How Algorithmic Trading Undermines Efficiency in Capital Markets
This Article argues that the rise of algorithmic trading undermines efficient capital allocation in securities markets. It is a bedrock assumption in theory that securities prices reveal how effectively public companies utilize capital. This conventional wisdom rests on the straightforward premise that prices reflect available information about a security and that investors look to prices to decide where to invest and whether their capital is being productively used. Unsurprisingly, regulation relies pervasively on prices as a proxy for the allocative efficiency of investor capital.
Algorithmic trading weakens the ability of prices to function as a window into allocative efficiency. This Article develops two lines of argument. First, algorithmic markets evidence a systemic degree of model risk—the risk that stylized programming and financial modeling fails to capture the messy details of real-world trading. By design, algorithms rely on pre-set programming and modeling to function. Traders must predict how markets might behave and program their algorithms accordingly in advance of trading, and this anticipatory dynamic creates steep costs. Building algorithms capable of predicting future markets presents a near-impossible proposition, making gaps and errors inevitable. These uncertainties create incentives for traders to focus efforts on markets where prediction is likely to be most successful, i.e., short-term markets that have limited relevance for capital allocation. Secondly, informed traders, long regarded as critical to filling gaps in information and supplying markets with insight, have fewer incentives to participate in algorithmic markets and to correct these and other informational deficits. Competing with high-speed, algorithmic counterparts, informed traders can see lower returns from their engagement. When informed traders lose interest in bringing insights to securities trading, prices are less rich as a result.
This argument has significant implications for regulation that views prices as providing an essential window into allocative efficiency. Broad swaths of regulation across corporate governance and securities regulation rely on prices as a mechanism to monitor and discipline public companies. As algorithmic trading creates costs for capital allocation, this reliance must also be called into question. In concluding, this Article outlines pathways for reform to better enable securities markets to fulfill their fundamental purpose: efficiently allocating capital to the real economy.
Associate Professor of Law, Vanderbilt Law School. I have benefitted tremendously from discussions and conversations with colleagues. For comments on earlier drafts, I owe sincere thanks to Professors Mehrsa Badaran, Adam Badawi, Brad Bernthal, Margaret Blair, Chris Brummer, Anthony Casey, Edward Cheng, Bhagwan Chowdhry, Onnig Dombalagian, Olivia Dixon, Paul Edelman, Adam Feibelman, Jesse Fried, Tracey George, Erik Gerding, David Hay, Jennifer Hill, Robert Jackson, Kathryn Judge, Donald Langevoort, Jeffrey Manns, Ronald Masulis, John Morley, Frank Partnoy, Elizabeth Pollman, Robert Thompson, Urska Velikonja, Morgan Ricks, Robert Reder, Michel Robe, Usha Rodrigues, Amanda Rose, JB Ruhl, Heidi Schooner, Andrew Schwartz, Chris Serkin, Kevin Stack, Randall Thomas, Pradeep Yadav and to participants at workshops at the University of Auckland Law School, University of Colorado Law School, National University of Singapore Law School, University of Sydney Law School and Tulane Law School. I also benefited greatly from discussions with experts in practice. All errors are my own.