J.P. Morgan & Co.

About: J.P. Morgan & Co. is a based out in . It is known for research contribution in the topics: Portfolio & Implied volatility. The organization has 328 authors who have published 436 publications receiving 14291 citations.

Programming Language Choices for Algo Traders: The Case of Pairs Trading

Abstract: In the last 20 years, relative value strategies have increased in popularity in various asset classes, including equity and commodity markets. Due to an increase in market participants, more sophisticated algorithms than those used in the past are now required to generate excess returns in pairs trading strategies. Sophisticated algorithms can cause an increase in complexity which, in-turn, increases computational run time. In our pairs trading example, C++ provides the best performance, however, it is also the most time consuming to implement. Among the languages that allow for faster development, Cython provides the best balance between run times and ease of prototyping.

Discussion of ‘Financial Reporting and Firm Valuation: Relevance Lost or Relevance Regained?’ by Luzi Hail (2013)

Abstract: †My initial comment relates to earnings and the relevance of the income statement. My understanding is that research typically focuses on reported earnings as gathered from Datastream, Worldcom and...

Dividend Modeling: Moving Forward Deterministic and Stochastic Cash Dividends

Abstract: The idea of this paper is to present different ways to model dividends. The first part evokes the most widespread model for dividend modeling, which considers that the dividend function is affine in the spot. The second part focuses on Forward Market models, i.e. models where we directly diffuse the forwards, and how these models are well suited to model dividends, even when stochastic rates are introduced. Eventually, we compare these two approaches for various dividend dependent products (Autocalls, Forward forwards, Dividend Options), and different vol models, mainly local volatility or stochastic volatility. We also mention in this article different techniques related to dividend modeling: the calibration to American Vanillas or the (already known) dividend averaging method.

Constant-Overhead Unconditionally Secure Multiparty Computation over Binary Fields.

Abstract: We study the communication complexity of unconditionally secure multiparty computation (MPC) protocols in the honest majority setting. Despite tremendous efforts in achieving efficient protocols for binary fields under computational assumptions, there are no efficient unconditional MPC protocols in this setting. In particular, there are no n-party protocols with constant overhead admitting communication complexity of O(n) bits per gate. Cascudo, Cramer, Xing and Yuan (CRYPTO 2018) were the first ones to achieve such an overhead in the amortized setting by evaluating \(O(\log n)\) copies of the same circuit in the binary field in parallel. In this work, we construct the first unconditional MPC protocol secure against a malicious adversary in the honest majority setting evaluating just a single boolean circuit with amortized communication complexity of O(n) bits per gate.

Less Volatile Portfolios: Strength through Simplicity

Abstract: One prominent trend in equity indexing in recent years has been indexes that target a lower volatility than a capitalization-weighted benchmark. To read the ground rule document of indexes using optimization procedures, one would think that it requires fine detailed predictions of return distributions and finely tuned constraints. In truth, the same outcome can be achieved with a far simpler set of rules, as the authors show in this article. The value of simplifying portfolio construction is twofold: robustness and stability. Robustness means lower sensitivity of the portfolio to input assumptions, decreasing the likelihood of changes in performance out of sample. Stability of solutions through time means that the portfolio can be allowed to run with fewer constraints, ensuring that the desired outcome (in this case lower volatility) can still be achieved without undue turnover in the portfolio. The authors demonstrate a simple method that can be used to build more robust volatility-lowering portfolios.