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This paper studies the information spillover effects over price and volatility across countries by using open-to-close (daytime) returns and close-to-open (overnight) returns of NASDAQ 100 and KOSDAQ 50 index futures data from January 1, 2001 to December 31, 2001. Based on the time-varying AR(1)-GARCH (1,1)-M models, we document that statistically significant conditional mean and volatility spillover effects from the daytime returns of NASDAQ 100 index futures to both overnight returns and daytime returns of KOSDAQ 50 index futures were observed. We also find that there were information spillover effects from overnight returns of NASDAQ 100 index futures to daytime returns of KOSDAQ 50 index futures returns because investors in Korean stock markets can get information on U.S. stock market movement on real time basis due to the ECN transaction with its trading hour overlapped. Finally, we find that the daytime returns of KOSDAQ 50 index futures significantly influence the overnight and daytime returns of the NASDAQ 100 index futures.

The purpose of the paper is to investigate the price interrelationship between the Taiwanese and US financial markets.

The trivariate GJR‐GARCH (1,1) model and event study were employed to investigate volatility asymmetry and overreaction phenomenon, respectively.

The empirical results show that return volatility reveals the asymmetric phenomenon, and the holding period returns on US index futures from the opening of the US index futures electronic trading to the opening of the Taiwanese stock market are an important reference for investors in the Taiwanese stock market. Additionally, the paper presents an overreaction of the Taiwan Stock Exchange Capitalization Weighted Stock Index to a drastic price rise of E‐min NASDAQ 100 Index futures at the opening of the Taiwanese stock market.

This paper deletes the observations arising from the different national holidays of the USA and Taiwan, to have the same number of observations in both markets, which might contaminate the empirical results.

Investors in the Taiwanese stock market tend to pay more attention to the fluctuations in the share prices of high‐technological companies in the USA.

Most of the previous studies regarding price transmission between the Taiwanese and US stock markets focused mainly on the Taiwanese market reactions to the overnight returns of the US market. This paper enlarges the current field by examining the lead‐lag relationship, the volatility asymmetry, and the overreaction phenomenon between the Taiwanese and US financial markets according to the most updated US stock index information.

The purpose of this paper is to examine the impact of trades by informed traders and uninformed traders on the asymmetric volatility relation, a stylized fact that has long been puzzling financial economists. Avramov, Chordia, and Goyal's hypothesized that asymmetric volatility, defined as the negative relationship between daily volatility and lagged unexpected return, is governed by the trading dynamics of informed traders and uninformed traders. However, the hypothesis has not been directly tested due to lack of a measure for informed and informed trades. The authors aim to test the hypothesis using a direct measure for informed trades and uninformed trades.

The authors employ the Computer Trade Reconstruction (CTR) data of Nasdaq‐100 index futures for the period of 2002 through 2004. The dataset contains a unique variable distinguishing informed trades and uninformed trades, allowing the authors to directly examine the impact of the trades (i.e. selling activities) on the asymmetric volatility relation.

Based on the Nasdaq‐100 index futures data, the asymmetric volatility relation is driven by the selling activity of uninformed traders, particularly from small‐size trades. These results are only significant during the first half of the period, which is more volatile than the second half. The selling impact of informed traders on the asymmetric volatility relation is at most weak for both subperiods.

While risk and returns are important for asset pricing and risk management, most financial researchers consider them from a fundamental perspective. This paper's results suggest that selling activity of uninformed traders can significantly influence asset return and volatility and, hence, deserves more attention from the researchers.

The paper is the first to provide a direct test for Avmarov et al.'s hypothesis and shows that uninformed trades cause the asymmetric volatility. The authors contribute to ongoing discussions of how noise trading behavior can impact asset return and volatility.

With the rapid development of the financial market, stock index futures have been the one of important financial instruments. Predicting stock index futures accurately can bring considerable benefits for investors. However, traditional models do not perform well in stock index futures forecasting. This study put forward a novel hybrid model to improve the predictive accuracy of stock index futures.

This study put forward a multivariate deep learning framework based on extreme gradient boosting (XGBoost) for stock index futures price forecasting. First, the original sequences were decomposed into several sub-sequences by variational mode decomposition (VMD), and these sub-sequences were reconstructed by sample entropy (SE). Second, the gradient boosting decision tree (GBDT) was used to rank the feature importance of influential factors, and the top influential factors were chosen for further prediction. Next, reconstructed sequence and the multiple factors screened were input into the bidirectional gate recurring unit (BiGRU) for modeling. Finally, XGBoost was used to integrate the modeling results.

For the sake of examining the robustness of the proposed model, CSI 500 stock index futures, NASDAQ 100 index futures, FTSE 100 index futures and CAC 40 index futures are selected as sample data. The empirical consequences demonstrate that the proposed model can serve as an effective tool for stock index futures prediction. In other words, the proposed model can improve the accuracy of stock index futures.

In this paper, an innovative hybrid model is proposed to enhance the predictive accuracy of stock index futures. Meanwhile, this method can be applied in other financial products prediction to achieve better forecasting results.

The purpose of this study is to extend the work of DeFusco, Ivanov and Karels by examining pricing deviation of DIA, SPY and QQQQ on intradaily basis.

The DIA is designed to be one hundredth of the DJIA, the SPY is designed to be one tenth of the S&P 500 and QQQQ is designed to be one fortieth of the NASDAQ 100. This feature of ETFs requires the estimation of the difference between the proportional level of the index and the price of the ETF, which is the ETF pricing deviation.

The paper finds that the DIA, SPY and QQQQ pricing deviations are 0.0429, −0.0743 and 0.4298, respectively. The findings indicate that the prices of DIA and QQQQ are on average lower than the underlying indexes. SPY is the exception having a price which is higher than the theoretical price of the S&P 500 index. The author finds that this is due to the increased demand for the SPY. Additionally, the paper provides an explanation for the large change (increase) in the pricing deviation of QQQQ after December 1, 2004 which DeFusco, Ivanov and Karels could not explain. On December 1, 2004 QQQQ trading was consolidated on NASDAQ. The paper finds negative growth in the volume of QQQQ after December 1, 2004 indicating decrease in popularity of this ETF. The decrease in popularity of QQQQ might explain the increase in its pricing deviation.

The paper uses high frequency data in the analysis of pricing deviation which might be artificially deflating standard errors and thus inflating the t‐test significance values.

The paper contributes to the ongoing search in the finance literature of precision ETF performance metrics.

The purpose of this paper is to examine the effect of trading volume and open interest on volatility of futures markets. The authors capture the size and change in speculative behaviour in futures markets by examining the role of liquidity variables (trading volume and open interest) in the behaviour of futures prices.

The sample includes daily data covering the period 1996-2014 from 36 international futures markets (including currencies, commodities, stock indices, interest rates and bonds). The authors employ a two-stage estimation methodology: first, the authors employ a E-GARCH model and consider the asymmetric response of volatility to shocks of different sign. Further, the authors consider a regression framework to examine the contemporaneous relationships between volatility, trading volume and open interest. To quantify the percentage of volatility that is caused by liquidity variables, the authors also regress the estimated volatilities on the measures of open interest and trading volume.

The authors find that: market depth has an effect on the volatility of futures markets but the direction of this effect depends on the type of contract, and there is evidence of a positive contemporaneous relationship between trading volume and futures volatility for all futures contracts. Impulse-response functions also show that trading volume has a more relevant role in explaining market volatility than open interest.

These results are recommended to financial managers and analysts dealing with futures markets.

To the best of the authors’ knowledge, no study has yet considered a complete database of futures markets to investigate the empirical relation between price changes (volatility), trading volume and open interest in futures markets.

The purpose of this paper is to find if erosion of value exists in grantor trust structured exchange traded funds. The author examines the performance of six currency exchange traded funds’ tracking errors and pricing deviations on intradaily-one-minute interval basis. All of these exchange traded funds are grantor trusts. The author also studies which metric is of more importance to investors in these exchange traded funds by examining how these performance metrics are related to the exchange traded funds’ arbitrage mechanism.

The Australian Dollar ETF (FXA) is designed to be 100 times the US Dollar (USD) value of the Australian Dollar, the British Pound ETF (FXB) is designed to be 100 times the USD value of the British Pound, the Canadian Dollar ETF (FXC) is designed to be 100 times the USD value of the Canadian Dollar, the Euro ETF (FXE) is designed to be 100 times the USD value of the Euro, the Swiss Franc ETF (FXF) is designed to be 100 times the USD value of the Swiss Franc and the Japanese Yen ETF (FXY) is designed to be 10,000 times the USD value of the Japanese Yen. The author uses these proportions to estimate pricing deviations. The author uses a moving average model based on an Elton et al. (2002) to estimate if tracking error or pricing deviation are more relevant in ETF arbitrage and thus to investors.

The author documents that the average intradaily tracking errors for the six currency ETFs are relatively small and stable. The tracking errors are highest for the FXF, 0.000311 percent and smallest for FXB, −0.000014 percent. FXB is the only ETF with a negative tracking error. All six ETFs average intradaily pricing deviations are negative with the exception of the FXA pricing deviation which is a positive $0.17; the rest of the ETFs pricing deviations are −0.3778 for FXB, −0.3231 for FXC, −0.2697 for FXC, −0.2697 for FXE, −0.6484 for FXF and −0.9273 for FXY. All exhibit skewness, kurtosis, very high levels of positive autocorrelation and negative trends, which suggests erosion of value. The author also found that these exchange traded funds’ arbitrage mechanism is more closely related to the exchange traded funds’ pricing deviation than tracking error.

The paper uses high-frequency one-minute interval data in the analysis of pricing deviation which might be artificially deflating standard errors and thus inflating the t-test significance values.

The paper is relevant to ETF investors and contributes to the continuing search in the finance literature of better ETF performance metric.

Prior research on additions to the S & P 500 and the smaller MidCap 400 and SmallCap 600 indexes reach different conclusions regarding the key variables that explain the cross-section of announcement period abnormal returns. Most notable in this regard is that liquidity measures, long thought to be of importance, do not appear to explain abnormal returns of the S & P 500 when other factors are controlled for. By contrast, they do appear to matter for additions to the smaller stock indexes. To explore this difference, the purpose of this paper is to analyze the abnormal returns upon announcement that a stock will be added to the Nasdaq-100 Index in a cross-sectional manner, controlling for several possible alternative factors.

This paper analyzes abnormal returns upon announcement that a stock will be added to the Nasdaq-100 Index. The authors consider several possible sources of the positive price effects in a multivariate setting that controls simultaneously for measures of liquidity, arbitrage risk, operating performance and investor interest and awareness. The authors then analyze both trading volume and the bid-ask spreads. The authors finally examine analyst and investor interest, focussing on changes in analyst coverage.

The authors find that only liquidity variables are significant, but that factors representing feedback effects on the firm’s operations and level of managerial effort are not. The authors find that the average bid/ask spreads of stocks added to the Nasdaq-100 index are lower after the addition. The authors also find that the number of analysts following a stock increases significantly after addition, verifying increased analyst interest. Both forms of evidence are consistent with the hypothesis that the additions are associated with enhanced liquidity for the stocks.

The authors conclude that what does happen to a Nasdaq stock when it is announced that it will be added to the Nasdaq-100 Index is that more analysts are drawn to it, and its market liquidity is enhanced. The authors conclude that what does not happen is that there is no evidence of significant effects of enhanced managerial effort or operating performance associated with the inclusion. This difference is noteworthy because it suggests that a certification effect of additions to the S & P indexes associated with S & P’s selection process are unique to it and do not apply to the Nasdaq-100 Index additions based on market cap alone. The results provide indirect evidence on the existence and significance of the certification effect associated with additions to the S & P indexes.

The purpose of this paper is to study the impact of the relaxation of mark-to-market (MTM) standards on community banks’ share prices. Mark-to-market valuation of securities became increasingly common in the late 1990s and 2000s, as regulators sought to create more transparent and more current depictions of bank financial positions. However, MTM accounting may be sub-optimal in the presence of severe market frictions, such as those experienced during the financial crisis of the late 2000s. To comply with capital requirements associated with MTM accounting, banks of the late 2000s dramatically liquidated portfolios with potentially solvent assets in illiquid markets, taking huge losses. During the financial crisis, mortgage-backed securities held by banks began to plummet in value. Banks were forced to either liquidate these assets even though there were no buyers or dramatically reduce the values of their portfolios based on fire-sale prices. On a cash-flow basis, these securities had value, as many mortgages bundled in these securities continued to be paid on time; however, with markets frozen, market prices did not reflect this value.

This study shows that, for a sample of 134 community banks, share prices increased after the MTM relaxation, even after accounting for a variety of other economic factors.

This paper shows that, perhaps counterintuitively, the steps taken by the Financial Accounting Standards Board to relax MTM accounting standards may have acted as a stabilizing factor on the market price of community bank shares by allowing banks to selectively liquidate assets, boosting asset prices until uncertainty was resolved.

This paper examines the impact of recent changes in accounting standards on the perceived risks associated with the banking sector. It specifically focuses attention on the impacts these changes had on community-based banks within the USA.

The prediction of stock market (SM) indices is a fascinating task. An in-depth analysis in this field can provide valuable information to investors, traders and policy makers in attractive SMs. This article aims to apply a correlation feature selection model to identify important technical indicators (TIs), which are combined with multiple deep learning (DL) algorithms for forecasting SM indices.

The methodology involves using a correlation feature selection model to select the most relevant features. These features are then used to predict the fluctuations of six markets using various DL algorithms, and the results are compared with predictions made using all features by using a range of performance measures.

The experimental results show that the combination of TIs selected through correlation and Artificial Neural Network (ANN) provides good results in the MADEX market. The combination of selected indicators and Convolutional Neural Network (CNN) in the NASDAQ 100 market outperforms all other combinations of variables and models. In other markets, the combination of all variables with ANN provides the best results.

This article makes several significant contributions, including the use of a correlation feature selection model to select pertinent variables, comparison between multiple DL algorithms (ANN, CNN and Long-Short-Term Memory (LSTM)), combining selected variables with algorithms to improve predictions, evaluation of the suggested model on six datasets (MASI, MADEX, FTSE 100, SP500, NASDAQ 100 and EGX 30) and application of various performance measures (Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error(RMSE), Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE)).