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 readingStock 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 readingIn 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 readingRecently, 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 readingIn 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 readingLet 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 readingThe 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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