Managing Editor’s Letter

Abstract

robert dunn General Manager The four issues of the 2019 inaugural publication of The Journal of Financial Data Science by all metrics indicate the success of the journal. Four of the articles published in JFDS were in the top 10 most downloaded articles across the Portfolio Management Research (PMR) platform. This is quite an accomplishment considering that JFDS represented just one year of articles. After publication of the first issue, articles in JFDS were featured in an opinion piece on the challenges of implementing machine learning by David Stevenson (“Machine Learning Revolution is Still Some Way Off”) published in the Financial Times. One of the articles in the inaugural issue is highlighted by Bill Kelly, the CEO of the CAIA Association, in an August 2019 blog (“Whatfore Art Thou Use of Alt-Data?”) in AllAboutAlpha. The Financial Data Professional Institute (FDPI), established by the CAIA Association, will be adopting at least f ive articles from JFDS as required reading for their membership exams. As researchers in this space produce papers, our expectation is that the journal will be well cited. In the first issue of Volume 2, there are nine articles which are summarized below. “Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies” is the first in a series of articles by Derek Snow dealing with machine learning in asset management. The series will cover the applications to the major tasks of asset management: (1) portfolio construction, (2) risk management, (3) capital management, (4) infrastructure and deployment, and (5) sales and marketing. Portfolio construction is divided into trading and weight optimization. The primary focus of the current article is on how machine learning can be used to improve various types of trading strategies, while weight optimization is the subject of the next article in the series. Snow classifies trading strategies according to their respective machine-learning frameworks (i.e., reinforcement, supervised and unsupervised learning). He then explains the difference between reinforcement learning and supervised learning, both conceptually and in relation to their respective advantages and disadvantages. Global equity and bond asset management require techniques that also put effort into understanding the structure of the interactions. Network analysis offers asset managers insightful information regarding factor-based connectedness, relationships, and how risk is transferred between network components. Gueorgui Konstantinov and Mario Rusev demonstrate the relation between global equity and bond funds from a network perspective. In their article, “The Bond–Equity–Fund Relation Using the Fama–French–Carhart Factors: A Practical Network Approach,” they show the advantages of graph theory to explain the collective b y gu es t o n Fe br ua ry 5 , 2 02 1. C op yr ig ht 2 02 0 Pa ge an t M ed ia L td .