Fair Game Model

In 1970, economist Eugene Fama pulled together decades of earlier research on the Random Walk Hypothesis. He built it into what he called the efficient market hypothesis (EMH). He presented that theory in terms of a fair game model.

The idea is simple to state and hard to beat. Suppose the current market price of a security already fully reflects everything knowable about it. Then the expected return based on that price is exactly what the security's risk deserves — no more, no less. Nobody can reliably out-earn what their risk-taking entitles them to. That holds however clever their strategy is.

The workbook: "In 1970 Eugene Fama, formalized the theory and organized the empirical evidences. Fama presented the efficient market hypothesis (EMH) in terms of a fair game model stating that in an efficient market, investors can be confident that the current market price fully reflects all available information about a security. Therefore, the expected return based upon this price is consistent with the risk of the security. Hence by design or by using any investment strategy or tool, investors can't derive above average risk adjusted return. Investors should receive normal returns consistent with the risk they are taking."

Fama then split the overall EMH into three testable sub-hypotheses by information set — weak-form, semi-strong-form and strong-form efficiency — each nested inside the next, with strong-form covering the widest information set (including inside information).

Illustrative figures — the workbook's fair game model passage itself carries no worked numbers.

A technical analyst studies six months of an NSE-listed IT company's price chart, currently trading at Rs 1,200, and flags what looks like a reliable breakout pattern pointing to a run-up toward Rs 1,400 within the quarter. Under the fair game model, that pattern is not a discovery of mispricing — weak-form efficiency implies past price data carries no predictive edge, so the pattern is, at most, a reflection of information already in the price. A quarter later the stock is at Rs 1,218, up 1.5% — in line with what the stock's risk warranted over that period, not the double-digit breakout the chart pattern implied.

Compare that to a fund manager who, on the same day, buys 1,000 shares of a small-cap stock at Rs 340 each (Rs 3,40,000), genuinely trading below its true value because the market has not yet priced in a recently announced Rs 50 crore contract win for the company. If the market truly is a fair game and semi-strong-form efficient, that mispricing should not persist once the contract announcement becomes public information: within days the stock re-rates to Rs 391, a 15% jump that lands almost entirely on the announcement day itself, taking the holding to Rs 3,91,000. An investor who buys only after the announcement, at Rs 391, earns just the ordinary risk-adjusted return from there — the gap has already closed, leaving no exploitable edge for investors who read the news after it is out.

Chapter 13, section 13.2 (Efficient Capital Markets and Random Walk Theory), introduces the fair game model as Fama's own framing of the EMH before splitting it into the three forms. Expect the fair game model tested as a name-and-year recall question — attributed to Fama, 1970 — and distinguished from the Random Walk Hypothesis that preceded it.

• "Fair game" does not mean zero expected return — it means the expected return equals what the security's risk warrants, no more and no less.
• The fair game model is Fama's framing of EMH, not a separate theory from it — do not treat the two as competing ideas.
• 1970 is Fama's formalisation date — the underlying random walk research predates it; do not attribute the earlier empirical work to Fama's 1970 paper.