Hi! Here's Iván from Noax Capital with this week's exciting newsletter, brimming with insights and discoveries on building robust investment strategies and risk models using Machine Learning.

In this edition, I am presenting the following sections:

• 🕹️ AI-Finance Insights: I summarize two must-read academic papers that mix cutting-edge ML/DL with Asset Pricing & Quant Finance:

  • DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions

  • Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling

• 🥐 Asset Pricing Insights: Today, I am preseting the paper: “Leveraging the Low-Volatility Effect in Modern Portfolio Management“

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“DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions”

👉 DeepVol processes raw high-frequency financial data through dilated causal convolutions to predict next-day volatility, eliminating the need for pre-computed realized measures.

👉 The architecture operates at 5-minute intervals using a one-day window, demonstrating that longer historical periods don't improve accuracy. Direct processing of raw data outperforms traditional approaches using handcrafted features.

👉 Testing on NASDAQ-100 data showed a 14.5% MAE reduction versus HEAVY model and 24.7% versus martingale benchmark. Out-of-sample MAE reached 7.288, with particularly strong performance during COVID-19 volatility.

👉 Model maintained accuracy across different market regimes and quickly adapted to volatility spikes. Adding realized measures as supplementary inputs provided no improvement.

👉 Implementation requires minimal data preprocessing and computational resources, making it practical for real-time trading applications.

👉 Extensive testing across multiple stocks confirms robust generalization capabilities, suggesting strong potential for portfolio-wide deployment.

“Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling”

👉 Novel approach combines supervised autoencoders with fractional differentiation and triple barrier labeling to enhance cryptocurrency trading performance. Study examines Bitcoin, Litecoin, and Ethereum from 2016-2022.

👉 Implementation leverages noisy data through balanced augmentation and optimized bottleneck size. Fractional differentiation maintains data memory while ensuring stationarity.

👉 30-minute strategy achieved highest information ratio (2.03) and starred information ratio (6.22) for Bitcoin, with lowest maximum drawdown (14.02%) versus buy-and-hold's 53.30%.

👉 Portfolio of SAE-MLP strategies outperformed buy-and-hold benchmarks despite high return correlations. 20-minute strategy delivered highest total return (619.80%) and annualized return (79.41%).

👉 Walk-forward validation demonstrates robust performance across different market regimes, including COVID-19 volatility period. Model shows strong adaptation to market changes while maintaining low drawdowns.

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Asset Pricing Insights

“Leveraging the Low-Volatility Effect in Modern Portfolio Management”

A new paper from Robeco researchers reveals innovative ways to capitalize on one of the most persistent market anomalies - the low-volatility effect. Keep reading! 👇

This research presents five practical strategies to leverage low-volatility investing, addressing common constraints that prevent investors from fully utilizing this powerful factor.

➡️ By combining low volatility with momentum and value factors, this strategy delivers impressive 12.5% annual returns compared to the market's 10.4%.

➡️The approach achieves a superior Sharpe ratio of 0.73 versus 0.51 for the market, while simultaneously reducing relative risk from 11.4% to 8.4%. This enhancement demonstrates how integrating multiple factors can amplify the low-volatility effect.

➡️ Moving beyond the traditional 60/40 portfolio, replacing it with a 70% Low-Vol+ and 30% bonds allocation achieves 10.6% returns while maintaining similar volatility levels.

➡️This approach boosts the Sharpe ratio to an impressive 0.82, providing significantly better risk-adjusted returns than conventional portfolios. The strategy particularly shines in preserving capital during market downturns.

➡️ Through careful application of 140% exposure to low-vol stocks, this approach delivers exceptional 15.7% annual returns compared to the market's 10.4%.

➡️This outperformance is achieved through modest leverage while maintaining strong risk control. An alternative implementation using futures requires only 30% leverage, offering a more capital-efficient solution.

➡️ This market-neutral strategy employs short positions to generate 9.6% returns with zero correlation to market movements. The approach has demonstrated remarkable consistency, producing positive returns in 83% of 12-month periods. This makes it an excellent portfolio diversifier, providing a unique, uncorrelated return stream.

➡️ Offering comparable downside protection at a fraction of the cost of traditional put options, this strategy reduces portfolio volatility from 15.4% to 9.2%. The approach proves particularly effective during moderate market declines, addressing a key weakness of put option strategies while avoiding their significant carry costs.

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