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Random Walks in Stock Market Prices

Fama, Eugene F.·1995·Financial Analysts Journal

Abstract

This article describes briefly and simply the theory of random walks and some of the important issues it raises concerning the work of market analysts. A discussion of two common approaches to predicting stock prices—the chartist (or technical) theories and the theory of fundamental (or intrinsic) value—allows the reader to put the theory of random walks into perspective. The theory of the market as efficient (at least semistrong efficient) and characterized as a random walk states that successive price changes in individual securities are independent and a series of stock price changes has no memory; thus, the past history of the series cannot be used to predict the future history. Empirical evidence indicates that, although price changes may not be strictly independent, the dependence is so slight that a simple buy-and-hold strategy beats any strategy based on mechanical trading rules. The implications of the market being a random walk are devastating for chartism. For fundamental value analysis, the implications are more complex. If the market is efficient, stock prices at any point in time represent good estimates of intrinsic value, so additional analysis is useless unless the analyst has new (private) information or insights. The challenge for each type of analysis is to show that their methods produce more return than a random sample of securities.

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Metadata

Authors
Fama, E. F.
Venue
Financial Analysts Journal
Published
1995-01
Volume
51
Issue
1
Pages
75-80
ISSN
  • 0015-198X (ISSN-L)
  • 1938-3312
OpenAlex
W2940897671
Cited by
1,227
References
0
FWCI
4.88
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Random Walks in Stock Market Prices. https://app.cochat.ai/papers/10.2469/faj.v51.n1.1861
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Fama, E. F. (1995). Random Walks in Stock Market Prices. Financial Analysts Journal. https://doi.org/10.2469/faj.v51.n1.1861

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.