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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
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.
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 the finance sector warranted? Evidence from financial adviser misconduct [Paper] (with I. Allahverdiyeva and T. Putnins)
Abstract: Using data from over one million financial advisers in the U.S., we estimate that 30% of advisers are involved in misconduct, but only about one-third of those are reported by regulators. Advisers with misconduct currently oversee around $6.9 trillion assets under management (AUM). The shares of adviser misconduct and unreported misconduct increase during the GFC, paralleling the erosion of trust in financial institutions. We estimate that misconduct by one adviser typically costs a firm about 5 clients and $10 million AUM annually. We also offer a list of characteristics associated with adviser misconduct to assist consumers, advisory firms, and regulators.
The good and evil of algos: Investment-to-price sensitivity and the learning hypothesis [Paper] (with Khaladdin Rzayev and Fariz Huseynov)
Abstract: An important dimension of heterogeneity in algorithmic trading (AT) is whether the algorithm is designed to supply or demand liquidity. We show that liquidity-supplying AT facilitates firm managers’ learning from stock prices by fostering information acquisition in markets, thereby increasing sensitivity of firms’ investment to stock price. By contrast, liquidity-demanding AT harms managerial learning from stock prices. Consequently, firm operating performance increases (decreases) with the level of liquidity-supplying (liquidity-demanding) AT. We use the staggered implementation of Autoquote in NYSE as a source of exogenous variation in AT to establish causality. Our findings highlight the real economic effects of AT.
The real effects of market manipulation [Paper] (with Inji Allahverdiyeva and Talis Putnins)
Abstract: Market manipulation distorts financial market prices, but does it have real economic effects on listed companies? We show that it does. Increased manipulation makes stock price signals less useful for firm managers seeking to learn about potential investment opportunities, thereby decreasing the sensitivity of firms' investments to stock prices. This leads to a decline in the quality of firms' investment decisions, and consequently, firm operating performance also decreases. Our findings suggest that the real economic consequences of market manipulation extend beyond the direct effects on secondary markets.
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.
Short selling constraints and their impact on corporate investment decisions [Paper] (with X. Deng)
Abstract: How do short selling constraints impact corporate investment decisions? We analyze two forms of short-sale constraint (short-sale ban and cost) in a model where firm value is endogenous to trading, due to feedback from the financial market to corporate investment decisions. We show that, compared to an economy with no constraint on short selling, introducing a cost on short sellers can increase firm value, but a large cost or a short-sale ban always harms it. Our model suggests that the impact of short-sale friction extends beyond trading in secondary markets.
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.
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? Experiencing a scam in the recent past (resp. a 1% increase in the scam investment ratio) decreases the probability of investing in a scam again by approximately 4% (resp. 0.30%).
However, experiencing a scam also decreases subsequent non-scam investment returns by around 14%. Victims increase their portfolio values, but the cash allocation and the standard deviation of daily portfolio returns suggest that victims become more (resp.
less) risk-tolerant in the short (resp. long) term after being scammed. Furthermore, less experienced investors tend to fall victim to multiple scams.
Equilibrium staking rewards: Implications for PoS blockchain security (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.
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