Case study – How to use Quantpedia

This is a case study of how to use Quantpedia’s database itself, with an aim to present a possible usage of strategies from our database.

We will pick several strategies, perform their backtest and create a composite strategy made of building blocks.


Strategies on are presented in a form of “Strategy Reviews”. Each strategy consists of several parts. We provide a very short description and fundamental reasons for functionality and performance, risk and various other characteristics extracted from source academic (see example below).

Each strategy is also described by several keywords. Users can use our Screening tool and screen categorised strategies.

For the purpose of this show-case, we have decided to focus on one trading style – seasonal anomalies.

The first filter in our screener is “Markets“, where we have picked equities. Secondly, various characteristics of strategies could be simply found by searching with keywords. In this case, we would choose the keyword „Seasonality“.

Lastly, we would pick “Only Free“ from the Free/Premium filter. Therefore we picked strategies which are available for free and no subscription is needed to read about them.

The four strategies we picked are:


The turn of the month is a well-known effect on stock indexes, with a simple idea that stock prices usually increase during the last four days and the first three days of each month.

It is due to reinvestments of savings, dividends, and interest at month-ends. We think that this strategy can be simplified even more by buying the SPY ETF on close at the end of the month and selling it on close of the first day in the following month.

Such a simple strategy with an easy execution still works in the present as our backtest shows.

The second anomaly is the Federal Open Market Committee Meeting Effect in Stocks. 

According to past research, the S&P 500 index average daily returns during FED meetings since 1980 are outstanding. They are more than 5 times greater than returns during other average days on the market.

Dates of FED meetings are publicly known and available. Therefore such an effect could be easily utilized in the seasonal strategy that would long the S&P 500 index during these FED meetings.

As the name of the third anomaly suggests this effect is another calendar anomaly. This one is connected with the Option-expiration week which is a week before options expiration. 

Option expiration day is Friday before each 3rd Saturday in each month.

The research suggests that stocks with large market capitalization, that have actively traded options, tend to have substantially higher average weekly returns during these „options expiration“ weeks. 

This leads to the construction of a simple market timing strategy. An investor buys the SPY ETF on close each Friday before 2nd Saturday in a month and sells it on close again in the next week’s Thursday.

The Payday effect is similar to the Turn-of-the Month anomaly.

After pay-days, investors seek to invest these funds which cause pushed up equity prices. However, many companies pay their employees twice a month, on the 15th day and at the end of the month. 

Therefore there should be a recognizable pattern in the middle of the month as well. Research confirms this hypothesis and abnormal returns truly exist in the middle of the month. 

Therefore, the simple strategy that utilizes this effect consists of buying the SPY ETF on close on the 15th day of each month and selling it on close the next day.


There are many options on how to form such a strategy, a very simple approach is investing the whole portfolio into SPY during “anomaly“ days.

We also recommend adding the trend factor into strategy, where one of the simplest ways how to do it is to trade only if the price of SPY is higher than its 200-day average.

Now, An investor can form one bigger strategy out of 4 selected calendar anomalies. Selected strategies are simple and easy to trade – investor only needs to invest in the S&P500 index, which can be easily made by the ETFs.

The composite strategy with the added trend factor has an annual performance of 7,47% with a maximal drawdown of only 10%.

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