Although the factors in asset pricing models offer a premium in the long run, they are undergoing bull and bear market cycles in the short term. One would expect that it is due to their connection to the business cycles as the factor premium represents a reward for bearing the macroeconomic risks. A novel study by Blitz (2021) finds that traditional business cycle indicators can’t explain much of the time variation of factor returns as the factors are a behavioral phenomenon driven by investor sentiment. To capture the large factor cyclical variation, the author proposes a quant cycle that is defined by the peaks and troughs in the factor returns corresponding to the bull and bear markets.
For example, a quant cycle starts with a normal stage that prevails about two-thirds of the time and gets interrupted by a major drawdown of value factor with a periodicity once per ten years. These drawdowns are caused either due to the rally of growth stocks (in a bullish environment) or the crash of value stocks (in a bearish environment). The growth rally is usually followed by a bear reversal, where the growth stocks that rallied during the previous stage crash, resulting in a strong rebound of the value factor. On the other hand, the bull reversal comes after the value crash and is typical for the rebound of the past biggest losers resulting in large negative returns for the momentum factor. Due to the drastic losses for trend-following strategies, bull reversals represent a tough challenge for multifactor portfolios. Therefore, by understanding the quant cycle an investor can form a multi-year outlook and choose the proper factor allocation accordingly.
Author: David Blitz
Title: The Quant Cycle
Traditional business cycle indicators do not capture much of the large cyclical variation in factor returns. Major turning points of factors seem to be caused by abrupt changes in investor sentiment instead. We infer a Quant Cycle directly from factor returns, which consists of a normal stage that is interrupted by occasional drawdowns of the value factor and subsequent reversals. Value factor drawdowns can occur in bullish environments due to growth rallies and in bearish environments due to crashes of value stocks. For the reversals we also distinguish between bullish and bearish subvariants. Empirically we show that our simple 3-stage model captures a considerable amount of time variation in factor returns. We conclude that investors should focus on better understanding the quant cycle as implied by factors themselves, rather than adhering to traditional frameworks which, at best, have a weak relation with actual factor returns.
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