Academic Insights for Smarter Investing.
Strategies designed for hedge funds and proprietary trading firms.
73 posts
Taking both long and short positions to exploit relative mispricings.
54 posts
Stocks
52 posts
Strategies focused on US-listed assets.
47 posts
Long-term strategies with monthly to multi-year holding periods, often fundamentals-driven.
46 posts
Long-term investment strategies suited for large institutional investors.
45 posts
Systematic strategies based on factors like value, momentum, quality, etc.
37 posts
Strategies accessible to individual investors and traders.
36 posts
Strategies reacting to corporate events like earnings, mergers, or insider activity.
31 posts
Strategies that only take long positions in assets.
30 posts
Data-driven trading using machine learning, deep learning, and AI-enhanced models.
29 posts
Multi-region investment strategies, including developed and emerging markets.
28 posts
🧠💰 From macro to micro alpha, LLMs are transforming asset pricing, forecasting, and trading. Here are reviewed recent academic papers that explore how GPT models (ChatGPT, GPT-4, DeepSeek, etc.) are reshaping financial research and investment strategies. Below is a curated snapshot
An explainable multi-agent system using fine-tuned GPT-4o models for crypto portfolio management. Specialized agents analyze news, factors, and charts, collaborate on decisions, and execute trades—outperforming benchmarks in returns, accuracy, and interpretability.
This paper introduces a multi-step prompt strategy called Classify-and-Rethink (CAR) to help ChatGPT overcome behavioral biases—especially the framing effect—in financial decision-making. Applied to gold news, CAR improves score rationality and generates higher Sharpe ratios.
This paper shows that large language models (LLMs) can predict stock returns in China using public news. The best models—especially an ensemble of multiple LLMs and Baichuan—produce daily trading signals that generate up to 91% annualized alpha, proving highly effective in emerging markets.
This paper shows ChatGPT can act as a robo-advisor, generating stock picks from news that yield up to 3% monthly alpha, especially on political and policy-related events. Its performance beats traditional textual analysis in both U.S. and Chinese markets.
GPT-4 can create alpha. By simply prompting it with price and volume definitions, it generates factors that deliver Sharpe ratios up to 4.49 and annual returns up to 66%—without using any financial data.
This paper introduces HAID—a novel measure that captures new information in earnings call Q&As by comparing executive answers to ChatGPT’s responses. A higher HAID predicts more trading, stronger price reactions, improved analyst forecasts, and greater liquidity.
ChatGPT-based sentiment from full earnings call transcripts predicts stock returns up to six months ahead. After its democratization, retail investors’ trading aligns more closely with AI insights—narrowing the gap with informed traders.
ChatGPT can generate profitable day trading signals by analyzing real-time Twitter news and selecting stock tickers to buy and sell. The strategy earns significant intraday alpha, especially from short positions, even without firm-specific prompts.
This paper shows that hedge funds adopting ChatGPT saw a 3–5% boost in annualized returns post-2022.
This paper introduces asset embeddings—vector representations of stocks learned from investor portfolios using techniques like BERT and Word2Vec. These embeddings outperform traditional characteristics in explaining return comovement, offering a new framework for understanding investor behavior.
ChatGPT can classify Federal Reserve policy tone and identify monetary shocks with high accuracy—matching or outperforming traditional methods. The paper shows GPT-4 can replicate expert-level reasoning, offering a powerful tool for analyzing central bank communication.