Research Topics

Most existing layer-1 blockchain protocols are based on proofs of work and proofs of stake. Those protocols are used to determine which miners or validators will be entitled to update the distributed ledger and earn the fees associated with the transactions in the mined blocks. More research is needed to determine which protocol would ensure the long term sustainability of blockchain and what is the best mechanism design for this protocol. See Proof-of-Work Cryptocurrencies: Does Mining Technology Undermine Decentralization?.

Modern blockchains enable the use of smart contracts to implement a broad range of financial services. Those services include borrowing/lending, derivative trading, insurance, and decentralized exchanges. Despite the potential gains of such systems, the adoption of DeFi (decentralized finance) exchanges will present a number of challenges and the need for guardrails. One example is the creation of arbitrage losses for investors trading in blockchain-based DEXs. See The Adoption of Blockchain-based Decentralized Exchanges.

A fundamental problem of second-generation blockchains, which support a broad range of financial services well and beyond cryptocurrency payments, is information leakage. This problem arises due to the public observability of transactions, which can be frontrun by malicious arbitrageurs while pending in the mempool. Frontrunning attacks not only lead to financial losses for traders of the DeFi ecosystem, but also congest the network and decrease the aggregate value of blockchain stakeholders. At the same time, frontrunning opportunities may benefit validators, who extract value from high transaction-fee bidding frontrunners. This is the so-called validator extractable value problem. The adoption of private communication channels depends upon the incentives provided to the users of the DeFi ecosystem. See The Evolution of Blockchain: From Public to Private Mempools.

Blockchain fee is a prominent feature of trading on decentralized exchanges (DEXs). In contrast to centralized exchanges (CEXs) where orders are executed continuously based on a price-time priority rule, which results in the arms race on speed among high-frequency traders, trading on DEXs is distinct: orders are executed in batches and the priority is based on the blockchain fees bid by submitters in descending order. Do high-blockchain-fee DEX trades convey any information? If so, do they reveal private information or simply respond to public information already released? See The Information Content of Blockchain Fees.

On centralized exchanges (CEXs) running a limit order book (LOB), market makers provide liquidity by actively submitting and re-pricing their quotes. Constant function market makers (CFMMs), the dominant mechanisms for decentralized exchanges (DEXs) on the blockchain, allow a new form of passive liquidity provision: liquidity providers (LPs) contribute assets to CFMM reserves that are subsequently available for trade with liquidity takers, at quoted prices that are algorithmically set. How is the return of providing liquidity on CFMMs determined? See Automated Market Making and Loss-Versus-Rebalancing.

Demand for blockchains such as Bitcoin and Ethereum is far larger than supply, necessitating a mechanism that selects a subset of transactions to include “on-chain” from the pool of all pending transactions. The idiosyncrasies of public blockchains require rethinking mechanism design from first principles, and in particular new notions of incentive-compatibility. Such blockchain-aware mechanism design played an important role in the evolution of Ethereum’s transaction fee mechanism, and in particular the adoption of EIP-1559. See Transaction Fee Mechanism Design.

In many settings, blockchain technology offers a decentralized alternative to a centralized service. However, decentralization often comes at an enormous efficiency cost; for example, chains like Bitcoin and Ethereum are very slow and expensive to operate when compared to their centralized counterparts. Understanding the economics of decentralization protocols—in other words, how prices and fees are set, how service is allocated, how infrastructure is provisioned, and related questions—is a core component in minimizing this efficiency cost. See Monopoly without a Monopolist: An Economic Analysis of the Bitcoin Payment System.

Compared with existing payment systems, Bitcoin’s throughput is low. Designed to address Bitcoin’s scalability challenge, the Lightning Network (LN) is a protocol allowing two parties to secure bitcoin payments and escrow holdings between them. In a lightning channel, each party commits collateral towards future payments to the counterparty and payments are cryptographically secured updates of collaterals. The network of channels increases transaction speed and reduces blockchain congestion. See Lightning Network Economics: Channels.

The growing adoption of smart contracts and blockchains brings new security risks that can lead to huge monetary losses. Billions of dollars worth of crypto assets have been stolen due to program errors and security vulnerabilities in smart contracts and blockchain systems. More research is needed to provide the correctness and security guarantees for blockchain programs according to their specifications with a reasonable effort, and more importantly, such guarantees should be machine-checkable without the need to trust any third party. See SciviK: A Versatile Framework for Specifying and Verifying Smart Contracts.

Permissioned blockchains are being used in number of contexts, including supply chains and other related industries. There is, at present, the lack of a framework for understanding the incentives of participants of a permissioned blockchain. Is adoption of blockchain socially beneficial and will such adoption arise in equilibrium? Our research has found that blockchain unequivocally benefits consumers, but that gains for the manufacturing sector are competed away. See Economics of Permissioned Blockchain Adoption.

Blockchain, and distributed ledger technology (DLT) more generally, have the potential for radically changing how securities markets operate, which would in turn dramatically alter how these markets should or should not be regulated. Interviews with about 100 persons who play prominent roles making these markets work or regulating them reveal a wide range of views on how DLT should or will affect the markets and their structure. While many highlighted the scope for dramatic cost reductions, others expressed skepticism, while still others questioned appetites for making DLT-based changes. See Distributed Ledger Technology and the Securities Markets of the Future. 

With so much investor-salient data coming in the form of text, our research aims at applying natural language processing techniques to large data sets to extract information. Our research has studied how equity, credit, rate, and currency markets respond to and influence news, earnings calls, and central bank communications. We have built models to investigate micro- and macro-efficiency of financial markets and the dynamics of information production. In future work, we plan to study how soft information (such as that obtainable from text corpora) can improve our understanding of price formation and the evolution of crypto and blockchain ecosystems. See Investor Information Choice with Macro and Micro Information and How News and Its Context Drive Risk and Returns Around the World.

Robo-advising has grown enormously over the last decade, offering a large range of financial services to investors, ranging from retirement planning to managing checking and saving accounts to meet investment goals. Robo-advisors democratize access to financial services by reducing barriers to entry through the imposition of low fees for assets under management and minimum required investment amounts. Increased adoption will depend on whether the robo-algorithm is able to personalize its recommendations to the risk preferences of users, and whether it can learn the needs and preferences of the clients served. See Robo-Advising: Learning Investors’ Risk Preferences via Portfolio Choices and Personalized Robo-Advising: Enhancing Investment Through Client Interaction.

Empirical and behavioral finance research shows that clients associate the notion of “risk” with the likelihood of not attaining their goals. For example, clients who want to pay for college expenses in five years, purchase a house in ten years, and have enough in their retirement accounts in thirty years worry about the likelihood of falling short of these goals. Robo-advisers are then confronted with the challenges of investing in risky assets to make sure that the goals are well funded by their deadlines. Robo-advisers trade off the immediate consumption of wealth to satisfy an upcoming goal versus saving and maintaining wealth in the portfolio to satisfy future goals of higher priority. See Goal Based Investment Management.