Month: July 2025

Quantitative trading based on market sentiment is a less developed area compared to traditional approaches. With the explosion of social media, advances in computing resources, and AI technology, sentiment-based trading is making progress. In this post, I will explore some aspects of sentiment trading.

Using ChatGPT to Extract Market Sentiment for Commodity Trading

A Large Language Model (LLM) is an advanced AI system trained on vast amounts of text data to understand, generate, and analyze human language. In finance, LLMs are used for tasks like analyzing earnings reports, generating market sentiment analysis, automating financial research, and enhancing algorithmic trading strategies.

Reference [1] examines the effectiveness of ChatGPT in predicting commodity returns. Specifically, it extracts commodity news information and forecasts commodity futures returns. The study gathers over 2.5 million articles related to the commodity market from nine international newspapers across three countries, covering a diverse set of 18 commodities.

Findings

-A novel Commodity News Ratio Index (CNRI) was developed using ChatGPT, derived from the analysis of more than 2.5 million news articles from nine international newspapers across 18 commodities.

-The CNRI effectively forecasts commodity futures excess returns over 1- to 12-month periods, demonstrating significant predictive power in both in-sample and out-of-sample regression analyses.

-ChatGPT was used to classify sentiment in commodity-related news as either positive or negative, based on headlines, abstracts, or full article content.

-The CNRI shows stronger forecasting accuracy during specific macroeconomic conditions—particularly economic expansions, contango market phases, and periods of declining inflation.

-This ChatGPT-based approach outperforms traditional text analysis methods, including BERT and Bag-of-Words, in predicting future returns in commodity markets.

-The study controlled for various business variables and economic indicators, confirming the independent predictive significance of the CNRI.

-Results indicate that the CNRI also holds macroeconomic insight, offering valuable signals on broader economic performance beyond commodity markets.

-Findings affirm the utility of ChatGPT in financial forecasting, showcasing the broader potential of LLMs in understanding and extracting actionable intelligence from complex financial text data.

-This research highlights the growing role of AI in finance, illustrating how LLMs can enhance decision-making for investors, analysts, and risk managers alike.

In short, ChatGPT proves useful in forecasting commodity market dynamics and provides valuable insights for investors and risk managers.

Reference

[1]Shen Gao, Shijie Wang, Yuanzhi Wang, Qunzi Zhang, ChatGPT and Commodity Return, Journal of Futures Market, 2025; 1–15

Using the Number of Confirmed Covid Cases as a Sentiment Indicator

COVID-19, the novel coronavirus, was a source of anxiety for markets and individuals around the world since its outbreak in December 2019. Many traders looked for ways to use the information on the spread of the virus to predict market movements.

In Reference [2], the authors established an intraday algorithmic trading system that would open a short position in the Eurostoxx 50 futures market if the number of new confirmed cases of Covid-19 increased in the previous day (suggesting that fear of the epidemic rises), and close by afternoon. The system will open a long position if the new confirmed cases of Covid-19 have decreased from the previous day. The trading system achieved an annual return of 423% and a Sharpe ratio of 4.74.

Findings

-Daily confirmed COVID-19 cases were used as a sentiment proxy, reflecting public fear and uncertainty in financial markets during the pandemic.

-Researchers built an intraday trading system for Eurostoxx 50 futures, responding to increases or decreases in new Covid-19 cases reported the previous day.

-The system opened short positions after rising case counts and long positions after declines, closing trades by the afternoon to reduce overnight exposure.

-This simple rule-based strategy delivered an annual return of 423% and a Sharpe ratio of 4.74, suggesting strong performance under extreme market stress.

-The study demonstrated that pandemic-related health data could serve as a reliable short-term predictor of market direction, especially during crisis periods.

-Results reinforce the idea that emotional triggers—like health fears—can impact trading behavior just as much as traditional economic indicators or financial models.

-During high-uncertainty environments, metrics that reflect collective anxiety, such as COVID-19 cases, can outperform classic sentiment tools like the VIX index.

-The strategy showed how non-financial data can be directly translated into market actions, offering practical tools for risk-aware investors and quant traders.

-Overall, the research contributes to behavioral finance by quantifying the influence of fear on asset prices in moments of extreme public concern.

The article presented new evidence that emotions have an impact on financial markets, especially in situations of extreme uncertainty. In these situations, investors may utilize a variety of investment techniques based on metrics reflecting the progression of fear.

Reference

[2] Gómez Martínez, R., Prado Román, C., &Cachón Rodríguez , G. (2021). Algorithmic trading based on the fear of Covid-19 in Europe, Harvard Deusto Business Research 10(2), 295-304.

Closing Thoughts

Together, these studies highlight the growing role of alternative data and AI-driven sentiment analysis in financial forecasting. From pandemic case counts to millions of news articles, both fear and information flow can shape markets in measurable ways. Whether through rule-based trading or LLM-powered indices, the findings underscore how emotion, uncertainty, and unstructured data are becoming key inputs in modern investment strategies.

Machine learning has many applications in finance, such as asset pricing, risk management, portfolio optimization, and fraud detection. In this post, I discuss the use of machine learning in forecasting volatility.

Using Machine Learning to Predict Market Volatility

The unpredictability of the markets is a well-known fact. Despite this, many traders and portfolio managers continue to try to predict market volatility and manage their risks accordingly. Usually, econometric models such as GARCH are used to forecast market volatility.

In recent years, machine learning has been shown to be capable of predicting market volatility with accuracy. Reference [1] explored how machine learning can be used in this context.

Findings

-Machine learning models can accurately forecast stock return volatility using a small set of key predictors: realized volatility, idiosyncratic volatility, bid-ask spread, and returns.

-These predictors align with existing empirical findings, reinforcing the traditional risk-return trade-off in finance.

-ML methods effectively capture both the magnitude and direction of predictor impacts, along with their interactions, without requiring pre-specified model assumptions.

-Large current-period volatility values strongly predict higher future volatility; small values have a muted or negative impact.

-LSTM models outperform feedforward neural networks and regression trees by leveraging temporal patterns in historical data.

-An LSTM using only volatility and return history over one year performs comparably to more complex models with additional predictors.

-LSTM models function as distribution-free alternatives to traditional econometric models like GARCH.

-Optimal lag length remains critical in LSTM performance and must be selected through model training.

-The study reports an average predicted realized volatility of 44.1%, closely matching the actual value of 43.8%.

-Out-of-sample R² values achieved are significantly higher than those typically reported in related volatility forecasting literature.

In short, the paper aimed to demonstrate the potential of machine learning for modeling market volatility. In particular, the authors have shown how the LSTM model can be used to predict market volatility and manage risks. The results suggest that this is a promising alternative approach to traditional econometric models like GARCH.

Reference

[1] Filipovic, Damir and Khalilzadeh, Amir, Machine Learning for Predicting Stock Return Volatility (2021). Swiss Finance Institute Research Paper No. 21-95

Machine Learning Models for Predicting Implied Volatility Surfaces

The Implied Volatility Surface (IVS) represents the variation of implied volatility across different strike prices and maturities for options on the same underlying asset. It provides a three-dimensional view where implied volatility is plotted against strike price (moneyness) and time to expiration, capturing market sentiment about expected future volatility.

Reference [2] examines five methods for forecasting the Implied Volatility Surface of short-dated options. These methods are applied to forecast the level, slope, and curvature of the IVS.

Findings

-The study evaluates five methods—OLS, AR(1), Elastic Net, Random Forest, and Neural Network—to forecast the implied volatility surface (IVS) of weekly S&P 500 options.

-Forecasts focus on three IVS characteristics: level, slope, and curvature.

-Random Forest consistently outperforms all other models across these three IVS dimensions.

-Non-learning-based models (OLS, AR(1)) perform comparably to some machine learning methods, highlighting their continued relevance.

-Neural Networks forecast the IVS level reasonably well but perform poorly in predicting slope and curvature.

-Elastic Net, a linear machine learning model, is consistently outperformed by the non-linear models (Random Forest and Neural Network) for the level characteristic.

-The study emphasizes the importance of model selection based on the specific IVS characteristic being forecasted.

-Performance evaluation is supported using the cumulative sum of squared error difference (CSSED) and permutation variable importance (VI) metrics.

-The research highlights the utility of Random Forest in capturing complex, non-linear patterns in IVS dynamics.

-Accurate IVS forecasting is valuable for derivative pricing, hedging, and risk management strategies.

This research highlights the potential of machine learning in forecasting the implied volatility surface, a key element in options pricing and risk management. Among the five methods studied, Random Forest stands out as the most consistent and accurate across multiple IVS features.

Reference

[2] Tim van de Noort, Forecasting the Characteristics of the Implied Volatility Surface for Weekly Options: How do Machine Learning Methods Perform? Erasmus University, 2024

Closing Thoughts

These studies highlight the growing effectiveness of machine learning in financial forecasting, particularly for market volatility and implied volatility surfaces. Models like LSTM and Random Forest demonstrate clear advantages over traditional methods by capturing complex patterns and dependencies. As financial markets evolve, leveraging such tools offers a promising path for enhancing predictive accuracy and risk management.