Can machine learning unlock new insights into high-frequency trading?

• URL: http://arxiv.org/abs/2405.08101v1
• Date: Mon, 13 May 2024 18:28:39 GMT
• Title: Can machine learning unlock new insights into high-frequency trading?
• Authors: G. Ibikunle, B. Moews, K. Rzayev,
• Abstract summary: We introduce new metrics to identify liquidity-demanding and -supplying HFT strategies. Our metrics have implications for understanding the information production process in financial markets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We design and train machine learning models to capture the nonlinear interactions between financial market dynamics and high-frequency trading (HFT) activity. In doing so, we introduce new metrics to identify liquidity-demanding and -supplying HFT strategies. Both types of HFT strategies increase activity in response to information events and decrease it when trading speed is restricted, with liquidity-supplying strategies demonstrating greater responsiveness. Liquidity-demanding HFT is positively linked with latency arbitrage opportunities, whereas liquidity-supplying HFT is negatively related, aligning with theoretical expectations. Our metrics have implications for understanding the information production process in financial markets.

Related papers

• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
  We have developed a multi-agent AI system called StockAgent, driven by LLMs. The StockAgent allows users to evaluate the impact of different external factors on investor trading. It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
  arXiv  Detail & Related papers  (2024-07-15T06:49:30Z)
• MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading [20.3106468936159]
  Reinforcement learning (RL) has become another appealing approach for high-frequency trading (HFT) We propose a novel Memory Augmented Context-aware Reinforcement learning method On HFT, empha.k.a. MacroHFT. We show that MacroHFT can achieve state-of-the-art performance on minute-level trading tasks.
  arXiv  Detail & Related papers  (2024-06-20T17:48:24Z)
• Stockformer: A Price-Volume Factor Stock Selection Model Based on Wavelet Transform and Multi-Task Self-Attention Networks [3.7608255115473592]
  This paper introduces Stockformer, a price-volume factor stock selection model that integrates wavelet transformation and a multitask self-attention network. Stockformer decomposes stock returns into high and low frequencies, meticulously capturing long-term market trends and abrupt events. Experimental results show that Stockformer outperforms existing advanced methods on multiple real stock market datasets.
  arXiv  Detail & Related papers  (2023-11-23T04:33:47Z)
• Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration [0.0]
  Reinforcement Learning (RL) is a branch of machine learning where agents learn by interacting with their environment. This paper dives deep into the integration of RL in statistical arbitrage strategies tailored for High-Frequency Trading (HFT) scenarios. Through extensive simulations and backtests, our research reveals that RL not only enhances the adaptability of trading strategies but also shows promise in improving profitability metrics and risk-adjusted returns.
  arXiv  Detail & Related papers  (2023-09-13T06:15:40Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading [5.75899596101548]
  We propose a Reinforcement Learning (RL) algorithm that trades based on signals from a learned predictive model. We test our algorithm on 20+ years of equity data from Bursa Malaysia.
  arXiv  Detail & Related papers  (2023-07-18T16:00:02Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling [11.430440350359993]
  We propose LARA, a novel price movement forecasting framework with two main components. LA-Attention extracts potentially profitable samples through masked attention scheme. RA-Labeling refines the noisy labels of potentially profitable samples. LARA significantly outperforms several machine learning based methods on the Qlib quantitative investment platform.
  arXiv  Detail & Related papers  (2021-07-26T05:52:42Z)
• REST: Relational Event-driven Stock Trend Forecasting [76.08435590771357]
  We propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks.
  arXiv  Detail & Related papers  (2021-02-15T07:22:09Z)
• A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
  We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
  arXiv  Detail & Related papers  (2020-12-10T01:15:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.