I am a senior manager at Cornerstone Research in New York City. I received a Ph.D. in Economics from the University of California, Berkeley.
I am a Ph.D. economist with more than 15 years of academic and professional experience in quantitative modeling, data analysis, leading teams, and managing high impact projects in quantitative finance, economics, data science, and risk management. I have a passion for solving complex problems to reach actionable recommendations using data-driven analysis.
At Cornerstone Research, I conduct financial, economic, and statistical analysis and provide client and expert support through all phases of commercial litigation and regulatory investigations. I focus on financial markets, market manipulation allegations, trading conduct, financial institutions, FinTech, and securities litigation. I have consulted on matters involving the U.S. Department of Justice (DOJ), the Commodity Futures Trading Commission (CFTC), and the Securities and Exchange Commission (SEC). My experience includes extensive work in matters involving alleged market manipulation in commodities, energy, and related futures and swap markets, consulting on regulatory investigations of the alleged manipulation of U.S. Treasury and related derivative markets, analyzing large, proprietary financial institutions datasets in the context of internal investigations and regulatory inquiries, and analyzing alleged spoofing activities in futures and options markets.
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Properties and Comparison of Risk Capital Allocation Methods (with Dóra Balog, Péter Csóka, and Miklós Pintér)
European Journal of Operational Research 259(2): 614-625, 2017.
Abstract: If a financial unit (a bank, an insurance company, a portfolio, the financial system of a country, etc.) consists of subunits (divisions, subportfolios, etc.), then the risk of the main unit should be allocated to the subunits using a risk capital allocation method in a fair way. We analyze seven methods widely discussed in the literature or used in practice (Activity based, Beta, Incremental, Cost gap, Marginal Risk Contribution, Shapley, and Nucleolus) in terms of ten reasonable fairness properties (Full Domain, Core Compatibility, Diversification, Strong Monotonicity, Incentive Compatibility Efficiency, Equal Treatment Property, Riskless Portfolio, Covariance, and Decomposition Invariance). We provide proofs or counterexamples for each method and the ten properties that we consider. We also computed how often on average Core Compatibility is satisfied in randomly generated risk capital allocation situations up to nine subunits in 24 treatments for all methods that do not satisfy Core Compatibility. We believe that through the descriptions of the examined methods our paper can serve as a useful guide for both practitioners and researchers.
Abstract: This paper models the learning and trading decision of investors in an economy with regular public announcements and costly information. Scheduled public announcements affect the information acquisition decision of traders, who in equilibrium focus their learning on stocks with upcoming announcements. When learning is endogenous, public announcements have a significant effect on information acquisition, price movements, and price informativeness. Using quarterly earnings announcements as regular and major information events, I document a number of patterns consistent with rational allocation of limited learning capacity. In the time-series, I show that costly information acquisition results in lower learning and price movements before announcements on busier weeks. In the cross-section of stocks, I find that learning and price movements are lower when other announcing firms are more valuable to learn about. The results suggest that information constraints matter for investors trading decisions, and that investors react in a somewhat rational fashion to their constraints. Consequently, learning plays a significant role in pre-announcement market movements, previously mainly attributed to leakage of insider information.