Chapter 1: Big Data in Finance: An Overview -- SECTION I: BIG DATA IN THE FINANCIAL MARKETS -- Chapter 2: Alternative Data -- Chapter 3: An Algorithmic Trading Strategy to Balance Profitability and Risk -- Chapter 4: High-Frequency Trading and Market Efficiency in the Moroccan Stock Market --...

Intro -- Copyright -- Title Page -- Preface -- About the Editors -- Acknowledgements -- Part 1 AI: Need to Know -- Chapter 1 The Future of AI in Finance -- The Promise of Deep Learning -- Business Applications in Finance -- Time for a Reality Check -- Safeguards and Systemic Risk -- Chapter 2...

We study how researchers can apply machine learning (ML) methods in finance. We first establish that the two major categories of ML (supervised and unsupervised learning) address fundamentally different problems than traditional econometric approaches. Then, we review the current state of...

We give explicit algorithms and source code for extracting factors underlying Treasury yields using (unsupervised) machine learning (ML) techniques, such as nonnegative matrix factorization (NMF) and (statistically deterministic) clustering. NMF is a popular ML algorithm (used in computer...

The common signs of development and its financing of cryptocurrency, artificial intelligence and basic income were determined. The main tendencies and problems of the development of cryptocurrency and artificial intelligence from their appearance to the present time were considered. The...

We give an explicit algorithm and source code for constructing risk models based on machine learning techniques. The resultant covariance matrices are not factor models. Based on empirical backtests, we compare the performance of these machine learning risk models to other constructions,...

I examine racial bias in the most popular home valuation algorithm and study the algorithm's impact on racial bias in transaction prices. I find statistically significant but economically small racial bias in the algorithm. For example, while Black buyers overpay by 9.3% in prices relative to...

Academics and practitioners have developed many models for volatility measurement and forecast – I estimate that the total number of available models to be about 200-300 if we count all modifications of intraday estimators, GARCH-type and continuous-time models.In practice, the estimate and...