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This paper aims to investigate the quickness, and test the accuracy, of liquidity taking high-frequency traders (HFT). This gives us important insights into a class of market participant who has come to be very influential in present markets.

The authors use the weekly natural gas (NG) storage report for the test because the information contained in the release often has a large effect on prices. Moreover, the NG market is heavily traded and liquid, and prone to high volatility. These factors make trading in this market attractive to HFT. The authors test for the profitability of those who trade in the first milliseconds after the report’s release; and for information leakage prior to the report.

The authors find those who trade within the first 50 ms accurately incorporate the information contained in the storage report into prices, and earn the majority of profits. In fact, HFT profits are decreasing in the time it takes them to trade after the announcement (measured to 200 ms). Further tests find no evidence of informed trading prior to the release of the report, and so the HFT reaction to the report incorporates the information contained therein into prices.

This is one of the few analyzes of the profitability of liquidity-taking HFT, and the only analysis that uses millisecond NG data. The data used is the exchanges original FIX/FAST messages.

To alert high frequency trading firms to the increased regulation and prosecution of manipulative trading practices during 2014 and early 2015.

Reviews four significant proceedings against high frequency trading firms (and/or individuals employed by such firms) and other developments from the relevant government agencies as a possible preview of the enforcement and prosecution of high frequency trading practices in 2015. Provides advice to high frequency trading firms on how to decrease the risk of regulatory or criminal actions against them in this changing environment.

Although the focus on high frequency trading has only recently begun to intensify, firms should be aware of the increased enforcement activity of the past year. These actions, both regulatory and criminal, have already resulted in large penalties and have helped initiate a strengthening of rules and regulations regarding manipulative trading practices, of which firms need to be aware and stay current.

High frequency trading firms should be aware of the recent regulatory and criminal actions in order to better evaluate their own practices and controls, to ensure that their trading patterns do not resemble manipulative practices, and to avoid similar actions.

Practical guidance from experienced litigators and securities regulatory lawyers, including a former SEC Assistant Chief Litigation Counsel and a former federal prosecutor, that consolidates and describes several recent actions and developments in one piece.

Are firms able to time the market? The purpose of this paper is to focus on the study of own stock trading, emphasizing both repurchase and resell operations on the open market as well as over the counter.

The authors use data on 37,997 own stock transactions from 2005 to 2015 of Euronext Lisbon listed firms. Following Dittmar and Field (2015), this paper uses relative transaction prices to ascertain the relative performance of own stock transactions, in the open market and over the counter.

Results show that firms can time both repurchases as well as resales. Firms repurchase (resell) at lower (higher) prices than those prevailing in the market. Moreover, market-timing ability proves to be higher after the bailout period and to be influenced by the own stock trading frequency. Trading on the open market allows for increased timing ability for own stock repurchasing and reselling activity. Finally, results show seasonal effects both in repurchase and resale performance. Also, more efficient but less valuable firms are more likely to be successful in timing the market.

The authors study both the repurchasing and the reselling activity of the same set of firms, of already issued stock, using high-frequency (daily) data. In addition, the authors study own stock trading both in the open market and OTC, and also study the impact of a major economic shift on the firms’ ability to time the market.

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.

The main objective of this study is to obtain new empirical evidence on non-synchronous trading effects through modelling the predictability of market indices.

The authors test for lead-lag effects between the Indian Nifty and Nifty Junior indices using Pesaran–Timmermann tests and Granger-Causality. Then, a simple test on overnight returns is proposed to infer whether the observed predictability is mainly attributable to non-synchronous trading or some form of inefficiency.

The evidence suggests that non-synchronous trading is a better explanation for the observed predictability in the Indian Stock Market.

The indication that non-synchronous trading effects become more pronounced in high-frequency data suggests that prior studies using daily data may underestimate the impacts of non-synchronicity.

The originality of the paper rests on various important contributions: overnight returns is looked at to infer whether predictability is more attributable to non-synchronous trading or to some form of inefficiency; the impacts of non-synchronicity are investigated in terms of lead-lag effects rather than serial correlation; and high-frequency data is used which gauges the impacts of non-synchronicity during less active parts of the trading day.

The purpose of this paper is to investigate the impact of the frequency of information acquisition on the frequency of stock trading. The authors also examined if the Big Five personality traits of investor influence the association between information acquisition and stock trading behavior.

The authors adopted NEO Five-Factor Inventory (Costa and McCrae, 1989) inventory to measure the Big Five personality traits of investors and examined the data collected from 541 individual investors of the Chinese stock market. To overcome the potential endogeneity bias, the authors followed two-stage least square method for estimating endogenous covariate by employing instrumental variable analysis. The authors performed probit regression to evaluate the moderating influence of investor personality traits on the association between information acquisition and stock trading behavior. The authors also performed several other tests to check the robustness of the key findings.

This research confirmed the previous findings that the more frequently investors acquire information, the more often they trade in stocks. Moreover, the authors added to the existing literature by providing empirical evidence that the Big Five personality traits moderate the relationship of information acquisition with stock trading behavior. Information acquisition tends to increase stock trading frequency in investors with conscientiousness, extraversion and agreeableness traits. On the other hand, it also has the tendency to decrease the intensity of stock trading in investors with openness and neuroticism traits.

The theoretical model in this study seeks to explain that the psychological factor, namely, investor personality, influences the way an investor interprets signals from information which in turn influences the investor decision to trade in securities. This research suggests that psychological characteristics of investors can be of relevance for policy makers in their attempts to improve their business in the financial services industry.

This study combines both information search literature and behavioral finance literature to investigate whether or not the information acquisition that relates to investors’ asset allocation decisions is influenced by investor personality. The study offers new theoretical insights into investors’ behavior due to the characteristics of the Chinese stock market which are uniquely different from other stock markets in the world. No previous study has been conducted so far in the Chinese stock market to explore variations in the impact of investors’ information acquisition on their stock trading by the Big Five personality and this paper strives to fill this research gap.

The paper's aim is to explore the impact of statistical arbitrage and high-frequency trading as hedge fund investment strategies that have a significant impact on the environment of corporations.

The paper is a meta-analysis of the role of investment strategies within complex systems.

The growth of hedge fund investment activity based on statistical arbitrage tends to produce a vulnerability; more funds using the strategy helps to create the profitable outcomes that the strategy relies upon. However, the growth also reduces the time lines of profitability and produces an underlying instability based on overlapping holdings and the use of leverage. The shortened timelines also create a further impetus towards technological competition and promotes high frequency trading, which then introduces further vulnerabilities based on “stop-loss cascades”.

Much of the trading creates a superficial form of liquidity, which gives a limited sense of market vulnerabilities. The basis of complex interactions between high frequency traders is also not clearly understood. Researchers and agents of policy ought to pay greater attention to the issues than is currently the case.

The area is one that is under-researched.

The purpose of this study is to identify appropriate autoregressive conditional duration (ACD) models that can capture the dynamics of tick-by-tick mid-cap exchange traded funds (ETFs) for the period July 2017 to December 2017 and accurately predict future trade duration values. The forecasted durations are then used to demonstrate the practical usefulness of the ACD models in quantifying an intraday time-based risk measure.

Through six functional forms and six error distributions, 36 ACD models are estimated for eight mid-cap ETFs. The Akaike information criterion and Bayesian information criterion and the Ljung-Box test are used to evaluate goodness-of-fit while root mean square error and the Superior predictive ability test are applied to assess forecast accuracy.

The Box-Cox ACD (BACD), augmented Box-Cox ACD (ABACD) and additive and multiplicative ACD (AMACD) extensions are among the best fits. The results obtained prove that higher degrees of flexibility do not necessarily enhance goodness of fit and forecast accuracy does not always depend on model adequacy. BACD and AMACD models based on the generalised-F distribution generate the best forecasts, irrespective of the trading frequencies of the ETFs.

To the best of the authors’ knowledge, this is the first study that analyses the empirical performance of ACD models for high-frequency ETF data. Additionally, in comparison to previous works, a wider range of ACD models is considered on a reasonably longer sample period. The paper will be of interest to researchers in the area of market microstructure and to practitioners engaged in high-frequency trading.

An agent-based market simulation is utilized to examine the impact of high frequency trading (HFT) on various aspects of the stock market. This study aims to provide a baseline understanding of the effect of HFT on markets by using a paradigm of zero-intelligence traders and examining the resulting structural changes.

A continuous double auction setting with zero-intelligence traders is used by adapting the model of Gode and Sunder (1993) to include algorithmic high frequency (HF) traders who retrade by marking up their shares by a fixed percentage. The simulation examines the effects of two independent factors, the number of HF traders and their markup percentage, on several dependent variables, principally volume, market efficiency, trader surplus and volatility. Results of the simulations are tested with two-way ANOVA and Tukey’s post hoc tests.

In the simulation results, trading volume, efficiency and total surplus vary directly with the number of traders employing HFT. Results also reveal that market volatility increased with the number of HF traders.

Increases in volume, efficiency and total surplus represent market improvements due to the trading activities of HF traders. However, the increase in volatility is worrisome, and some of the surplus increase appears to come at the expense of long-term-oriented investors. However, the relatively recent development of HFT and dearth of appropriate data make direct calibration of any model difficult.

The simulation study focuses on the structural impact of HF traders on several aspects of the simulated market, with the effects isolated from other noise and problems with empirical data. A baseline for comparison and suggestions for future research are established.

The purpose of this paper is to explain what information is contained in mutual funds' trading behaviors and to try to further assess the impact on the stock market.

The objective is achieved by an empirical examination using the high‐frequency intraday data. The main methods used for the research are the autoregressive conditional duration model and the UHF‐GARCH model.

This paper gives an empirical study of mutual funds' behavior on two aspects. The first aspect is the direct impact on micro variables. The results show that mutual funds changing their positions will have different influences to the spread, adding position broadens the spread, while decreasing position makes the spread narrow; behaviors of funds change the clustering characteristic of the duration. The second aspect is the impact on the relationships among micro variables. The results indicate that trading started by liquidity buyers will make volatility larger.

This paper supposes funds as informed traders and individual investors as liquidity traders in China's stock market. If it is not true, some interpretations of empirical results would be wrong. The authors' results may help researchers to understand the information content of funds' trading behaviors in the microstructure aspect.

The paper is an original work, which will be interesting to scholars in market microstructure and to practitioners in the Chinese stock market. The main contributions of the paper are: the use of high‐frequency data to study funds' behaviors and combine the trading duration and investors' trading behavior to analyze the information content of trading behaviors; second, the use of 14 stock samples in the Shanghai Stock Exchange to do the empirical study, which ensures the reliability of the results.