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## September 2018

### Saad Mouti, UC Berkeley: On Optimal Options Book Execution Strategies with Market Impact

We consider the optimal execution of a book of options when market impact is a driver of the option price. We aim at minimizing the mean-variance risk criterion for a given market impact function. First, we develop a framework to justify the choice of our market impact function. Our model is inspired from Leland’s option replication with transaction costs where the market impact is directly part of the implied volatility function. The option price is then expressed through a Black–…

Find out more »### Tamas Batyi, UC Berkeley: Capacity constraints in earning, and asset prices before earnings announcements

This paper proposes an asset pricing model with endogenous allocation of constrained learning capacity, that provides an explanation for abnormal returns before the scheduled release of information about firms, such as quarterly earnings announcements. In equilibrium investors endogenously focus their learning capacity and acquire information about stocks with upcoming announcements, resulting in excess price movements during this period. I show cross-sectional heterogeneity in stock returns and institutional investors' information demand before quarterly earnings announcements that are consistent with the model.…

Find out more »### Haosui (Kevin) Duanmu, UC Berkeley: Nonstandard Analysis and its Application to Markov Processes

Nonstandard analysis, a powerful machinery derived from mathematical logic, has had many applications in probability theory as well as stochastic processes. Nonstandard analysis allows construction of a single object - a hyperfinite probability space - which satisfies all the first order logical properties of a finite probability space, but which can be simultaneously viewed as a measure-theoretical probability space via the Loeb construction. As a consequence, the hyperfinite/measure duality has proven to be particularly in porting discrete results into their…

Find out more »### Ben Gum, AXA Rosenberg: A Deep Learning Investigation of One-Month Momentum

The one-month return reversal in equity prices was first documented by Jedadeesh (1990), who found that there was a highly significant negative serial correlation in the monthly return series of stocks. This is in contrast to the positive serial correlation of the annual stock returns. Explanations for this effect differ, but the general consensus has been that the trailing one-month return includes a component of overreaction by investors. Since 1990, the one-month return reversal effect has decayed substantially, which has led…

Find out more »## October 2018

### Dangxing Chen, UC Berkeley: Predicting Portfolio Return Volatility at Median Horizons

Commercially available factor models provide good predictions of short-horizon (e.g. one day or one week) portfolio volatility, based on estimated portfolio factor loadings and responsive estimates of factor volatility. These predictions are of significant value to certain short-term investors, such as hedge funds. However, they provide limited guidance to long-term investors, such as Defined Benefit pension plans, individual owners of Defined Contribution pension plans, and insurance companies. Because return volatility is variable and mean-reverting, the square root rule for extrapolating…

Find out more »### Jacob Steinhardt, Stanford: Robust Learning: Information Theory and Algorithms

This talk will provide an overview of recent results in high-dimensional robust estimation. The key question is the following: given a dataset, some fraction of which consists of arbitrary outliers, what can be learned about the non-outlying points? This is a classical question going back at least to Tukey (1960). However, this question has recently received renewed interest for a combination of reasons. First, many of the older results do not give meaningful error bounds in high dimensions (for instance,…

Find out more »### Tingyue Gan, UC Berkeley: Asymptotic Spectral Analysis of Markov Chains with Rare Transitions: A Graph-Algorithmic Approach

Parameter-dependent Markov chains with exponentially small transition rates arise in modeling complex systems in physics, chemistry, and biology. Such processes often manifest metastability, and the spectral properties of the generators largely govern their long-term dynamics. In this work, we propose a constructive graph-algorithmic approach to computing the asymptotic estimates of eigenvalues and eigenvectors of the generator. In particular, we introduce the concepts of the hierarchy of Typical Transition Graphs and the associated sequence of Characteristic Timescales. Typical Transition Graphs can…

Find out more »### Xiang Zhang, SWUFE: Proliferation of Anomalies and Zoo of Factors – What does the Hansen–Jagannathan Distance Tell Us?

Recent research finds that prominent asset pricing models have mixed success in evaluating the cross-section of anomalies, which highlights proliferation of anomalies and zoo of factors. In this paper, I investigate that how is the relative pricing performance of these models to explain anomalies, when comparing their misspecification errors– the Hansen–Jagannathan (HJ) distance measure. I find that a traded-factor model dominates others in a specific anomaly by incorporating the multiple HJ distance comparing inference. However, different from the current research of…

Find out more »## November 2018

### Chi Zhang, Kamyar Kaviani, Nikita Vemuri, and Simon Walter (UC Berkeley) – Putting the ‘I’ in IPO

As an alternative to traditional loans, young people could issue securities that pay dividends that depend on their future financial success in life. This type of a personal IPO is especially desirable for young people, who for example may need money for a college education, because it allows them to shift the risk of repayment to investors who bet on their future success, unlike in a traditional loan setting. In this seminar we will report a framework for estimating an…

Find out more »### Michael Ohlrogge, Stanford University: Bankruptcy Claim Dischargeability and Public Externalities: Evidence from a Natural Experiment

In 2009, the Seventh Circuit ruled in U.S. v. Apex Oil that certain types of injunctions requiring firms to clean up previously released toxic chemicals were not dischargeable in bankruptcy. This was widely perceived to represent a split with Sixth Circuit precedent, although Supreme Court cert was denied. Numerous legal commentators wrote of the significance of this decision in strengthening incentives for firms, and their creditors, to reduce the likelihood of costly environmental damage that would no longer be dischargeable in the…

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