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Filter trading is a technical trading rule that has been used extensively to test the efficient market hypothesis in the context of long-term trading. In this paper, the authors adopt the rule to analyze intraday trading, in which an open position is not left overnight. This paper aims to explore the relationship between intraday filter trading profitability and intraday realized volatilities. The bivariate thin plate spline (TPS) model is chosen to fit the predictor-response surface for high frequency data from the Hang Seng index futures (HSIF) market. The hypotheses follow the adaptive market hypothesis, arguing that intraday filter trading differs in profitability under different market conditions as measured by realized volatility, and furthermore, the optimal filter size for trading on each day is related to the realized volatility. The empirical results furnish new evidence that range-based realized volatilities (RaV) are more efficient in identifying trading profit than return-based volatilities (ReV). These results shed light on the efficiency of intraday high frequency trading in the HSIF market. Some trading suggestions are given based on the findings.

Among all the factors that affect the profit of filter trading, intraday realized volatility stands out as an important predictor. The authors explore several intraday volatilities measures using range-based or return-based methods of estimation. The authors then study how the filter trading profit will depend on realized volatility and how the optimal filter size is related to the realized volatility. The bivariate TPS model is used to model the predictor-response relationship.

The empirical results show that range-based realized volatility has a higher predictive power on filter rule trading profit than the return-based realized volatility.

First, the authors contribute to the literature by investigating the profitability of the filter trading rule on high frequency tick-by-tick data of HSIF market. Second, the authors test the assumption that the magnitude of the intraday momentum trading profit depends on the realized volatilities and aims to identify a relationship between them. Furthermore, the authors consider several intraday realized volatilities and find the RaV have the higher prediction power than ReV. Finally, the authors find some relationship between the optimal filter size and the realized volatilities. Based on the observations, the authors also give some trading suggestions to the intraday filter traders.

Intraday volatility characteristics throughout the trading week are examined at the emerging Borsa Istanbul (BIST) stock exchange. Using five-minute (and 15-minute) intervals, accentuated intraday volatility patterns at the microstructure level are examined during the stock market open and close in the morning and in the afternoon sessions. Volatility is highest when markets open in the morning. The second highest is during the afternoon open. The third highest is before the market closes for the day. Volatility before the market close has increased in recent years. These characteristics are seen every trading day. There are also differences: Monday returns are lowest, Friday returns are highest, and Monday morning volatility is highest of the entire trading week. Day-of-the-week and intraday accentuated volatility smile anomalies are jointly investigated using the longest intraday sample period in the emerging country stock exchange literature. Investment companies and professionals can utilize the results for risk management and hedging by avoiding highly volatile opening and closing periods. Arbitrageurs, speculators, and risk takers should trade during these highly volatile periods. Heightened volatility is increased difficulty in price discovery, thus inefficiency. Market participants, exchanges, and public prefer efficient markets. The research presents evidence of trading days, and periods during the trading day, when the exchange becomes more efficient. This is the first research that explores day-of-the-week effect from intraday volatility perspective in an emerging market, and provides useful recommendations in designing risk management strategies at market microstructure level.

This study investigates the impact of simultaneously replacing both midday single-price call auction and lunch break with multi-price continuous trading on intraday volatility–volume patterns as well as the intraday volatility–volume nexus.

The analysis utilises 150 m tick-by-tick transaction data related to 333 stocks traded on Borsa Istanbul Equity Market covering a period of 2 months prior to and following the change. In addition to graphic comparisons, the study uses difference in mean tests, panel-fixed generalized least squares (GLS), panel-random GLS and random-effects linear models with AR(1) disturbance regression estimations.

The results show that intraday volatility and trading volume form an inverse J-shape and are positively correlated. It is observed that the implementation of the regulation change decreased intraday volatility and increased trading volume. Additionally, the results indicate a negative volatility–liquidity and a positive volume–liquidity relationship, supporting the mixture of distribution hypothesis.

Enhanced market efficiency provides greater opportunity for investment and risk management. Investors can benefit from the findings on the intraday volatility–volume nexus, which is an indicator of informed trading, and regulatory authorities can use volume to oversight volatility.

This very rare regulation change of the simultaneous replacement of the lunch break and midday call auction with continuous trading is investigated in the context of intraday volume and volatility. This study also expands upon some important findings on the volume–volatility nexus for the Turkish Stock Market.

The purpose of this study is to analyze the overnight and intraday returns of the most traded American Depositary Receipts (ADRs) of Asian companies, understand the different levels of volatilities realized in these asynchronous markets and develop trading strategies based on empirical findings.

This study presents an empirical analysis on the overnight and intraday returns of Asian ADRs. The authors propose a measure to quantify the relative contributions of the intraday and overnight returns to the ADR's total volatility. Furthermore, the return difference between S&P500 index and each ADR is fitted to an Ornstein–Uhlenbeck model via maximum-likelihood estimation.

This study finds that ADRs' overnight returns are more volatile, whereas the intraday returns are significantly more strongly correlated with the US market returns. The return spreads between the S&P500 and ADRs are found to be a mean-reverting time series and motivate a pairs trading strategy.

The methodology used in this study is not limited to Asian ADRs and can be adapted to analyze the overnight and intraday returns of other non-Asian ADRs and stocks.

Investors should be aware of the overnight price fluctuations while intraday traders may consider strategies that capture the mean-reverting return spread between an ADR (or an Exchange-Traded Funds [ETF] of Asian stocks) and the S&P500 index ETF (SPY).

ADRs are among the most popular securities for investing in foreign (non-US) companies. The total global investments in ADRs are estimated to be close to US$1tn. Understanding the risks of ADRs is important to not only individual/institutional investors but also regulators.

This study provides a new measure to quantify and compare the relative contributions of volatility by overnight and intraday returns. Optimized pairs trading strategies involving ADRs and ETFs are developed and backtested.

The purpose of this paper is to study the relation between financial analysts’ ratings of firms’ disclosure policies and the intraday pattern in spreads between specialists’ bid and ask price quotes.

Measure of the disclosure policy is based on financial analysts’ ratings of the quality of firms’ annual reports, quarterly and other information, and investor relations activities. The bid‐ask spread is the ask price minus the bid price. Time‐weighted bid‐ask spreads were measured over half‐hour trading intervals. Generalized method of moments is used to estimate regressions of bid‐ask spreads on disclosure policy ratings and controls for trading volume, price volatility, and share price.

It was found that spreads are uniformly lower for firms with higher‐rated disclosure policies in all half‐hour trading intervals during the day. In addition, increases in spreads in the first two half‐hours of trading are smaller for firms with higher‐rated disclosures. Finally, our evidence suggests spreads increase more in the last half‐hour of trading for firms with better disclosure policies, and subsequent tests suggest this is due to greater end‐of‐day liquidity trading.

These results suggest that disclosure policy is a determinant of both the level and pattern of intraday bid‐ask spreads. Firms with higher‐rated disclosure policies have a more liquid market for their shares, which is theoretically linked to a lower cost of capital. In addition, better disclosure mitigates the decrease in market liquidity typically observed at the open of daily trading.

Better disclosures can help reduce market frictions.

This paper is the first to study the relation between disclosure policy and intraday spread patterns.

The purpose of this paper is to study the effect of different parts (predictable and impact) of different types of speculative behavior (intraday speculation, medium-term speculation and long-term speculation) on future fluctuations in the underlying index.

The authors input information about heterogeneous speculative behavior into the HAR-RV model to study the effect of different parts (predictable and impact) of different types of speculative behavior (intraday speculation, medium-term speculation and long-term speculation) on the future fluctuation of the underlying index.

The authors find that the increase in intraday speculation will exacerbate spot market volatility; and the expected increase of long-term value speculation can reduce market volatility, but the shock of speculation will exacerbate market volatility.

The authors suggest that regulators should strictly limit speculative intraday trading, and also focus on the long-term value speculation that decreases market volatility, in order to guide the benign development of the markets that stabilize abnormal market fluctuations.

First, in view of the correlation between the futures and spot markets, the authors put forward a new proxy for the speculation degree. Second, the authors input heterogeneous speculative behavior into the HAR-RV model to study the effects of different parts (predictable and impact) on different types of speculative behavior (intraday speculation, medium-term speculation and long-term speculation) on the future fluctuation of the underlying index.

The purpose of this paper is to study short sales trading as part of the New York Stock Exchange (NYSE) batch open and National Association of Securities Dealers Automated Quotations (NASDAQ) opening cross. The paper examines whether short transactions at the open can predict future returns.

The study tests to see if short transactions in the NYSE opening batch trade and NASDAQ opening cross are informative of future returns.

It is found that a stock's opening‐trade short volume is predictive of its short volume for the rest of trading day, positively related to its previous‐day price change, and positively related to its overnight price change at the opening trade on option‐expiration Fridays when the stock is part of the Standard and Poor (S and P) 500 index.

While previous research shows that intraday short sale trades are informative, this is the first paper to examine the opening trade of the day, and whether these short sales are informative.

Studies on transfers to a more regulated section show an increase in information disclosure and stocks’ liquidity levels. Classical theories suggest that volatility should also be reduced. This study aims to analyse the long-term effects of a section transfer to a more regulated section (TSE 1/TSE 2) on stock return volatility.

This study uses an empirical framework relying on two-sample t-tests and panel regressions. These use robust standard errors and control for fixed effects, day effects and macroeconomic factors. The return variance of comparable stocks’ benchmark sample, instead of market variance, is used as a control variable. Comparable stocks operate within the same industry and do not transfer during the sample period. The authors test our results’ robustness using generalized autoregressive conditional heteroskedasticity estimates.

The study’s main findings show that pre-transferred stocks are more volatile than the stocks’ benchmark sample. The transfer to a more regulated section leads to a gradual decrease in the total daily stock return volatility, intraday return volatility and overnight return volatility.

To the best of my knowledge, this study is the first to empirically address the volatility change caused by the stocks’ transfer to a more regulated section. This study highlights the benefits of choosing section transfers to reduce volatility.

The purpose of this paper is to examine what the optimal time is in a typical trading day for investors to buy/sell stocks in the Australian stock market.

The study mainly focuses on the S&P/ASX200. Each trading day, between 10:00 a.m. and 4:00 p.m., is divided into 30-min blocks. The effectiveness of easily implementable trading strategy to purchase the index in the morning and sell at the close is tested. The study controls for the excess overnight price volatility to improve the effectiveness of the investment strategy. This trading strategy is compared against other 66 possible day-trading combinations.

The results show that the trading strategy of buying in the first 30 min of the trading session and close off the position during the last 30 min obtains higher returns than other 66 strategies.

The day-trading strategy proposed in this study is very simple and therefore can be easily implemented by investors including individual investors.

To the best of our knowledge, this is the first study which constructs a trading strategy using the J- or U-shaped intraday return pattern.

The algorithmic trading has advanced exponentially and necessitates the evaluation of intraday stock market forecasting on the grounds that any stock market series are foreseen to follow the random walk hypothesis. The purpose of this paper is to forecast the intraday values of stock indices using data mining techniques and compare the techniques’ performance in different markets to accomplish the best results.

This study investigates the intraday values (every 60th-minute closing value) of four different markets (namely, UK, Australia, India and China) spanning from April 1, 2017 to March 31, 2018. The forecasting performance of multivariate adaptive regression spline (MARSplines), support vector regression (SVR), backpropagation neural network (BPNN) and autoregression (1) are compared using statistical measures. Robustness evaluation is done to check the performance of the models on the relative ratios of the data.

MARSplines produces better results than the compared models in forecasting every 60th minute of selected stocks and stock indices. Next to MARSplines, SVR outperforms neural network and autoregression (1) models. The MARSplines proved to be more robust than the other models.

Forecasting provides a substantial benchmark for companies, which entails long-run operations. Significant profit can be earned by successfully predicting the stock’s future price. The traders have to outperform the market using techniques. Policy makers need to estimate the future prices/trends in the stock market to identify the link between the financial instruments and monetary policy which gives higher insights about the mechanism of existing policy and to know the role of financial assets in many channels. Thus, this study expects that the proposed model can create significant profits for traders by more precisely forecasting the stock market.

This study contributes to the high-frequency forecasting literature using MARSplines, SVR and BPNN. Finding the most effective way of forecasting the stock market is imperative for traders and portfolio managers for investment decisions. This study reveals the changing levels of trends in investing and expectation of significant gains in a short time through intraday trading.