Advances in Financial Machine Learning

CHAPTER 22 High-Performance Computational Intelligence and Forecasting Technologies

Kesheng Wu and Horst D. Simon

22.1 Motivation

This chapter provides an introduction to the Computational Intelligence and Forecasting Technologies (CIFT) project at Lawrence Berkeley National Laboratory (LBNL). The main objective of CIFT is to promote the use of high-performance computing (HPC) tools and techniques for analysis of streaming data. After noticing the data volume being given as the explanation for the five-month delay for SEC and CFTC to issue their report on the 2010 Flash Crash, LBNL started the CIFT project to apply HPC technologies to manage and analyze financial data. Making timely decisions with streaming data is a requirement for many business applications, such as avoiding impending failure in the electric power grid or a liquidity crisis in financial markets. In all these cases, the HPC tools are well suited in handling the complex data dependencies and providing a timely solution. Over the years, CIFT has worked on a number of different forms of streaming data, including those from vehicle traffic, electric power grid, electricity usage, and so on. The following sections explain the key features of HPC systems, introduce a few special tools used on these systems, and provide examples of streaming data analyses using these HPC tools.

22.2 Regulatory Response to the Flash Crash of 2010

On May 6, 2010, at about 2:45 p.m. (U.S. Eastern Daylight Time), the U.S. stock market ...