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Machine Learning and Big Data in Quantitative Investing

May 31, 2019

Machine learning enables computers or machines to learn from data directly without being explicitly programmed. By nature, machine learning models can capture nonlinearities better than traditional models can. To extract valuable information hidden in a large dataset, you need to use modern tools for processing big data and machine learning together. Big data generally refers to a large volume of data that is hard to process using existing techniques that require in-memory computation. Data used in or stored by financial institutions that is typically considered big data includes: The most basic example of the role of data in finance is technical analysis in which the relationships among price, volume, and time are aggregated into technical indicators for predicting future movements in price.

Spotlight

iOLAP, Inc.

iOLAP, Inc. is a Dallas-based strategic data consultancy specializing in Big Data, Business Intelligence and Data Warehousing strategy and solutions. We are completely focused on these markets and bring a client-centric and business-focused perspective to all of our engagements. iOLAP has been in business since 1999 with in-depth expertise across all Data Warehousing and Business Intelligence technology areas, including the newest leading-edge Business Analytics and Big Data architectures. We serve some of the world’s largest companies across all major industries. Unlike the expensive, big-name, and general technology consulting companies, we don’t try to do a little of everything. We do Big Data and Business Intelligence.

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Machine Learning and Big Data in Quantitative Investing