J.P. Morgan's quantitative investing and derivatives strategy team, led Marko Kolanovic and Rajesh T. Krishnamachari, has just issued the most comprehensive report ever on machine learning in financial services. Titled, 'Big Data and AI Strategies' and sub headed, 'Machine Learning and Alternative Data Approach to Investing', the report says that machine learning will become crucial to the future functioning of markets. Analysts, portfolio managers, traders and chief investment officers all need to become familiar with machine learning techniques. If they don't they'll be left behind: traditional data sources like quarterly earnings and GDP figures will become increasingly irrelevant as managers using newer datasets and methods will be able to predict them in advance and to trade ahead of their release. At 280 pages, the report is too long to cover in detail, but I've pulled out the most salient points and put it into a cool infographic:
Bank of America Merrill Lynch hired Rajesh Krishnamachari, formerly a senior quantitative strategist and researcher at J.P. Morgan, as the head of data science for equities in New York last month. BofA's new equities-focused data-science team is using machine learning and artificial intelligence to get insights from proprietary data and develop new products that have an impact on the top and bottom line of the business. A Bank of America spokeswoman confirmed his employment but declined to comment further. Krishnamachari joined J.P. Morgan's equity derivatives quantitative research team in 2014. Primarily using Python, Java and the XGBoost software library, he designed and back-tested systematic options, VIX and equities trading strategies, as well as an ultra-high-frequency execution algorithm for trading VIX futures.
Machine learning in essence, is the research and application of algorithms that help us better understand data. By leveraging statistical learning techniques from the realm of machine learning, practitioners are able to draw meaningful inferences from and turn data into actionable intelligence. Furthermore, the availability of several open source machine learning tools, platforms and libraries today enables absolutely anyone to break into this field, utilizing a plethora of powerful algorithms to discover exploitable patterns in data and predict future outcomes. This development in particular has given rise to a new wave of DIY retail traders, creating sophisticated trading strategies that compete (and in some cases, outperform others) in a space previously dominated by just institutional participants. In this introductory blog post, we will discuss supportive reasoning for, and different categories of machine learning.