3730 Walnut Street
567 Jon M. Huntsman Hall
Philadelphia, PA 19104
Gerry Tsoukalas is an assistant professor at the Wharton School at the University of Pennsylvania, teaching the Wharton MBA core in Business Analytics, as well as graduate and undergraduate-level electives in advanced Mathematical Modeling (with applications in finance).
His research examines how technology impacts firm operating strategy and financing. Recent areas of application include how to optimally design and operate blockchain and fintech platforms. His work has appeared in leading academic journals, including Management Science, Operations Research, and M&SOM. He serves on the editorial board of Management Science, as an Associate Editor.
Professor Tsoukalas completed his undergraduate studies in France, receiving degrees in Physics from the University of Paris, and Aeronautical Engineering from the Institut Supérieur de l’Aéronautique et de l’Espace-Supaero (2005). He completed his graduated studies in the US, receiving a Masters in Aeronautics & Astronautics from MIT (2007) and a PhD from the Management Science & Engineering Department at Stanford University (2009-2013). He was also previously a doctoral scholar at the MIT Operations Research Center (2010-2011).
Professor Tsoukalas has experience working with a variety of firms and startups in the technology and financial services industries (PayPal, Forest Park, Indiegogo, Rabt Inc, Moody’s, etc.) and on policy matters with government agencies and think tanks (Center of Planing and economic research KEPE, Wharton public policy initiative WPPI, etc.). Prior to joining academia, he was a structured products trader at Morgan Stanley in London (2007-2009). He has also consulted for and advised several startups, proprietary investment firms and hedge funds, including EvA Funds (2010-2011), and Weiss Asset Management (2012-2013), and has held stints in several international banks, including Barclays Capital (2006) and Societe Generale (2005).
Jingxing (Rowena) Gan, Gerry Tsoukalas, Serguei Netessine (2020), Initial Coin Offerings, Speculation, and Asset Tokenization, Management Science, forthcoming.
Abstract: Initial Coin Offerings (ICOs) are an emerging form of fundraising for Blockchain-based startups. We examine how ICOs can be leveraged in the context of asset tokenization, whereby firms issue tokens backed by future assets (i.e., inventory) to finance growth. We (i) make suggestions on how to design such \asset-backed" ICOs---including optimal token floating and pricing for both utility and equity tokens (aka, Security Token Offerings, STOs)---taking into account moral hazard (cash diversion), product characteristics and customer demand uncertainty, (ii) make predictions on ICO success/failure, and (iii) discuss implications on rm operating strategy. We show that in unregulated environments, ICOs can lead to significant agency costs, underproduction, and loss of rm value. These inefficiencies, however, fade as product margins and demand characteristics (mean/variance) improve, and are less severe under equity (rather than utility) token issuance. Importantly, the advantage of equity tokens stems from their inherent ability to better align incentives, and thus continues to hold even absent regulation.
Description: 2019 INFORMS Section on Finance Best Student Paper Award Honorable Mention
Brett H. Falk and Gerry Tsoukalas, Inference in Networks with Limited Information.
Abstract: Using financial networks as a backdrop, we develop a new framework for privacy-preserving network analytics. Adopting the debt and equity models of Eisenberg and Noe (2001) and Elliott et al. (2014) as proof of concept, we show how aggregate-level statistics required for stress testing and stability assessment can be derived on real network data, without any individual node revealing its private information to any third party, be it other nodes in the network, or even a central agent. Our work helps bridge the gap between the theoretical models of financial networks that assume agents have full information, and the real world, where information sharing is hindered by privacy and security concerns.
Abstract: Using airlines as a backdrop, we study optimal overbooking policies with endogenous customer demand, i.e., when customers can internalize their expected cost of being "bumped". We first consider the traditional setting in which compensation for bumped passengers is fixed and booking limits are the airline's only form of control. We provide sufficient conditions under which demand endogeneity leads to lower overbooking limits in this case. We then consider the broader problem of joint control of ticket price, bumping compensation and booking limit. We show that price and compensation can act as substitutes, which reduces the general problem to a more tractable one-dimensional search for optimal overbooking compensation, and effectively allows the value of flying to be decoupled from the cost of being bumped. Finally, we extend our analysis to the case of auction-based compensation schemes, and demonstrate that these generally outperform fixed compensation schemes. Numerical experiments to gauge magnitudes suggests that fixed-compensation policies that account for demand endogeneity can significantly outperform those that do not, and that auction-based policies bring smaller but significant additional gains.
Jiri Chod, Trichakis Nikos, Gerry Tsoukalas, Henry Aspegren, Mark Weber (2018), On the Financing Benefits of Supply Chain Transparency and Blockchain Adoption, Management Science, Forthcoming.
Abstract: We develop a theory that shows signaling a firm's fundamental quality (e.g., its operational capabilities) to lenders through inventory transactions to be more efficient---it leads to less costly operational distortions---than signaling through loan requests, and we characterize how the efficiency gains depend on firm operational characteristics such as operating costs, market size, and inventory salvage value. Signaling through inventory being only tenable when inventory transactions are verifiable at low enough cost, we then turn our attention to how this verifiability can be achieved in practice and argue that blockchain technology could enable it more efficiently than traditional monitoring mechanisms. To demonstrate, we develop b_verify, an open-source blockchain protocol that leverages Bitcoin to provide supply chain transparency at scale and in a cost effective way. The paper identifies an important benefit of blockchain adoption---by opening a window of transparency into a firm's supply chain, blockchain technology furnishes the ability to secure favorable financing terms at lower signaling costs. Furthermore, the analysis of the preferred signaling mode sheds light on what types of firms or supply chains would stand to benefit the most from this use of blockchain technology.
Description: 2018 INFORMS Technology, Innovation Management & Entrepreneurship Best Working Paper Award, Third Prize
Abstract: Blockchain-based platforms often rely on token-weighted voting (``τ-weighting'') to efficiently crowdsource information from their users for a wide range of applications, including content curation, and on-chain governance. We examine the effectiveness of such decentralized platforms at harnessing the ``wisdom'' and ``effort'' of the crowd. We find that τ-weighting generally discourages truthful voting, and erodes the platform's predictive power unless users are ``strategic enough'' to unravel the underlying aggregation mechanism. Platform accuracy decreases with the number of truthful users and the dispersion in their token holdings, and in many cases, platforms would be better off with an unweighted ``1/n'' mechanism. When, prior to voting, strategic users can exert effort to endogenously improve their signals, users with more tokens generally exert more effort---a feature often touted in marketing materials as a core advantage of τ-weighting---however, this feature is not attributable to the mechanism itself, and more importantly, the ensuing equilibrium fails to achieve the first-best accuracy of a centralized platform. The optimality gap decreases as the distribution of tokens across users approaches a theoretical optimum, that we derive, but, tends to increase with the dispersion in users' token holdings.
Elena Belavina, Simone Marinesi, Gerry Tsoukalas (2018), Rethinking Crowdfunding Platform Design: Mechanisms to Deter Misconduct and Improve Efficiency, Management Science, Forthcoming.
Abstract: Lacking credible rule enforcement mechanisms to punish entrepreneurial misconduct, existing reward-based crowdfunding platforms can leave campaign backers exposed to two sources of risk: the risk that entrepreneurs run away with backers' money (funds misappropriation) and the risk of product misrepresentation (performance opacity). In contrast to prior work, which has mainly focused on studying the first, we examine the adverse consequences of both. We show that not only do both risks have a material impact on crowdfunding efficiency, but they cannot even be analyzed in isolation: rather, their joint presence leads to complex interactions that either dampen or amplify their individual adverse effects. In light of these results, we find that a simple deferred payment scheme with escrow, which the literature argues to be optimal, cannot overcome both sources of friction. We then propose two new designs that Pareto dominate this benchmark. The first design does not rely on escrow, and thus requires less involvement on the part of the platform---but cannot achieve optimality. The second design can restore full efficiency, but requires the platform to take a more active role: we thus provide guidance on how to ease its practical implementation.
Vlad Babich, Simone Marinesi, Gerry Tsoukalas (2017), Does Crowdfunding Benefit Entrepreneurs and Venture Capital Investors?, M&SOM, Forthcoming.
Abstract: We study how a new form of entrepreneurial finance - crowdfunding - interacts with more traditional financing sources, such as venture capital (VC) and bank financing. We model a multi-stage bargaining game, with a moral-hazard problem between entrepreneurs and banks, and a double-sided moral-hazard problem between entrepreneurs and VCs. We decompose the economic value of crowdfunding into cash gains or losses, costs of bad investments avoided, and project-payoff probability update. This economic value is generally shared between entrepreneurs and VC investors, benefiting both. In addition, crowdfunding can alleviate the under-investment problem due to moral-hazard frictions. Furthermore, crowdfunding allows some projects to gain access to both VC and bank financing and the competition between those investor classes benefits entrepreneurs. However, competition from other investors reduces value to VC investors, who may walk away from the deal entirely. This can also hurt entrepreneurs who lose out on valuable VC expertise.
Abstract: We develop a new theory of supplier diversification based on buyer risk. When suppliers are subject to the risk of buyer default, buyers may take costly action to signal creditworthiness so as to obtain more favorable terms. But once signaling costs are sunk, buyers sourcing from a single supplier become vulnerable to future holdup. Although ex ante supply base diversification can be effective at alleviating the holdup problem, we show that it comes at the expense of higher upfront signaling costs. We resolve the ensuing trade off and show that diversification emerges as the preferred strategy in equilibrium. Our theory can help explain sourcing strategies when risk in a trade relationship originates from the sourcing firm, e.g., SMEs or startups; a setting which has eluded existing theories so far.
Quantitative methods have become fundamental tools in the analysis and planningof financial operations. There are many reasons for this development: the emergence of a whole range of new complex financial instruments, innovations in securitization, the increased globalization of the financial markets, the proliferation of information technology and the rise of high-frequency traders, etc. In this course, models for hedging, asset allocation, and multi-period portfolio planning are developed, implemented, and tested. In addition, pricing models for options, bonds, mortgage-backed securities, and other derivatives are studied. The models typically require the tools of statistics, optimization, and/or simulation, and they are implemented in spreadsheets or a high-level modeling environment, MATLAB. This course is quantitative and will require extensive computer use. The course is intended for students who have strong interest in finance. The objective is to provide students the necessary practical tools they will require should they choose to join the financial services industry, particularly in roles such as: derivatives, quantitative trading, portfolio management, structuring, financial engineering, risk management, etc. Prospective students should be comfortable with quantitative methods such as basic statistics and the methodologies (mathematical programming and simulation) in OIDD612 BusinessAnalytics and OIDD321 Management Science (or equivalent). Students should seek permission from the instructor if the background requirements are not met.
"Managing the Productive Core: Business Analytics" is a course on business analytics tools and their application to management problems. Its main topics are optimization, decision making under uncertainty, and simulation. The emphasis is on business analytics tools that are widely used in diverse industries and functional areas, including operations, finance, accounting, and marketing.
Quantitative methods have become fundamental tools in the analysis and planningof financial operations. There are many reasons for this development: the emergence of a whole range of new complex financial instruments, innovations in securitization, the increased globalization of the financial markets, the proliferation of information technology and the rise of high-frequency traders, etc. In this course, models for hedging, asset allocation, and multi-period portfolio planning are developed, implemented, and tested. In addition, pricing models for options, bonds, mortgage-backed securities, and other derivatives are studied. The models typically require the tools of statistics, optimization, and/or simulation, and they are implemented in spreadsheets or a high-level modeling environment, MATLAB. This course is quantitative and will require extensive computer use. The course is intended for students who have strong interest in finance. The objective is to provide students the necessary practical tools they will require should they choose to join the financial services industry, particularly in roles such as: derivatives, quantitative trading, portfolio management, structuring, financial engineering, risk management, etc. Prospective students should be comfortable with quantitative methods, such as basic statistics and the methodologies (mathematical programming and simulation) taught in OIDD 612 Business Analytics or OIDD 321 Management Science (or equivalent). Students should seek permission from the instructor if the background requirements are not met.
New research shows lenders could make more prudent decisions.