ESG Investing (sometimes called Socially Responsible Investing) is becoming a current trend, and its proponents characterize it as a modern, sustainable, and responsible way of investing. Some people love it, others see it as just another fad that will soon be forgotten. We at Quantpedia have decided to immerse in academic research related to this trend to understand it better. How are ESG scores measured? What are the common problems in ESG data? Are there any systematic ESG factor strategies that offer outperformance? These are some of the areas we wanted to explore, and we invite you on this journey with us …

Professor Roger Ibbotson is one of the most respected and influential researchers of the current era. His book “Stocks, Bonds, Bills, and Inflation” is a classic and often serves as a reference for information about capital market returns. Therefore we always pay attention to his publications. His actual work, “Popularity – A Bridge between Classical and Behavioral Finance”, which is written with Thomas M. Idzorek, Paul D. Kaplan, and James X. Xiong, is now available on SSRN.

In their work, authors explain the term “Popularity” from an asset pricing point and show how “Popularity” can be a broad umbrella under which nearly all market premiums and anomalies (including the traditional value and small-cap) can fall. They develop a formal asset pricing model that incorporates the central idea of “Popularity”, which they call the “popularity asset pricing model” (PAPM). Based on this model, they predict characteristics as a company’s brand, reputation, and perceived competitive advantage to be new equity factors.

It’s a long read, but we at Quantpedia really recommended it for all equity portfolio managers …

Authors: Ibbotson, Idzorek, Kaplan, Xiong

Title: Popularity: A Bridge between Classical and Behavioral Finance

Everyone who lived during the 2007 and 2009 crisis knows what the biggest weakness of the equity momentum strategy was. It was right during the spring of 2009 when the financial markets were on its inflection point when the momentum strategy crashed. Right after that inflection point, stocks which were the biggest losers during the previous year performed exceptionally well and caused strong under-performance of classical long-short momentum strategy. How can we prevent this situation from happening again? That’s the topic of our favorite new recent study written by Matthias Hanauer and Steffen Windmueller. They analyze three momentum risk management techniques – idiosyncratic momentum, constant volatility-scaling, and dynamic scaling, to find the remedy for momentum crashes. It’s our recommended read for this week for equity long-short managers …

Authors: Matthias Hanauer and Steffen Windmueller

Title: Enhanced Momentum Strategies

We at Quantpedia absolutely love long-term studies, and academic research paper written by Bhardwaj, Janardanan, and Rouwenhorst is really exceptional. There are a lot of studies covering a long history of equity and bond markets. But futures markets are not covered so well, and that’s the reason why is this paper so valuable. An additional plus is that study covers also delisted contracts, which makes the study’s data quality even better. Quantpedia’s recommended read to anyone interested in asset allocation into commodities …

Authors: Bhardwaj, Janardanan and Rouwenhorst

Title: The Commodity Futures Risk Premium: 1871–2018

Financial academics have described so many equity factors that the whole universe of them is sometimes called “factor zoo”. Therefore, it is no surprise that there is a quest within an academic community to bring some order into this chaos. An interesting research paper written by Favilukis and Zhang suggests explaining a lot of equity factors with momentum anomaly. They show that very often, up to 50% of the equity factor returns can be linked to returns of momentum strategy. This link is especially prevalent in short legs of equity factors.

Authors: Favilukis, Zhang

Title: One Anomaly to Explain Them All

No doubt, the correct datasets are the key when one does some analysis in the financial markets. Nowadays, futures contracts are widely spread and popular among practitioners. However, each delivery month is connected with a different price where the price of the underlying asset should stand at a given date in the future (the expiration date). The industry standard for backtesting futures strategies is to construct one data sequence from a stream of contracts. Our short article shows the importance of choosing the correct methodology for building continuous futures contracts data series…