In this series of articles, we’ll dive deeper into factor investing, how they are constructed and found, and how you can start investing in factors. We’ll delve deeper into individual factors and their innovations, but for now – let’s start with the basics.

Factor investing, sometimes known as smart beta, is to invest in a diversified portfolio that focuses on specific common risk factors found in the market. These portfolios consist of companies that share a common characteristic, such as their size or market capitalization, their book-to-market ratio, or whether these companies invest aggressively or more conservatively.

The reason why diversified portfolios are used, or portfolios with many stocks and not just a few, is to ensure that these portfolios are actually exposed to the risks of that factor and thereby capture its returns. If you were to invest in a single stock in attempt to have some factor exposure, it would likely result in taking on more company specific risk or idiosyncratic risk. However, if you make your portfolio more diversified with companies that share a common measurable trait, you largely eliminate idiosyncratic risk and are left with systematic risk or beta. Hence, factor investing is often known as smart beta. BlackRock is a well-known example of an investment manager that has a product line (ETFs) called Smart Beta portfolios.

To isolate these characteristics, companies are sorted by let’s say, their size. Larger cap firms are placed into one portfolio while smaller cap firms into another portfolio. This allows us to see how big and small firms perform in different market conditions by looking at the portfolio’s return. In practice, we generally see that small-cap outperform large-cap – this was first discovered by Rolf Banz (1981). You could stop here and simple start investing in companies that are small in relative market capitalization, but what if you would want to isolate this size effect – to just capture this size premium? Well, remember the two portfolios with small companies and big companies? We can use them to isolate this size effect by shorting the portfolio with big firms and invest in the portfolio with small firms using the proceeds from the short. Now you have created the famous SMB (small-minus-big) risk-factor which is also a zero-cost strategy because we borrowed money from the short position to go long in small companies. A similar process applies to other risk factors, whether it is the momentum or value factor, or the quality and low-volatility phenomenon. We will further discuss how to actually make these factors yourself in later articles.

The study of how the returns of these different types of companies behave in the real world is called empirical finance. A famous duo in financial academia, Kenneth French and Eugene Fama, have published a paper in 1993 that summarized how the returns of some risk-factors - more specifically size, value, and the overall market factor – can explain the average returns on stocks better than the capital asset pricing model (CAPM). While they did not actually discover anything new, these factors were already known, but they did help put the puzzle pieces together and show that the CAPM was not the best model, that market risk was not the only risk for which investors should care about. You should also care about how big a company is and if you were to invest in small companies, you should expect a higher return from them.

 So why do these factors exist – why do small firms outperform large firms or why do past winners do better than past losers in the future? What drives their returns? And what are its implications for you as an investor? Well, why they exist is not completely know – often times these factors are called anomalies because they defy what the CAPM expects. But what drives their return is often attributed to risk, investor behavior, and simple statistical flukes or chance. For example, let’s return to our size premium – a generally accepted economic intuition as to why small cap outperform big cap is because smaller firms tend to be riskier. And obviously, the more risk you take, the more returns you should expect.

Finally, what are the implications for you as an investor? Imagine that you give some money to an investment manager and he or she promises you some nice returns because they are very skilled and thereby can generate ‘alpha’. Alpha is returns beyond what can be expected from some benchmark. You are required to pay a certain fee for their skills and troubles and these fees are generally not very cheap. At the end of the year, you see that your investment was well placed because you had some nice returns, but after some thorough analysis you realize that the returns were simple generated from investing in common risk factors. Remember, you paid a handsome fee for their skills and not for following something that is simple to replicate. Hence, it is called a ‘common’ risk factor because it is well known. In reality, you might have paid for an overpriced manager while you could have achieved similar returns for much less by investing in a passive fund. In reality, you paid for beta and not alpha. This is quite common, and something you might call ‘closet indexing’, and it costs investors like you, a lot of fees that could have been spent better.