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Efficient market theory

As explained in Investing for Programmers

Efficient market theory is the theory that asset prices incorporate available information so consistently that persistent excess returns are difficult to achieve.

Efficient market theory holds that an asset’s price approximately reflects its intrinsic value when investors have access to relevant information. Under this view, persistently beating the market by buying undervalued stocks is difficult. section 8.1.3 section 10.2

The theory creates a difficult forecasting target for machine learning: past performance may provide limited help in finding the rare companies whose exceptional returns drive overall market gains. Market behavior can also be chaotic and interconnected across countries. section 8.1.3

Practical ways to interpret the theory include:

  • Use fundamental analysis to estimate what a company is worth from balance sheets and other company documents, then form a view about undervaluation or overvaluation. section 10.2
  • Use technical analysis to study price development over time and identify trends that may inform purchase timing. section 10.2
  • Treat claims of market outperformance cautiously: investors such as Warren Buffett dispute the theory, but disagreement does not make model predictions reliable. section 8.1.3 section 10.2

Rare winners and ordinary stocks

A market can contain many stocks with negative cumulative returns while a small number of exceptional companies raise the market’s average return above alternatives such as bonds. Searching historical performance data for a repeatable hidden advantage may therefore offer limited assistance. section 8.1.3

How Investing for Programmers covers it

How deep it goes
Defines pricing and intrinsic value, then contrasts fundamental and technical analysis without developing a formal market-efficiency model.
Examples
Uses balance sheets, price charts, established companies, new companies, historical stock returns, and bonds as illustrations.
What you can do after
Evaluate market-prediction claims while comparing fundamental analysis, technical analysis, and machine-learning approaches.
How it connects
Connects intrinsic value, fundamental analysis, technical analysis, machine learning, forecasting, and random walk behavior.

This explainer was generated by Manning's knowledge graph from the text of Investing for Programmers. The section links point to the parts of the book it draws on.