← All lessons Track 04 · Quantitative finance · Lesson 4.3

Beta, alpha and alpha decay

6 min read

A fund that returned 20% sounds impressive, until you learn the market returned 25% and the fund took more risk. Beta and alpha are how professionals separate skill from exposure.

Beta

Beta measures how much a stock or portfolio moves with the market. A beta of 1 moves in line with it. 1.5 moves 50% more, in both directions. 0.5 moves half as much. Utilities and staples tend to have low betas, while tech and small caps tend to have high ones.

Market exposure is cheap: an index fund delivers beta for a tiny fee. So no one should pay high fees for it.

Alpha

Alpha is the return left over after accounting for the risk taken. The Capital Asset Pricing Model gives the simplest version:

Expected return = risk-free rate + beta × (market return − risk-free rate)

Anything above that is alpha. Modern versions add more factors (size, value, momentum, quality), because returns from simply tilting towards those factors can be bought cheaply too. Much of what once looked like stock-picking skill turned out to be factor exposure.

Alpha decay

An edge rarely lasts. Once a profitable pattern is known, capital piles in until the excess return disappears.

  • Publication: a well-known study by McLean and Pontiff looked at nearly a hundred return anomalies from academic papers. Returns were about a quarter lower out of sample, and more than half lower after publication.
  • Crowding: as more funds run similar strategies, entry and exit become more expensive, and crowded trades can unwind violently together, as in the 2007 quant crash.
  • Speed: for short-term signals, alpha can decay in seconds or milliseconds, which is why latency matters so much in lesson 4.4.

Why it matters for you

Before trusting a strategy, ask: is this beta in disguise? Is it a known factor? Has it survived after being published? And is the edge large enough to survive trading costs?

Key takeaway

Beta is the return you get for showing up. Alpha is the return for being right when others are wrong, and it shrinks as soon as others catch on.

Educational content only, not financial advice. Investing involves risk and you can lose money.

Sources (2)
  1. McLean and Pontiff (2016): Does Academic Research Destroy Stock Return Predictability?
  2. Jegadeesh and Titman (1993): Returns to Buying Winners and Selling Losers
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