Deep Learning Statistical Arbitrage

• URL: http://arxiv.org/abs/2106.04028v1
• Date: Tue, 8 Jun 2021 00:48:25 GMT
• Title: Deep Learning Statistical Arbitrage
• Authors: Jorge Guijarro-Ordonez, Markus Pelger and Greg Zanotti
• Abstract summary: We propose a unifying conceptual framework for statistical arbitrage and develop a novel deep learning solution. We construct arbitrage portfolios of similar assets as residual portfolios from conditional latent asset pricing factors. We extract the time series signals of these residual portfolios with one of the most powerful machine learning time-series solutions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Statistical arbitrage identifies and exploits temporal price differences between similar assets. We propose a unifying conceptual framework for statistical arbitrage and develop a novel deep learning solution, which finds commonality and time-series patterns from large panels in a data-driven and flexible way. First, we construct arbitrage portfolios of similar assets as residual portfolios from conditional latent asset pricing factors. Second, we extract the time series signals of these residual portfolios with one of the most powerful machine learning time-series solutions, a convolutional transformer. Last, we use these signals to form an optimal trading policy, that maximizes risk-adjusted returns under constraints. We conduct a comprehensive empirical comparison study with daily large cap U.S. stocks. Our optimal trading strategy obtains a consistently high out-of-sample Sharpe ratio and substantially outperforms all benchmark approaches. It is orthogonal to common risk factors, and exploits asymmetric local trend and reversion patterns. Our strategies remain profitable after taking into account trading frictions and costs. Our findings suggest a high compensation for arbitrageurs to enforce the law of one price.

Related papers

• Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer [1.4061979259370274]
  We implement the PolyModel theory for constructing a hedge fund portfolio. We create quantitative measures such as Long-term Alpha, Long-term Ratio, and SVaR. We also employ the latest deep learning techniques (iTransformer) to capture the upward trend.
  arXiv  Detail & Related papers  (2024-08-06T17:55:58Z)
• 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)
• Mean-Variance Portfolio Selection in Long-Term Investments with Unknown Distribution: Online Estimation, Risk Aversion under Ambiguity, and Universality of Algorithms [0.0]
  This paper adopts a perspective where data gradually and continuously reveal over time. The performance of these proposed strategies is guaranteed under specific markets. In stationary and ergodic markets, the so-called Bayesian strategy utilizing true conditional distributions, based on observed past market information during investment, almost surely does not perform better than the proposed strategies in terms of empirical utility, Sharpe ratio, or growth rate, which, in contrast, do not rely on conditional distributions.
  arXiv  Detail & Related papers  (2024-06-19T12:11:42Z)
• Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
  This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
  arXiv  Detail & Related papers  (2023-10-02T12:33:28Z)
• 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)
• Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management [0.0]
  Reinforcement learning (RL) methods do not rely on explicit forecasts and are better suited for multi-stage decision processes. Experiments were conducted on three markets in different economies with different overall trends.
  arXiv  Detail & Related papers  (2022-02-13T10:12:09Z)
• Universal Trading for Order Execution with Oracle Policy Distillation [99.57416828489568]
  We propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution. We show that our framework can better guide the learning of the common policy towards practically optimal execution by an oracle teacher with perfect information.
  arXiv  Detail & Related papers  (2021-01-28T05:52:18Z)
• Learning Risk Preferences from Investment Portfolios Using Inverse Optimization [25.19470942583387]
  This paper presents a novel approach of measuring risk preference from existing portfolios using inverse optimization. We demonstrate our methods on real market data that consists of 20 years of asset pricing and 10 years of mutual fund portfolio holdings.
  arXiv  Detail & Related papers  (2020-10-04T21:29:29Z)
• Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network [71.25144476293507]
  We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market. With representation learning, we derived an embedding called Stock2Vec, which gives us insight for the relationship among different stocks. Our hybrid framework integrates both advantages and achieves better performance on the stock price prediction task than several popular benchmarked models.
  arXiv  Detail & Related papers  (2020-09-29T22:54:30Z)
• Learning from eXtreme Bandit Feedback [105.0383130431503]
  We study the problem of batch learning from bandit feedback in the setting of extremely large action spaces. In this paper, we introduce a selective importance sampling estimator (sIS) that operates in a significantly more favorable bias-variance regime. We employ this estimator in a novel algorithmic procedure -- named Policy Optimization for eXtreme Models (POXM) -- for learning from bandit feedback on XMC tasks.
  arXiv  Detail & Related papers  (2020-09-27T20:47:25Z)
• Time your hedge with Deep Reinforcement Learning [0.0]
  Deep Reinforcement Learning (DRL) can tackle this challenge by creating a dynamic dependency between market information and hedging strategies allocation decisions. We present a realistic and augmented DRL framework that: (i) uses additional contextual information to decide an action, (ii) has a one period lag between observations and actions to account for one day lag turnover of common asset managers to rebalance their hedge, (iii) is fully tested in terms of stability and robustness thanks to a repetitive train test method called anchored walk forward training, similar in spirit to k fold cross validation for time series and (iv) allows managing leverage of our hedging
  arXiv  Detail & Related papers  (2020-09-16T06:43:41Z)

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.