Orchestrating Information Acquisition

We study how to orchestrate information acquisition in an environment where bidders endowed with original estimates (“types”) about their private values can acquire further information by incurring a cost. We consider both single-round and fully sequential short-listing rules. The optimal single-round shortlisting rule admits the set of most efficient bidders that maximizes expected virtual surplus adjusted by the second-stage signal and information acquisition cost. When shortlisting is fully sequential, at each round, the most efficient remaining bidder is admitted provided that her conditional expected contribution to the virtual surplus is positive. (JEL D44, D82, D83)

Projection of Private Values in Auctions

We explore how taste projection—the tendency to overestimate how similar others’ tastes are to one’s own—affects bidding in auctions. In first-price auctions with private values, taste projection leads bidders to exaggerate the intensity of competition and, consequently, to overbid—irrespective of whether values are independent, affiliated, or (a)symmetric. Moreover, the optimal reserve price is lower than the rational benchmark, and decreasing in the extent of projection and the number of bidders. With an uncertain common-value component, projecting bidders draw distorted inferences about others’ information. This misinference is stronger in second-price and English auctions, reducing their allocative efficiency compared to first-price auctions. (JEL D11, D44, D82, D83)

HashWires: Hyperefficient Credential-Based Range Proofs

This paper presents HashWires, a hash-based range proof protocol that is applicable in settings for which there is a trusted third party (typically a credential issuer) that can generate commitments. We refer to these as “credential-based” range proofs (CBRPs). HashWires improves upon hashchain solutions that are typically restricted to micro-payments for small interval ranges, achieving an exponential speedup in proof generation and verification time. Under reasonable assumptions and performance considerations, a Hash-Wires proof can be as small as 305 bytes for 64-bit integers. Although CBRPs are not zero-knowledge and are inherently less flexible than general zero-knowledge range proofs, we provide a number of applications in which a credential issuer can leverage HashWires to provide range proofs for private values, without having to rely on heavyweight cryptographic tools and assumptions.

Obviousness around the clock

Li (Am Econ Rev 107(11):3257–3287, 2017) introduces a theoretical notion of obviousness of a dominant strategy, to be used as a refinement in mechanism design. This notion is supported by experimental evidence that bidding is closer to dominance in the dynamic ascending-clock auction than the static second-price auction (private values), noting that dominance is theoretically obvious in the former but not the latter. We replicate his experimental study and add three intermediate auction formats that decompose the designs’ differences to quantify the cumulative effects of (1) simply seeing an ascending-price clock (after bid submission), (2) bidding dynamically on the clock, and (3) getting (theoretically irrelevant) drop-out information about other bidders. The theory predicts dominance to become obvious through (2), dynamic bidding. We find no significant behavioral effect of (2), however, while the feedback effects (1) and (3) are highly significant. We conclude that behavioral differences between second-price and ascending-clock auctions offer rather limited support for the theory of obviousness and that framing has surprisingly large potential in mechanism design.

Spoofing the Limit Order Book: A Strategic Agent-Based Analysis

We present an agent-based model of manipulating prices in financial markets through spoofing: submitting spurious orders to mislead traders who learn from the order book. Our model captures a complex market environment for a single security, whose common value is given by a dynamic fundamental time series. Agents trade through a limit-order book, based on their private values and noisy observations of the fundamental. We consider background agents following two types of trading strategies: the non-spoofable zero intelligence (ZI) that ignores the order book and the manipulable heuristic belief learning (HBL) that exploits the order book to predict price outcomes. We conduct empirical game-theoretic analysis upon simulated agent payoffs across parametrically different environments and measure the effect of spoofing on market performance in approximate strategic equilibria. We demonstrate that HBL traders can benefit price discovery and social welfare, but their existence in equilibrium renders a market vulnerable to manipulation: simple spoofing strategies can effectively mislead traders, distort prices and reduce total surplus. Based on this model, we propose to mitigate spoofing from two aspects: (1) mechanism design to disincentivize manipulation; and (2) trading strategy variations to improve the robustness of learning from market information. We evaluate the proposed approaches, taking into account potential strategic responses of agents, and characterize the conditions under which these approaches may deter manipulation and benefit market welfare. Our model provides a way to quantify the effect of spoofing on trading behavior and market efficiency, and thus it can help to evaluate the effectiveness of various market designs and trading strategies in mitigating an important form of market manipulation.

Real and Private-Value Assets

Real and private-value assets—defined here as the sum of real estate, infrastructure, collectibles, and noncorporate business equity—compose an investment class worth an estimated $84 trillion in the U.S. alone. Furthermore, private values can affect pricing in many other financial markets, such as that for sustainable investments. This paper introduces the research on real assets and private values that can be found in this special issue. It also reviews recent advances and highlights new research directions on a number of topics in the real assets space that we believe to be particularly important and exciting.