, while returns during trading hours are negative. The overnight volatility is lower than the trading volatility. Estimating the value at risk and expected shortfall by incorporating the daytime and overnight returns into a joint distribution with a copula method, we find that the risk contribution of trading hours is substantially higher than that of nontrading hours. The results are not consistent with the usual risk-return tradeoff. For US ETF and futures markets, we also show that overnight returns can forecast both the in-sample and out-of-sample returns during the first half-hour (with a negative relation) and last half-hour (with a positive relation) of trading hours.

Informed trading and the price impact of block trades: A high frequency trading analysis

Using high frequency data from the London Stock Exchange (LSE), we investigate the relationship between informed trading and the price impact of block trades on intraday and inter-day basis. Price impact of block trades is stronger during the first hour of trading; this is consistent with the hypothesis that information accumulates overnight during non-trading hours. Furthermore, private information is gradually incorporated into prices despite heightened trading frequency. Evidence suggests that informed traders exploit superior information across trading days, and stocks with lower transparency exhibit stronger information diffusion effects when traded in blocks, thus informed block trading facilitates price discovery.

Informed Trading and the Price Impact of Block Trades

Using high frequency data from the London Stock Exchange (LSE), we investigate the relationship between informed trading and the price impact of block trades on intraday and inter-day basis. Price impact of block trades is stronger during the first hour of trading; this is consistent with the hypothesis that information accumulates overnight during non-trading hours. Furthermore, private information is gradually incorporated into prices despite heightened trading frequency. Evidence suggests that informed traders exploit superior information across trading days, and stocks with lower transparency exhibit stronger information diffusion effects when traded in blocks, thus informed block trading facilitates price discovery.

Linkages between European National Stock Markets During Trading and Non-Trading Hours

The paper documents differences in the structures of dependence between stock markets during trading and non-trading hours. We study this issue on the basis of analysis of the dynamic dependence between five selected European stock markets during the period from 1997 to the beginning of 2013. The markets are represented by their main stock indices. The analysis is performed by means of Markov-switching copula models with three regimes. The adopted approach enables us to avoid limiting ourselves to elliptical distributions for the bivariate returns, and allows to assess dependence in tails of the bivariate distributions. Moreover, due to the use of copulas with Markov-switching there is no necessity to make any a priori assumptions concerning the time or cause of a change in the dependence structure.

Linkages between European national stock markets during trading and non-trading hours

ABSTRACTThe paper documents differences in the structures of dependence between stock markets during trading and non-trading hours. We study this issue on the basis of analysis of the dynamic dependence between five selected European stock markets during the period from 1997 to the beginning of 2013. The markets are represented by their main stock indices. The analysis is performed by means of Markov-switching copula models with three regimes. The adopted approach enables us to avoid limiting ourselves to elliptical distributions for the bivariate returns, and allows to assess dependence in tails of the bivariate distributions. Moreover, due to the use of copulas with Markov-switching there is no necessity to make any a priori assumptions concerning the time or cause of a change in the dependence structure.

Analysis of Realized Volatility for Nikkei Stock Average on the Tokyo Stock Exchange

We calculate realized volatility of the Nikkei Stock Average (Nikkei225) Index on the Tokyo Stock Exchange and investigate the return dynamics. To avoid the bias on the realized volatility from the non-trading hours issue we calculate realized volatility separately in the two trading sessions, i.e. morning and afternoon, of the Tokyo Stock Exchange and find that the microstructure noise decreases the realized volatility at small sampling frequency. Using realized volatility as a proxy of the integrated volatility we standardize returns in the morning and afternoon sessions and investigate the normality of the standardized returns by calculating variance, kurtosis and 6th moment. We find that variance, kurtosis and 6th moment are consistent with those of the standard normal distribution, which indicates that the return dynamics of the Nikkei Stock Average are well described by a Gaussian random process with time-varying volatility.

Square It Off: Trading Strategy

In this paper we propose a strategy to short stocks intraday and beat the market over a period of time. Our strategy for BSE Sensex gives returns of more than 40,000 absolute points while the market climbed less than 25,000 points in the same period. We use 20 years data from 1995 to 2014. We expand our sample to multiple indices across the globe. There is a similar trend on most of the other indices. We slice the data on the basis of trend to provide granularity to our results. We also provide a survey of literature on trading strategies, trading during non-trading hours and overnight information content.

On the Trading Behavior of Emerging Market ETFs

Emerging market exchange-traded funds (ETFs) are the subject of the current article. In particular, a sample of 40 iShares listed in the United States and exposed to several emerging stock markets in the Americas, Europe, Asia, and South Africa is employed to study several issues surrounding the trading behavior of emerging market ETFs. The issues examined concern the behavior of their return and volatility during trading and nontrading hours; their performance relative to market performance; the correlation of emerging ETF returns with the stock market of the United States; their tracking error and the persistence in tracking error; the pricing inefficiencies in terms of deviations between the trading prices and net asset values of ETFs; the persistence in these inefficiencies, which may or may not be arbitrageable; the reaction implications for the pricing of ETFs due to the existence of these inefficiencies; and finally, the determinants of ETFs’ trading activity. The results are comprehensive and indicative of the trading behavior of U.S. ETFs invested in emerging market indexes covering the Americas, Europe, Asia, and South Africa.

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.

Effects of Hormones on Financial Risk Taking: A Research in Istanbul Stock Exchange

Intraday pattern of stock returns are usually U-shaped on one-session markets and W-shaped on two-session markets. Today, the most accepted reason for these consistent patterns is the information accumulation during the non-trading hours. Nevertheless, there might be another reason that has grown out of the investor himself/herself. If so, this characteristic must work approximately the same for all the people in the world and must be endogenous. It leads us to the thought that this must be either a psychological or a physiological characteristic. Financial decision-making occurs on a rational thinking process, which is constrained by psychological factors. Physiological aspects may well affect those psychological factors, as we know hormones affect human behavior. Some hormones display a daily routine profile just as the intraday return. Hormones are the main biological factors that affect our actions. Cortisol turned out to be the most suitable hormone for such a research as it displays an inverse J-shaped daily profile and retains the characteristic as keeping people strong, which helps them adapt to long term stress and give them self-confidence that we foresee and covered in the paper. On the other hand, the pattern of this hormone is more in consistency with the intraday volatility rather than intraday return. Intraday volatility displays either a distorted U-shaped or an L-shaped daily profile in several stock exchanges around the world, which leads us to the thought that some time intervals must be more volatile than other time intervals within one day. The major objective of the study is to discover the association between daily volatility in Istanbul Stock Exchange (ISE) as a measure of aggregate risk taking and cortisol hormone levels of individuals, taken from previous research. Why volatility is chosen for this research also retains a conceptual reason. Return is not a measure for investor profile or risk taking of the investors. However, volatility is the measure of risk taking and indirectly self-confidence. The volatility part of the research is based on the immediate data from ISE in two different years. Each day was divided into day periods and the volatility of each period was calculated by the standard deviation of immediate data. Unlike our expectations, no significant relationship was found between daily cortisol level and intraday market volatility. Even though the cortisol level and volatility display consistency in the morning, the afternoon rise of the volatility in ISE distorts the relationship.