The Impact of Algorithmic Trading in a Simulated Asset Market

Mukerji, Purba; Chung, Christine; Walsh, Timothy; Xiong, Bo

doi:10.3390/jrfm12020068

Cited by 15 publications

(10 citation statements)

References 15 publications

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“…In Mukerji et al (2019), the authors have studied the impact of AT in asset markets where human and algorithmic traders conduct trades using technical and fundamental analysis, apart from statistical arbitrage strategies. The objectives of this study include explaining the impact of AT on market quality.…”

Section: Theoretical Background and Literature Surveymentioning

confidence: 99%

Accounting for unadjusted news sentiment for asset pricing

Eachempati

Srivastava

2021

QRFM

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Purpose A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment. Design/methodology/approach Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results. Findings Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6. Originality/value The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.

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Section: Theoretical Background and Literature Surveymentioning

confidence: 99%

Accounting for unadjusted news sentiment for asset pricing

Eachempati

Srivastava

2021

QRFM

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“…In fact, HFTs accounted for 68.3% of the total trading volume in the stock market [ 8 ]. Furthermore, HFTs currently account for the majority of orders shown in the order book [ 9 , 10 , 11 ]. The availability of high-frequency trading data has triggered the academic study of HFTs [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Individual High-Frequency Traders’ Buy–Sell Order Strategy Based on Multivariate Hawkes Process

Watari

Takayasu

2022

Entropy

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Traders who instantly react to changes in the financial market and place orders in milliseconds are called high-frequency traders (HFTs). HFTs have recently become more prevalent and attracting attention in the study of market microstructures. In this study, we used data to track the order history of individual HFTs in the USD/JPY forex market to reveal how individual HFTs interact with the order book and what strategies they use to place their limit orders. Specifically, we introduced an 8-dimensional multivariate Hawkes process that included the excitations due to the occurrence of limit orders, cancel orders, and executions in the order book change, and performed maximum likelihood estimations of the limit order processes for 134 HFTs. As a result, we found that the limit order generation processes of 104 of the 134 HFTs were modeled by a multivariate Hawkes process. In this analysis of the EBS market, the HFTs whose strategies were modeled by the Hawkes process were categorized into three groups according to their excitation mechanisms: (1) those excited by executions; (2) those that were excited by the occurrences or cancellations of limit orders; and (3) those that were excited by their own orders.

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“…The electronic exchange is a fully automated trading system programmed to incisively enforce order precedence, pricing and the matching of buy and sell orders. Each order's pricing, submission and execution is performed using sophisticated algorithmic trading strategies, which account for 85% of the equity market's trading volume (Mukerji et al, 2019). Highfrequency trading (HFT, or high-frequency trader), a subset of algorithmic trading, is characterised by exceptionally high speeds, minuscule timeframes and complex programs for initiating and liquidating positions (SEC, 2014).…”

Section: Introductionmentioning

confidence: 99%

Deep Hawkes Process for High-Frequency Market Making

Kumar¹

2021

Preprint

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High-frequency market making is a liquidity-providing trading strategy that simultaneously generates many bids and asks for a security at ultra-low latency while maintaining a relatively neutral position. The strategy makes a profit from the bid-ask spread for every buy and sell transaction, against the risk of adverse selection, uncertain execution and inventory risk. We design realistic simulations of limit order markets and develop a high-frequency market making strategy in which agents process order book information to post the optimal price, order type and execution time. By introducing the Deep Hawkes process to the high-frequency market making strategy, we allow a feedback loop to be created between order arrival and the state of the limit order book, together with self-and cross-excitation effects. Our highfrequency market making strategy accounts for the cancellation of orders that influence order queue position, profitability, bid-ask spread and the value of the order. The experimental results show that our trading agent outperforms the baseline strategy, which uses a probability density estimate of the fundamental price. We investigate the effect of cancellations on market quality and the agent's profitability. We validate how closely the simulation framework approximates reality by reproducing stylised facts from the empirical analysis of the simulated order book data.

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The Impact of Algorithmic Trading in a Simulated Asset Market

Cited by 15 publications

References 15 publications

Accounting for unadjusted news sentiment for asset pricing

Accounting for unadjusted news sentiment for asset pricing

Analysis of Individual High-Frequency Traders’ Buy–Sell Order Strategy Based on Multivariate Hawkes Process

Deep Hawkes Process for High-Frequency Market Making

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