AlphaX: An AI-Based Value Investing Strategy for the Brazilian Stock Market

ArXiv ID: 2508.13429 “View on arXiv”

Authors: Paulo André Lima de Castro

Abstract

Autonomous trading strategies have been a subject of research within the field of artificial intelligence (AI) for aconsiderable period. Various AI techniques have been explored to develop autonomous agents capable of trading financial assets. These approaches encompass traditional methods such as neural networks, fuzzy logic, and reinforcement learning, as well as more recent advancements, including deep neural networks and deep reinforcement learning. Many developers report success in creating strategies that exhibit strong performance during simulations using historical price data, a process commonly referred to as backtesting. However, when these strategies are deployed in real markets, their performance often deteriorates, particularly in terms of risk-adjusted returns. In this study, we propose an AI-based strategy inspired by a classical investment paradigm: Value Investing. Financial AI models are highly susceptible to lookahead bias and other forms of bias that can significantly inflate performance in backtesting compared to live trading conditions. To address this issue, we conducted a series of computational simulations while controlling for these biases, thereby reducing the risk of overfitting. Our results indicate that the proposed approach outperforms major Brazilian market benchmarks. Moreover, the strategy, named AlphaX, demonstrated superior performance relative to widely used technical indicators such as the Relative Strength Index (RSI) and Money Flow Index (MFI), with statistically significant results. Finally, we discuss several open challenges and highlight emerging technologies in qualitative analysis that may contribute to the development of a comprehensive AI-based Value Investing framework in the future

Keywords: autonomous trading, value investing, reinforcement learning, backtesting bias, AlphaX strategy, Equities

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper applies standard value investing metrics (P/E, P/B) and AI models without heavy mathematical derivations, but it emphasizes bias-controlled backtesting and performance metrics against Brazilian benchmarks, indicating a practical, implementation-focused approach.

  flowchart TD
    A["Research Goal:<br>Develop AI Value Investing Strategy<br>for Brazilian Stock Market"] --> B
    subgraph B ["Methodology & Data"]
        B1["Historical Price Data<br>Brazilian Equities"] --> B2{"Computational Simulations"}
        B2 --> B3["Value Investing Logic + AI<br>Reinforcement Learning"]
        B4["Bias Control &<br>Backtesting Validation"] --> B2
    end
    B --> C
    subgraph C ["Key Findings"]
        C1["Outperforms Major<br>Brazilian Benchmarks"]
        C2["Superior to Technical<br>Indicators RSI & MFI"]
        C3["Statistically Significant<br>Risk-Adjusted Returns"]
    end
    C --> D["Outcome:<br>AlphaX Strategy Framework<br>Ready for Deployment"]