The best of 2018 - Quantitative Investing
The winning and commended papers for all 15 categories at the 2018 Savvy Investor Awards were announced on December 4th. The first link below will take you straight to our blog for the Best Quant Paper category, where you can view both the winning paper and the other commended papers that were recognized.
Additionally, we've included some great quant papers from the past few weeks, most of which just missed the cutoff for this year's awards.
TOP QUANT PAPERS OF 2018
The winners of this year's award for Best Quant Paper were Eugene Fama and Kenneth French. Click here for more information about their paper as well as other commended papers recognized in the Savvy Investor Awards.
TOP PAPERS FROM THE LAST FEW WEEKS
Popular Methods for Forecasting Long-Term Equity Trends (PGIM Institutional Advisory & Solutions, 2018)
For compliance reasons, this paper is only accessible in the United States
Many investors need to make long-term asset class forecasts for planning and portfolio construction purposes. In this 16-page paper by PGIM, the authors examine the empirical performance of two different approaches to forecasting future ten-year equity returns: a regression methodology using CAPE and a more traditional “building block” approach.
This 114 page paper from CFA Institute Research Foundation takes a step back and examines the entire Investment management industry, from fundamental theories and active management insights, to trends currently present, and a look at what the future might hold in store.
Investors are increasingly showing interest in risk premia strategies across asset classes. Carry is one of the most studied premia. To successfully execute a risk premia strategy, it is important to have a detailed understanding of how individual premia returns are affected by macroeconomic conditions.
FTSE Russell shows that a bottom-up approach to multi-factor index construction that is based upon multiple tilting provides advantages to a top-down approach based upon selection and weighting.
This study proposes a multi-factor approach to outperforming an index by removing both 'lottery' stocks and 'safety' stocks.
Popularity: A Bridge Between Classical and Behavioral Finance (CFA Institute Research Foundation, 2018)
This tome (authored by CFA Institute Research Foundation) examines the differences between classical finance and behavioral finance as well as how they can complement each other.
Machine Learning & Investing Part 1: From Linear Regression to Ensembles of Decision Stumps (O'Shaughnessy AM blog, 2018)
How can machine learning techniques be applied to financial practices? O'Shaughnessy Asset Management looks at a particular tool called decision stumps to show how it can potentially encapsulate more data than a simple linear regression.
In this paper, the ECB analyzes Eurozone corporate bond spreads and the factors that contributed to them over a 15 year period.