High Frequency Trading Research Articles

Building a High-Frequency Trading Algorithm Using an Ordinary Differential Equation Recurrent Neural Network

Building a High-Frequency Trading Algorithm Using an Ordinary Differential Equation Recurrent Neural Network

Quantifying the High-Frequency Trading "Arms Race"

Quantifying the High-Frequency Trading "Arms Race"

A Decision Support System for Trading in Apple Futures Market Using Predictions Fusion

In the last decade, High-Frequency Trading (HFT) has become a popular issue in the futures market, which has attracted much attention from numerous researchers. In this study, an intelligent decision support system is proposed for apple futures high-frequency trading. First, three eXtreme Gradient Boosting (XGBoost) based models use the feature inputs from multiple time scales for return and direction prediction. Then, based on a pre-designed trading rule, the signals of long and short-selling are determined, and corresponding transactions are executed. In order to retain considerable profits in time and to avoid serious losses possibly caused by sudden and huge price changes toward the opposite direction as predictions, a position closing function is implemented in the trading rule. Meanwhile, Particle Swarm Optimization (PSO) is employed to optimize the parameters of the trading rule as well as the XGBoost parameters. By evaluating the experimental results, we observed that the proposed approach successfully achieved the best performance in terms of direction prediction accuracy, transaction returns, as well as return/risk ratio. It could be inferred from the experimental results that the proposed approach could provide decision support and beneficial reference for market traders involved in high-frequency trading of the apple futures.

The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets

Cryptocurrencies have gained a lot of attention since Bitcoin was first proposed by Satoshi Nakamoto in 2008, highlighting the potential to play a significant role in e-commerce. However, relatively little is known about cryptocurrencies, their price behaviour, how quickly they incorporate new information and their corresponding market efficiency. To extend the current literature in this area, we develop four smart electronic Bitcoin markets populated with different types of traders using a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm. We apply the STGP technique to historical data of Bitcoin at the one-minute and five-minute frequencies to investigate the formation of Bitcoin market dynamics and market efficiency. Through a plethora of robust testing procedures, we find that both Bitcoin markets populated by high-frequency traders (HFTs) are efficient at the one-minute frequency but inefficient at the five-minute frequency. This finding supports the argument that at the one-minute frequency investors are able to incorporate new information in a fast and rationale manner and not suffer from the noise associated with the five-minute frequency. We also contribute to the e-commerce literature by demonstrating that zero-intelligence traders cannot reach market efficiency, therefore providing evidence against the hypothesis of Hayek (1945; 1968). One practical implication of this study is that we demonstrate that e-commerce practitioners can apply artificial intelligence tools such as STGP to conduct behaviour-based market profiling.

Pricing of Arithmetic Asian Options under Stochastic Volatility Dynamics: Overcoming the Risks of High-Frequency Trading

This research extended the model developed by Hull and White by integrating Taylor-series expansion into the model for deriving approximate analytical solutions for stochastic volatility forward-starting Asian options. Numerical experiments were performed to compare the proposed model with the Monte Carlo model over numerous simulations and demonstrated that the developed model has a pricing accuracy greater than 99%. Furthermore, the computation time was approximately 10−5 s for each simulation. The model’s outstanding computational performance demonstrates its capability to address the challenges of high-frequency trading.

Analisis Kesiapan Otoritas Pajak dalam Pemajakan atas Ekonomi Digital

The patterns, forms of human behavior and the presence of many new forms of business today such as e-commerce, payment services, application services, online advertising, cloud computing, high frequency trading, participative networked platforms are influenced by the digital revolution as formulated by the OECD. Social phenomena as an effect of the rapid development of information and communication technology (ICT) known as the digital economy will certainly also change the patterns of business individuals and organizations throughout the world. With the presence of various new business models, there are many opportunities for tax avoidance schemes that are usually dominated by multinational entities (MNE). Based on this, the OECD recommends several action plans, one of which is about the digital economy. The need to detect the digital economy by the tax authorities of world countries such as Indonesia is now very large. Related to this the Indonesian government issued a unilateral regulation namely Perpu Number 1 of 2020 which later became Law No. 2 of 2020, one of which regulates taxation of the digital economy. The Indonesian tax authority seeks to respond to the dynamics of the internal and external environment called organizational readiness. This study wants to analyze whether the Indonesian tax authority is ready to tax the digital economy.

Predict high-frequency trading marker via manifold learning

High-frequency trading (HFT) has continuously evolved and dominated trading in the past decades. However, HFT trading marker prediction is a rarely investigated problem in Fintech literature. In this study, we first propose a novel manifold learning based HFT trading marker prediction model: M-SCAN to handle this challenge. Our study takes advantage of manifold embeddings of HFT data and seeks potential markers among the outliers in density-based clustering. We further propose HFT trading marker evaluation algorithms to validate the prediction effectiveness besides unveiling trading marker discovery via visualization. Our results demonstrate locally linear embedding (LLE) outperforms its peers in capturing trading markers in terms of accuracy and complexity for its local data structure keeping mechanism in embedding though different stocks may demonstrate their model preference under M-SCAN. Our studies also propose novel entropy and variance concentration ratios (VCR) to quantify HFT data and show that a high-entropy dataset is more likely to have better trading marker prediction than a low-entropy one. To the best of our knowledge, it is the first study in HFT trading marker prediction and will inspire coming studies in this area.

High Frquency Trading a Boon and Bane

High-Frequency Trading is a modern solution to a time-old problem. This research article was drafted by our group for internal class discussion. We have tried to cover from introduction to the explanation - topology on how HFT works and interesting topics like- Automated market-making, Market Arbitrage Latency Arbitrage, Liquidity rebate trading. Legality and Regulations of HFTs in countries like The European Union, Germany, United Kingdom, United States, Singapore, China with our recommendations.

Analysis of the Impact of High-Frequency Trading on Artificial Market Liquidity

Many empirical studies have discussed market liquidity, which is regarded as a measure of a booming financial market. Further, various indicators for objectively evaluating market liquidity have also been proposed and their merits have been discussed. In recent years, the impact of high-frequency traders (HFTs) on financial markets has been a focal concern, but no studies have systematically discussed their relationship with major market liquidity indicators, including volume, tightness, resiliency, and depth. In this study, we used agent-based simulations to compare the major liquidity indicators in an artificial market where an HFT participated was compared to one where no HFT participated. The results showed that all liquidity indicators in the market where an HFT participated improved more than those in the market where no HFT participated. Furthermore, as a result of investigating the correlations between the major liquidity indicators in our simulations and the extant empirical literature, we found that market liquidity can be measured not only by the major liquidity indicators but also by execution rate. Therefore, it is suggested that it could be appropriate to employ execution rate as a novel liquidity indicator in future studies.

A financial market with singular drift and no arbitrage

We study a financial market where the risky asset is modelled by a geometric Itô-Lévy process, with a singular drift term. This can for example model a situation where the asset price is partially controlled by a company which intervenes when the price is reaching a certain lower barrier. See e.g. Jarrow and Protter (J Bank Finan 29:2803–2820, 2005) for an explanation and discussion of this model in the Brownian motion case. As already pointed out by Karatzas and Shreve (Methods of Mathematical Finance, Springer, Berlin, 1998) (in the continuous setting), this allows for arbitrages in the market. However, the situation in the case of jumps is not clear. Moreover, it is not clear what happens if there is a delay in the system. In this paper we consider a jump diffusion market model with a singular drift term modelled as the local time of a given process, and with a delay theta > 0 in the information flow available for the trader. We allow the stock price dynamics to depend on both a continuous process (Brownian motion) and a jump process (Poisson random measure). We believe that jumps and delays are essential in order to get more realistic financial market models. Using white noise calculus we compute explicitly the optimal consumption rate and portfolio in this case and we show that the maximal value is finite as long as theta > 0. This implies that there is no arbitrage in the market in that case. However, when theta goes to 0, the value goes to infinity. This is in agreement with the above result that is an arbitrage when there is no delay. Our model is also relevant for high frequency trading issues. This is because high frequency trading often leads to intensive trading taking place on close to infinitesimal lengths of time, which in the limit corresponds to trading on time sets of measure 0. This may in turn lead to a singular drift in the pricing dynamics. See e.g. Lachapelle et al. (Math Finan Econom 10(3):223–262, 2016) and the references therein.

Intraday interactions between high-frequency trading and price efficiency

We examine intraday interactions between high-frequency trading (HFT) and the informational quality of prices to provide evidence on the role of HFT in the price discovery process. HFT and price efficiency display reverse L-shaped patterns with low levels at the open and strong improvement across the trading day. Using the panel vector auto-regression model, we find bi-directional causality between HFT and price efficiency: High-frequency traders react actively to movements in price efficiency, while greater intensity of HFT is associated with more efficient prices. Overall, HFT activities can be considered as efficient ways to incorporate information quickly and accurately into prices.

Does VPIN provide predictive information for realized volatility forecasting: evidence from Chinese stock index futures market

PurposeUsing intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300).Design/methodology/approachThe authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI.FindingsThe empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics.Originality/valueThe study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.

Competition among liquidity providers with access to high-frequency trading technology

We model endogenous technology adoption and competition among liquidity providers with access to High-Frequency Trading (HFT) technology. HFT technology provides speed and information advantages. Information advantages may restore excessively toxic markets. Speed advantages may reduce resource costs for liquidity provision. Both effects increase liquidity and welfare. However, informationally advantaged HFTs may impose a winner’s curse on traditional market makers, who in response reduce their participation. This increases resource costs and lowers the execution likelihood for market orders, thereby reducing liquidity and welfare. This result also holds when HFT technology dominates traditional technology in terms of costs and informational advantages.

Innovation in the Stock Market and Alternative Trading Systems

Is something wrong with the structure of the U.S. stock market? Both industry participants and scholars have recently faulted the equity market for its lack of innovation. In particular, they have emphasized that stock exchanges may not have the right incentives to provide innovative solutions to market problems. I argue that understanding the quality of innovation in the equity market requires bringing other types of stock trading venues—particularly “alternative trading systems” (“ATSs”)—into the picture. My central claim is that ATSs have markedly superior incentives to innovate than exchanges for reasons rooted in their governing law and business models. First, ATSs face far lighter regulatory hurdles to beginning operations and changing structure than an exchange, and some important regulations that limit exchange experimentation do not apply to ATSs at all. Second, ATSs are subject to fewer disclosure obligations than exchanges, and as a result they can, in practice, retain their innovations as “trade secrets,” while this is exceptionally difficult for exchanges. Third, ATSs and exchanges have different revenue sources. This is important because ATSs benefit less from the status quo market design than exchanges and thus lack reasons to preserve that structure against desirable innovations, especially those reducing opportunities for latency arbitrage. The result is that ATSs produce the very innovations that some scholars have sought, but failed to find in exchanges, such as frequent batched auctions. I use newly available data on ATSs to inform and illustrate these arguments. I conclude by noting that the consequences of ATS innovations for the equity market as a whole are profoundly ambiguous, because it is unclear how much trading volume an ATS can ultimately attract.

Crypto Covered Interest Parity Deviations

Studying deviations from covered interest rate parity (CIP) in the Bitcoin/US-Dollar (BTC/USD) market, we find large CIP deviations of up to 15% until Q1/2018. Afterwards, CIP deviations have been subdued, which we attribute to the market entry of high-frequency traders (HFTs). We argue that these market entries have increased efficiency of cryptocurrency markets with respect to CIP as well as liquidity, volatility, and bid-ask spreads. Remarkably, these efficiency gains are larger for the less liquid cryptocurrency Litecoin. Employing a difference-in-differences design, we show that the launch of the BTC/USD future at the Chicago Mercantile Exchange (CME) did not affect market efficiency. Finally, remaining CIP deviations after Q1/2018 seem mostly related to increased credit risk of certain crypto exchanges.

High-Frequency Trading as an Impediment to Long-Term and Sustainable Finance: Identifying a Regulatory Gap that Can Put the Goals of the European Action Plan on Financing Sustainable Growth at Risk

High-Frequency Trading as an Impediment to Long-Term and Sustainable Finance: Identifying a Regulatory Gap that Can Put the Goals of the European Action Plan on Financing Sustainable Growth at Risk

High-frequency trading with fractional Brownian motion

In the high-frequency limit, conditionally expected increments of fractional Brownian motion converge to a white noise, shedding their dependence on the path history and the forecasting horizon and making dynamic optimisation problems tractable. We find an explicit formula for locally mean–variance optimal strategies and their performance for an asset price that follows fractional Brownian motion. Without trading costs, risk-adjusted profits are linear in the trading horizon and rise asymmetrically as the Hurst exponent departs from Brownian motion, remaining finite as the exponent reaches zero while diverging as it approaches one. Trading costs penalise numerous portfolio updates from short-lived signals, leading to a finite trading frequency, which can be chosen so that the effect of trading costs is arbitrarily small, depending on the required speed of convergence to the high-frequency limit.

Autonomous self-evolving forecasting models for price movement in high frequency trading: Evidence from Taiwan

Among FinTech research and applications, forecasting financial time series data has been a challenging task because this kind of data is typically quite noisy and non-stationary. A recent line of financial research centers around trading through financial data on the microscopic level, which is the holy grail of high-frequency trading (HFT), as the higher the data frequency, the more profitable opportunities may appear. The advancement in HFT modeling has also facilitated more understanding towards price formation because the supply and demand of a stock can be comprehended more easily from the microstructure of the order book. Instead of traditional statistical methods, there has been increasing demand for the development of more reliable prediction models due to the recent progress in Computational Intelligence (CI) technologies. In this study, we aim to develop novel CI-based methodologies for the forecasting task of price movement in HFT. Our goal is to conduct a study for autonomous genetic-based models that allow the forecasting systems to self-evolve. The results show that our proposed method can improve upon the previous ones and advance the current state of Fintech research.

Do speed bumps curb low-latency investment? Evidence from a laboratory market

Exchanges implement intentional trade delays to limit the harmful impact of low-latency trading. Do such “speed bumps” curb investment in fast trading technology? Data are scarce since trading technologies are proprietary. We build an experimental trading platform where participants face speed bumps and can invest in fast trading technology. We find that asymmetric speed bumps, on average, reduce investment in speed by only 20%. Increasing the magnitude of the speed bump by one standard deviation further reduces low-latency investment by 8.33%. Finally, introducing a symmetric speed bump leads to the same investment level as no speed bump at all.

Are high–frequency traders informed?

Are high–frequency traders (HFTs) informed? To address this question, we examine HFTs' activity in the call auction environment, where speed-related trading is limited and signal processing capacity becomes more relevant. To model the call market, we consider the Kyle (1989) rational expectations framework for strategic trading. The test we propose for detecting informed HFTs in this market assesses potential deviations of the informativeness of HFTs' aggregate (net) demand, from the informativeness of the aggregate demand submitted by the rest of the traders. Data from the Euronext Paris preopening phase indicate that informed HFTs are present in the market just before the opening. Our results provide useful guidance for the assessment of the influence of HFTs’ quotes on price quality, an important issue for market regulators and policy makers.