How to time the market with News Sentiment Analysis?

Stress Index Strategy Enhanced with Financial News Sentiment Analysis

July 05, 2024

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Hi! Here's Iván 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 three must-read academic papers that mix cutting-edge ML/DL with Asset Pricing & Quant Finance:

    • Stress Index Strategy Enhanced with Financial News Sentiment Analysis

    • Forecasting Realized Volatility Using Machine Learning Models

    • Stock Picking with Machine Learning

  • 💊 AI Essentials: Today, I'm thrilled to share an outstanding video titled "Machine Learning for Beginners in 2024." This course begins with a Machine Learning Roadmap for 2024, emphasizing career paths and beginner-friendly theory. It then moves on to hands-on practical applications and a comprehensive end-to-end project using Python.

  • 🥐 Asset Pricing Insights: In this edition, I recommend the paper "Dissecting Characteristics Nonparametrically," which introduces a novel nonparametric approach to predicting stock returns using firm characteristics. Unlike traditional linear models, this method does not assume a specific relationship between characteristics and returns, allowing for more flexibility and accuracy in predictions.

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AI-Finance Insights

“Stress Index Strategy Enhanced with Financial News Sentiment Analysis”

🔊 How to time the market with News Sentiment Analysis? 👇

The paper introduces a novel risk-on risk-off strategy for the stock market, combining a financial stress indicator with sentiment analysis derived from ChatGPT reading Bloomberg's daily market summaries. The main contributions and findings are as follows:

👉 The study leverages market stress forecasts based on volatility and credit spreads, enhanced with financial news sentiment derived from GPT-4. This combination significantly improves performance, resulting in higher Sharpe ratios and reduced maximum drawdowns.

👉 Empirical results demonstrate the strategy's robustness and effectiveness across multiple equity markets, including NASDAQ, S&P 500, and six major global equity markets. The Dynamic SI+News strategy consistently outperforms other strategies, achieving the highest Sharpe ratios (0.81 for S&P 500, 0.89 for NASDAQ) and Calmar ratios (0.56 for S&P 500, 0.62 for NASDAQ), while also managing market downturns more effectively.

“Forecasting Realized Volatility Using Machine Learning Models”

🔊 How to forecast realized volatility using machine learning? 👇

This paper investigates the application of machine learning (ML) techniques to predict realized volatility (RV) in financial markets, comparing their performance against traditional HAR-family models.

Two main takeaways:

👉 The study evaluates various ML models, particularly focusing on the Order Book Machine Learning (OB-ML) model. These models incorporate high-frequency limit order book (LOB) data, news sentiment, and historical volatility measures to forecast RV. The OB-ML model demonstrates superior performance on normal volatility days compared to traditional HAR-family models, achieving better mean squared error (MSE) and quasi-likelihood (QLIKE) loss function scores.

👉 Empirical analysis highlights that ML models, especially OB-ML, outperform HAR-family models on normal volatility days but underperform during high volatility periods. Key variables influencing RV predictions include mid prices at various LOB levels, mean bid, and mean ask prices. The robustness checks confirm the OB-ML model's efficacy across different configurations, with optimal performance requiring more complex models during high volatility periods.

“Stock Picking with Machine Learning”

🔊 How to use machine learning for stock picking and portfolio management? 👇

This paper presents a comprehensive study on applying machine learning (ML) models to improve stock selection and portfolio performance.

Two main takeaways:

👉 The study evaluates several ML models, including deep neural networks (DNN), long short-term memory networks (LSTM), elastic net (ENet), least absolute shrinkage and selection operator (LASSO), principal component analysis (PCA), random forest (RF), and boosting methods. These models use equity factors, fundamental data, and technical indicators to predict stock returns and identify outperforming stocks.

👉 Empirical analysis reveals that ML-based portfolios significantly outperform traditional benchmarks like the S&P 500 index and equally weighted portfolios. The ensemble model achieves the highest Sharpe ratios, up to 0.84, with annualized returns of 20.8% and a volatility of 24.8%. DNN and LSTM models also show strong performance, with Sharpe ratios of 0.78 and 0.74, respectively.

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AI-Essentials

📹 Check out this engaging course on "Machine Learning for Beginners in 2024." The course starts with a Machine Learning Roadmap for 2024, highlighting career paths and beginner-friendly theory. Then it progresses to hands-on practical applications and a comprehensive end-to-end project using Python. 👇

Asset Pricing Insights

“Dissecting Characteristics Nonparametrically”

The paper introduces a novel nonparametric approach to predicting stock returns using firm characteristics. Unlike traditional linear models, this method does not assume a specific relationship between characteristics and returns, allowing for more flexibility and accuracy in predictions.

The methodology leverages an additive model to address the "curse of dimensionality," significantly improving prediction accuracy. This model captures nonlinear relationships and is robust to outliers, enhancing its out-of-sample predictive power and adaptability to various market conditions.

Empirical results demonstrate the superior performance of this nonparametric approach. For equally weighted portfolios, it achieves a Sharpe ratio of 3.42, compared to 2.26 for linear models. It also selects fewer characteristics while maintaining or improving accuracy, showcasing its efficiency and robustness in financial markets.

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