Understanding Financial Market Behavior through Empirical Game-Theoretic Analysis

Financial market activity is increasingly controlled by algorithms, interacting through electronic markets. Unprecedented information response times, autonomous operation, use of machine learning and other adaptive techniques, and ability to proliferate novel strategies at scale are all reasons to question whether algorithmic trading may produce dynamic behavior qualitatively different from what arises in trading under direct human control. Given the high level of competition between trading firms and the significant financial incentives to trading, it is desirable to understand the effect incentives have on the behavior of agents in financial markets. One natural way to analyze this effect is through the economic concept of a Nash equilibrium, a behavior profile of every agent such that no individual stands to gain by doing something different. Some of the incentives traders face arise from the complexities of modern market structure. Recent studies have turned to agent-based modeling as a way to capture behavioral response to this structure. Agent-based modeling is a simulation paradigm that allows studying the interaction of agents in a simulated environment, and it has been used to model various aspects of financial market structure. This thesis builds on recent agent-based models of financial markets by imposing agent rationality and studying the models in equilibrium. I use empirical game-theoretic analysis, a methodology for computing approximately rational behavior in agent-based models, to investigate three important aspects of market structure. First, I evaluate the impact of strategic bid shading on agent welfare. Bid shading is when agents demand better prices, lower if they are buying or higher if they are selling, and is typically associated with lower social welfare. My results indicate that in many market environments, strategic bid shading actually improves social welfare, even with some of the complexities of financial markets. Next, I investigate the optimal clearing interval for a proposed market mechanism, the frequent call market. There is significant evidence to support the idea that traders will benefit from trading in a frequent call market over standard continuous double auction markets. My results confirm this statement for a wide variety of market settings, but I also find a few circumstances, particularly when large informational advantages exist, or when markets are thin, that call markets consistently hurt welfare, independent of frequency. I conclude with an investigation on the effect of trend following on market stability. Here I find that the presence of trend followers alters a market’s response to shock. In the absence of trend followers, shocks are small but have a long recovery. When trend followers are present, they alter background trader behavior resulting in more severe shocks that recover much more quickly. I also develop a novel method to efficiently evaluate the effect of shock anticipation on equilibrium. While anticipation of shocks does make markets more stable, trend followers continue to be profitable.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144031/1/ebrink_1.pd

Computational Explorations of Information and Mechanism Design in Markets

Markets or platforms assemble multiple selfishly-motivated and strategic agents. The outcomes of such agent interactions depend heavily on the rules, regulations, and norms of the platform, as well as the information available to agents. This thesis investigates the design and analysis of mechanisms and information structures through the ``computational lens\u27\u27 in both static and dynamic settings. It both addresses the outcome of single platforms and fills a gap in the study of the dynamics of multiple platform interactions. In static market settings, we are particularly interested in the role of information, because mechanisms are harder to change than the information available to participants. We approach information design through specific examples, i.e., matching markets and auction markets. First, in matching markets, we study the situation where the matching is preceded by a costly interviewing stage in which firms acquire information about the qualities of candidates. We focus on the impact of the signals of quality available prior to the interviewing stage. We show that more ``commonality\u27\u27 in the quality of information can be harmful, yielding fewer matches. Second, in auction markets, we design an information environment for revenue enhancement in a sealed-bid second price auction. Much of the previous literature has focused on signal design in settings where bidders are symmetrically informed, or on the design of optimal mechanisms under fixed information structures. Here, we provide new theoretical insights for complex situations like corporate mergers, where the sender of the signal has the opportunity to communicate in different ways to different receivers. Next, in dynamic markets, we focus on two dimensions: (1) the effects of different market-clearing rules on market outcomes and (2) the dynamics of multiple platform interactions. Considering both dimensions, we investigate two important real-world dynamic markets: kidney exchange and financial markets. Specifically, in kidney exchange, we analyze the performance of different market-clearing algorithms and design a competing-market model to quantify the social welfare loss caused by market competition and exchange fragmentation. Here, we present the first analysis of equilibrium behavior in these dynamic competing matching market systems, from the viewpoints of both agents and markets. To improve the performance of kidney exchange in terms of both social welfare and individual utility, we analyze the benefit of convincing directed donation pairs to participate in paired kidney exchange, measured in terms of long-term graft survival. We provide the first empirical evidence that including compatible pairs dramatically benefits both social welfare and individual outcomes. For financial markets, in the debate over high frequency trading, the frequent call (Call) mechanism has recently received considerable attention as a proposal for replacing the continuous double auction (CDA) mechanisms that currently run most financial markets. We examine agents\u27 profit under CDA and frequent call auctions in a dynamic environment. We design an agent-based model to study the competition between these two market policies and show that CALL markets can drive trade away from CDAs. The results help to inform this very important debate

A Review of ISO New England's Proposed Market Rules

This report reviews the proposed rules for restructured wholesale electricity markets in New England. We review the market rules, both individually and collectively, and identify potential problems that might limit the efficiency of these markets. We examine alternatives and identify the key tradeoffs among alternative designs. We believe that the wholesale electricity market in New England can begin on December 1, 1998. However, improvements are needed for long-run success. We have identified four major recommendations: 1. Switch to a multi-settlement system. 2. Introduce demand-side bidding. 3. Adopt location-based transmission congestion pricing, especially for the import/export interfaces. 4. Fix the pricing of the ten minute spinning reserves.Auctions; Multiple Object Auctions; Electricity Auctions

Size Discovery

Size-discovery mechanisms allow large quantities of an asset to be exchanged at a price that does not respond to price pressure. Primary examples include "workup" in Treasury markets, "matching sessions" in corporate bond and CDS markets, and block-trading "dark pools" in equity markets. By freezing the execution price and giving up on market clearing, size-discovery mechanisms overcome concerns by large investors over their price impacts. Price-discovery mechanisms clear the market, but cause investors to internalize their price impacts, inducing costly delays in the reduction of position imbalances. We show how augmenting a price-discovery mechanism with a size-discovery mechanism improves allocative efficiency

Examining the Trade-Off between Settlement Delay and Intraday Liquidity in Canada's LVTS: A Simulation Approach

The author explores a fundamental trade-off that occurs between settlement delay and intraday liquidity in the daily operation of large-value payment systems (LVPS), with specific application to Canada's Large Value Transfer System (LVTS). To reduce settlement delay, participants generally must maintain greater intraday liquidity in the system. Intraday liquidity and settlement delay can be costly for LVPS participants, and improvements in the trade-off are desirable. The replacement of standard queuing arrangements with a complex queue-release algorithm represents one such improvement. These algorithms are expected to lower intraday liquidity needs and speed up payments processing in an LVPS. Simulation analysis is used to empirically test this proposition for the case of Canada's LVTS. The analysis is conducted using a payment system simulator developed by the Bank of Finland, called the BoF-PSS2. The author shows that increased use of the LVTS central queue (which contains a complex queue-release algorithm) reduces settlement delay associated with each level of intraday liquidity considered, relative to a standard queuing arrangement. Some important issues emerge from these results.Payment, clearing, and settlement systems

Computational Modeling and Design of Financial Markets: Towards Manipulation-Resistant and Expressive Markets

Electronic trading platforms have transformed the financial market landscape, supporting automation of trading and dissemination of information. With high volumes of data streaming at high velocity, market participants use algorithms to assist almost every aspect of their decision-making: they learn market state, identify trading opportunities, and express increasingly diverse and nuanced preferences. This growing automation motivates a reconsideration of market designs to support the new competence and prevent potential risks. This dissertation focuses on designing (1) manipulation-resistant markets that facilitate learning genuine market supply and demand, and (2) expressive markets that facilitate delivering preferences in greater detail and flexibility. Advances towards each may contribute to efficient resource allocation and information aggregation. Manipulation-Resistant Markets. Spoofing refers to the practice of submitting spurious orders to deceive others about supply and demand. To understand its effects, this dissertation develops an agent-based model of manipulating prices in limit-order markets. Empirical game-theoretic analysis on agent behavior in simulated markets with and without manipulation shows that spoofing hurts market surplus and decreases the proportion of learning traders who exploit order book information. That learning behavior typically persists in strategic equilibrium even in the presence of manipulation, indicating a consistently spoofable market. Built on this model, a cloaking mechanism is designed to deter spoofing via strategically concealing part of the order book. Simulated results demonstrate that the benefit of cloaking in mitigating manipulation outweighs its efficiency cost due to information loss. This dissertation explores variations of the learning-based trading strategy that reasonably compromise effectiveness in non-manipulated markets for robustness against manipulation. Regulators who deploy detection algorithms to catch manipulation face the challenge that an adversary may obfuscate strategy to evade. This dissertation proposes an adversarial learning framework to proactively reason about how a manipulator might mask behavior. Evasion is represented by a generative model, trained by augmenting manipulation order streams with examples of normal trading. The framework generates adapted manipulation order streams that mimic benign trading patterns and appear qualitatively different from prescribed manipulation strategies. Expressive Markets. Financial options are contracts that specify the right to buy or sell an underlying asset at a strike price in the future. Standard exchanges offer options of predetermined strike values and trade them independently, even for those written on the same asset. This dissertation proposes a mechanism to match orders on options related to the same asset, supporting trade of any custom strike. Combinatorial financial options---contracts that define future trades of any linear combination of underlying assets---are further introduced to enable the expression of demand based on predicted correlations among assets. Optimal clearing of such markets is coNP-hard, and a heuristic algorithm is proposed to find optimal matches through iterative constraint generation. Prediction markets that support betting on ranges (e.g., the price of S&P) offer predetermined intervals at a fixed resolution, limiting the ability to elicit fine-grained information. The logarithmic market scoring rule (LMSR) used in this setting presents two limitations that prevent its scaling to large outcome spaces: (1) operations run in time linear in the number of outcomes, and (2) loss suffered by the market can grow unbounded. By embedding the modularity properties of LMSR into a binary tree, this dissertation shows that operations can be expedited to logarithmic time. A constant worst-case loss can also be achieved by designing a liquidity scheme for intervals at different resolutions.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167942/1/xintongw_1.pd

Essays on Bidding Behavior in Auctions

The following thesis presents the results of three experimental studies that investigate how changes in the auction environment or auction rules affect bidding behavior and the auction outcome in a variety of auctions. The first study is concerned with the impact of ambiguity about one’s competitiveness. In particular, bidders are either informed or not informed about the upper limit of the support of a uniform distribution from which their competitors’ bids are drawn. Their relative bid is found to decrease under ambiguity – an effect, which is not predicted by standard ambiguity theories alone. A combination of smooth ambiguity and nonlinear probability weighting is shown to organize the experimental results. The second study investigates how two forms of favoritism affect the auction outcome. In one of the treatments, an ex ante preferred bidder is given the right of first refusal. In the other treatment, the seller elicits the preferred bidder’s valuation with an incentive compatible transfer. The good is then sold to the non-preferred bidder(s) via an auction with a reserve price that optimizes the expected joint payoff of the seller and the preferred bidder. The formal analysis for risk-neutral bidders is based to a great extend on Burguet and Perry (2009). Observed behavior partly deviates from the theoretical predictions. In particular, the auction with an optimal reserve price does not maximize the joint payoff of the seller and the preferred bidder. Most of the deviations can be explained by accounting for risk aversion. The third study is concerned with multi-unit uniform-price auctions in the context of emission trading. It investigates the impact of auction frequency on the ability of the secondary market to achieve cost-efficient emission reduction. In the experiment, frequent auctioning does not increase the allocative efficiency (due to better information in later trading periods) but leads to higher price variability and thus higher total abatement costs