Case study: predicting a competing bid

How do top quantitative trading firms use generative AI?  @versorinvestments , a $1.4B[1] quantitative investment boutique in the asset management industry, reveals how human ingenuity drives its AI-powered investment research and machine learning in finance pipeline. Partners DeWayne Louis and Nishant Gurnani explain how they combine supervised machine learning, natural language processing, and alternative data—from credit card receipts to job postings—to generate investment insights and forecast returns across global equity markets. We discuss why strong quant trading strategies start with clean data, how to avoid data-mining traps, and why top quantitative researchers think like market scientists, not model-builders.We dive into Versor’s flagship hedge fund strategies, from its quant merger arbitrage framework that predicts competing bids to its global equities tactical trading (GETT) strategy capturing dislocations in global equity markets. Nishant and DeWayne unpack what “positive convexity” means in practice, how to design market-neutral quant trading strategies uncorrelated to CTAs, and how Versor’s 30-year research lineage from Investcorp reflects true capital markets innovation. They share lessons on quant research culture, hiring IIT-trained talent, and how disciplined portfolio construction and human-guided AI define the next generation of machine learning in finance and algorithmic trading.We also discuss...How alternative data investing drives alpha in the modern AI quant hedge fund ecosystemBuilding models for event-driven investing strategies and predicting competing bids in merger arbitrage hedge funds – read more here.How Versor’s managed futures strategy achieves diversification and positive convexity investment performanceIdentifying global dislocations through global equity index futures trading and relative value signalsConstructing market-neutral portfolios through advanced market neutral quantitative strategies – read more here

Why Versor’s success as a research-driven hedge fund comes from blending data science with human intuitionTurning unstructured data in finance — from job postings to credit card data — into tradable insightsDesigning an algorithmic trading platform that scales across multiple asset classes and geographiesApplying machine learning hedge fund strategies to model complex market behaviorsHow disciplined portfolio construction quant strategies optimize risk-adjusted returnsThe evolution of data-driven investing hedge funds and how AI is reshaping portfolio managementThe future of quant talent recruitment in finance and why deep research skills beat brainteasersLessons from 30 years of capital markets innovation and systematic alpha generationFuture of AI in hedge funds — read more here: https://www.linkedin.com/pulse/quant-intel-agentic-ai-quantitative-investing-versorinvestments-cygxf/ Why human-guided AI remains critical in building resilient, high-Sharpe machine learning hedge fund strategies