This study examines the effects of macroeconomic policies on financial markets using a novel approach that combines Machine Learning (ML) techniques and causal inference. It focuses on the effect of interest rate changes made by the US Federal Reserve System (FRS) on the returns of fixed income and equity funds between January 1986 and December 2021. The analysis makes a distinction between actively and passively managed funds, hypothesizing that the latter are less susceptible to changes in interest rates. The study contrasts gradient boosting and linear regression models using the Double Machine Learning (DML) framework, which supports a variety of statistical learning techniques. Results indicate that gradient boosting is a useful tool for predicting fund returns; for example, a 1% increase in interest rates causes an actively managed fund's return to decrease by-11.97%. This understanding of the relationship between interest rates and fund performance provides opportunities for additional research and insightful, data-driven advice for fund managers and investors.

A document by Navin Kamuni. Click on the document to view its contents.

This study examines the fundamental theory necessary for comprehending Variational Autoencoders (VAEs) and generative latent time-series models. We will also explain how these models extend the principles of VAEs to the domain of time-series data by incorporating temporal dependencies into the latent space. By leveraging the probabilistic nature of VAEs and the temporal dependencies captured by generative latent time-series models—researchers and practitioners can generate synthetic data for various applications, ranging from image generation to time-series forecasting. Through experiments and examples, we showcase the efficacy of these models in generating synthetic data that closely resembles the characteristics of the original dataset.

In this study, the method used involves segmenting images into fixed-sized square regions called superpixels and then analyzing features of each superpixels to determine whether it belongs to the foreground or background. The study also discusses the process of selecting relevant features and testing different combinations. These features include characteristics of the superpixel itself—its local relationships with neighboring superpixels, and global features of the entire image. Importantly, it emphasizes making the decision-making process understandable—achieved by using a decision tree model for analyzing the collected features. This study focuses on teaching a self-driving system how to accurately separate foreground (movable areas like floors) from background (non-movable areas) in images captured inside buildings. This approach aims to strike a balance between entirely automated segmentation models like Convolutional Neural Networks (CNNs)—which lack explainability, and purely knowledge-based segmentation models, where all information is predefined. This information is stored in a database for analysis. Specifically, it addresses the scenario of an autonomous vehicle navigating public spaces without endangering people.