Market Microstructure Research Articles

High‐Frequency Instruments and Identification‐Robust Inference for Stochastic Volatility Models

ABSTRACTWe introduce a novel class of stochastic volatility models, which can utilize and relate many high‐frequency realized volatility (RV) measures to latent volatility. Instrumental variable methods provide a unified framework for estimation and testing. We study parameter inference problems in the proposed framework with nonstationary stochastic volatility and exogenous predictors in the latent volatility process. Identification‐robust methods are developed for a joint hypothesis involving the volatility persistence parameter and the autocorrelation parameter of the composite error (or the noise ratio). For inference about the volatility persistence parameter, projection techniques are applied. The proposed tests include Anderson‐Rubin‐type tests and their point‐optimal versions. For distributional theory, we provide finite‐sample tests and confidence sets for Gaussian errors, establish exact Monte Carlo test procedures for non‐Gaussian errors (possibly heavy‐tailed), and show asymptotic validity under weaker assumptions. Simulation results show that the proposed tests outperform the asymptotic test regarding size and exhibit excellent power in empirically realistic settings. The proposed inference methods are applied to IBM's price and option data (2009–2013). We consider 175 different instruments (IVs) spanning 22 classes and analyze their ability to describe the low‐frequency volatility. IVs are compared based on the average length of the proposed identification‐robust confidence intervals. The superior instrument set mostly comprises 5‐min HF realized measures, and these IVs produce confidence sets which show that the volatility process is nearly unit‐root. In addition, we find RVs with higher frequency yield wider confidence intervals than RVs with slightly lower frequency, indicating that these confidence intervals adjust to absorb market microstructure noise. Furthermore, when we consider irrelevant or weak IVs (jumps and signed jumps), the proposed tests produce unbounded confidence intervals. We also find that both RV and BV measures produce almost identical confidence intervals across all 14 subclasses, confirming that our methodology is robust in the presence of jumps. Finally, although jumps contain little information regarding the low‐frequency volatility, we find evidence that there may be a nonlinear relationship between jumps and low‐frequency volatility.

Bibliometric Study of the Impact of Algorithmic Trading on Market Microstructure: Trends Patterns and Implications

Bibliometric Study of the Impact of Algorithmic Trading on Market Microstructure: Trends Patterns and Implications

A Hybrid LSTM-KNN Framework for Detecting Market Microstructure Anomalies:

This paper proposes a novel hybrid LSTM-KNN framework for detecting market microstructure anomalies in high-frequency credit default swap (CDS) markets. The framework integrates the temporal learning capabilities of Long Short-Term Memory networks with the pattern recognition strengths of K-Nearest Neighbors classification to identify price jumps and market anomalies. Through analysis of high-frequency CDS market data spanning from 2020 to 2023, encompassing over 2.5 million data points from five major CDS indices, the research demonstrates significant improvements in jump detection accuracy. The hybrid model achieves a 92.8% accuracy rate, representing a 15.2% improvement over traditional statistical methods and an 8.5% enhancement compared to standalone deep learning approaches. The framework maintains computational efficiency with an average processing latency of 48.2 milliseconds, enabling real-time market applications. The empirical analysis reveals strong correlations between detected jumps and market liquidity conditions, with bid-ask spreads and order book imbalances identified as critical predictive indicators. The research contributes to both theoretical understanding of market microstructure dynamics and practical applications in risk management and market surveillance.

Assessment of a Counterintuitive Trading Strategy Based on Market Microstructure

In general, day trading has little support from empirical research and a justified reputation of being dangerously unsound. It often relies on positive price momentum, regardless of the underlying value of the stock. Here, we assess multiple versions of a protocolized day trading strategy that relies on overreaction to bad corporate news, as reflected in premarket prices. Relevant stocks are purchased at the open, when there is an imbalance between buy and sell orders, and held for no longer than a single trading day. The companies in question constitute a basket of deplorables, and the trading strategies rely on mean regression rather than momentum, and also benefit from high levels of inter-day price volatility as the process of price discovery proceeds. Observed returns were positive for all versions of the strategy. Conceptual support is provided by the notion that active money managers have individual incentives to immediately rush toward the door, and those incentives should continue into the future. In conclusion, we have described an unusual and counterintuitive strategy that relies on a conceptually plausible, yet rare and fleeting market inefficiency. It is based on the microstructure of the stock market.

The Next Chapter of Big Data in Finance

Abstract The second special issue on big data in finance showcases advancements in research related to data of large size, high dimension, and complex structure since the first NBER/RFS big data conference. The papers published in this next chapter address some questions that were proposed in the initial special issue in 2021. Other papers are more directly connected to recent developments in the market. We discuss some new research directions, following on the papers published here. They include evaluating market microstructure reforms, understanding medium-frequency trading, improving missing data imputations, and deepening data valuation. We look forward to more developments to follow.

Multi-Agent Reinforcement Learning for High-Frequency Trading Strategy Optimization

This study presents a novel multi-agent reinforcement learning (MARL) framework for optimizing high-frequency trading strategies. The proposed approach leverages the StarCraft Multi-Agent Challenge (SMAC) environment, adapted for financial markets, to simulate complex trading scenarios. We implement a Value Decomposition Network (VDN) architecture combined with the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to coordinate multiple trading agents. The framework is evaluated using high-frequency limit order book data from the FI-2010 dataset, augmented with derived features to capture market microstructure dynamics. Experimental results demonstrate that our MARL-based strategy significantly outperforms traditional algorithmic trading approaches and single-agent reinforcement learning models. The strategy achieves a Sharpe ratio of 2.87 and a maximum drawdown of 12.3%, showcasing superior risk-adjusted returns and robust risk management. Comparative analysis reveals a 9.8% improvement in annualized returns over a single-agent Deep Q-Network approach. Furthermore, the implementation of our strategy shows a positive impact on market quality metrics, including a 2.3% decrease in effective spread and a 15% reduction in price impact. These findings suggest that the proposed MARL framework not only enhances trading performance but also contributes to market stability and efficiency in high-frequency trading environments.

Forecasting Digital Asset Return: An Application of Machine Learning Model

ABSTRACTIn this study, we aim to identify the machine learning model that can overcome the limitations of traditional statistical modelling techniques in forecasting Bitcoin prices. Also, we outline the necessary conditions that make the model suitable. We draw on a multivariate large data set of Bitcoin prices and its market microstructure variables and apply three machine learning models, namely double deep Q‐learning, XGBoost and ARFIMA‐GARCH. The findings show that the double deep Q‐learning model outperforms the others in terms of returns and Sortino ratio and is capable of one‐step‐ahead sign forecast of the returns even on synthetic data. These critical insights in forecasting literature will support practitioners and regulators to identify an economically viable cryptocurrency forecasting return model.

Fear, extreme fear and U.S. stock market returns

This paper investigates the multiscale co-movement between the levels and changes in VIX and SKEW indices and the returns of the S&P 500 market indices at the aggregate and sector levels of data. By utilizing the wavelet localized multiple correlation (WLMC) method over the most recent time period (January 3, 2006 - October 26, 2023), we demonstrate that very short-term market participants, such as traders and speculators, are watchful about the combined information from the VIX and SKEW levels; while the changes in VIX and SKEW are more informative for stock returns in the medium- to long-term, which should concern the decisions of investors and portfolio managers. We also show that VIX in general exerts a more dominant influence on the S&P 500 Index returns than SKEW, but more for VIX changes than for VIX levels. The very short-run informativeness of VIX and SKEW for stock returns is markedly pronounced around the European debt crisis (2012) and the COVID-19 meltdown (2020). The stock sector analysis reveals that defensive sectors are sensitive to the SKEW index, whereas the cyclical sectors are predominantly related to the fluctuations in the VIX index. Overall, our findings provide useful new insights into the stock market sentiment and the resulting market participants’ behavior at the market microstructure level. As such, they may be of interest not only to traders and investors, but also to financial analysts and government policy-makers in their attempts to understand the multiscale relationship between monetary policy and the stock market.

Are stablecoins the money market mutual funds of the future?

This paper is the first to provide a comprehensive comparison of two financial instruments: stablecoins and money market mutual funds (MMFs). We observe similar reserve asset backing for fiat reserve backed (FRB) stablecoins and MMFs, similar importance of sponsor support, and the same negative association between macroeconomic indicators and peg deviations. Both instruments serve as short-term facilities for investors to park funds and their primary market microstructure is similar. However, FRB stablecoins exhibit larger dispersions from the dollar peg, significantly higher volatility, and a lack of transparency in their market infrastructure. Larger FRB stablecoins show reduced volatility compared to their smaller counterparts, with peg deviation drivers more closely resembling those of MMFs. We conclude that FRB stablecoins demonstrate remarkable similarities to MMFs and have the potential to become the MMFs of the future.

Information Asymmetry Effect on Stock Liquidity Effect on Dividend Payout in Market Microstructure in Indonesia

The impact of market microstructure in shaping corporate governance has been distinguished in several previous research. Market Microstructure becomes prominent factor in emerging markets. This study tested the effect of market microstructure, in the scheme of stock liquidity, on dividend payouts. This study has set focus mainly on the impact of stock liquidity on the corporate payout policy. Later, the researcher examined the interaction given to the stock liquidity factor with information transparency and agency problem properties since the researcher believed that transparency is linked to the and, subsequently, affecting the corporate agents. This research examined observations using quantitative research method, Tobit regression on 256 non-financial firms of the Indonesian capital market during 2010-2018. Stock liquidity significantly impacted dividend policy. The result showed robustness with other stock liquidity measures. This study found that information asymmetry and agency problems had a moderation effect on stock liquidity effect on dividend payout. However, the interaction of stock liquidity and disparity of control rights and cash flow rights did not give a moderation effect on the dividend payout policy. The result supports the notion that dividend payout is an outcome of firm good governance and an inverse of dividend signaling theory.

A bibliometric review of Market Microstructure literature: Current status, development, and future directions

We present a bibliometric literature review of the research conducted in the field of Market Microstructure over the past two decades. Covering 981 articles from the Web of Science (WOS) database published from 2004–2023, we summarise the publication trends and identify the most important contributors to the field. We map the conceptual structure and identify the popular themes using the Bibliometrix package run in R software. We also suggest several future research topics. Given the growing interest in applying high-frequency data in the finance literature, our study helps in understanding the application of Market Microstructure theories in modern financial markets.

Unleashing the Potential of the Indian Fintech for Financial Inclusion: A Qualitative Analysis

The fintech revolution has many ramifications for the financial market microstructure, regulatory framework and entire society. Policymakers across the world have begun to pin their hopes on digital initiatives and the fintech for financial inclusion. They believe that the fintech can lead to better access to finance. This study provides a thematic analysis of the qualitative data leading to four major themes in the newly evolving Indian fintech landscape. The themes that emerged from the in-depth interviews with the key informants, are evolution, potential, market strategy and regulatory. The findings suggest that fostering training and awareness amongst the customers is imperative to build up their trust. Regulatory interventions should also be based on proper engagement and learning with other stakeholders of the ecosystem.

Does speculation impair informational efficiency? New evidence from the Indian agricultural commodity market

Purpose This study explores the association between market efficiency and speculation. The government of India temporarily banned the futures trading of various commodities several times citing the presence of speculation. Many controversies exist about this topic; thus, this study clarifies the association between market efficiency and speculation and investigates whether market reforms altered this association.Design/methodology/approach The data for nine commodities is collected from the National Commodity and Derivative Exchange (NCDEX) for 2005–2022. Regression analysis and Automatic Variance Ratio (AVR) were adopted to inspect the informational efficiency and influence of speculation in the commodity market. Furthermore, this study uses different sub-samples to understand the changes in the market microstructure and its effects on market quality.Findings The results confirm an inverse and significant relationship between information efficiency and speculation and a deviation from the random walk process observed. Therefore, return predictability exists in the market. This study confirms that market reforms do not reduce the influence of speculation on market efficiency. The study concludes that the market is not weak-form efficient.Research limitations/implications This study has certain limitations, since this study is empirical in nature, it may possess the limitations of empirical research.Originality/value This paper has dual novelty. First, this study investigates the effects of market reforms. Second, this study captures the influence of speculation in the Indian agricultural commodity market by considering the market microstructure aspects.

On the influence of conventional and automated market makers on market quality in cryptoeconomic systems

Decentralized exchanges (DEXs) have become an alternative to centralized exchanges (CEXs) for trading assets in the form of tokens in cryptoeconomic system markets. The emergence of DEXs is strongly driven by their potential to tackle challenges for market quality originating from CEXs by design, such as opaque market-making strategies and centralization of power. However, it remains unclear to what extent DEXs can enhance market quality compared to CEXs. A core reason for this is the lack of an analysis concept for investigating influences of market makers, including automated market makers (AMMs) used in DEXs and conventional market makers used in CEXs, on market quality in cryptoeconomic systems. To better understand influences of market makers on market quality in cryptoeconomic systems, we developed an analysis concept based on our formal price model grounded in established concepts of market microstructure. We demonstrate the usefulness of the analysis concept by examining conventional market makers on CEXs (i.e., Binance and Coinbase) and automated market makers (AMMs) on DEXs (i.e., Uniswap v2 and Uniswap v3). The main purpose of this work is to support the analysis of influences of different market makers on market quality in cryptoeconomic systems. This is useful to better understand how cryptoeconomic systems can ensure high market quality and safeguard market participants, when issuing tokens.

Understanding temporal dynamics of jumps in cryptocurrency markets: evidence from tick-by-tick data

Cryptocurrency markets have recently attracted significant attention due to their potential for high returns; however, their underlying dynamics, especially those concerning price jumps, continue to be explored. Building on previous research, this study examines the presence and clustering of jumps in an extensive tick data set covering six major cryptocurrencies traded against Tether on seven leading exchanges worldwide over nearly 2.5 years. Our analysis reveals that jumps occur on up to 58% of trading days, with negative jumps predominating in both frequency and size. Notably, we observe systematic clustering of jumps over time, especially in Bitcoin and Ethereum, indicating interconnected market dynamics and potential predictive power for market movements. By employing high-frequency econometric tools, we identify temporal patterns in jump occurrence, highlighting heightened activity during specific trading hours and days. We also find evidence of jumps influencing intraday returns, underscoring their significance in short-term price dynamics. Our findings enhance understanding of the cryptocurrency market microstructure and offer insights for risk management and predictive modeling strategies. Nevertheless, further research is needed to develop robust methodologies for detecting and analyzing co-jumps across multiple assets.

Stock Liquidity Sidedness and Share Repurchase

We show that stock liquidity sidedness plays a vital role in share repurchase decisions. Specifically, we investigate whether share repurchases are affected differently by upside and downside liquidity, which respectively correspond to positive and negative return days. Using a sample of U.S. publicly-listed firms over four decades, we demonstrate that upside liquidity has a larger effect on repurchase initiation, amount, and ratio. Managers of repurchasing firms are more concerned with upside liquidity because they expect their repurchase activities to propel stock prices upward rather than down. The relationship between liquidity sidedness and repurchase decisions is more pronounced for financially constrained firms but less so for firms with higher stock price informativeness. We mitigate potential endogeneity concerns by using the market and industry liquidity sidedness as instruments and exploiting the 2003 Amendment to SEC Rule 10b-18 as an exogenous shock to the information advantages of repurchasing firms. Overall, our findings underscore the importance of market microstructure in shaping corporate policies.

Transaction Costs and Capacity of Systematic Corporate Bond Strategies

Can systematic corporate bond investments generate attractive returns net of costs? To answer this question, we apply the principle of market microstructure invariance and obtain bond transaction costs increasing in trade size. As the size of the bond fund increases, the market impact reduces net returns to zero. High-turnover strategies hit capacity constraints fast. Low-turnover credit risk–focused strategies have much higher capacities that can be further increased by constraining portfolio rebalancing in realistic ways. Transaction costs do not absorb the corporate bond risk premium even in the largest possible market portfolios.

Synergizing quantitative finance models and market microstructure analysis for enhanced algorithmic trading strategies

In today’s complex financial markets, “Algorithmic Trading” has become very important. The study delves into the amalgamation of four pivotal indicators - Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) to create and develop a potent trading strategy. Through intensive backtesting and parameter tuning, our study demonstrates 60.63 % profitable trades on the National Stock Exchange (NSE), India, surpassing the standalone indicators. The Weapon Candle Strategy created using the four indicators presents its efficiency as it was able to achieve a profit factor of 1.882. This suggests that when these four technical indicators combined to make a strategy, it can provide significantly more accurate and reliable trading signals compared to using a combination of two or three indicators. Algorithmic traders should use a multi-indicator approach to achieve a more comprehensive understanding of the market and make informed trading decisions.

Revolutionizing finance with bitcoin and blockchain: a literature review and research agenda

PurposeOur analysis is targeted at researchers in the fields of economics and finance, and we place emphasis on the incremental contributions of each paper, key research questions, study methodology, main conclusions and data and identification tactics. By focusing on these critical areas, our review seeks to provide valuable insights and guidance for future research in this rapidly evolving and complex field.Design/methodology/approachThis paper conducts a structured literature review (SLR) of Bitcoin-related articles published in the leading finance, economics and accounting journals between 2018 and 2023. Following Massaro et al. (2016), SLR is a method for examining a corpus of scholarly work to generate new ideas, critical reflections and future research agendas. The goals of SLR are congruent with the three outcomes of critical management research identified by Alvesson and Deetz (2000): insight, critique and transformative redefinition.FindingsThe present state of research on Bitcoin lacks coherence and interconnectedness, leading to a limited understanding of the underlying mechanisms. However, certain areas of research have emerged as significant topics for further exploration. These include the decentralized payment system, equilibrium price, market microstructure, trading patterns and regulation of Bitcoin. In this context, this review serves as a valuable starting point for researchers who are unacquainted with the interdisciplinary field of bitcoin and blockchain research. It is essential to recognize the potential value of research in Bitcoin-related fields in advancing knowledge of the interaction between finance, economics, law and technology. Therefore, future research in this area should focus on adopting innovative and interdisciplinary methods to enhance our comprehension of these intricate and evolving technologies.Originality/valueOur review encompasses the latest research on Bitcoin, including its market microstructure, trading behavior, price patterns and portfolio analysis. It explores Bitcoin's market microstructure, liquidity, derivative markets, price discovery and market efficiency. Studies have also focused on trading behavior, investors' characteristics, market sentiment and price volatility. Furthermore, empirical studies demonstrate the advantages of including Bitcoin in a portfolio. These findings enhance our understanding of Bitcoin's potential impact on the financial industry.

Lessons from the Demise of the Brent Crude Oil Futures Contract on the Singapore Exchange

This paper highlights the lessons drawn from the demise of the Brent Crude Oil futures contract that was traded on the Singapore Stock Exchange (SGX). We analyze the market microstructure of the contract prior to its failure—specifically, the number of trades, trading volume, open interest, bid–ask spread, and volatility. We find a steady decline in the mean volume, open interest, and number of trades as the contracts near their demise. The bid–ask spread of the contract also widens. Investigations of the mutual offset feature of the Brent Crude Oil futures contract between SGX and the International Commodity Exchange (ICE) provides evidence that trading volume, open interest, and the number of trades increase significantly during 4:00–5:45 PM local time when mutual offset is available.