Factor investing has been around for decades although it has become more refined in recent years. What began as stock pricing prowess has given way to quantitative methods. Researchers employ optimization algorithms to sift through mountains of data in search of new factors that have led to higher returns.

John Burr Williams was a pioneer of factor investing. In the early 20th century, he created a methodology to estimate stock prices based on each company’s intrinsic value. In later decades, Benjamin Graham sifted through stacks of annual reports looking for companies that had hidden cash on their books. Warren Buffet still selects companies based on their predictable cash-flows. Finding companies with specific fundamental factors has helped all these investors achieve outsized returns.

The institutionalization of factor investing began about 30 years ago. More efficient computing power enabled researchers to filter through thousands of pieces of information quickly to find similar characteristics among outperforming securities.

Factor investing started in the early 1960s with the discovery of “beta” by Nobel Laureate William Sharpe. It’s defined as the risk of “the market”. Although beta wasn’t called a factor at the time, it is a factor that is in every stock portfolio, even if that portfolio has only a few securities. Every portfolio value goes up and down with the market to some extent. The measure of the regression is the beta of a portfolio.

The first factor other than beta to be statistically documented was in small company stocks. The returns of very small stocks have had a higher return than large company stocks even after adjusting for a greater sensitivity to market beta. This factor was documented by Rolf Banz in 1981. He coined his discovery as the “size effect”.

No one could definitively explain why the size effect occurred. Academics surmised that small company stock had higher risk than large company stock, and that part of that risk could be explained by higher beta. However, they couldn’t agree on where the excess return in addition to higher beta was coming from.

This is one of the ironies of factor investor. The excess return from a factor doesn’t require an explanation that everyone agrees on. What is required is that the excess premium is robust and that it is persistent over independent time periods and in markets around the globe. Factors must also be investible. A methodology must exist to capture factor returns in portfolios net of cost with reasonable levels of turnover.

In 1977, Sanjoy Basu tested the notion that value factors explained differences in portfolio returns that were unrelated to beta or the size effect. He indentified a positive and consistent relationship between price-earnings ratios (P/E) and portfolio return that couldn’t be explained by other risk factors. Since 1977, researchers have found many value factors that provided an excess return. They include, but are not limited to: price-earnings, price-to-cash-flow, and price-to-book. Stocks with high fundamental value relative to price (value stocks) outperformed stocks with low fundamental value relative to price (growth stocks).

Eugene Fama and Ken French released ground-breaking research in 1992 on the cross-section of expected stock returns that, among other things, measured book-to-market (BtM) returns for highest BtM (or value) stocks compared to the lowest BtM (or growth) stocks. They found large excess returns from value stocks relative to growth stocks in the U.S. equity markets. Their research led to the next step in valuation models, the Fama-French Three Factor Model.

The discovery of factors in the market has lead to a growth of factor-based mutual funds, exchange-traded funds (ETFs) and other products. A leader in this space is Dimensional Fund Advisers, known to advisers as DFA. The company was formed in 1981 to capture the size premium as outlined by Banz. During the 1990s, the firm expanding their offerings into three-factor investing by embracing Fama-French research.

Recent years have discovered more factors and more factor-based products. These include but are not limited to: momentum, profitability and liquidity factors.

The fund industry has started using terms such as smart beta, intelligent beta and alternative beta to identify risk factors. I believe these other beta terms confuse investors and should be avoided. In asset pricing theory, beta is a measure of market sensitivity. Let’s leave it at that.

I’ll discuss whether factor investing should be part of your portfolio in my next article. Multifactor investing has benefits and drawbacks — and there’s always the risk that factors that occurred in the past will disappear in the future.