Large language models (LLMs) are evolving rapidly, from early text-generation systems to the latest models capable of sophisticated reasoning and multi-step decision-making. Reference applies LLMs to the development of a gold trading system. Unlike traditional quantitative models, which are typically built around forecasting future prices or returns, the authors …

AI is evolving rapidly across many areas of life, and trading is no exception. Large Language Models (LLMs) have increasingly been used as tools to assist financial analysts with research, data synthesis, and decision-making. More recently, researchers and practitioners have moved beyond analyst assistance and begun exploring LLMs as autonomous …

Regime detection is important in portfolio management and remains an active area of research, particularly in the age of machine learning and AI. Reference proposes a trading strategy based on machine learning, combined with regime detection using a Hidden Markov Model. Specifically, the machine learning technique used is LightGBM, …

Backtesting is an essential part of quantitative strategy development, and naturally, strategies are often selected based on strong backtest performance. However, an important question when evaluating backtested strategies is how much of the results reflects skill versus luck. Reference examines this issue by analyzing 1,726 commercially marketed strategies from …

There is a noteworthy line of research that decomposes asset or strategy returns into daytime and overnight components. This type of decomposition has been discussed previously in the context of the volatility risk premium. Reference follows a similar approach, examining SPY and nine sector ETFs over the period 1999 …

Retail options trading is rising rapidly, driven by factors such as the growth of retail brokers, the popularity of social media, and more flexible working hours. Alongside this trend, there has been an increased interest in research on retail options trading behavior. We recently discussed retail options trading and its …

Machine learning (ML) is increasingly applied in finance. In trading in particular, researchers aim to determine whether ML can generate consistent alpha. Reference revisits this question. The authors utilize a long sample dataset from 1970 to 2024 and evaluate a wide range of ML models, including linear, nonlinear, and …

Options trading volume has risen sharply in recent years, and a significant portion of this increase is attributed to the growing participation of retail traders. We have discussed retail options trading behavior in previous posts. Reference continues this line of research by examining how retail investors trade stock options …

Pairs trading is a classic quantitative trading strategy. Despite its widespread use, it continues to attract research attention. A recent line of research focuses on grouping underlyings into clusters with similar characteristics. We recently discussed such grouping using fundamental metrics. Reference also attempts to improve the pair selection process, …

Regression is one of the oldest predictive methods used in finance and remains widely applied today. Reference revisits this “simple” approach by employing logistic regression, which is particularly suited for modeling binary outcomes, such as whether an asset’s price will increase or decrease. The author uses cumulative returns over …