Weekly Recap
Research on investing, macro, and trading.
Welcome to this week’s collection of links to the latest research and insights on quant investing. Below, you’ll find a curated list where each title links to the source for more information. Thank you for reading!
ESG
Drawing Up the Bill: Are ESG Ratings Related to Stock Returns Around the World? (Alves et al.)
Using a very comprehensive dataset covering global stock returns and ESG ratings, the authors find little relation between ratings and stock returns.
Lecture Notes
Mathematics of Neural Networks (Smets)
The field of deep learning/neural networks has seen a rapid development during the recent decade. These lecture notes cover the mathematics of this field.
Lecture notes on ridge regression (van Wieringen)
Extensive lecture notes on ridge regressions, lasso, elastic net, and related techniques.
Machine Learning and Large Language Models
Can ChatGPT Generate Stock Tickers to Buy and Sell for Day Trading? (Cho)
The study explores ChatGPT's ability to generate profitable day trading strategies by analyzing news data, highlighting its potential to identify mispriced stocks.
Moving Targets (Cohen and Nguyen)
Managers are found to strategically change performance metrics in investor communications, leading to negative future returns for firms.
Synthetic Data Applications in Finance (Potluru et al.)
This is a thorough review of finance applications of synthetic data, with a generous reference list for further reading.
AI at the Frontier of Economic Research (Giesecke)
This is a survey paper on applications of machine learning and large language models in economics and finance.
FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications (Konstantinidis et al.)
The authors present a finance-specific language model, FinLlama, fine-tuned on financial news to enhance trading strategies with improved sentiment analysis.
Advanced Statistical Arbitrage with Reinforcement Learning (Ning and Lee)
A model-free approach to statistical arbitrage based on reinforcement learning is proposed, outperforming other methods.
Improving Deep Learning of Alpha Term Structures from the Order Book (Kolm and Westray)
The paper evaluates deep learning models for predicting high-frequency stock returns, finding simple LSTM models often outperform more complex architectures.
Macro
Maximally Forward-Looking Core Inflation (Goulet Coulombe et al.)
The authors introduce a new approach to measuring core inflation using a supervised learning method that improves inflation prediction accuracy.
Quant Blogs
Trading 0DTE Options with the IBKR Native API (Robot Wealth)
Macro trends and equity allocation: a brief introduction (Macro synergy)
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