Deep Learning for Portfolio Optimization

• URL: http://arxiv.org/abs/2005.13665v3
• Date: Sat, 23 Jan 2021 18:19:33 GMT
• Title: Deep Learning for Portfolio Optimization
• Authors: Zihao Zhang, Stefan Zohren, Stephen Roberts
• Abstract summary: Instead of selecting individual assets, we trade Exchange-Traded Funds (ETFs) of market indices to form a portfolio. We compare our method with a wide range of algorithms with results showing that our model obtains the best performance over the testing period.
• Score: 5.833272638548154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We adopt deep learning models to directly optimise the portfolio Sharpe ratio. The framework we present circumvents the requirements for forecasting expected returns and allows us to directly optimise portfolio weights by updating model parameters. Instead of selecting individual assets, we trade Exchange-Traded Funds (ETFs) of market indices to form a portfolio. Indices of different asset classes show robust correlations and trading them substantially reduces the spectrum of available assets to choose from. We compare our method with a wide range of algorithms with results showing that our model obtains the best performance over the testing period, from 2011 to the end of April 2020, including the financial instabilities of the first quarter of 2020. A sensitivity analysis is included to understand the relevance of input features and we further study the performance of our approach under different cost rates and different risk levels via volatility scaling.

Related papers

• Conformal Predictive Portfolio Selection [10.470114319701576]
  We propose a framework for predictive portfolio selection using conformal inference, called Conformal Predictive Portfolio Selection ( CPPS) Our approach predicts future portfolio returns, computes corresponding prediction intervals, and selects the desirable portfolio based on these intervals. We demonstrate the effectiveness of our CPPS framework using an AR model and validate its performance through empirical studies.
  arXiv  Detail & Related papers  (2024-10-19T15:42:49Z)
• AAPM: Large Language Model Agent-based Asset Pricing Models [4.326886488307076]
  We propose a novel asset pricing approach, which fuses qualitative discretionary investment analysis from LLM agents and quantitative manual financial economic factors. The experimental results show that our approach outperforms machine learning-based asset pricing baselines in portfolio optimization and asset pricing errors.
  arXiv  Detail & Related papers  (2024-09-25T18:27:35Z)
• Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback [110.16220825629749]
  Learning from preference feedback has emerged as an essential step for improving the generation quality and performance of modern language models. In this work, we identify four core aspects of preference-based learning: preference data, learning algorithm, reward model, and policy training prompts. Our findings indicate that all aspects are important for performance, with better preference data leading to the largest improvements.
  arXiv  Detail & Related papers  (2024-06-13T16:17:21Z)
• Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization [49.396692286192206]
  We study the use of deep reinforcement learning for responsible portfolio optimization by incorporating ESG states and objectives. Our results show that deep reinforcement learning policies can provide competitive performance against mean-variance approaches for responsible portfolio allocation.
  arXiv  Detail & Related papers  (2024-03-25T12:04:03Z)
• 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)
• Joint Latent Topic Discovery and Expectation Modeling for Financial Markets [45.758436505779386]
  We present a groundbreaking framework for financial market analysis. This approach is the first to jointly model investor expectations and automatically mine latent stock relationships. Our model consistently achieves an annual return exceeding 10%.
  arXiv  Detail & Related papers  (2023-06-01T01:36:51Z)
• A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks [1.5773159234875098]
  This paper presents a systematic approach to portfolio optimization using two approaches, the hierarchical risk parity algorithm and the Eigen portfolio on seven sectors of the Indian stock market. The backtesting results of the portfolios indicate that the performance of the HRP portfolio is superior to that of its counterpart on both training and test data for the majority of the sectors studied.
  arXiv  Detail & Related papers  (2022-10-03T14:51:24Z)
• Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model [0.0]
  This paper presents an algorithmic approach for designing optimum risk and eigen portfolios for five thematic sectors of the NSE of India. The prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31, 2020. An LSTM model is designed for predicting future stock prices. Seven months after the portfolios were formed, on Aug 3, 2021, the actual returns of the portfolios are compared with the LSTM-predicted returns.
  arXiv  Detail & Related papers  (2022-02-06T07:41:20Z)
• Stock Portfolio Optimization Using a Deep Learning LSTM Model [1.1470070927586016]
  This work has carried out an analysis of the time series of the historical prices of the top five stocks from the nine different sectors of the Indian stock market from January 1, 2016, to December 31, 2020. Optimum portfolios are built for each of these sectors. The predicted and the actual returns of each portfolio are found to be high, indicating the high precision of the LSTM model.
  arXiv  Detail & Related papers  (2021-11-08T18:41:49Z)
• Deep Stock Predictions [58.720142291102135]
  We consider the design of a trading strategy that performs portfolio optimization using Long Short Term Memory (LSTM) neural networks. We then customize the loss function used to train the LSTM to increase the profit earned. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA.
  arXiv  Detail & Related papers  (2020-06-08T23:37:47Z)

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.