3 Factors Explain 90% of Stock Returns

Three Factors Drive 90% of Stock Market Returns, Landmark Study Finds

A groundbreaking 1993 research paper has fundamentally reshaped our understanding of investing. Published in the Journal of Financial Economics, the study by Eugene Fama and Kenneth French revealed that just three key factors could explain about 90% of the differences in returns across diversified stock portfolios. This research continues to influence how investors and academics view the stock market.

Challenging the Old Model

Before Fama and French, the main idea in finance was the Capital Asset Pricing Model (CAPM), developed in the 1960s. CAPM suggested that a stock’s expected return was mainly linked to its ‘beta.’ Beta measures how much a stock’s price tends to move compared to the overall market. A higher beta meant higher expected returns. This model was so influential that its co-creator, William Sharpe, won a Nobel Prize for it.

However, researchers noticed that CAPM didn’t explain everything. Certain types of stocks consistently earned higher returns than CAPM predicted. These unexplained returns were called ‘anomalies.’ The core problem was a ‘joint hypothesis problem’: you couldn’t prove CAPM was right without knowing if markets were efficient, and you couldn’t prove markets were efficient without a correct pricing model. It was a circular issue.

Introducing the Three-Factor Model

Fama and French built upon this by identifying three specific factors that explained these anomalies:

• Market Factor: This is similar to CAPM’s beta, representing how a stock moves with the overall market. It shows whether stocks generally went up or down with the market.
• Size Factor (SMB – Small Minus Big): This factor captures the historical tendency for smaller companies to generate higher returns than larger companies. If small stocks outperform large ones, the SMB premium is positive.
• Value Factor (HML – High Minus Low): This factor relates to a company’s valuation. It looks at the difference in returns between ‘value’ stocks (companies with low prices relative to their book value) and ‘growth’ stocks (companies with high prices relative to their book value). When value stocks do better, the HML premium is positive.

The researchers argued that investors demand higher expected returns for taking on these specific types of risks associated with company size and valuation. Therefore, these factors, not just market beta, were crucial for understanding stock returns.

Groundbreaking Results

To test their theory, Fama and French created 25 different portfolios based on combinations of company size and value characteristics. They then used statistical methods called time series regressions to see how well their three-factor model explained the historical returns of these portfolios from 1963 to 1991.

The results were striking. The Fama-French three-factor model explained about 90% of the variation in returns across these portfolios, a significant leap from CAPM’s estimated 60%. This meant that most of the differences in how these stock groups performed could be attributed to their exposure to market risk, company size, and value characteristics.

Furthermore, the model found very small ‘alphas’ – returns not explained by the factors – for most portfolios. This suggested that once size and value risks were accounted for, there were few persistent, unexplained excess returns left. The only exception was small-cap growth stocks, which had lower returns than the model predicted, leaving room for further study.

Market Impact and Investor Takeaways

The Fama-French paper challenged the idea that anomalies were simply signs of inefficient markets. Instead, it presented them as compensation for systematic risks that investors were exposed to. This perspective shifted the focus from finding market ‘mispricings’ to understanding and managing risk exposures.

What Investors Should Know:

• Factor Investing: The research supports the idea that investors can potentially achieve higher long-term returns by tilting their portfolios towards certain factors like size and value. This approach is known as ‘factor investing’ or ‘smart beta.’
• Active Management Reconsidered: The model also provided a new lens for evaluating actively managed funds. If a fund appears to outperform the market, the Fama-French model helps determine if that outperformance is due to genuine skill or simply exposure to well-established risk factors. If it’s the latter, investors might achieve similar results more cheaply through passive factor-based investments.
• Evolution of Models: The academic world continued to build on this work. In 2015, Fama and French introduced a five-factor model, adding ‘profitability’ and ‘investment’ factors. This updated model further improved explanatory power to around 95%. These models are now considered workhorses in academic finance.
• Practical Application: Companies like Dimensional Fund Advisors (co-founded by Eugene Fama) and Avantis Investors have built investment products based on this factor-based research. These funds aim to provide investors with diversified exposure to these factors at a low cost, often through ETFs.

In essence, the 1993 Fama-French paper provided a more comprehensive framework for understanding why different investments perform differently. It highlighted that while market movements are important, factors like company size and valuation play a critical role in driving long-term investment returns.

Source: The Finance Paper That Changed Everything (YouTube)