This is a summary of links featured on Quantocracy on Monday, 04/29/2024. To see our most recent links, visit the Quant Mashup. Read on readers!
-
Initial Test of Trading Forex News Announcements [Dekalog Blog]My first test of trading forex news announcements is to test the efficacy of breakouts immediately following a news announcement related to the US dollar, specifically, only the high impact news as shown on the forexfactory calendar in red. The intention would be to capture some of the profit available from the big movements resulting from surprise news or simply market manipulation around these
-
How to use autoencoders to create feature embeddings [PyQuant News]Embeddings are used in neural networks to transform large, sparse data into manageable, dense formats. In other words, they simplify complex data, making it easier to analyze. We can use embeddings to capture dense information about drivers of stock returns. This approach is a great way to select pairs and diversify portfolio risk. By the end of todays newsletter, youll have code to train an
-
Factor Investing Is Dead, Long Live Factor Investing! [Finominal]Market-neutral multi-factor products have reached all-time highs However, long-only multi-factor products have consistently underperformed Portfolio construction and implementation matters INTRODUCTION Investors have been style investing since the inception of stock markets. Some chase trends, while others focus on quality or cheap stocks. Factor investing is not new, but it did experience a
-
MLMs: do they work better than traditional approaches? [Alpha Architect]Can AI models improve on the failures in predicting returns strictly from a practical point of view? In this paper, the possibilities are tested with a battery of AI models including linear regression, dimensional reduction methods, regression trees and neural networks. These machine learning models may be better equipped to address the multidimensional nature of stock returns when compared to