How Quant Trading Turned Math Into Wall Street Edge
A Hebrew-language explainer traces how quantitative trading moved from a fringe idea to a dominant force on Wall Street, and why it still carries serious risks. The article argues that stock-market success is hard not only because of human bias, but because returns are highly concentrated, with only a small share of companies generating most of the market’s wealth.
Several studies are cited to show how narrow winners are. One Arizona State University study by Prof. Hendrik Bessembinder examined more than 29,000 stocks from 1926 to 2023 and found that 58% underperformed U.S. government bonds, about 38% beat them only modestly, and just 4% produced most market wealth. A 2023 global study of more than 64,000 stocks found that about 55% of U.S. stocks and 57% of non-U.S. stocks lagged U.S. Treasuries. Fidelity data cited in the article says the seven “Magnificent Seven” stocks, Apple, Microsoft, Amazon, Nvidia, Alphabet, Meta and Tesla, accounted for more than 42% of the S&P 500’s return in 2025. The piece also recalls the dot-com bubble, when 15 companies drove 75% to 80% of S&P 500 profits and 85% to 90% of Nasdaq gains.
The story credits Louis Bachelier with the early mathematical view of markets, but says Edward Thorp made the approach practical. Thorp, known for his blackjack work and the book “Beat the Dealer,” later applied similar thinking to finance, helping create one of the first model-based hedge funds in the late 1960s. He used statistical arbitrage, delta hedging, mean reversion and pairs trading, including a recent oil example in which a Brent-WTI mismatch created about a 6% gain when the spread normalized.
The article then turns to Jim Simons, a Berkeley-trained mathematician who worked in geometry, topology, physics and cryptanalysis before founding Renaissance Technologies in the early 1980s and the Medallion fund in 1988. Medallion reportedly delivered average annual gross returns of about 66% and net returns of around 39%, turning $1,000 into more than $90 million by 2022 and generating over $100 billion in trading profits. It also says quant strategies now manage an estimated 25% to 30% of hedge fund assets and 60% to 70% of U.S. and European securities volume, but remain vulnerable, as shown by the August 2007 “quant crisis,” when many market-neutral funds simultaneously lost 10% to more than 20% during a severe liquidity shock. The article closes by saying AI may further democratize quant trading for retail investors.
