Out of many existing seasonal effects, one is the market maxim “Sell in May and go away” or a Halloween effect. The profitability stems from a simple finding that on average stocks deliver close to zero returns in the six-month period from May through October while giving a risk premium only from November through April. Although the idea is pretty simple, academic research has found this effect to be profitable. As more recent research is suggesting, this anomaly could be enhanced, and become even more profitable (more information can be found in our screener). Additionally, this effect is global since this pattern is present in almost every country in the world.
Similar findings can be found, for example, in the paper of Jacobsen and Bouman: The Halloween Indicator, ‘Sell in May and Go Away’: Another Puzzle. Quoting the authors: “We document the existence of a strong seasonal effect in stock returns based on the popular market saying ‘Sell in May and go away’, also known as the ‘Halloween indicator’. According to these words of market wisdom, stock market returns should be higher in the November-April period than those in the May-October period. Surprisingly, we find this inherited wisdom to be true in 36 of the 37 developed and emerging markets studied in our sample. The ‘Sell in May’ effect tends to be particularly strong in European countries and is robust over time.” Although this seasonal indicator appears to be a very powerful stock market timing tool, that has been known for decades, it has not been widely covered in academic literature. The author hypothesizes that this may be because there is, as yet, no well-established consensus about the underlying causes of this remarkable pattern. This could also be supported by Jacobsen and Bouman: “While we have examined a number of possible explanations, none of these appears to explain the puzzle convincingly.” In spite of disagreement on whether the strategy is connected with the optimism cycle, psychology, or even another cause, the performance connected with practical usage of the strategy based on this effect must be backed by the research.
Value and momentum strategies are very well documented by As we have previously mentioned, there could be two possible explanations. Firstly, according to Kamstra, Kramer, and Levi (2003) or Garret, Kamstra, and Kramer (2004), the seasonal pattern can be attributed to a time-varying equity premium influenced by the Seasonal Affective Disorder (SAD) effect, the so-called winter depression. The link may be there because of evidence taken from psychological literature shows that depression lowers one’s willingness to take a risk. Kamstra, Kramer, and Levi state that: “SAD is an extensively documented medical condition whereby the shortness of the days in fall and winter leads to depression for many people. Experimental research in psychology and economics indicates that depression, in turn, causes heightened risk aversion. Building on these links between the length of the day, depression, and risk aversion, we provide international evidence that stock market returns vary seasonally with the length of the day, a result we call the SAD effect.”
Another possibility is that the seasonal results stem from the optimism cycle in which, in the last quarter of the year, investors start looking forward to the next calendar year. At first, they are usually too optimistic about the economic outlook (about the growth prospects for the economy and earnings), and this optimism results initially in attractive returns on stocks. However, several months into the year, reality catches up with them. Investors become more pessimistic, and the stock market experiences a summer lull. In this way, psychological factors repeatedly make a fool of investors. Therefore, in the six months from November through April, investors should overweight equities, and during the summer period from May through October, they should be underweight.
Backtest period from source paper
Confidence in anomaly's validity
Notes to Confidence in Anomaly's Validity
OOS back-test shows significantly negative performance. It looks, that in sample back-test from source research paper might have been data mined.
Notes to Indicative Performance
return during 6 months (November-April) in global equity market, rest of the time investor could be in cash and earn extra return, data from table 1 Panel A
Period of Rebalancing
Notes to Period of Rebalancing
Notes to Estimated Volatility
Number of Traded Instruments
Notes to Number of Traded Instruments
Notes to Maximum drawdown
Notes to Complexity Evaluation
CFDs, ETFs, funds, futures
Simple trading strategy
Be invested in global equity markets during November – April period, stay in cash during May-October period (alternatively go long in stocks from countries from northern hemisphere during winter period and long in stocks from countries from southern hemisphere during summer period; alternatively go long in cyclical companies during winter period and short defensive stocks and switch positions during the summer period)
Hedge for stocks during bear markets
Partially - The selected strategy is a class of “Market Timing” strategies that try to rotate out of equities during the time of stress. Therefore the proposed strategy isn’t mainly used as an add-on to the portfolio to hedge equity risk directly, but it is more an overlay that can be used to manage the percentual representation of equities (or “equity-like assets”) in a portfolio. “Equity Market Timing” strategy can decrease the overall risk of equities in a portfolio, and it can improve the risk-adjusted returns. Moreover, as strategy’s goal is to hold equity market only in a positive times for equity market factor and be out of equities otherwise, therefore this logic can be maybe used to create amended market timing strategy (using original rules) which is out of equities during positive times and holds bonds (or goes short equities) during bad times. This new amended strategy can be maybe used as a hedge/diversification to equity market risk factor during bear markets. However, performance/risk characteristics and overall correlation and quality of suggested amended strategy can find out only by rigorous backtest and source academic research paper doesn’t give us any clues on how it will perform…
Out-of-sample strategy's implementation/validation in QuantConnect's framework