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.

The U.S. (and the world’s) economy is currently entering a recession. Right now, everybody can see it, the only question is how deep it will be. But is it possible in a real-time predict if the economy will enter a recession? And will that information help us to better set % allocation of equities in our portfolio? Most of the macroeconomic data shows recession in macroeconomic reports with a significant lag. There are multiple different forecasting models which try to predict recession or at least estimate the probability that we are entering into one. We are presenting one interesting research paper written by Jonathan Hartley which shows that prediction markets (betting markets created for the purpose of trading the outcome of events) can be successfully used as a complementary tool in various economic forecasting tools. Prediction markets can be used to measure risk in U.S. equities, credit spreads, the U.S. Treasury yield curve, and U.S. dollar foreign exchange rates.

Author: Hartley

Title: Recession Prediction Markets and Macroeconomic Risk in Asset Prices

A month ago, nobody could expect what March would bring to us. Every person and every company around the world had to turn around its existence completely. That change is still in progress, and we are no exception. The ordinary life of every member of our team has been affected by measures used against coronavirus threat. But we were able to adjust to a new situation flexibly, and we are bringing you your periodical stream of quant research.

We were able to double our rate of research as ten 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 15 new and amended around 20 older backtests written in QuantConnect code. Our database currently contains 260 strategies with out-of-sample backtests/codes.

Also, five new blog posts you may find interesting have been published on our Quantpedia blog. One is about an interesting academic research paper:

Markets are in turmoil, and there exist very few investors who are unscathed by current global events related to coronavirus pandemic. It’s a good time to revisit how are various groups of algorithmic trading strategies navigating current troubled times. The selected sample for this short article consists of 7 well-known equity factor strategies – size, value, momentum, quality, investment, short-term reversal and low volatility factors.

Our analysis shows that we have two groups of factors: strong winners and bad losers. There is no middle ground. A current bear market is ruthless, equity long-short factor strategies either totally nailed it and had a stellar performance or totally disappointed.