Estimating Cost of Equity with the FFC Factor Model

Imagine you’re lending money to a friend to start a small business, maybe a cool coffee shop or a tech startup. Before you hand over your hard-earned cash, you’d naturally want to know what kind of return you can expect. This expected return is essentially the ‘cost’ your friend has to pay for using your money. For companies, this is very similar, but instead of friends, they get money from investors who buy stocks – and the return these investors expect is the cost of equity capital.

Now, figuring out this cost of equity isn’t as simple as asking your friend for a percentage. It’s more complex for big companies traded on the stock market. Investors are essentially taking a risk when they buy stock; the company might do well, or it might not. The riskier the investment, the higher the return investors will demand. So, how do we measure this risk and translate it into the cost of equity? That’s where models like the FFC factor specification come in.

FFC stands for Fama-French Carhart, named after the researchers who developed and refined this approach. It’s a way to estimate the cost of equity by looking at different factors that influence a company’s stock returns, and therefore, its risk. Think of it like trying to predict the weather. You wouldn’t just look at the temperature; you’d consider wind, humidity, and cloud cover too. The FFC model does something similar for stock returns; it looks beyond just the overall market.

The core idea is that there are certain characteristics of companies that are consistently linked to higher or lower stock returns. The FFC model typically considers four main factors. The first is the market factor, which is essentially the overall performance of the stock market. This is the most basic risk – the risk that the entire market might go up or down.

The second factor is size. Historically, smaller companies have tended to have higher returns than larger companies. This might sound counterintuitive, but it’s because smaller companies are generally considered riskier. They might be newer, less established, and more vulnerable to economic downturns. Think of it like investing in a small local bakery versus a giant national chain; the bakery could grow quickly, but it’s also more likely to struggle.

The third factor is value. Value stocks are companies that look ‘cheap’ relative to their fundamentals, things like their earnings or assets. These companies might be out of favor for some reason, or they might be in industries that are currently struggling. The FFC model suggests that value stocks tend to outperform growth stocks, which are companies expected to grow rapidly in the future. Value stocks might be seen as riskier because there’s often a reason they’re cheap; they might be facing challenges. It’s like buying a house that needs some renovation; it’s cheaper upfront, but there’s more uncertainty and potential work involved.

The fourth factor, added later by Mark Carhart, is momentum. This factor captures the tendency of stocks that have performed well recently to continue performing well in the short term, and vice versa for poorly performing stocks. It’s like following a trend; if a stock has been going up, investors might expect it to keep going up for a while. Momentum can be seen as a risk factor because these trends can reverse quickly, and chasing momentum can be risky if you get in at the wrong time.

So, how does this help estimate the cost of equity? The FFC model uses historical stock market data to determine how sensitive a company’s stock returns are to each of these four factors. For example, a small, value-oriented company might be highly sensitive to the size and value factors. By quantifying these sensitivities, and combining them with the expected return for each factor, the model can provide an estimate of the company’s expected stock return. And remember, this expected stock return is essentially the cost of equity.

The FFC model is a more sophisticated way to estimate cost of equity than simpler models that only consider market risk. It acknowledges that different types of companies have different risk profiles and it attempts to capture these nuances. While it’s not a perfect predictor, as no model can perfectly foresee the future, it provides a more nuanced and potentially more accurate estimate of the cost of equity, which is crucial for companies when making investment decisions and for investors when evaluating stock valuations.