Deep Portfolio Optimization via Distributional Prediction of Residual Factors

• URL: http://arxiv.org/abs/2012.07245v1
• Date: Mon, 14 Dec 2020 04:09:52 GMT
• Title: Deep Portfolio Optimization via Distributional Prediction of Residual Factors
• Authors: Kentaro Imajo and Kentaro Minami and Katsuya Ito and Kei Nakagawa
• Abstract summary: We propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors. We demonstrate the efficacy of our method on U.S. and Japanese stock market data.
• Score: 3.9189409002585562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent developments in deep learning techniques have motivated intensive research in machine learning-aided stock trading strategies. However, since the financial market has a highly non-stationary nature hindering the application of typical data-hungry machine learning methods, leveraging financial inductive biases is important to ensure better sample efficiency and robustness. In this study, we propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors, which is known to be generally useful for hedging the risk exposure to common market factors. The key technical ingredients are twofold. First, we introduce a computationally efficient extraction method for the residual information, which can be easily combined with various prediction algorithms. Second, we propose a novel neural network architecture that allows us to incorporate widely acknowledged financial inductive biases such as amplitude invariance and time-scale invariance. We demonstrate the efficacy of our method on U.S. and Japanese stock market data. Through ablation experiments, we also verify that each individual technique contributes to improving the performance of trading strategies. We anticipate our techniques may have wide applications in various financial problems.

Related papers

• Transfer learning for financial data predictions: a systematic review [0.0]
  Financial time series data pose significant challenges for accurate stock price prediction. Traditional statistical methodologies made assumptions, such as linearity and normality, which are not suitable for the non-linear nature of financial time series. Machine learning methodologies are able to capture non linear relationship in the data.
  arXiv  Detail & Related papers  (2024-09-24T20:52:32Z)
• AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
  We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
  arXiv  Detail & Related papers  (2024-03-19T09:45:33Z)
• 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)
• 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)
• Factor Investing with a Deep Multi-Factor Model [123.52358449455231]
  We develop a novel deep multi-factor model that adopts industry neutralization and market neutralization modules with clear financial insights. Tests on real-world stock market data demonstrate the effectiveness of our deep multi-factor model.
  arXiv  Detail & Related papers  (2022-10-22T14:47:11Z)
• 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)
• The Adaptive Multi-Factor Model and the Financial Market [0.0]
  The introduction of constituents like Exchange-Traded Funds, and the wide-use of advanced technologies such as algorithmic trading, results in a boom of the data. Traditional statistical methods always suffer from the high-dimensional, high-correlation, and time-varying instinct of the financial data. With the proposed methodologies, we can have more interpretable models, clearer explanations, and better predictions.
  arXiv  Detail & Related papers  (2021-07-30T03:05:03Z)
• Financial Markets Prediction with Deep Learning [11.26482563151052]
  We propose a novel one-dimensional convolutional neural networks (CNN) model to predict financial market movement. The customized one-dimensional convolutional layers scan financial trading data through time, while different types of data, such as prices and volume, share parameters ( Kernels) with each other. Our model automatically extracts features instead of using traditional technical indicators.
  arXiv  Detail & Related papers  (2021-04-05T19:36:48Z)
• Interpretable ML-driven Strategy for Automated Trading Pattern Extraction [2.7910505923792646]
  We propose a volume-based data pre-processing method for financial time series analysis. We use a statistical approach for assessing the performance of the method. Our analysis shows that the proposed method allows successful classification of the financial time series patterns.
  arXiv  Detail & Related papers  (2021-03-23T09:55:46Z)
• Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction [3.9189409002585562]
  There are several challenges in financial markets hindering practical applications of machine learning-based models. We propose the Trader-Company method, a novel evolutionary model that mimics the roles of a financial institute and traders. Our method predicts future stock returns by aggregating suggestions from multiple weak learners called Traders.
  arXiv  Detail & Related papers  (2020-12-18T13:19:27Z)
• Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning [100.73223416589596]
  We propose a cost-sensitive portfolio selection method with deep reinforcement learning. Specifically, a novel two-stream portfolio policy network is devised to extract both price series patterns and asset correlations. A new cost-sensitive reward function is developed to maximize the accumulated return and constrain both costs via reinforcement learning.
  arXiv  Detail & Related papers  (2020-03-06T06:28:17Z)

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.