Our mathematicians, statisticians, engineers and computer scientists perform fundamental research and advanced quantitative analysis, and utilize cutting–edge technology to identify and create new strategies in the investment arena.
Our organization and our people are diligent students of several of the best schools of statistical learning (in the tradition of Hastie, Tibshirani and Friedman at Stanford) as well as machine learning. We deploy state-of-the-art software, including many packages from the R statistical language such as caret and mlr along with their underlying boosting, bagging and tree-based classification and regression methods.
It is this core belief in the academic foundation and principles of our work that inform our approach to investing.
As our researchers develop new strategies they write and share white papers to explain their findings.
A Practitioner’s Defense of Return Predictability
[Paper Link; How to replicate “Practitioner’s Defense PDF; White Paper Indicators PDF]
- Published in The Journal of Portfolio Management
Our first white paper is authored by Blair Hull and Xiao Qiao and explores the issues and opportunities of market timing and return predictability.
It is this core belief in the academic foundation and principles of our work that inform our approach to investing. In “A Practitioner’s Defense of Return Predictability,” a white paper authored by Blair Hull and Xiao Qiao, the issues and opportunities of market timing and return predictability are explored.
The paper discusses a six-month horizon model that leverages correlation screening to combine a constantly changing collection of twenty academically referenced variables in order to demonstrate forecasting efficacy. Using a walk forward simulation in which positions in SPY are taken proportional to the model forecast equity risk premium, this approach indicates simulated strategy yields of more than twice the annual returns of a buy-and-hold strategy and a corresponding Sharpe ratio four times that of buy-and-hold.
Return Predictability and Market-Timing: A One-Month Model
- Forthcoming in the Journal of Investment Management
In 2017, the Hull team released the results of its current research in a paper called “Return Predictability and Market-Timing: A One-Month Model”. We believe the paper is unique in that it provides a link to the model’s forecasts. To our knowledge, no other research paper gives access to real time predictions of the equity risk premium.
Seasonal Effects and Other Anomalies
The most recent white paper revisits a series of popular anomalies: seasonal, announcement and momentum. The Hull team investigates the creation of a seasonal anomaly and trend model composed of the Sell in May (SIM), Turn of the Month (TOM), Federal Open Market Committee pre-announcement drift (FOMC) and State Dependent Momentum (SDM). Similar to the previous two white papers, model conclusions and recommendations are included in the Daily Report.