As an old investing adage goes, “Everybody’s a genius in a bull market.” It is easy to fall victim to the Dunning-Kruger effect, where attribution bias makes us mistake our luck for abilities. When the business cycles change, there are great problems with precise stock price predictability. And this is not the only problem for humans, who are baffled by many mental heuristics. Machine learning algorithms experience similar problems, too. What is happening, and why is it so? A new paper by Wang, Fu, and Fan gives an explanation and proposes some remedies …

We mentioned multiple times that we at Quantpedia love historical analysis that spans over a long period of time as it offers a unique glimpse into the different macro environments and periods of political and economic instabilities. These long-term studies help a lot in risk management, and they also help investors set the right expectations about the range of outcomes in the future. Historical analysis of equity and fixed-income markets is not rare, but currency markets are less explored. Therefore, we are happy to investigate a recent paper by Joseph Chen that analyzes carry, momentum, and value strategies in the currency markets over the 200-year history.

Hello all,

– A new Pragmatic Asset Allocation report
– A reminder for Quantpedia Awards 2024 competition with a $15.000 prize pool
– MesoSim discount announcement
– 11 new Quantpedia Premium strategies have been added to our database
– 8 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 9 new backtests written in QuantConnect code
– 5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

There was a time in history when researchers believed that we, as a human species, act ultimately reasonably and rationally (for example, when dealing with financial matters). What arrived with the advent of Animal spirits (Keynes) and later Behavioral Finance pioneers such as Kahneman and Tversky was the realization that it is different from that. We often do not do what is in our best interest; quite the contrary. These emotions are hardly reconcilable with normal reasoning but result in market anomalies.

Researchers love to find causes and reasons and link behavioral anomalies to stock market performance. A lot of anomalies are related to various sentiment measures, derived from a alternative data sources and today, we present an interesting new possible relationship – investors’ mood and sentiment proxied by music sentiment!

In the rapidly evolving landscape of cryptocurrency markets, understanding the underlying dynamics that drive price movements and investor sentiment can be a matter of survival. However, there are myriad facets of trading reality, and the only thing that we can do is to slowly understand them one after another, one step at a time. This article picks one corner of the cryptocurrency market and sheds a little light on it. We have already written a few times about the importance of the introduction of Bitcoin futures and their impact on the Bitcoin price. Therefore, in this article, we will specifically examine Bitcoin’s behavior around the critical events when Bitcoin futures expire.

The degree of stock market efficiency is a fundamental question of finance with considerable implications for the efficiency of capital allocation and, hence, the real economy. Return predictability is a cornerstone that allows investors to estimate their returns with ranging precision. Some anomalies allow one to exploit loopholes in global markets and capture substantial alpha, which violates the Efficient Market Hypothesis (EMH). However, whether this alpha arrives from risk premia or its source is mispricing is still puzzling academics around the globe, and they wrap their head around solving these tricky question.