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DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection

Donghee Choi, Jinkyu Kim, Mogan Gim, Jinho Lee, Jaewoo Kang

TL;DR

DeepClair is introduced, a novel framework for portfolio selection that leverages a transformer-based time-series forecasting model to predict market trends, facilitating more informed and adaptable portfolio decisions.

Abstract

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.

DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection

TL;DR

DeepClair is introduced, a novel framework for portfolio selection that leverages a transformer-based time-series forecasting model to predict market trends, facilitating more informed and adaptable portfolio decisions.

Abstract

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.
Paper Structure (25 sections, 9 equations, 2 figures, 4 tables)

This paper contains 25 sections, 9 equations, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Time-series Forecasting Transformers
  4. Reinforcement Learning adopting to Downstream Tasks
  5. Parameter Efficient Fine-Tuning Method
  6. Problem Statement
  7. DeepClair
  8. Market Price Forecasting Model
  9. Training Procedures by Time Series Forecasting
  10. Portfolio Optimization Framework
  11. Asset Scoring Module
  12. Portfolio Selection Module
  13. Market Scoring Module
  14. Adaptation of Forecasting Module using LoRA
  15. Formulating Portfolio Selection Vectors
  16. ...and 10 more sections

Figures (2)

  • Figure 1: The model architecture of DeepClair is detailed in Sec \ref{['sec:deepclair']}. Task 1 involves pre-training the forecasting module using a supervised learning approach using Market Price Forecasting Model $M_1$ described in Sec \ref{['subsec:market_price_forecasting_model']}. In Task 2, we use the forecasting model $M_1$ with pre-trained weights from Task 1, fine-tuning a relatively small number of parameters with LoRA techniques. During investment trials through RL, this fine-tuned model is used for the portfolio selection task, defined as a portfolio optimization framework $M_2$ described in Sec \ref{['subsec:portfolio_optimization_framework']}. The portfolio selection module generates long $\mathbf{w}_t^{long}$ and short $\mathbf{w}_t^{short}$ position vectors with the outputs $\mathbf{v}_t$. Based on the forecasting outcomes, DeepClair determines the long and short ratios of each asset using the vector $\rho_t$. The actual portfolio ratios for each asset by combining the outputs $\mathbf{w}_t$ from the Portfolio Selection Module and $\rho_t$ from the Market Scoring Module.
  • Figure 2: Nasdaq Index for two notable characteristics of market periods and the changing patterns of $\rho$. DeepClair adjusts its position during the bearish market of the crisis periods, transitioning from P1 to P2, and during the subsequent recovery period, transitioning from P2 to P3 in both (a) and (b).