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MENTAL MODEL · M12693

Random Walk Theory

Random Walk Theory
BusinessHigh supportFinance
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Updated 2026-09-10

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INTRODUCTION

English translation pending.

CORE DEFINITION

Random walk theory holds that successive changes in a variable are independent draws, so the best prediction of the next value is the current value plus an unpredictable increment. It was formalized for financial prices by Louis Bachelier and later developed by Eugene Fama into the efficient market hypothesis. The core claim is that if price changes are independent, chart patterns and trend extrapolation carry no predictive information. The qualification is that independence is an approximation: markets show fat tails, volatility clustering, and some long-horizon predictability.

SCAFFOLDING EFFECT

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Reduce cognitive load

- Forecast check: ask what evidence shows that past values predict future ones for this series. - Model choice: use independent increments as the baseline before adding any structure. - Tail awareness: add fat tails and volatility clustering, which a pure random walk omits.

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Anchor fast decisions

If each increment is independent of the last, then the variance of the total grows linearly with the number of steps while the expected value stays put, so uncertainty widens without any directional tendency. No pattern in past values can then improve the forecast, because the past carries no information about the next draw. Real prices depart from this in the tails, which is why the model is a baseline rather than a complete description.

MINIMUM ACTION

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Source support: Explicit

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    wiki.mbalib.comhttps://wiki.mbalib.com/wiki/%E9%9A%8F%E6%9C%BA%E6%BC%AB%E6%AD%A5%E7%90%86%E8%AE%BAZH · Explicit
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