Increase Alpha: Performance and Risk of an AI-Driven Trading Framework

ArXiv ID: 2509.16707 “View on arXiv”

Authors: Sid Ghatak, Arman Khaledian, Navid Parvini, Nariman Khaledian

Abstract

There are inefficiencies in financial markets, with unexploited patterns in price, volume, and cross-sectional relationships. While many approaches use large-scale transformers, we take a domain-focused path: feed-forward and recurrent networks with curated features to capture subtle regularities in noisy financial data. This smaller-footprint design is computationally lean and reliable under low signal-to-noise, crucial for daily production at scale. At Increase Alpha, we built a deep-learning framework that maps over 800 U.S. equities into daily directional signals with minimal computational overhead. The purpose of this paper is twofold. First, we outline the general overview of the predictive model without disclosing its core underlying concepts. Second, we evaluate its real-time performance through transparent, industry standard metrics. Forecast accuracy is benchmarked against both naive baselines and macro indicators. The performance outcomes are summarized via cumulative returns, annualized Sharpe ratio, and maximum drawdown. The best portfolio combination using our signals provides a low-risk, continuous stream of returns with a Sharpe ratio of more than 2.5, maximum drawdown of around 3%, and a near-zero correlation with the S&P 500 market benchmark. We also compare the model’s performance through different market regimes, such as the recent volatile movements of the US equity market in the beginning of 2025. Our analysis showcases the robustness of the model and significantly stable performance during these volatile periods. Collectively, these findings show that market inefficiencies can be systematically harvested with modest computational overhead if the right variables are considered. This report will emphasize the potential of traditional deep learning frameworks for generating an AI-driven edge in the financial market.

Keywords: deep learning, feed-forward networks, recurrent networks, signal processing, quantitative trading, Equities

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper uses established deep learning architectures (feed-forward, recurrent networks) without heavy mathematical derivations, focusing on practical implementation rather than novel theory. It demonstrates high empirical rigor with detailed backtesting, risk metrics (Sharpe >2.5, max drawdown ~3%), regime analysis, and performance evaluation across 814 equities, though it omits full disclosure of proprietary model details.

  flowchart TD
    A["Research Goal<br>Uncover & exploit subtle patterns<br>in financial markets"] --> B["Methodology<br>Domain-focused DNN: FFNN & RNNs<br>Curated Features"]
    B --> C["Data & Inputs<br>Price, Volume, Cross-sectional<br>800 US Equities Daily"]
    C --> D["Computation<br>Minimal overhead, robust to low SNR"]
    D --> E["Backtesting & Metrics<br>Sharpe Ratio, Max Drawdown, Correlation"]
    E --> F{"Outcomes"}
    F --> F1["Sharpe > 2.5"]
    F --> F2["Max Drawdown ~3%"]
    F --> F3["Near-zero correlation with S&P 500"]
    F --> F4["Stable in volatile markets"]