The strategy that keeps MAKING BILLIONS to INSTITUTIONAL traders: PEAD.

I'm Matteo Conti. I spent 7 years as a market maker for a large investment bank, where I generated over €30M in trading PnL, and now I run my own hedge fund where I automate every strategy I trade. This channel is about cutting through the noise that retail trading gurus push, and teaching you how professionals actually think. PEAD, post-earnings announcement drift, is one of the most documented market inefficiencies in financial history. Institutional traders have quietly made billions off it since the 1980s. Most retail traders ignore it for one reason: nobody ever told them it exists. In this video I walk through the academic research that documents it, then extract a simple, mechanical, fully rule-based strategy straight out of those papers and backtest it. === THE RESEARCH === Four papers, 53 years. Discovered by Ball and Brown in 1968, formalized by Bernard and Thomas in 1989, modernized by Livnat and Mendenhall in 2006, and confirmed across 200+ studies by Fink in 2021. Most anomalies get arbitraged away within a few years of publication. PEAD has stubbornly refused to disappear. === THE STRATEGY, IN FULL === This is the by-the-book version I walk through in the video. Tested on a basket of 20 large-cap US stocks across multiple sectors, 658 earnings events, 2018 to 2026. Entry conditions • LONG when a company reports earnings above consensus AND the stock reacts positively on the reaction day • SHORT when a company reports earnings below consensus AND the stock reacts negatively on the reaction day • If the surprise and the price reaction disagree, no trade (this is the concordant filter) • Reaction day: same day for BMO announcements, next session for AMC. Enter at the open of the following session Exit condition • Time-based exit: hold for 60 trading days, then close. No stop loss, no take profit in the basic version. The drift is statistical, not directional, and tight stops on a 60-day hold cut winners systematically Position sizing • Flat 10% of capital per position. Simple, no optimization, runs on a 100k account without insane single-stock risk === HOW TO USE THIS VIDEO === If you take one thing away: you do not need to be a coder, and you do not need expensive data, to do real research. The process: 1. Find an edge in academic papers, not in guru threads 2. Extract three things from the research: entry, exit, sizing 3. Source the data 4. Hand the rules to an LLM and have it code the simplest possible version 5. Backtest it as-is, no optimization, no curve fitting Only then add complexity, one isolated step at a time Tools used: • MultiCharts with PowerLanguage for the coding, backtest and automation • Claude to write the code from the strategy rules • Zacks.com for free historical earnings data === DISCLAIMER === This video is for educational purposes only. Nothing in it is financial advice, investment advice, or a recommendation to buy or sell any security or derivative. The strategy shown is not a finalized system: the stock basket was picked manually, and no out-of-sample testing or further validation was performed. It exists to demonstrate that a decades-old, well-documented edge can be pulled out of academic research with free tools. Backtest results are historical and not predictive of future performance. Trading futures and other leveraged instruments involves substantial risk of loss and is not suitable for every investor. Do your own research and consult a licensed professional before risking real capital. If this was useful, follow the channel and drop a word in the comments. That's all the algorithm needs, and there's much more coming. #trading #trader #algotrading #algorithmictrading #quantfinance #PEAD #systematictrading #backtesting #tradingstrategy #earnings #stockmarket #tradingeducation #marketinefficiency #quantitativetrading