Algorithmic Trading Is Reshaping Financial Markets

The advent of digital technologies has created a very new and vastly different financial landscape. Today's buying and selling of securities is conducted mostly by computer programs that react within nanoseconds – faster than any human could – to the subtlest market fluctuations. In a new report published by the Centre for Economic Policy Research (CEPR), co-written with Darrel Duffie (Stanford University), Laura Veldkamp (Columbia University’s Graduate School of Business), and Xavier Vives (Spain's Instituto de Estudios Superiores de La Empresa), we come to grips with how technologies are fundamentally changing the way banks, brokers, exchanges, and dealers do their work, and what it means for investors, for privacy and income inequalities.

The 'sell side' industry, concerned with the building up and liquidating of securities portfolios, has undergone enormous changes over the past fifty years, mainly due to the computerization of trading, and more recently the emergence of big data.

Those familiar, iconic scenes in which mobs of colorfully garbed brokers tussle and bustle, waving frantically and hollering orders on stock exchange floors, though still sometimes staged for marketing purposes, are a thing of the past.

There are at least two types of technologies driving the so-called 'electronification' of trading.

First, exchanges have automated the process by which they match buyers and sellers of securities. Imagine, for example, that you want to buy 1,000 shares of L'Oréal stock. Your bank or broker might send your order to Euronext, one of the exchanges on which L'Oréal is traded. Euronext receives, buys and sells orders like this all the time, using computers and algorithms to match them. This is already a profound change, but now consider, Euronext is also accumulating massive amounts of data, about submitted orders, about realized transactions and so on, which it can then resell to other intermediaries and investors. In this respect, securities trading platforms are increasingly looking like other digital platforms, like Facebook, Google or Twitter, and the share of their revenues coming from the sale of data is growing very quickly (at an annual rate of about 13% since 2012). Like Big Tech does, trading platforms could pay you to trade with them, just so you will use their platforms and generate more data!

The second type of technology involves industry participants automating their decisions on the buying or selling of securities. This use of algorithms to make portfolio decisions is what we call algorithmic trading. An asset manager can buy or sell millions of shares of a given stock in a day in response to investors' inflows and outflows in his or her fund. This is the same process of automation that we see in other industries. We are removing humans and replacing them with machines.

Some specialized trading firms, known as high-frequency traders, use algorithms that rely on extremely fast, less-than-a-millisecond, access to information, including to market data sold by electronic trading platforms.

With extremely fast access to this kind of market data, these firms can take advantage of small differences in the price of the same stock on two different trading platforms.

Some of them pay to have their computer servers housed near trading platform servers – they may even rent rack space in the same room, thus gaining some nanoseconds in the delivery of key information, which can make all the difference.

The question of what effect these developments may have on trading costs for other market participants is controversial, raising many issues that are now at the center of policy debate in the EU and North America.

The European Securities and Markets Authority (ESMA) and various national bodies, such as the Autorité des Marchés Financiers (AMF) in France, are the key regulatory bodies for securities markets in the EU, while the Securities and Exchange Commission (SEC) and the Commodity Futures Trading Commission (CFTC) cover U.S. markets.

A number of issues related to the impact of new technologies are up for consideration by regulators.For instance, does the electronification of financial markets actually reduce the costs at which investors can build and liquidate their portfolios? This could mean far larger returns for investors on their savings.

Does algorithmic trading make financial markets more stable, or less so? Do trading platforms have too much market power in the pricing of their market data? We think the development of central bank digital currencies technology should be targeted at solving market failures.

Another question we address in our report is whether trading should be slowed down. The issue here is that high-frequency traders might be deriving excessive profits, at the expense of other participants.

There is also a concern about trading on platforms with less stringent transparency requirements than the main exchanges. The volume of this so-called 'dark trading' is growing, now accounting for about 40% to 50% of equity trading in the EU, raising debate over whether these platforms should be more strictly regulated.

Finally, another issue is to what extent algorithms might destabilize financial markets, resulting in large price swings.

In the coming years, I expect exchanges' business models to continue to rely increasingly on the monetization of data generated by trading. This means exchange platforms will compete with one another to attract users who generate that data, very much like Big Tech does. 

The increasing use of consumer data allows for efficiency gains but also involves potential risks in terms of privacy, diminished competition, and increased income inequality. This trend accelerated during the COVID-19 pandemic, and, if it continues, this will put strong competitive pressures on securities dealers, and it will eventually reduce trading costs for investors.

At some point, the data generated by trading may become more profitable than the trading itself. So, there may come a time when trading platforms start to go to greater lengths to attract users. For example, they could simply pay you to trade with them, just so you will use their platforms and generate more data!

Applications

Central Bank Digital Currencies Could Address Failures. One of the broader messages in the report is about the suitability of central bank digital currencies (CBDCs). We think the development of CBDC technology should be targeted at solving market failures. We also point out that the increasing use of consumer data allows for efficiency gains but also involves potential risks in terms of privacy, diminished competition, and increased income inequality.

In short, the electronification of market securities has real policy and economic consequences that we must understand and address.

Methodology

The current report is the fourth in the CEPR's 'Future of Banking' series and part of the Banking Initiative launched by the IESE Business School in October 2018 with support from Citi. The goal of the initiative is to study new developments in banking and financial markets. The Center for Economic Policy Research (CEPR) is an independent, non-partisan and non-profit organization, founded in 1983 to enhance the quality of economic policy making in Europe.