UC Berkeley

The landscape of the modern financial market is rapidly changing due to innovations in trading methods and information technologies. Do innovations in financial markets improve market quality? How do traders change their trading behavior and information acquisition? How should exchange platforms and policymakers react to financial innovations? To answer these questions, I first analyze strategic information acquisition by traders and the impact of imposing market structures with different informational environments. Chapter 1 considers the quality aspect of information (i.e., the precision of private information), and Chapter 2 considers the speed aspect of information (i.e., how quickly a trader can process information and act on it). Finally, Chapter 3 analyzes one of the latest innovations in financial markets: the blockchain technology and decentralization.

In Chapter 1, I extend the canonical model of Kyle (1985) and accommodate strategic information acquisition by an informed trader and a fair disclosure regulation. A fair disclosure policy tries to mitigate information asymmetry between informed and uninformed traders by disseminating material information to all market participants. The literature has suggested that such a policy should discourage information acquisition by a potential informed trader, as it diminishes the value of privately possessing information. However, my model shows that a trader may exhibit the opposite reaction. In particular, if the disclosure policy provides a more precise public signal about asset fundamentals, it can promote information acquisition by a potential informed trader. This effect is referred to as the crowding-in effect. The crowding-in effect competes against the intended effect of fair disclosure, leading to an ambiguous reaction of private information production by an informed trader.

Chapter 2 deals with financial innovations in the speed of trading and information processing. It analyzes high-frequency traders (HFTs) and intentional delays imposed by exchange platforms. HFTs are ultra-fast traders who exploit sophisticated information and communication technologies in order to acquire information and take short-term arbitrage opportunities. The speed advantage of HFTs imposes an adverse selection cost on other traders, making a market less liquid. A growing number of exchanges have adopted intentional delays to exogenously slow down HFTs and to protect liquidity providers against latency arbitrage. However, analogous to Chapter 1, my model shows that intentional delays have the crowding-in effect on speed acquisition by HFTs. Even though an exchange tries to slow down HFTs by exogenously imposing a delay on their transactions, HFTs may try to process information more quickly and move faster. As the crowding-in effect competes against the intended effect of intentional delays, the reaction of equilibrium market quality to the imposition of delays becomes ambiguous.

Chapter 3 considers the recent innovations in blockchain and decentralized exchanges. A growing number of exchanges are built on a decentralized information management system of the Ethereum blockchain, and they have implemented a novel market-making algorithm called Constant Product Market Makers (CPMM) to execute transactions. I consider the coexistence of a centralized exchange with the traditional order-book mechanism and a decentralized exchange with the CPMM. Informed and uninformed traders are endogenously differentiated between the traditional and the new market platforms and re-configure the informativeness of order flow on each exchange. The model demonstrates that liquidity on a decentralized exchange with the CPMM is positively associated with that on a centralized exchange.