April was more business-as-usual month for Quantpedia team as eleven new Quantpedia Premium strategies have been added into our database, and six new related research papers have been included in existing Premium strategies during last month.

Additionally, we have produced 17 new backtests written in QuantConnect code. Our database currently contains over 270 strategies with out-of-sample backtests/codes.

Also, five new blog posts you may find interesting have been published on our Quantpedia blog:

Financial markets are full of pricing anomalies, and their existence is often explained by human behavior. Behavioral finance postulates that cognitive irrationality is manifested in biases like the disposition effect (the tendency of people to sell assets that have increased in value, but keeping assets that have dropped in value in portfolio) or overconfidence bias (the tendency of people to be more confident in their own abilities). There are some papers which directly link investment decision making caused by these biases to actual physiology of investors (for example, a known impact of testosterone on investment performance). A new research paper written by Nofsinger, Patterson, and Shank examines not only testosterone but also cortisol levels of testing subjects and then compares their performance in a mock investment contest. Both hormones are strongly related to higher portfolio turnover and inability to accept losses, with cortisol levels even more significant than testosterone.

Author: Nofsinger, Patterson, Shank

Title: On the Physiology of Investment Biases: The Role of Cortisol and Testosterone

Federal Open Market Committee meetings (aka FED meetings) have a significant influence on the number of different assets (see for example our article related to drift in equities during FED meetings). The main channel which FED uses to influence the US economy is the level of short term interest rates. Therefore, it’s not a surprise that FED meetings have influence also on long-term interest rates. But just how big? Bigger than most people think. We are presenting one interesting research paper written by Sebastian Hillenbrand, which shows that the whole secular decline in equity yields and long-term interest rates since 1980 was realized entirely in a 3-day window around FOMC meetings. Now, that’s called the influence …

Author: Hillenbrand

Title: The Secular Decline in Long-Term Yields around FOMC Meetings

Quantpedia’s main goal is and always has been to help our readers to navigate in the ocean of academic research related to systematic investment strategies and quant trading. Our main product offering, the Quantpedia’s Premium database of algo/quant/systematic trading strategies, is tailored to an advanced audience.

But we also have readers who are complete beginners or aspiring quants and are looking for a complete educational package with a lot of explanation. Therefore, we have partnered with the QuantInsti and created a new tutorial course from beginners to an intermediate audience.

Tick data is the most granular high-frequency data available, and so is the most useful in market microstructure analysis. Unfortunately, tick data is also the most susceptible to data corruption and so must be cleaned and conditioned prior to being used for any type of analysis.  

This article, written by Ryan Maxwell, examines how to handle and identify corrupt tick data (for analysts unfamiliar with tick data, please try an intro to tick data first).

In many academic fields like physics, chemistry or natural sciences in general, laws do not change. While economics and theory of investing try to find rules that would be true and always applicable, it is not that simple, there is a “complication“ – human. Psychology of humans is very complex. In the one hand, it creates anomalies in the market, that academics study and practitioners use. On the other hand, after an anomaly is discovered, often, the strategy becomes less profitable.

While for academics, it is just another research question, investors may be worried that the anomaly is arbitraged away, and it will become unprofitable in their portfolios. In this article, we will look deeper on whether the anomaly can be arbitraged away, if the profits are lower for the specific strategy once the strategy becomes well-known, and even if the strategies can be timed. Quantpedia‘s readers are often interested in these common topics, and we will try to shed some light on them.