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A central theme in finance is the risk – expected return relation. Practitioners have traditionally used the capital asset pricing model (CAPM) to determine likely return on investments. The CAPM predicts that stocks with high market beta are riskier and are therefore likely to yield higher returns. However, given its empirical failure, several empirical factor models have emerged that do a much better job in explaining the cross section of average stock returns.
These models employ certain variables, often referred to as factors. Several of the well-known models include factors based on profitability, investment, the market portfolio as a risk factor, size and book-to-market.
Respectively, these deal with excess returns in terms of stocks of highly profitable versus poor profitability firms, low versus high previous year asset growth firms, small versus big stocks and high- versus low-book-to-market stocks.
«When using these models, it turned out that stocks with high exposure, especially to the profitability and investment factors, also have higher returns on average», says Ilan Cooper, Professor at BI Norwegian Business School.
Evaluating the risks
In a study, Cooper and his colleague investigated why stocks with higher covariances with the profitability and investment factors were riskier. Covariance is a measure of how changes in one variable is associated with changes in a second variable.
«More specifically, we wanted to discover what kind of risks are captured by these two factors», explains Cooper.
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In their paper, they studied if the prominent factor models were consistent with the Intertemporal Capital Asset Pricing Model (ICAPM), an alternative to the CAPM where wealth as a state variable is included. Among others, they tested whether the models could forecast the market returns and market volatility.
«It turns out that various versions of the profitability factors can indeed predict positively the future market returns with economic and statistical significance at various horizons. Secondly, the investment factor can forecast negatively the market volatility», says Cooper.
The researchers also examined whether the factor can forecast economic activity, by using aggregate economic activity as an alternate proxy for aggregate wealth, and the growth in industrial production and the Chicago FED index of economic activity.
Warning for momentum investors
«We found that the investment factor can positively and strongly forecast future economic activity», says Cooper.
Overall, their findings show that the prominent factor models in the empirical asset pricing literature might be risk factors in the context of the ICAPM.
«Our results also suggest that investing in market anomalies, such as in low investment stock and highly profitable stocks, as well as other anomalies is not a free lunch», says Cooper.
In other words, investors who invest in stock with high expected returned are taking risks of deterioration in investment opportunities. Investors should therefore be cautious when investing in such anomalies, Cooper argues.
«While momentum is also considered a profitable investment strategy, it is not without risk. In fact, momentum investors might attain poor returns exactly in bad times, such as poor outlook for the markets and times of high stock volatility».
Only a small number of academic publications ranks as the best in their field world-wide. Last year, 16 BI researchers published papers in these ABS4* journals. These journals are considered among the highest in terms of impact factor and have the highest requirements of data and rigour in theory. Throughout 2019, we highlight these academics, their research and its impact on society.
Cooper, Ilan; Maio, Paulo. (2018). Asset Growth, Profitability, and Investment Opportunities. Management Science 2018.
Text: Eivind Lindkvist Johansen, Communications Advisor, BI