Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management

ArXiv ID: 2309.16679 “View on arXiv”

Authors: Unknown

Abstract

Financial portfolio management describes the task of distributing funds and conducting trading operations on a set of financial assets, such as stocks, index funds, foreign exchange or cryptocurrencies, aiming to maximize the profit while minimizing the loss incurred by said operations. Deep Learning (DL) methods have been consistently excelling at various tasks and automated financial trading is one of the most complex one of those. This paper aims to provide insight into various DL methods for financial trading, under both the supervised and reinforcement learning schemes. At the same time, taking into consideration sentiment information regarding the traded assets, we discuss and demonstrate their usefulness through corresponding research studies. Finally, we discuss commonly found problems in training such financial agents and equip the reader with the necessary knowledge to avoid these problems and apply the discussed methods in practice.

Keywords: Deep Learning, Financial Trading, Supervised Learning, Reinforcement Learning, Sentiment Analysis, Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper covers advanced deep learning architectures (RNNs, LSTMs, Transformers, DRL) and discusses mathematical concepts like non-stationarity and normalization, but the excerpt focuses on literature review and theoretical challenges rather than presenting specific backtests, code, or statistical performance metrics.

  flowchart TD
    A["Research Goal: DL for Financial Portfolio Management"] --> B["Data Collection & Preprocessing"]
    
    subgraph B ["Data Sources"]
        B1["Price Data<br>OHLCV"]
        B2["Sentiment Data<br>Online Sources"]
    end

    B --> C["Methodology"]
    
    subgraph C ["Dual Approach"]
        C1["Supervised Learning<br>for Prediction"]
        C2["Reinforcement Learning<br>for Decision Making"]
    end

    C --> D["Computational Process<br>Deep Learning Models"]
    
    subgraph D ["Model Types"]
        D1["Recurrent Networks<br>LSTM/GRU"]
        D2["Transformers<br>Attention Mechanism"]
        D3["Actor-Critic RL<br>Agent"]
    end

    D --> E["Key Findings & Outcomes"]
    
    subgraph E ["Results"]
        E1["Sentiment Enhances<br>Performance"]
        E2["RL Outperforms<br>Traditional Methods"]
        E3["Multi-Asset<br>Optimization"]
        E4["Practical Training<br>Guidelines"]
    end