How Algorithmic Trading Undermines Efficiency in Capital Markets
65 Pages Posted: 29 Mar 2015 Last revised: 31 Jul 2016
Date Written: March 27, 2015
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 modelling 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. This anticipatory dynamic creates steep costs. Building algorithms capable of predicting future markets presents a near-impossible proposition, making gaps and errors inevitable. Uncertainties also 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 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 swathes 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.
Keywords: Securities regulation, algorithmic trading, high frequency trading, market efficiency, price formation, insider trading, fraud, disclosure, long-term investing, co-location, exchanges, value efficiency, financial stability
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