One of the very often promoted attributes of Bitcoin is said to be its “safe heaven” characteristic. Some cryptocurrency proponents advocate that Bitcoin can be used as a store of value mainly during the economic and financial crisis. We argue that it’s not so.

Bitcoin (and all cryptocurrencies too) is, in our opinion, fundamentally more similar to stocks of small companies from the technological sector. It is a very speculative bet on blockchain technology. It may seem unrelated to the broader equity market (like the S&P 500 index) during normal times. But when a stressful time comes, investors are more concerned to meet a deadline for the next mortgage payment. This is the time when the speculative bets are closed, and cash is raised. And this is precisely the time when Bitcoin falls as equities do too.

Financial markets are in panic mode. Everybody is talking about the next bear market and economic implications of spreading coronavirus to the whole world. People are split into two groups. One group reasons that a new covid-19 virus is just a stronger flu. Other are worried and draw parallels to Spanish flu pandemic with tens of millions of dead.

We would like to show you two charts which can explain why the high market volatility can be completely rational.

Mirror, mirror on the wall, what’s the best factor model of them all? We at Quantpedia are probably not the only one asking this question. A lot of competing factor models are described in the academic literature and used in practice. That’s the reason why we consider a new research paper written by Matthias Hanauer really valuable. He compared several commonly employed factor models across non-U.S. developed and emerging market countries and answered the question from the beginning of this paragraph. Which model seems the winner? The six-factor model proposed in Barillas et al. (2019) that substitutes the classic value factor in the Fama and French (2018) six-factor model for a monthly updated value factor …

Authors: Hanauer

Title: A Comparison of Global Factor Models

Dear readers,

Four new Quantpedia Premium strategies have been added into our database, and five new related research papers have been included in existing Premium strategies during last month. Plus, I am happy to announce that we have expanded our team of analysts and starting in March, we plan to more than double our rate of research. Our plan is to add around 100 new strategies derived out of academic research until the end of 2020.

Additionally, we have produced 19 new backtests written in QuantConnect code. Our database currently contains 245 strategies with out-of-sample backtests/codes.

Once upon a time, there was everybody’s favourite finance tool in a town – Capital Asset Pricing Model, which was liked and used by nearly everyone. But a few decades ago, it went out of fashion. Easier accessibility of cheap finance databases allowed a lot of researchers to dig deeper into those data. They uncovered a tremendous amount of evidence for a lot of market anomalies not consistent with CAPM. A new research paper written by Park and Wang shows that CAPM is maybe not completely useless. The rise of automated trading causes individual stocks’ returns to align more closely with the market. Intraday correlation in the equity market is rising, and so is the fraction of firms’ returns that are explained by market returns …

Authors: Park, Wang

Title: Did Trading Bots Resurrect the CAPM?

Various risk parity methodologies are a popular choice for the construction of better diversified and balanced portfolios. It is notoriously hard to predict the future performance of the majority of asset classes. Risk parity approach overcomes this shortcoming by building portfolios using only assets’ risk characteristics and correlation matrix. A new research paper written by Lohre, Rother and Schafer builds on the foundation of classical risk parity methods and presents hierarchical risk parity technique. Their method uses graph theory and machine learning to build a hierarchical structure of the investment universe. Such structure allows better division of assets into clusters with similar characteristics without relying on classical correlation analysis. These portfolios then offer better tail risk management, especially for skewed assets and style factor strategies.

Authors: Lohre, Rother and Schafer

Title: Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-Asset Multi-Factor Allocations