Pensions’ return forecasts appear inversely related to their funded ratios, potentially indicating a structural bias.
An analysis of public pensions’ asset allocations suggests that their forecasts may prove too optimistic.
An overview of Rademacher Averages, a fundamental concept from statistical learning theory that can be used to derive uniform sample-dependent bounds to the deviation of samples averages from their expectations.
Two Sigma researchers discuss notable advances in deep learning, optimization algorithms, Bayesian techniques, and time-series analysis presented at 2016's Conference on Neural Information Processing Systems (NIPS).
Given a large graph, the authors we aim at producing a concise lossy representation (a summary) that can be stored in main memory and used to approximately answer queries about the original graph much faster than by using the exact representation.
With the recent fall in correlations across asset classes, allocators may find diversification more beneficial for their portfolios.
How can asset allocators quantify the effects of political risk on financial markets? A simple and tractable empirical approach.
Berkeley’s Professor David E. Culler discusses the future of data science, the “Berkeley view” of the field, and the biggest challenges for data scientists today.
A Jack Treynor Prize-winning paper co-authored by a Two Sigma researcher provides a convenient method for summarizing risk exposure in credit portfolios.
During the past five years, forecasters repeatedly have proffered overly optimistic forecasts for long-term growth and excessively pessimistic near-term forecasts