This article “Market anomaly: monday and friday effects” was written by Sergey Okun – Senior Financial Analyst at I Know First, Ph.D. in Economics.

Highlights:

• A correctly identified market anomaly can generate a profit for an investor that enables them to beat the market.
• We have tested the Weekend effect anomaly and we have found that exploration of this anomaly cannot provide the desired return for an investor today.
• The I Know First AI algorithm can identify working market anomalies that could be difficult for an ordinary investor to recognize.

Any investor is interested in finding a strategy that allows them to extract additional returns without taking a corresponding grade of risk. Market Anomaly is a change in the price or return of a security that cannot be directly linked to current relevant information known in the market or to the release of new information into the market. It is a stock behavior that is systemically repeated from one period to another which an investor can use to generate a positive return, and where the market efficiency concept does not work.

Anomalies can appear, disappear, and reappear with almost no warning. Moreover, before exploiting a market anomaly, a researcher should find supporting evidence that the market anomaly is consistent over reasonably long periods. Otherwise, a detected market anomaly may largely be an artifact of the sample period chosen. Also, wishing to detect a profitable anomaly a researcher can pitfall in the process of data snooping. Data snooping is the practice of determining a model by extensive searching through a dataset for statistically significant patterns. In general, an initial hypothesis is developed which is based on economic rationale. Tests are then conducted on objectively selected data to either confirm or reject the original hypothesis. However, with data snooping, the process is typically reversed: data are examined with the intent to develop a hypothesis, instead of developing a hypothesis first. This is done by analyzing data in various manners and even utilizing different empirical approaches until you find support for the desired result, in this case, a profitable anomaly. Enough data snooping often can detect a trading strategy that would have worked in the past by chance alone. But in an efficient market, such a strategy is unlikely to generate abnormal returns on a consistent basis in the future.

Here we test the weekend effect anomaly or Monday and Friday anomalies. The weekend effect is a phenomenon in financial markets in which the stock market is underperforming on Mondays showing much lower or sometimes consistently negative returns while on Fridays the situation is reversed and the average return is much higher than the weekly average. The weekend effect addresses investors’ irrationality. There is no clear economical explanation of the weekend effect existence. For instance, we can mention a psychological reason – people are pessimistic about the first working day and they hate Mondays, and for the same reason they like Fridays, which could be considered as a fulfilling explanation.

Let’s analyze the existence of the weekend effect in the S&P500 returns for the recent 5 years period from January 18^th, 2018 to January 13^th, 2023.

According to Figure 3, we can notice that Monday’s mean is around -0.02% while Friday’s mean and Other’s mean are around 0.04% and 0.05%, respectively. Therefore, we can notice that the average return on Tuesday, Wednsday, and Thursday is higher than Friday’s average return, which goes into controversy with the idea that the stock market outperforms on Friday. At the same time, the hypothesis that the stock market underperforms on Monday could still be true.

The unequal variance t-test allows us to test the equality of variances to identify whether returns on Monday and Friday are different from the return on Other days. Firstly, we calculate the difference between Monday’s mean and the Other day’s mean, and between Friday’s mean and the Other day’s mean which are -0.0718% and -0.0103 respectively. We can notice that in both cases returns on Monday and Friday are lower than on Other days. Figure 1 enables us to calculate the standard deviation for the t-tests, which are 0.1127% for Monday and 0.0974% for Friday. Figure 2 allows us to specify the number of degrees of freedom for the unequal variance t-stat tests which are 345 and 424 for Monday and Friday respectively. Therefore, we can calculate the t-stats for Monday and Friday, which are -0.64 and -0.11 respectively. Consequently, p-values for Monday and Friday are equal to 52.45% and 91.56% respectively, which means that we cannot reject the null hypothesis about the absence of Monday or Friday effects. Therefore, we have not found statistical support that Monday and Friday’s effects have worked for the last 5 years.

Let’s check for an even longer period to validate our findings. Below in Figure 4, we can notice our estimation to identify the weekend effect for the period of January 4^th, 2000, to January 13^th, 2023.

According to Figure 4, we can notice that Monday’s and Friday’s means are around 0% while Other’s mean is around 0.04%. Therefore, we can notice that the average return on Tuesday, Wednsday, and Thursday is higher than Friday’s average return, which goes into controversy with the idea that the stock market outperforms on Friday. Also, the hypothesis that the stock market underperforms on Monday is not verified by the statistical test. The p-values for Monday and Friday are equal to 29.94% and 22.83% respectively, which means that we cannot reject the null hypothesis about the absence of Monday or Friday effects.

Does it mean, according to our results, that Monday and Friday’s effects have never existed? The answer is no because the existence of these effects is very well documented in the past periods of the 20^th century. Below, we can notice the statistical result of identifying the weekend effect from January 3^rd, 1928, to January 13^th, 2023.

According to Figure 5, we can notice that Monday’s mean is around -0.07% while Friday’s mean and Other’s mean are around 0.052% and 0.055%, respectively. The p-values for Monday and Friday are equal to 0.00% and 89.82% respectively, which means that we can reject the null hypothesis about the absence of the Monday effect, but we still cannot reject the null hypothesis about the absence of the Friday effect. Therefore, we can sum up that we have found statistical significance evidence of the existence of the Monday effect in the historical data, which has not appeared since 2000. Also, we cannot confirm the existence of the Friday effect. It is possible, if we check certain decades of the 60s, the 70s, or the 80s, we could identify the Friday effect in the stock market.

Exploit Market Anomalies with I Know First

A correctly identified market anomaly can generate a profit for an investor that enables him to beat the market. However, recognizing a working anomaly is a challenging task where a mistake can cost money. AI can identify a working market anomaly that could be difficult for an ordinary investor to recognize. The I Know First predictive algorithm is a successful attempt to discover the rules of the market that enable us to make accurate stock market forecasts. Taking advantage of artificial intelligence and machine learning and using insights of chaos theory and self-similarity (the fractals), the algorithmic system is able to predict the behavior of over 13,500 markets. The key principle of the algorithm lays in the fact that a stock’s price is a function of many factors interacting non-linearly. Therefore, it is advantageous to use elements of artificial neural networks and genetic algorithms. How does it work? At first, an analysis of inputs is performed, ranking them according to their significance in predicting the target stock price. Then multiple models are created and tested utilizing 15 years of historical data. Only the best-performing models are kept while the rest are rejected. Models are refined every day, as new data becomes available. As the algorithm is purely empirical and self-learning, there is no human bias in the models and the market forecast system adapts to the new reality every day while still following general historical rules.

I Know First has used algorithmic outputs to provide an investment strategy for institutional investors. Below you can see the investment result of our S&P 500 Stocks package which was recommended to our clients for the period from January 1^st, 2020 to January 18^th, 2023 (you can access our forecast packages here).

The investment strategy that was recommended by I Know First accumulated a return of 70.86%, which exceeded the S&P 500 return by 47.33%.

Conclusion

Any investor is interested in finding a strategy that allows him to extract additional returns without taking a corresponding grade of risk. Here, we have tested the Weekend effect anomaly and we see that exploration of this anomaly cannot provide the desired return for an investor today. A correctly identified market anomaly can generate a profit for an investor that enables them to beat the market. However, recognizing a working anomaly is a challenging task where a mistake can cost money. The I Know First AI algorithm can identify working market anomalies that could be difficult for an ordinary investor to recognize.

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Please note-for trading decisions use the most recent forecast.