top of page
Publications
Ambiguous price formation [Paper, Internet Appendix] (with X. He)
Journal of Mathematical Economics, 2023
Abstract: We study the price and liquidity of an asset in a model where market makers face ambiguity about the asset payoff. This ambiguity explains liquidity deteriorations and improvements in financial markets. We show that the ambiguity influences how market makers perceive adverse selection risk, and therefore, distorts market liquidity. The perceived adverse selection risk increases (resp. decreases) with the ambiguity when the market maker is sufficiently (resp. insufficiently) ambiguity averse. Our model also helps to understand how ambiguity and ambiguity aversion of market makers impact price and liquidity dynamics under various trading histories.
Toward a general model of financial markets [Paper] (with X. He)
Artificial Intelligence, Learning and Computation in Economics and Finance, 2023, 71-100, Springer
Abstract: This paper discusses the idea of reconciling efficient market hypothesis and behavioral finance using the literature of decision theories and information sciences. The focus is centered on the precision and reliability of information and the broad definition of rationality. The main thesis advanced is that the roots of behavioral anomalies are the imprecision and reliability of information. We propose a general framework that subsumes efficient and inefficient markets as special cases. We also show that the proposed framework helps to understand behavioral anomalies.
Working papers
Equilibrium staking rewards: Implications for PoS blockchain security [Paper available upon request] (with K. Nguyen, T. Nguyen and T. Putnins)
Abstract: The security of PoS blockchains is at the mercy of external return opportunities. We model how capital flows among staking opportunities. Consistent with the model, we document that higher opportunity costs decrease the staked dollars, thereby compromising PoS blockchains’ security. A 1% increase in TradFi yields equates to a $17 billion drop in staked dollars, and a 1% increase in staking yields in other blockchains decreases a specific chain's stakes by around $1 billion. We derive security measures for PoS blockchains, estimate them for major PoS blockchains, and use them to assess the overall security of the entire PoS ecosystem.
Once bitten twice shy: Learning about scams [Paper] (with I. Allahverdiyeva and T. Putnins)
Abstract: Do investors learn from their mistakes after falling victim to a scam? On average, experiencing a scam in the recent past decreases the probability of reinvesting in a scam by around 4% and reduces subsequent non-scam investment returns by approximately 14%. Victims increase their market exposure (portfolio values) after a scam. The cash allocation and the standard deviation of daily portfolio return suggest that victims become more (resp. less) risk-tolerant in the short term (resp. long term) after a scam. We also explore characteristics associated with varying learning rates among investors and find that less experienced investors tend to learn less.
Can blockchain-based atomic settlements improve traditional financial markets? [Paper] (with R. Gaudiosi and T. Putnins)
Abstract: Atomic settlement involves the conditional exchange of two assets. We model atomic settlement in permissionless and permissioned blockchains and apply it to traditional financial markets. We find that a permissionless blockchain is optimal for US
equities and foreign exchange, and a permissioned blockchain is optimal for US corporate bonds and treasury bills, and the gold market. We estimate that transitioning to optimal blockchain settlement could potentially improve gains from trade in foreign exchange by $17 billion, Nasdaq by $12 billion, US corporate bonds by $6 billion, gold market by $4 billion, and US treasury bills by $420 million annually.
Scam alert: Can cryptocurrency scams be detected early? [Paper] (with I. Allahverdiyeva and T. Putnins)
Abstract: Public blockchains have given rise to a new type of scam known as a “rug pull”. We find these scams are pervasive: 44% of tokens in major decentralized exchanges (DEXs) are scams, causing losses of $1.5 billion to investors. We show that scams differ from legitimate tokens in key characteristics, including the token creators launch multiple liquidity pools, do not lock their liquidity provider tokens, imitate other tokens, and create the liquidity pool shortly after the token’s release. We find these and other characteristics can detect scams before investors fall prey. Our scam index has practical applications in cryptocurrency surveillance and scam prevention.
Is public distrust of finance sector warranted? Evidence from financial adviser misconduct [Paper] (with I. Allahverdiyeva and Talis Putnins)
Abstract: Financial advisers are the primary interface between the public and the finance sector. Using data from over one million financial advisers in the U.S., we find that 30% of advisers are involved in misconduct, but only about one-third of those advisers are reported by regulators. We estimate that advisers involved in misconduct currently oversee around $6.9 trillion assets under management. The shares of adviser misconduct and unreported misconduct increase during the GFC, paralleling the erosion of trust in financial institutions. Our findings suggest that public distrust of the finance sector is warranted.
The real effects of market manipulation [Paper] (with Inji Allahverdiyeva and Talis Putnins)
Abstract: Market manipulation distorts financial market prices, but does that matter for listed companies? We show that it does. Increased manipulation makes stock price signals less useful for corporate managers, thereby decreasing the quality of real investment decisions by companies. Consequently, firm operating performance decreases. Our results highlight that the real economic consequences of market manipulation go beyond the direct effects on secondary markets.
Buying frenzies, short selling costs and their impact on investment efficiency [Paper] (with X. Deng)
Abstract: The collaboration on social media platforms like Reddit to buy ’meme’ stocks and short squeeze large institutional short sellers has drawn considerable attention recently. How does such retail buying frenzy impact firms’ investment efficiency? We model retail buying frenzy as a cost on informed and manipulative short sellers when the firm manager learns about the quality of an investment opportunity from the stock price. We show that, compared to an economy with no friction on short selling, introducing a small cost on short sellers can improve firms’ investment efficiency, but a large cost or a short-sale ban always harms it.
Algorithmic trading and investment-to-price sensitivity [Paper] (with Khaladdin Rzayev and Fariz Huseynov)
Abstract: Does the increased prevalence of algorithmic trading (AT) produce real economic effects? We find that AT contributes to managerial learning by fostering the production of new information, thereby increasing firms’ investment-to-price sensitivity. We show that AT improves managers‘ earnings forecast accuracy and encourages information acquisition of market participants, and the impact of AT on investment-to-price sensitivity is stronger for stocks with greater managerial learning, suggesting that AT increases the revelatory efficiency of stock prices. While, in aggregate, AT contributes positively to managerial learning, we also show that a subset of AT, namely opportunistic AT, is harmful to managerial learning.
Learning about adverse selection in markets [Paper] (with Xue-Zhong He and Talis Putnins)
Abstract: How does a market learn about the number of informed traders and thus adverse selection risk? We show that trade sequences convey information about adverse selection risk. Consequently, buy/sell order imbalances can destabilize markets, triggering extreme price movements, flash crashes, and liquidity evaporation. The increasing prevalence of these effects in markets can be explained by more active learning about adverse selection by competitive, high-frequency market makers. We use our model to estimate the uncertainty in adverse selection risk for US stocks and show that it decreases market liquidity and increases extreme price movements.
Ambiguity and information tradeoffs [Paper]
Abstract: We develop a model where investors face ambiguity about the number of informed traders. This ambiguity creates complementarities in information acquisition because investors’ effective ambiguity aversion changes with the number of informed traders, resulting in multiple equilibria in the information market. Complementarities are prominent when the level of ambiguity is high, the ambiguity attitude of traders is not extreme (not too ambiguity averse or seeking), noise in the asset payoff is high and informed traders trade more aggressively. This ambiguity also generates both undervaluation and overvaluation in financial markets.
bottom of page