Quantitative Asset Allocation Made Easy

Using machines and algos to trade any markets

This selection of insights focuses on everything quant investing. It is dedicated to Jim Simons, the founder of one of the most famous quant hedge funds in the history of finance—Renaissance Technologies—who sadly passed away recently. To keep his memory alive, here are some of the latest perspectives on machine learning, trend-following, momentum trading, among other things quant related.

Honey, I Shrunk the Trend-Following (Man Group)

Traditional portfolios need additional sources of diversification more than they have done so far this century. Trend-following, the authors argue, can meet this need.

Capturing Unpredictability: Trend Following Alpha (Aspect Capital)

Trend following CTAs have been able to capitalise on bull and bear markets across asset classes in recent years. Will this development continue?

Can Machines Time Markets? The Virtue of Complexity in Return Prediction (AQR)

Machine learning techniques have flourished in environments with high predictability and large amounts of data—can they be applied to investing with any success?

Trend-Following and Risk Factor Diversification 2022-2023 (Quantica Capital)

This paper’s authors outline the very different market dynamics in 2022 and 2023 for trend-following CTAs.

Advances in Machine Learning Approaches - A Panel Discussion (PMR)

Most of the challenges to effective financial model training and deployment involve data. Read this panel discussion to find out if these can be solved.

Using ML Programs to Forecast the Equity Risk Premium (Alpha Architect)

One of today's common questions is whether more complicated methods using newly developed machine learning models can provide superior forecasts.

FX Carry + Value + Momentum Strategies over Their 200+ Year History (Quantpedia)

This paper shows that the carry trade return would have been surprisingly robust throughout history.

Improving Factor Strategies (CFA Institute Research & Policy Center)

More research is needed on implementation shortfalls caused by frictions such as trading costs and discontinuous trading when it comes to factor strategies.

Modeling Conditional Factor Risk Premia: A Proposal (The Journal of Finance)

The model used in this paper allows allocators to estimate the conditional factor risk premia for nontraded factors using the improved non-linear option exposures.

Artificial Intelligence Could Compound Finance Misconceptions (Price Action Lab)

Can artificial intelligence help with trading based on the news? This article provides an interesting response to this evolving problem.

Same-Weekday Momentum

One of the findings of this paper is that institutional trading as an important driver of stock momentum.

Man Versus Machine (Investments & Wealth Institute)

Does artificial intelligence pose a threat to financial advisors? There is no clear answer to this conundrum—yet.