Have you ever wondered if your trading asset trends or mean-reverts? Everyone involved in trading or investments daily solves the task of – What trading strategy should I apply to my assets to generate profits? As always, we at Quantpedia will try to help you a bit with this never-ending task with our new tool/report, which will be unveiled next week for all Quantpedia Pro subscribers. The following article serves as an introduction to the methodology we will use to find new trading edges for you automatically.

The summer is slowly approaching; therefore, our new article will be on a little lighter tone. We will examine a research paper on a periodic event with sentiment implications. The authors (Abudy, Mugerman, Shust) focused on a specific song competition – the Eurovision Song Contest, an international song competition organized annually. They examined a positive swing in investor mood in the winning country the day after the Eurovision Song Contest and documented an average abnormal return of 0.381%. On the contrary, they did not find any negative sentiment in other participating countries.

Phenomenal innovation, new technologies, growth of social media, and e-commerce have been characteristics of the last decades. BigTech companies such as Google, Facebook (Meta), Amazon, Apple, and Microsoft are becoming so increasingly popular. So now, in connection to the actual carnage on the financial markets, the question arises: are BigTech firms the new “Too Big to Fail”?

When someone mentions a financial crisis, most people immediately think of the global financial crisis of 2007-2008. Even though this is the most significant economic crisis in recent years, there have been many more significant crisis periods in the past 100 years. This article examines the biggest crises in three asset classes: stocks, bonds, and commodities, during the past century. Additionally, we analyze the behavior of our trend-following strategy during each of the crisis periods and propose it as a hedge for the stock, bond, and/or commodity markets.

The Monte Carlo method (Monte Carlo simulations) is a class of algorithms that rely on a repeated random sampling to obtain various scenario results. Monte Carlo simulations are used to predict the probability of different outcomes when it would be difficult to use other approaches such as optimization. The main aim is to create alternative scenarios, which account for possible risk and help with decision making. The simulations are used in various fields, from finance and quantitative analysis to engineering or science. We plan to unveil our new “Monte Carlo” report for Quantpedia Pro clients in a next few days, and this article is our introduction to different methodologies that can be used for Monte Carlo calculation.