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

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.

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

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

Every hedge fund manager and every trader wants to know what strategies are employed in a fund ran by his competition. The curiosity is even stronger if we want to see how strategies are mixed in the kitchen of the most successful hedge funds. Top performing funds are usually notoriously secretive about their portfolios. But we still can learn something from the history of their monthly returns. One such interesting methodology is described in a research paper written by Canepa, Gonzalez, and Skinner. Their analysis hints that the top-performing hedge funds are usually successful because they are able to manage their factor exposure better. They are not dependent so much on classical equity risk factors as average funds are. And if they are exposed to some risk factor, the top-performing hedge funds are able to close underperforming factor strategy sooner than average funds.

Authors: Canepa, Gonzales, Skinner

Title: Hedge Fund Strategies: A non-Parametric Analysis

There are many calendar / seasonal anomalies by which we can enhance our strategies to gain more return. One of the least frequent but still very interesting anomalies is for sure the Pre-Election Drift in the stock market in the United States. This year is the election year, and public discussion is getting more heated. The current president of the United States and candidate for re-election, Donald Trump, is a peculiar figure who split the population of the United States into two parts, ones who hate him and those who love him. We can probably expect volatile market moves as we will move closer to this year’s presidential election. But this post will not be about politics but about trading. In this post, we will try to uncover a pattern in historical data that shows significant market moves a few days before elections…

Authors: Vojtko, Cisar

Title: Pre-Election Drift in the Stock Market