In my last post I introduced Fundamental Return Moment Estimation. This short post applies the resulting estimates to build superior minimum variance portfolios. Both standard mean-variance-, as well as minimum variance portfolio optimization suffer from…

Continue reading## Fundamental Parameter Estimation I

Stock returns are notoriously noisy and as a result, little can be learned from historical data. One of the major buidling blocks of academic finance, the Markowitz portfolio optimization with its mean-variance framework is rarely...

Continue reading## Local Effects in the Cross-Section of stock Returns

In this post, I want to introduce local effects in the cross-section of stock returns. Different from risk factors/ market anomalies, calculated using raw (global) measures, local measures that relate single stocks to their peers...

Continue reading## Deep Characteristic Portfolios

We overcome the low performance of standard portfolio optimization by modeling portfolio weights as functions of stock characteristics. Instead of predicting asset returns in a first step and subsequently modeling portfolio weights in a second step, we...

Continue reading## Factor Investing: Deep Time Series Factor Momentum

Recently, I presented two new concepts related to risk factor cross-correlations. Namely, I showed you cross-correlation time-series factor momentum and I used deep neural networks to predict the returns of the value factor. Today I...

Continue reading## Factor Investing: Predicting Factor Returns with Neural Networks

In the last post, I showed you cross-correlation time-series factor momentum. This strategy times factors by utilizing auto-cross-correlations in factor data (see Gupta & Kelly (2019) for a comprehensive study). This week, I will extract...

Continue reading## Factor Investing: Cross-Correlation Time-Series Momentum

Let me introduce you to time-series factor momentum, a strategy that attempts to harness the auto-correlation in factor returns (see Gupta & Kelly (2019) for a comprehensive study). Factor returns are frequently significantly auto-correlated, which...

Continue reading## Diversifying Estimation Errors with Unsupervised Machine Learning

Markowitz Portfolio Optimization is haunted by estimation errors in mean and covariance estimates. Instead of optimizing the Sharpe ratio out-of-sample, the optimizers tend to maximize the estimation Error (Michaud 1989). The estimation error maximization property...

Continue reading## Factor Chasing: The Case for Cross-Country Factor Momentum

Factor momentum is persistent and observable in global equity markets (Gupta & Kelly (2019)). We take the perspective of a value investor that faces disappointing returns in domestic markets and attempts to chase positive value returns over the world. A strategy that buys winning international...

Continue reading## Factor Zoo Manifold Learning: Extracting Cross-Sectional Signals from High Dimensions

The factor zoo challenges financial research and raised doubts regarding post-publication robustness and data snooping. Feng et al. (2020) find that only a few new anomalies are enough to explain the cross section of stock returns, while the other factors are redundant. Another way...

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