Factor investing is usually discussed as a way to pursue higher expected returns, but it can also diversify the sources of those returns. Market, value and profitability premia do not reliably perform well or badly at the same time, reducing reliance on any single factor being rewarded immediately.

Across the complete calendar-year histories available from the Fama–French Data Library, the market, value and profitability premia were never all negative in the same year in either the United States or developed markets excluding the United States. This does not prevent a factor-tilted equity portfolio from losing money, but it shows how different equity premia have historically experienced their difficult periods at different times.

Regression analysis is widely used in investment research to understand what drives the returns of a fund, stock or portfolio. At its simplest, a regression measures how changes in one variable relate to changes in another. More advanced factor models extend this idea by examining whether an investment’s returns can be explained by its exposure to the market, smaller companies, value companies and other systematic factors.

The results help separate returns into factor-related performance, unexplained average return and period-by-period residuals. Alpha represents the return left unexplained by the factors included in the model, whilst coefficients measure the investment’s sensitivity to each factor. Statistics such as R², standard errors, t-statistics and confidence intervals then help assess how well the model explains the historical return pattern and how much confidence should be placed in its estimates.

Tracking difference and tracking error are both used to judge how closely a fund follows its benchmark, but they answer different questions. Tracking difference tells you the actual return gap over a specific period: did the fund beat or lag the index, and by how much? For passive funds, this figure is often slightly negative because real-world funds face costs that an index does not, including ongoing charges, transaction costs and cash drag.

Tracking error, by contrast, tells you how variable that return gap was along the way. A fund can have a low tracking error whilst still consistently lagging the index by a small amount. Put simply, tracking difference is where the fund finished relative to the benchmark; tracking error is how smooth or erratic the journey was.

Recent market history can make it feel like stock markets mostly go up. That is understandable if your investing experience has been shaped by the post-2010 to 2025 period. The problem is that it can distort expectations about what ‘normal’ looks like. Negative returns are a routine part of equity investing, especially over short horizons.