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BAML hires top machine-learning quant from J.P. Morgan

Rajesh Krishnamachari, J.P. Morgan, Bank of America Merrill Lynch, BAML, BofA, Merrill, Merrill Lynch, Bank of America, machine learning, ML, artificial intelligence, AI, data science, data scientists, big data, Wall Street, banks, banking, bankers, trading, traders, research, researchers, quant strategy, quant research, quant investing, quant trading, systematic trading, algo trading, algo traders, algorithmic trading

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.

In the middle of the following year, Krishnamachari was promoted to the J.P. Morgan global macro quantitative and derivatives strategy team within the global research group and appointed to the Quant Research Council overseeing applications of data science and AI. As a member of this elite quant team led by MD Marko Kolanovic, whose peers on the Street nicknamed him Gandalf and who CNBC refers to as “the man who moves markets” and “half-man, half-God,” Krishnamachari analyzed market data using machine learning, signal processing and financial econometrics to propose discretionary and systematic equities, FICC and derivatives trades.

Krishnamachari cemented his reputation as co-author with Kolanovic of the influential book-length report Big Data and AI Strategies: Machine Learning and Alternative Data Approach to Investing, which outlined how to use advanced machine learning techniques, including neural networks, and alternative data, including sentiment, commercial transaction and satellite imagery, to design systematic trading and quantitative investment strategies across all asset classes. It cites stock selection, sector rotation and portfolio construction as three examples where you can use machine learning. Various universities use it as a textbook.

In addition to adding Krishnamachari in March, toward the end of last year BAML added ex-DTCC, J.P. Morgan and Morgan Stanley technologist Mieko Shibata and two senior Goldman Sach executives: Howard Sloan, ex-MD and chief information officer of Goldman Sachs Bank USA, and Caroline Arnold, now an MD and the global head of enterprise technology. J..P Morgan, meanwhile, has been losing AI staff after Graham Giller quit for Deutsche Bank and Geoffrey Zweig quit for Facebook.

Goldman Sachs continues to expand its algorithmic trading business, and in January, the bank brought on board managing director Mathew Rothman, the former head of global quantitative equity research at Credit Suisse and an ex-MD at Lehman Brothers and Barclays.


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