# Day of the Month Seasonality Part 3: Nikkei 225, Hang Seng, STI

This is the third and final post investigating day of the month seasonality effects in global equity market indices. In part 1 we looked at U.S. indices; in part 2 we saw that the effects were even more powerful in three major European markets. In this post I will analyze three Asian indices: the Nikkei 225 (Japan), the Hang Seng Index (Hong Kong), and the Straits Times Index (Singapore).

## The methodology:

As with the European indices, the Asian ones have relatively short histories. In order to get a long enough sample of results, I shortened the initial look-back period to 2500 trading days. The exact steps to re-create the results below are the following:

- Standardize every month to 21 trading days; round to the nearest integer when the number of days in a month is different.
- Start by using the last 2500 days; keep increasing the sample size until you reach 5000 days. After that use a moving window of the last 5000 days of daily returns and estimate the average return on every (standardized) day of the month.
- Rank the days by their past returns. If the next day is in the top 6, buy on close and sell on the next close.
- Move forward by one trading day and repeat from step 2.

A technical note: I am using QuantLib‘s holiday calendar functions to calculate the number of trading days in a particular month. There are problems with QuantLib, especially when looking further back in time, that result in an inaccurate trading day count for certain months. The effect is rather small as only a tiny number of months are affected, but the results should be even better if these problems were to be corrected.

## The results:

### Nikkei 225:

The equity curves:

And the statistics:

### Hang Seng Index:

The equity curves:

And the statistics:

### Straits Times Index:

The equity curves:

And the statistics:

## Calendars:

Here’s the updated list of average (standardized) day of the month returns over the last 5000 days for the indices we have looked at. The last and first few days of the month seem to be the best worldwide. The days around day #5 and day #15 seem to be the worst, again across the board. Beyond that there are few similarities among these markets.

## Conclusions:

The main conclusion to be drawn from these results is simple:

**The majority of permanent upwards stock market movements happen on a small number of days, and it is easy to predict which days these will be.**

How can we use this knowledge? Setting up automatic investment plans to buy 4-5 days before the end of the month is one obvious implication. Going in too early or too late could have a significant negative impact on your returns over the long term.

If you swing trade any of these indices, it should take less to convince you to go long on these special days, and vice versa on the short side. Of course, there are long stretches of time during which the day of the month effect performs badly; it is not a trading rule in itself and as such should be treated with caution.

### Shorting

Unfortunately there does not seem to be any consistent edge in day of the month seasonality for the short side. Given the general upward trend of equity markets over time, this is not all that surprising. It is possible that using a bear market filter we could uncover something useful, and I might revisit the topic in the future.

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