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.

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Ness Digital Engineering designs and builds digital platforms and software that help organizations engage customers, differentiate their brands, and drive revenue growth. Our customer experience designers, software engineers and data experts partner with clients to develop roadmaps that identify ongoing opportunities to increase the value of their digital products and services. Through agile development of minimum viable products (MVPs), our clients can test new ideas in the market and continually adapt to changing business conditions—giving our clients the leverage to lead market disruption in their industries and compete more effectively to drive revenue growth. For more information, visit www.ness.com

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