A recent spring 2022 crisis in the cryptocurrency market emphasized the importance of market-neutral crypto trading strategies. It’s not enough just to HODL crypto market and hope for the everlasting bull market. Therefore, we continue our series of research articles about the cryptocurrency market and offer an analysis of the skewness anomaly. So after our description of the skewness effect in commodities, an article about the multi-asset skewness strategy, and observation of the skewness/lottery effect in ETFs, we have one more asset class, where we can find lottery/skewness anomaly – in cryptocurrencies.

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.

We focused the first part of this article more on tendencies and trends among these US Equity Value strategies. Today we will talk solely about specific top-performing equity value strategies. More specifically, about numbers #7 to #1 from Quantpedia’s ranking. We will also take a look at combining all the value strategies together into a diversified portfolio of value strategies.

Hello all,

What have we accomplished in the last month?

– A new Quantpedia Pro report – Monte Carlo Analysis
– 9 new Quantpedia Premium strategies have been added to our database
– 15 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 10 new backtests written in QuantConnect code
– And finally, 5+3 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

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.