Frequency Trading Research Articles

Strategic liquidity provision in high-frequency trading

We construct a Kyle (1985)-type market model in which fast and slow traders are present. We perform numerical calculations after deriving the equilibrium condition, described as a simultaneous equation system. A major finding is that the fast trader, who has an advantage in trade frequency, acts as a liquidity provider, taking the opposite position against the slow trader if the difference in frequency is significant. Our theoretical results appear to be consistent with the empirical results of previous studies.

Review of Recent Research Directions and Practical Implementation of Low-Frequency Algorithmic Trading

Financial trading has undergone substantial technological evolution, with automation taking center stage, leading to approximately 80% of US market trades being executed by computer systems, predominantly by large financial institutions. The rise of algorithmic trading, poised to engage smaller entities, international markets, and individual traders, drives this article’s exploration of research in this field. Providing a comprehensive overview, it outlines the evolution of trading practices and defines algorithmic trading as a computer-powered tool aiding investment decisions. The article details the steps involved in algorithmic trading, covering opportunity identification, quantitative research, implementation, testing phases, and continuous monitoring. It also examines prevalent programming languages and open-source platforms facilitating algorithm development. Focusing on trading frequencies across financial instruments, it delves into high-frequency trading as a subset, alongside methodologies like technical and fundamental analysis, time series analysis, option trading strategies, and machine learning techniques used in algorithm creation. Categorized by trading frequencies, analytical approaches, involved financial instruments, and analysis objectives, the reviewed papers contribute insights into algorithmic trading’s diverse landscape and methodologies, offering valuable perspectives for industry participants and researchers alike.

Algorithm-Based Low-Frequency Trading Using a Stochastic Oscillator and William%R: A Case Study on the U.S. and Korean Indices

Using stochastics in stock market analysis is widely accepted for index estimation and ultra-high-frequency trading. However, previous studies linking index estimation to actual trading without applying low-frequency trading are limited. This study applied William%R to the existing research and used fixed parameters to remove noise from stochastics. We propose contributing to stock market stakeholders by finding an easy-to-apply algorithmic trading methodology for individual and pension fund investors. The algorithm constructed two oscillators with fixed parameters to identify when to enter and exit the index and achieved good results against the benchmark. We tested two ETFs, SPY (S&P 500) and EWY (MSCI Korea), from 2010 to 2022. Over the 12-year study period, our model showed it can outperform the benchmark index, having a high hit ratio of over 80%, a maximum drawdown in the low single digits, and a trading frequency of 1.5 trades per year. The results of our empirical research show that this methodology simplifies the process for investors to effectively implement market timing strategies in their investment decisions.

When is cross impact relevant?

Trading pressure from one asset can move the price of another, a phenomenon referred to as cross impact. Using tick-by-tick data spanning 5 years for 500 assets listed in the United States, we identify the features that make cross-impact relevant to explain the variance of price returns. We show that price formation occurs endogenously within highly liquid assets. Then, trades in these assets influence the prices of less liquid correlated products, with an impact velocity constrained by their minimum trading frequency. We investigate the implications of such multidimensional price formation mechanism on interest rate markets. We find that the 10-year bond future serves as the primary liquidity reservoir, influencing the prices of cash bonds and futures contracts within the interest rate curve. Such behaviour challenges the validity of the theory in Financial Economics that regards long-term rates as agents anticipations of future short term rates.

Profitability of technical trading rules in the Chinese stock market

This article examines the profitability of technical analysis on the Shanghai Stock Exchange Composite Index (SSEC) and the Growth Enterprise Market (GEM) Index, employing stepwise generalized error rate control procedures to address the data-snooping bias. Our comprehensive study encompasses 38,456 trading rules, extending beyond prior research by incorporating simple rules alongside their contrarian and complex counterparts. Upon rectifying data-snooping bias, we show that only a few complex rules outperform SSEC in different testing sub-periods, and no trading rules outperform GEM. As the outperforming rules are based on relatively high frequent trades, most lucrative rules yield negative returns in the out-of-sample periods once transaction costs were considered. Our new findings shed light on the efficiency of the Chinese stock market from 2010 to 2021.

When Vegas Comes to Wall Street: Associations Between Stock Price Volatility and Trading Frequency Amongst Gamblers

AbstractBoth gambling and trading involve risk-taking in exchange for potential financial gains. In particular, speculative high-risk high-frequency trading closely resembles disordered gambling behaviour by attracting the same individuals who tend to be overconfident, sensation-seekers, and attracted to quick large potential payoffs. We build on these studies via an incentivised experiment, in which we examine how manipulated levels of market volatility affected trading frequency. Gamblers (N=604) were screened based on the existence of household investments and recruited across the four categories of the Problem Gambling Severity Index. The volatility of stocks was manipulated between-participants (high vs. low). Participants traded fictitious stocks and were provided bonuses based on the results of their trading activity (M=US$4.77, range=[0, 16.99]). Participants traded more often in the high-volatility market, and this finding remained robust after controlling for financial literacy, overconfidence, age, and gender. Many investors trade more frequently than personal finance guides advise, and these results suggest that individuals are more likely to commit this error in more volatile markets. Exploratory analyses suggest that the effect of the volatility manipulation was strongest amongst gamblers who were at low-risk of experiencing gambling harms. As they might be otherwise considered low-risk, these individuals could be overlooked by protective gambling interventions yet nonetheless suffer unmitigated financial harms due to unchecked excessive trading.

Analisis Perbandingan Volume Perdagangan Saham, Abnormal Return dan Frekuensi Saham Perusahaan Sektor Energi Yang Terdaftar di Bursa Efek Indonesia Sebelum dan Sesudah Pelarangan Ekspor Batubara untuk Ketahanan Energi Dalam Negeri 2022

This study aims to find out and compare the trading volume activity, abnormal return and frequency of trading shares of energy companies before and after the announcement of the ban on export coal for Domestic Market Obligation in Indonesia.The population in this study are companies that are included in the Energy Sector listed at IDX. The samples in this study used purposive sampling, where only 17 companies met the criteria. In this study, the data used is 14 days before and after the announcement of the ban on export coal for Domestic Market Obligation in Indonesia. The hypothesis test used is Wilcoxon Signed Rank Test. The result showed that: 1) there is a significant differences in the trading volume and frequency trading share of sample companies before and after the ban on export for Domestic Market Obligation, 2) there is no significant difference in the abnormal return of sample companies before and after the ban on export for Domestic Market Obligation.

Modeling the Effects of Volatility and Uncertainty in Psychological Errors on Collective Decision-Making of Investors in the Iranian Capital Market

Objective: The aim of the present study is to model the effects of volatility and uncertainty in psychological errors on the collective decision-making of investors in the Iranian capital market. Methodology: To test the hypotheses, a sample consisting of 204 companies from those listed on the Tehran Stock Exchange was selected using the systematic elimination method over a 5-year period from 2018 to 2022. The research data were collected on a monthly basis with a panel data approach and were analyzed using three multiple linear regression models. These models were calculated using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) and Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) methods, with the help of Excel 2016 and Eviews13 software for statistical analysis. Findings: According to the results of the statistical tests of the research hypotheses, the volatility of the psychological error index and investors' trading behavior has a significant and negative impact on abnormal stock returns. Additionally, the volatility of the psychological error index has a significant and positive impact on the volume and frequency of abnormal stock trades. However, the volatility of investors' trading behavior has a significant and negative impact on the volume and frequency of abnormal stock trades. Conclusion: The results of the analysis of the EGARCH and TGARCH conditional heteroskedasticity models indicate asymmetry in the influence of collective investment decisions from past data and past volatility, which is consistent with the Prospect Theory that claims positive and negative returns have different effects on investor utility.

A Wavelet Analysis of Bitcoin Price Volatility Dynamic

The cryptocurrency market has experienced significant growth in international finance in recent years, attracting a large number of investors. This has led to a substantial increase in the overall trading volume[1], surpassing 1.2 trillion USD in July2023. Bitcoin in particular, has garnered significant interest from both advanced and emerging economies. Notably, Bitcoin is legal tender in El Salvador and the Central African Republic. The cryptocurrency market possesses distinct characteristics that set it apart from traditional markets such as exchanges, commodities, and equities. Its decentralization, facilitated by Blockchain technology, stands as a key differentiating factor. This technology enables anonymous trading of cryptocurrencies, with the identities of market participants, account holders, and electronic wallet managers remaining unknown. Consequently, there is an inherent ambiguity when it comes to characterizing the behavior of these investors or determining their preferred trading horizons. In this context, an important question arises: how do investors in the cryptocurrency market navigate price volatility? Furthermore, how do their trading strategies, which are inherently tied to their investment horizons, impact each other and consequently influence market prices? The heterogeneity hypothesis, which is a prevalent assumption in financial markets, is particularly relevant when exploring how investors in the cryptocurrency market navigate price volatility. The diverse trading strategies and approaches to handling price fluctuations among heterogeneous investors impact and influence the overall volatility and price dynamics of cryptocurrencies. The heterogeneity market hypothesis[2] refers to the existence of differences among investors or traders regarding their beliefs, information, risk preferences, investment strategies, and time horizons. In a heterogeneous market, investors, traders and financial institutions have varying views and expectations about the future performance of assets. These differences can manifest in various ways, such as market information access, market liquidity provision, trading strategies and market structure. While numerous empirical studies have explored the concept of market heterogeneity in relation to traditional assets such as those conducted by Müller et al. (1993;1997), Lux and Marchesi (2000), LeBaron (2000), Dacorogna et al. (2001) and Benhmad (2011), there is a paucity of research on this topic specifically focusing on cryptocurrencies. This paper aims to contribute to the existing literature that investigates the hypothesis of investor heterogeneity in traditional markets, including forex, stocks, and commodities. Specifically, we focus on the cryptocurrency market, which is predominantly dominated by Bitcoin. As of November 2021[3], Bitcoin holds the distinction of being the most actively traded cryptocurrency, with a market capitalization exceeding 1.2 trillion USD. Given its significance, Bitcoin garners substantial interest from investors across various categories. Therefore, this research seeks to uncover potential synergies among participants[4] in the cryptocurrency market and identify investor types and investment horizons prevalent in this market. By understanding these factors, we can gain insights into the dynamic volatility displayed by Bitcoin’s price. To tackle this issue, we investigate Bitcoin’s price volatility, analyzing causal relationships among short-term, medium and long-term traders by using wavelet transform to decompose volatility at different trading frequency scales[5] considered, then Granger causality test will be employed to determine if changes in one scale impact others. Furthermore, we extend this analysis by employing the nonlinear causality test proposed by Hmamouche. Y (2020). This nonlinear causality test goes beyond the limitations of the linear causality test and helps identifying potential nonlinear causal effects that may exist between the volatilities at different frequency scales, which could be overlooked by the linear causality test. The rest of this paper is organized as follows: Section 2 presents the theoretical framework relatively to the heterogeneity market hypothesis. Section 3 focuses on the empirical review. Section 4 and 5 turns to the data and methodology. Section 6 provides the empirical findings. Section 7 concludes.

Hotspots of zoonotic disease risk from wildlife hunting and trade in the tropics

AbstractWildlife hunting and trade creates numerous direct human–animal contact opportunities that can facilitate transmission of zoonotic diseases pantropically. Yet, there is a lack of knowledge of which species and areas have a high risk of zoonotic disease spillover due to wildlife hunting and trade. We combined maps of hunting pressure, recent data sets on human‐shared pathogens, and trade volumes for 614 mammalian host species to map zoonotic disease risk from wildlife hunting and trade pantropically. We also assessed the relationship between zoonotic disease risk from hunting and probability of emerging zoonotic diseases using generalised least square regression. We identified hotspots of zoonotic disease risk from hunting in central America, China, and central Africa. When considering wildlife trade frequency, high risk occurs in southern Africa and China. Zoonotic disease risk from wildlife hunting aligns significantly with the risk of zoonotic emerging infectious diseases, suggesting that hunting is a key driver of zoonotic disease outbreaks of pandemic potential. Interventions that regulate the hunting of host species and considers the socioeconomic motivations behind hunting could reduce the risk of zoonotic disease spillover.

THE INFLUENCE OF FINANCIAL CONFIDENCE, FINANCIAL SOCIALIZATION, HERDING, AND MENTAL ACCOUNTING ON INVESTMENT DECISION AMONG GENERATION Z IN JAKARTA

This research purpose is to examine the influence of financial confidence, financial socialization, herding, and mental accounting on investment decision among Generation Z in Jakarta. The samples for this research are investors who were born between 1995-2010 (Generation Z) and living in Jakarta. The cluster sampling method was used to collect 205 respondents from a survey that was shared online on social media through google forms. The data of this research is analyzed through the use of a structural equation system using the SmartPLS.3.2.9 program. The result of this research shows that the investment decision of Gen Z investors in Jakarta is significantly influenced by financial confidence, financial socialization, herding, and mental accounting. This research also found that a lower frequency of trading which happens in Gen Z investors tends to overcome the negative impact of herding.

Differences In Stock Trade Frequency Due To Announcement Of Large-Scale Social Restrictions (Psbb) Due To Covid-19 In Transportation Companies Listed On The Indonesia Stock Exchange

Differences In Stock Trade Frequency Due To Announcement Of Large-Scale Social Restrictions (Psbb) Due To Covid-19 In Transportation Companies Listed On The Indonesia Stock Exchange

The effect of financial literacy on long-term recognition and short-term trade in mutual funds: Evidence from Japan

We explore how financial literacy affects mutual fund investments through unique survey data from a broad spectrum of Japanese households. Our evidence reveals the dual effects of literacy on investments over long and short horizons. While the acquisition of financial knowledge promotes long-term investment in mutual funds, it also has the side effect of encouraging short-term investment. Specifically, people with high financial literacy share a fundamental recognition that mutual funds are suitable for long-term wealth creation. However, highly literate people intend to sell mutual funds immediately after short-term price appreciation, which could lead to frequent trading turnover. This is consistent with the knowledge bias effect whereby knowledgeable people falsely react to information, contrary to long-term investments.

Institutional Investors, CSR Report Readability and the Moderating Role of ESG Performance

This study examines the impact of institutional investors on the readability of corporate social responsibility (CSR) reports in the Chinese polluting industry. All available CSR reports in the listed polluting industries in China from 2010 to 2019 are analyzed to develop a readability score for each company. To differentiate the heterogenous motivation of institutional investors, we classify institutional investors into long-term and short-term investors based on their holding purpose and trading frequency. The results show that long-term institutional investors with long-term horizons have strong motivation to engage in the CSR reports disclosure. However, short-term institutional investors pursue near-term interests and they may have weak incentives to participate in the corporate governance and improve CSR report readability. Furthermore, we also find that the positive relationship between long-term institutional investors and CSR readability is stronger in polluting firms with higher environmental, social, and governance (ESG) performance. The significant role of institutional investors in promoting CSR reporting highlights the importance for polluting firms to acknowledge the needs of large and influential investors.

Can Lexicographic Goal Programming, Artificial Neural Networks, and Value-at-risk Methods be Effective in Analysis of 50 Highest Trade Frequency Issuers Optimum Portfolio

This study aims to describe the optimal stock portfolio as a series of processes for selecting a stock portfolio that suits the needs and desires of investors. The variable used is the daily closing price of the stock exchanges listed on the Indonesia Stock Exchange for the period September 1, 2016 to September 1, 2022 for 50 stocks with the highest trading frequency in the first quarter of 2022 using the value at risk, lexicographic goal method, programming, neural networks, and artificial neural networks. It is known that the value-at-risk method in generating shortfalls is carried out using a valuable approach that exceeds the threshold where the assumption of a 5% tail minimum return is generalized with the Pareto distribution. The input of the neural network in producing the output obtained through the PACF and ACF plots is t1. Issuer price forecasts are obtained using the neural network method and the backpropagation algorithm for each issuer, an SSMS of Rp. 1808.3, ICBP of Rp. 12,717, ASII Rp. 6832.1, BBNI of Rp. 6234.6, INCO of Rp. 2781.8, CPIN of Rp. 12,763, TLKM worth 2630.1, SCMA Rp. 2885.5, and WSKT worth Rp. 1575.7. The issuers obtained have a small MAPE value, which is below 10%. Programming the lexicographical objectives resulted in the nine selected issuers having a minimum standard of risk with maximum returns. Of the nine stocks selected, CPIN, SSMS, BBNI, ICBP, TLKM, ASII, INCO, SCMA and WSKT.
 Keywords: return, value at risk, lexicographic goal programming, backpropagation

Does Corporate Governance Quality Influence Insider Trading around Private Meetings between Managers and Investors?

SYNOPSIS We examine the effectiveness of corporate governance in influencing insider trading around private in-house meetings (hereafter “private meetings”) between management and investors in China. Consistent with better corporate governance curbing (1) disclosure of nonpublic price-sensitive information and (2) insider trading, we find that better governance quality is associated with reduced insider trading frequency, value, and profitability around private meetings. Firms with better corporate governance appear to exchange less price-sensitive information with outsider investors around private meetings, which limits the opportunity to make profitable insider trades. Our results are economically significant and robust using instrumental variable and propensity score matching approaches to address endogeneity. We argue that improving corporate governance quality may be a partial substitute for costly government regulation designed to curb insider trading around private meetings. JEL Classifications: G34; G14; G18.

Satu Dekade IFRS dan Relevansi Nilai Laba pada 50 Biggest Market Capitalization

<p><em>This study aims to examine whether the convergence of the International Financial Reporting Standards (IFRS) can increase the Value Relevance of earnings on the financial statements of companies listed on the Indonesia Stock Exchange (ISE). This study covers the period before the IFRS convergence (pre- IFRS) in 2010 and after the IFRS convergence (post-IFRS) in 2019. The research sample is a public company that is included in the 50 largest market capitalization, 50 biggest trading frequencies, 50 biggest trading volumes, and 50 biggest trading values. The study was conducted by comparing the adjusted R square value in multiple linear regression models in the period before and after IFRS, and comparing the adjusted R square value between net income and comprehensive income after IFRS. The results of the study prove that there is an increase in the relevance of the value of earnings after the implementation of IFRS and comprehensive income has a value relevance that is higher than net income. The results of the study prove that there is an increase in the relevance of the value of earnings after the implementation of IFRS and comprehensive income has a value relevance that is higher than net income.</em></p>

Psychological profile and investment decisions

We conduct a study to test whether psychological factors influence stock trading behavior in a sample of 176 individuals. Using a trading simulation game, we combined financial data with demographic information, psychological traits, and risk attitudes. Our findings indicate that conscientiousness is a significant factor in explaining higher trading volume and greater risk-taking. Furthermore, demographics and risk attitude measures influenced individual investment choices. These results demonstrate that financial decision-making and the frequency of stock trading are influenced not only by our attitudes toward risk but also by our level of conscientiousness. These findings have important implications for modeling decisions under risk and providing financial advice.

Market-making with search and information frictions

We develop a dynamic model of trading through market-makers that incorporates two canonical sources of illiquidity: trading (or search) frictions, which imply that investors can rebalance their portfolio only with a delay; and information frictions, which imply that market-makers face some degree of adverse selection. We use this model to study the effects of various technological innovations and regulatory initiatives that have reduced trading frictions in over-the-counter markets. Our main result is that reducing trading frictions can lead to less liquidity, as measured by bid-ask spreads. The key insight is that more frequent trading makes investors' behavior less dependent on asset quality. As a result, dealers learn about asset quality more slowly and set wider bid-ask spreads to compensate for this increase in uncertainty. We also show that widening bid-ask spreads do not necessarily correspond to a decline in trading volume or welfare.

Analisis Frekuensi Perdagangan, Volume Perdagangan dan Kapitalisasi Pasar Terhadap Return Saham Pada Perusahaan Perbankan BUMN Terdaftar di Bursa Efek Indonesia

This study aims to determine whether there is an influence on the return of banking stocks in the BUMN sector, which is seen based on trading frequency, trading volume and market capitalization. This type of quantitative research uses the event study method. The population and sample used are all BUMN banks listed on the Indonesia Stock Exchange. This hypothesis testing is a different test using the Wilcoxon Test, namely the Z test, through SPSS. The results showed that there was no significant influence between the frequency and volume of trading with stock returns, but market capitalization had a significant influence on stock returns. While simultaneously on the overall influence of independent variables on stock returns in BUMN banking companies.