Calendar month effects on stock performance is an area of interest. Early investors observed that markets seem to behave in a certain way during specific times of the year. For example, we have the January effect in the US, where stocks tend to do better in January. Then you may hear the saying “Sell in May and go away”. However, most of these observations are merely hypothesis as deterministic evidence remain elusive. And do they still hold today?
A Simple Study on The Calendar Month Effects On S&P 500
Let’s look at the pattern for the US stock market. The results in the table below are computed using data from the S&P 500 price index from 1928 to 2022 for a total of 95 years. It shows the performance characteristics of each calendar month such as the average, maximum, minimum return, and volatility across the period. We also constructed a NAV series for each calendar month e.g. the January series is constructed assuming you are fully invested in the S&P 500 in January each year and are flat in the rest of the months.
Key Observations
👉 If we focus solely on the average return, we will find that while January’s performance looks decent, it falls short of even making it to the Top 3. The champion goes to July. April is the runner-up and December comes in third place. January is the fourth.
👉 If we look at returns per unit risk i.e. Sharpe, then December took the crown. July falls to the 2nd place and January squeezes into the 3rd.
👉 Neither is the most quoted "May" the worst month. In terms of ranking from bad to worst, it would be May, Feb, and Sep based on their Sharpe.
👉 September looks notably worse off than the rest – lowest average return, the record holder for the worst month, the poorest Sharpe, and it also has the lowest probability of delivering a positive return.
Would I use a strategy based solely on these seasonal observations?
So should I just the top and bottom 3 months to trade? That is I go long during April, July, and December and go short in February, May, and September. Because that would be what the data suggests. But no, I am not going to. Why?
▶️ The rationale is not strong enough. No one can explain why things happen the way they did. Just like I have no idea why the January effect seems to exist in the 70s-90s, I have no clue why it wanes after 2000 either.
▶️ The sample size is actually not as big as it seems. When fundamental reasoning fails, we turn to statistics. While we have 95 years of data sounds like an awfully long period, there is only 1 specific calendar month each year. So we only have 95 observations for each month to work with. There is no hard and fast rule for determining a good sample size. Basically, the more the merrier. This is so that we have a better chance of ruling out fluke results, in particular for cases that are not well understood.
▶️ Historical behaviors that far back can change. The data stretches all the way back to 1928. Given the differences in demographics, culture, environment, educational, political, economic, and technological situations today, are data that far back still relevant?
▶️ One or two key events can shake up the results. Let’s take October as an example. It is not a particularly impressive month. But if we dive a bit deeper, we will realize that 2 key events have a major responsibility for where it is today — (1) Black Monday, and (2) the continued fallout from the collapse of Lehman Brothers after September 2008. If we removed these 2 events, October would jump and become one of the top performers. Such events could have happened in any of the months.
▶️ It is difficult to determine when to pull the plug. Knowing when to call it quits and cut losses should be an integral part of everyone’s investment plan. This includes answering fundamental questions like when to pull the plug on a strategy. To give such seasonal strategies reasonable room to run, we are going to need many years before we can fairly decide if the premise is still valid. And none of us have that many years to give. So we may end up having to settle for less optimal criteria to decide whether the strategy goes or stay.
These are just my thoughts. It certainly does not represent what everyone thinks. For example, those with large portfolios and many different strategies may be less inhibited to try out new ideas as long as any fallout can be mitigated. At the end of the day, a lot boils down to our own investment philosophy and risk preference. Whether you are right or wrong, the market will decide.
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