Foreword

• Author: Christine A. Parlour
• DOI: http://doi.org/10.1111/fima.12302
• Date: 01 December 2019
• Published date: 01 December 2019

DOI: 10.1111/fima.12302

INTRODUCTION

Foreword

FinTechis a catchall phrase that covers a myriad of changes in the marketplace. The purpose of this special issue is to

lay out some of the ways in which we as researchers can approach FinTechand what it tells us about the world.

First, as is always the case with a new area, besides developing an institutional understanding of these changes,

there are new techniques and skills needed. The introductory paper by Sanjiv Das, “The Futureof FinTech,’” provides a

broad overviewof both the areas that will most likely be affected by FinTech,and also of the new tools that are inherent

in the area that we can use to sharpen our analysis. Foremostamong them is the whole area of machine learning. It goes

without saying that the machines do not “learn’” but instead identify patterns. Such patterns can be very useful but, of

course, it is up to us as economists to clarify what they “mean.’’

The paper by Jagtiani and Lemieux “The Roles of Alternative Data and Machine Learning in FinTech’’ is a careful

analysis of the effect of industry machine learning on real outcomes. Researchers have been lucky in having access

to the Lending Club data and the authors use this to provide a nuanced look at how their internal credit algorithms

affect lending patterns. The traditional measure of credit worthiness is the FICO score, but Jagtiani et al. illustratehow

the correlations between this measure and the internal measures (rating grades) declined to 35%. In spite of that, the

rating grades were informative about default probability.Thus, the alternative data can help credit worthy borrowers

with a low FICO score to obtain loans at a better rate. In this case, having access to more data and being able to use it

to predict default relax some borrowers’ constraints.

To some extent,pricing credit risk is a familiar problem, but FinTech comprises more than using new techniques.

Bitcoin and its Blockchainemerged over a decade ago and have survived as a robust example of Decentralized Finance

(DeFi). Jain, McInish, and Miller provide us with “Insights from Bitcoin Trading.’’ In this paper, they investigate what

BitCoin markets can tell us about existingfinancial markets. Specifically, the microstructure literature has established

various stylized relationships and theories, such as commonality in returns and volume. They find strong support for

commonality in liquidity in this cryptocurrency; indeed, one factor explainsabout 70% of the variance in hourly volume.

Similar to the results from foreign exchange,they find a strong local effect in trading volume. Ideally, if our understand-

ing of financial markets is robust, patterns and observations we observe in one market should also appear in other

markets. This paper establishes that theydo.

Similarly,Hu et al., “Cryptocurrencies: Stylized Facts on a New Investible Instrument,’’ compare the return proper-

ties of a set of over200 large cryptocurrencies. These data provide an interesting insight into assets that are effectively

unregulated. They thus provide a useful benchmark against which we can compare more conventionalfinancial assets.

In addition, the authors consider some properties of initial coin offerings and note the size of the first day returns. By

this measure, IPO first day returns may be too low.

Finally,Routledge in “Machine Learning and Asset Allocation’’ points the way to new analyses that we haveto under-

take if we want to understand how investorsare taking advantage of the new ways of analyzing data.

These papers barely scratch the surface of the different research channels that FinTechhas opened up. However,

each of them points the way forward. I hope you enjoyreading them as much as I have.

Christine A. Parlour

Haas School of Business, University of California - Berkeley

c

2019 Financial Management Association International

Financial Management. 2019;48:979. wileyonlinelibrary.com/journal/fima 979