DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection
Utilizing market forecasts is pivotal in optimizing portfolio selection strategies. We introduce DeepClair, a novel framework for portfolio selection. DeepClair leverages a transformer-based time-series forecasting model to predict market trends, facilitating more informed and adaptable portfolio decisions. To integrate the forecasting model into a deep reinforcement learning-driven portfolio selection framework, we introduced a two-step strategy: first, pre-training the time-series model on market data, followed by fine-tuning the portfolio selection architecture using this model. Additionally, we investigated the optimization technique, Low-Rank Adaptation (LoRA), to enhance the pre-trained forecasting model for fine-tuning in investment scenarios. This work bridges market forecasting and portfolio selection, facilitating the advancement of investment strategies.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep Reinforcement LearningTime SeriesTime Series ForecastingSimilar Papers 제목 키워드 기반
Log-Optimal Portfolio Selection Using the Blackwell Approachability Theorem
We present a method for constructing the log-optimal portfolio using the well-calibrated forecasts of market values. Dawid's notion of calibration and the Blackwell approachability theorem are used for computing well-cal…
Using Machine Learning to Forecast Market Direction with Efficient Frontier Coefficients
We propose a novel method to improve estimation of asset returns for portfolio optimization. This approach first performs a monthly directional market forecast using an online decision tree. The decision tree is trained …
Portfolio OptimizationProbabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels
In the effort to achieve carbon neutrality through a decentralized electricity market, accurate short-term load forecasting at low aggregation levels has become increasingly crucial for various market participants' strat…
Computational EfficiencyDensity EstimationLoad ForecastingPortfolio OptimizationMarket-Based Portfolio Variance
The investor, who holds his portfolio and doesn't trade his shares, at current time can use the time series of the market trades that were made during the averaging interval with the securities of his portfolio to assess…
Time SeriesVine Copula based portfolio level conditional risk measure forecasting
Accurately estimating risk measures for financial portfolios is critical for both financial institutions and regulators. However, many existing models operate at the aggregate portfolio level and thus fail to capture the…