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Speed Acquisition∗ (preliminary and incomplete) (tentative title) Speed Acquisition∗ Shiyang Huang† Bart Zhou Yueshen‡ this version: November 17, 2016 ∗ This paper tremendously benefits from discussions with Dion Bongaerts, James Brugler, Sarah Draus, John Kuong, Joël Peress, Neal Galpin, Naveen Gondhi, Vincent Gregoire, Bruce Grundy, Semyon Malamud, Lyndon Moore, Mark van Achter, and Zhong Zhuo. In addition, comments and feedback from participants at seminars hosted by The University of Melbourne and Rotterdam School of Management are greatly appreciated. There are no competing financial interests that might be perceived to influence the analysis, the discussion, and/or the results of this article. † Huang is at School of Economics and Finance, The University of Hong Kong; [email protected]; K.K. Leung Building, The University of Hong Kong, Pokfulam Road, Hong Kong. ‡ Yueshen is at INSEAD; [email protected]; INSEAD, 1 Ayer Rajah Avenue, Singapore 138676. (preliminary and incomplete) (tentative title) Speed Acquisition Abstract Speed has become a signature of modern financial markets. This paper theoretically studies investors’ endogenous speed acquisition, alongside their investment in information. Investments in speed and in information are shown intrinsically linked via a novel tension between investors’ intra- and intertemporal competition. Due to this tension, advances in speed technology exert a non-monotone effect on investors’ information acquisition, aggregate price efficiency, as well as firms’ real investment decisions. On the flip side, advances in information technology might also hurt investors’ speed acquisition, slowing down the price discovery process. Other testable implications for market qualities are also discussed. Keywords: speed, information, technology, price discovery, price efficiency JEL code: D40, D84, G12, G14 (There are no competing financial interests that might be perceived to influence the analysis, the discussion, and/or the results of this article.) 1 Introduction Price discovery is a fundamental function of financial markets. It involves two steps: First, investors acquire information about the underlying security. Second, via trading, such information is incorporated in price. The first step determines the amount of information that the price can eventually reflect, i.e. the magnitude of price discovery. The second step determines how soon the price reflects the acquired information, i.e. the speed of price discovery. Speed is an indispensable dimension of the price discovery process. This is because trading takes time: Not all investors with information instantaneously gather together; neither do their trading orders. If the informed only slowly arrive in the market, the resulting price discovery process will also be slow. All else equal, the market is more efficient (in price) if the informed investors trade faster.1 To date, the literature has mainly emphasized information acquisition, i.e. the magnitude aspect of price discovery, following the pioneering works by Grossman and Stiglitz (1980) and Verrecchia (1982). This paper complements this canonical perspective by studying investors’ speed acquisition, alongside their information acquisition. Indeed, speed has risen as a focal point of modern financial securities markets. Trading companies buy real estate to co-locate with exchanges; they pay for extra wide cables to directly “speak to” the servers; billions of dollars have been spent on infrastructures like transatlantic fiber-optic cables and microwave towers. While such microstructure advancement has sped up trading, the decision process to implement a trading idea has been arguably slowed down by the tightening regulatory environment seen in recent years. The increased burden of bookkeeping and compliance, following regulations like Dodd-Frank Act and Volcker Rule, could have hindered, for example, the process for buy-side analysts to convince fund managers of new trading strategies. 1 Under the assumption of (semi-)strong efficient market (Fama, 1970), all information is incorporated in price instantaneously. This can be thought of as an extremely fast price discovery process, implicitly requiring all informed investors be present and trade at the same time to clear the market. 1 A set of questions arise together with these phenomena: How much speed technology should investors acquire? Is speed technology favored over information technology? How does speed acquisition affect investors’ demand for information, and vice versa? Most importantly, what are the implications for the overall quality of price discovery and market efficiency? This paper develops a model to address these questions. The model considers an economy populated with atomless investors, who first endogenously invest in both speed and information technology and then trade a risky asset. The information technology allows an investor to receive a more precise private signal about the asset, improving his information rent. The speed technology allows him to trade ahead of his peers. The sooner he trades, the less price discovery has occurred and the more valuable is his private signal. These are the direct effects of the two technologies. To fix the idea, consider a hedge fund for example. The fund’s information acquisition involves investments in, for example, analyzing companies’ annual reports, sending analysts for firm visits, buying data feeds, or building up machines to perform quantitative analysis. Such expenditure will improve the precision of the fund’s private signal. The fund’s speed acquisition covers different aspects. It can spend on microstructure technology, like colocation or microwave, to speed up the implementation of every single trade by micro/milliseconds. It can also invest in technologies to streamline the compliance process, so that the overall execution of trading ideas can be expedited by hours or even days, ahead of its competitors. The model reveals that the magnitude and the speed of price discovery are intrinsically connected via investors’ competition for information rent—the indirect effects. First, there is intratemporal competition: An investor acquires less (more) information when there are more (fewer) competitors trading at the same time with him. Second, there is a novel effect of intertemporal crowding out: When fast investors in aggregate acquire more information, the price becomes more efficient in the short-run. This hurts slow investors as there is less information rent remaining. As a result, speed acquisition can crowd out information acquisition. By the same token, too much information in the short-run can reduce investors incentive to become fast, crowding out speed acquisition. The 2 equilibrium is found where investors optimally choose their technology, accounting for both the direct and indirect effects, together with the investment costs. The model yields testable predictions on how technology shocks affect market quality. The key driver is the pair of countervailing effects, intratemporal competition v.s. intertemporal crowding out. For example, while an advancement in speed technology hastens the price discovery process, it non-monotonically affects the eventual magnitude: On the one hand, as more investors acquire speed and become fast, there is less intratemporal competition for the slow investors. Hence they acquire more information. On the other, because there are more fast investors, the improved short-run price efficiency intertemporally crowds out slow investors’ information acquisition. The net effect on the eventual (long-run) magnitude of price discovery depends on the strength of these two effects. In particular, it is possible that the eventual magnitude of price discovery actually shrinks, even though the process speeds up in the short-run. The worrying result of faster price discovery at the cost of long-run efficiency echoes the empirical finding by Weller (2016), who shows that aggressive algorithmic trading deters information acquisition. Dugast and Foucault (2016) share a similar prediction based on time-consuming information acquisition, a feature differs with the current paper as in their model speed and information investments are mutually exclusive. A model application is then developed to illustrate the intrinsic connection between speed and magnitude of price discovery, via the lens of a real-decision making firm. Specifically, the firm is trying to learn about an underlying factor—e.g., the economic condition—from the financial market to make real investment decisions. The novelty lies in that the firm can also time its investment: While waiting is costly due to discounting, it allows the firm to learn more precisely about the factor. As the speed technology advances, the firm is more willing to invest early due to the faster price discovery process. However, because of the intertemporal crowding out effect, the magnitude of the long-run price discovery can be hurt by the speed technology. Consequently, while some firms rush to make investment decisions, those who wait might have to make less reliable decisions. The overall real efficiency is thus non-monotonically affected by the speed technology. 3 Further predictions are derived on how speed and information technology affects differently other market quality metrics like return volatility, trading volume, market depth, and trading costs. In particular, because of the new angle of speed, the model is able to separately examine the patterns of short-run market quality measures and the long-run ones. This paper contributes to three strands of the literature. First,
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