Managing Editor’s Letter

Cathy Scott General Manager and Publisher The lead article in this issue is by the co-editor of this journal, Marcos López de Prado, “Machine Learning for Econometricians: The Readme Manual.” As he notes, econometric tools are typically applied in investment research despite the fact that they are poorly suited for uncovering statistical patterns in financial data. This is because of the unstructured nature of financial datasets, as well as the complex relationships involved in financial markets. Researchers and analysts working for asset managers overlook these limitations as they take the view that econometric approaches are more appropriate than machine learning methods. One of their objections to using machine learning is that their tools are not transparent (i.e., it is a black box approach to problem solving). López de Prado demonstrates why it is not the case that machine learning is a black box. For each analytical step of the econometric process, he identifies a corresponding step in machine learning analysis. By clearly stating this correspondence, López de Prado has facilitated and reconciled the adoption of machine techniques among econometricians, offering a bridge from classical statistics to machine learning. The process of meta-labeling, introduced by López de Prado, is used as the machine learning layer of an investment strategy that can determine the size of positions, filter out false-positive signals from backtests, and improve performance metrics. In “Meta-Labeling: Theory and Framework,” Jacques Francois Joubert provides an overview of meta-labeling’s theoretical framework (including its architecture and applications). Then the author describes the methodology for three controlled experiments designed to break meta-labeling down into three components: information advantage, modeling for false positives, and position sizing. The three experiments validated that meta-labeling not only improves classification metrics but also significantly improves the performance of various types of primary investment strategies. Because of this attribute of meta-labeling, this article provides a good case study of how machine learning can be applied in financial markets. Studies have shown that security prices are driven by information beyond the financial information reported by companies in their filings with the Securities and Exchange Commission. This information includes news and investor-based sentiment. In “FinEAS: Financial Embedding Analysis of Sentiment,” a new language representation model for sentiment analysis of financial text called “financial embedding analysis of sentiment” (FinEAS) is introduced by Asier Gutiérrez-Fandiño, Petter N. Kolm, Miquel Noguer i Alonso, and Jordi Armengol-Estapé. Their approach is based on transformer language models that are explicitly developed for sentence-level analysis which builds on Sentence-BERT, a sentence-level extension of vanilla BERT. The authors argue that the new approach generates sentence embeddings that are of higher quality that significantly improve sentence/document-level tasks such as financial sentiment analysis. Using a large-scale financial news dataset from RavenPack, the authors demonstrate that for financial sentiment analysis the new model outperforms several state-of-the-art models. The authors make the model code publicly available. Deep reinforcement learning (DRL) has attracted substantial interest from practitioners. However, its application has been limited by the need for practitioners to