Insights

We examine whether the rise in stock buybacks has artificially propped up equity prices, suppressed market volatility, and weakened corporate balance sheets.

A Two Sigma AI engineer outlines several approaches for understanding how machine learning models arrive at the answers they do.

Speaking at the 2019 Milken Institute Global Conference, Two Sigma co-founder David Siegel discusses the challenges and opportunities AI offers for individuals, companies, and societies.

We introduce a new paper proposing a methodology for using historical data to quantify the return premia for major asset-class based factors.

A proposed methodology for using historical data to quantify the return premia for major asset-class based factors.

Two Sigma engineers explore key challenges and opportunities they encountered while systematically rebuilding cloud security processes in an automated, agile manner.

Speaking on a panel at the 2019 World Economic Forum, Two Sigma co-founder David Siegel discusses key challenges and opportunities as computers assume greater decision-making power globally.

A Two Sigma engineer shares key lessons learned while building a high-performance metrics system based on customized open source building blocks.

Modern large-scale ML applications require stochastic optimization algorithms to be implemented on distributed computational architectures. A key bottleneck is the communication overhead for exchanging information such as stochastic gradients among different workers. In this paper, to reduce the communication cost, we propose a convex optimization formulation to minimize the coding length of stochastic gradients.