1. Market Risk Premium and Risk-Free Rate used for 69 countries in 2019: a Survey by Pablo Fernandez (University of Navarra – IESE Business School) and Mar Martinez (IESE Business School) and Isabel Fernández Acín (University of Navarra – University of Navarra, Students)

2. 151 Trading Strategies by Zura Kakushadze (Quantigic Solutions LLC), and Juan A. Serur (University of CEMA)

This is a 271-page preview version of a new book “151 Trading Strategies”, which provides detailed descriptions, including more than 550 mathematical formulas, for more than 150 trading strategies across a host of asset classes and trading styles. These include stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility, real estate, distressed assets, cash, cryptocurrencies, weather, energy, inflation , global macro, infrastructure, and tax arbitrage. Some strategies are based on machine learning algorithms such as artificial neural networks, Bayes, and k-nearest neighbors. The book also includes source code for illustrating out-of-sample backtesting, around  2,000 bibliographic references, and more than 900 glossary, acronym and math definitions. The presentation is intended to be descriptive and pedagogical and of particular interest to finance practitioners, traders, researchers, academics, and business school and finance program students. The book was recently published by Palgrave Macmillan, an imprint of Springer Nature.

I got the idea and was inspired to write this book following the success of my paper “101 Formulaic Alphas”, which provides explicit formulas – that are also computer source code – for 101 real-life quantitative trading signals (alphas).  “151 Trading Strategies” takes this concept to the next level: instead of focusing on quant trading alphas or any particular asset class, it goes across essentially all asset classes and a number of trading styles, so it comes as no surprise that it took almost 9 months to write it.  — Zura Kakushadze

3. A Brief Introduction to the Basics of Game Theory by Matthew O. Jackson ( Stanford University – Department of Economics)

4. Time-Series Momentum: A Monte-Carlo Approach by Clemens Struck (University College Dublin) and Enoch Cheng (University of Colorado at Denver – Department of Economics)

During my days as an undergraduate student, my professors showed me a set of very simple trading strategies such as the carry trade. Such strategies are usually referred to as “factor investment”. Bringing prior trading experience to the degree, I found it hard to believe that one can make serious money by following a set of naïve trading rules. Not surprisingly, many factor investors have experienced disappointment by the real-life performances of such strategies.
 
An observation I made early was that all of these rules seem to work fine in historical backtests over very short periods of time usually not longer than 10 or 20 years. Often, the sample periods contain only a single extreme market situation (e.g. a larger recession) that determines the overall performance of a strategy. Effectively, there is just one observation. 
 
While I see the need for historical backtests, I believe these tests are insufficient. Bootstrapping has been suggested as an alternative. I think we need to go one step further as bootstrapping repeats the same extreme events again and again. Thus, when you bootstrap from a historical sample with one extreme event, you repeatedly analyze the same extreme event. That’s why we looked out for something else and found Monte-Carlo approaches in the risk management literature and hand-tailored them to assess factor investment strategies. – Clemens C. Struck

5. Global Factor Premiums by Guido Baltussen (Erasmus University Rotterdam (EUR)) and Laurens Swinkels (Erasmus University Rotterdam (EUR)) and Pim van Vliet (Robeco Asset Management – Quantitative Investing)

This paper shows very strong evidence on the main strategies underlying factor-based investing. Over the years several anomalous, but persistent patterns in returns have been discovered by fellow academics and ourselves. These patterns are also known as ‘return factors’ and are a hot topic in the investment industry, with tremendous growth in asset managed based on factors. 

At the same time, many studies turn out to be hard to replicate. There is bias to positive results which is referred to as ‘p-hacking’. This p-hacking is a serious concern, but it can be addressed. For example, by raising the statistical bar. Furthermore, replication studies have become more common in social sciences. Campbell Harvey has put p-hacking on the financial research agenda with his AFA Presidential Address.

Nobody knows 100% sure if factors keep on working in the future. But what if the results were fake to start with? We, therefore, apply the same cures which are proposed by the leading scientists. Replicate previous studies, raise the statistical bar and apply extensive, deep datasets. It is our duty to apply the highest possible standards when doing research.

Over the past years, we have constructed a very extensive and deep historical dataset stretching back to 1800 with which we test 24 global factor premiums. We replicate several previous studies which typically go back ‘only’ 30-40 years, with some very strong and remarkable findings. – Guido Baltussen, Laurens Swinkels, and Pim van Vliet