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Q1 hedge fund letters, conference, scoops etc
The seasonal effect, namely that equities do better from November through April, is well-known. This article provides a rigorous statistical test of the effect and a trading strategy that profits from it.
My long-term study supporting this observation can be found here. A related switching strategy model with cyclical and defensive ETFs is described here.
The seasonality of the S&P 500 is easily verified. The S&P 500 with dividends from 1960 onward returned on average 1.92% for the six-month periods May through October, the “bad-period.” For the other six months, the “good-period,” from November through April, the average return was 8.47%.
Visually observing is not an adequate way to assess effectiveness. It is more rigorous – but nonetheless quite easy – to statistically demonstrate that the six months from November to April are usually good-periods for equities. The null hypothesis H0 is the default position, namely that there is no difference between the average returns of the good-periods and bad-periods, the average return hereinafter referred to as the “H0-return”.
Quantifying stock market seasonality with likelihood ratios
In evidence-based medicine, likelihood ratios assess the reliability of a diagnostic test, leading to improved patient outcomes and refined drug regimens. In finance, likelihood ratios can quantify the reliability of a financial test as well. For example, one can check the dependability of a recession indicator, as described here.
In medicine, likelihood ratios estimate how much the probability that a patient has a particular disease changes from before a diagnostic test is given to after its result is known. One can use the same concept to determine the probability of stock market performance over a particular period in the year when the outcome of a relevant indicator’s test is positive or negative.
The time over which I tested this is from January 1960 to April 2019, consisting of 59 cyclical good-periods (condition positive) and 59 cyclical bad-periods (condition negative) for stocks, totaling 118 six-month periods, and showing an average return of 5.20% for all periods, the H0-return.
An indicator can be sending one of the following four messages, depending on the actual return for a period and its magnitude relative to the H0-return. For the condition positive the possibilities are:
- a correct call that the return for the good-period is greater than the H0-return (true positive, TP); or
- a false call (false positive, FP) when the return for the good-period is less than the H0-return.
For the condition negative the possibilities are:
- a valid negative call that the return for the bad-period is less than the H0-return (true negative, TN); or
- a wrong negative call (false negative, FN) when return for the bad-period is greater than the H0-return.
How often one of those conditions occurs over the observation period are the raw data for the analysis, shown in the Table-1 for my specific investigation.
Read the full article here by Georg Vrba, Advisor Perspectives
