Blog Details
Exploring Seasonality in Wall Street, Europe, and Asia-Pacific Stock Markets (Apr 27, 2023)
Risks of Contagion Remain in Europe
Elsewhere, the negative sentiment that has been following Credit Suisse around prior to the recent banking developments was further exacerbated when its main shareholder, the Saudi National Bank, publicly stated it cannot be relied upon for more financial support. Yesterday, news of a CHF 50 billion loan from the Swiss National Bank and an announcement that the bank looks to reduce some of its senior debt sent the share price up as much as 33% in the premarket. The 50 basis point hike by the ECB despite the turmoil suggests the central bank has complete faith in its tools that can be deployed should they need to, mainly via the Transmission Protection Instrument.
Have you ever wondered whether or not certain months of the year are consistently better or worse for stock markets? If such a relationship does exist, then we would describe it as seasonal. Seasonality is something you often see in all kinds of data. For example, you can probably guess which months of the year ice cream sales consistently spike or when people repeatedly purchase more winter clothing.
Businesses use seasonality every year to plan their ventures, but can investors do the same with stock markets? If we assume that markets are efficient, reflecting all available information at a given time, then changes in stock prices every day (or in this case every month) should behave as white noise. In other words, we should not be able to predict the future based on past performance because markets are random.
We will put this simplified theory to the test for global stock markets. We will analyze the Dow Jones, S&P 500 and Nasdaq Composite on Wall Street. Then, we will do the same for the DAX 40, FTSE 100 and CAC 40 to gauge European indices. Finally, I will wrap up with a few major benchmark indices from the Asia-Pacific region: the Nikkei 225, ASX 200 and Hang Seng Index.
Each heatmap chart below contains identical parameters:
- Average monthly percent change (+2.5 means the index rose 2.5% on average in that month)
- Which months are significant (based on a 95% confidence interval from a regression model)
- The average goodness of fit between the 3 regression models (1 for each stock index)
These parameters will be discussed in further detail below.
Dow Jones, S&P 500, Nasdaq Composite Monthly Seasonality
Starting with Wall Street, I have used data on the Dow Jones and S&P 500 since 1950. For the Nasdaq Composite, information since 1973 was used. The most recent month pulled was from December 2022, which applies to all European and Asia-Pacific indices as well. Are there any seasonal effects? Yes!
Historically, it seems that April has been the best month for Wall Street. On average the Dow, S&P and Nasdaq rallied +1.67% in this month. These are also statistically significant. In other words, there is about a 2.6% chance that this +1.67% effect on Wall Street is due to chance within the context of this study.
Meanwhile, September is the only month where all 3 indices have averaged negative and are statistically significant. In this month, the indices averaged -0.8%. I have also highlighted which other months are statistically significant with a “*”. Note that January seems to be a very strong month for the Nasdaq, but it is not significant because, over the years, this month has become progressively weaker.
Finally, and arguably more importantly, notice that the average goodness of fit between the 3 models is 3% (out of a maximum of 100%). What does that mean? On average, about 3% of the variation on Wall Street since the 1950s (‘70s for the Nasdaq) can be explained by seasonality alone (without factoring in any other variables like monetary policy or the unemployment rate).
In other words, while we found meaningful seasonality in April and September, seasonality by itself did a very poor job of explaining how stock markets traded in these months.