Jacob Gelfand

Jacob Gelfand

Director, Quantitative Investment Strategies, Research and Analytics, Northwestern Mutual

Biography

Mr. Jacob Gelfand, CFA, Director of Quantitative Strategy and Research, Investment Risk Management, is responsible for macro research and analysis for the fixed income, currencies, equity and cross-asset class portfolios.
His focus areas include global markets risk analysis, asset allocation, investment style and risk management, relative value analysis and other quantitative and global macro aspects of risk and portfolio management.

Mr. Gelfand joined Northwestern Mutual in 2004 as a member of the Fixed Income Department at Mason Street Advisors (MSA). In 2015, he joined the Investment Strategy Division, and in 2016, became a member of the Investment Risk Management Division. Before joining Northwestern Mutual, Mr. Gelfand was a Senior Consultant with Cap Gemini, serving clients in investment management and financial industries. Mr. Gelfand is an adjunct faculty in the Lubar School of Business, University of Wisconsin- Milwaukee, where he teaches classes on Global Investments, and Options and Derivatives. Mr. Gelfand received a MS in Computer Science with Honors from the Moscow State University of Civil Engineering (MSUCE), Russia, and an MBA in Finance and Strategy with Honors from the University of Chicago Booth School of Business. He holds the Chartered Financial Analyst (CFA) designation.

TBC-NEWS SENTIMENT ANALYSIS IN EM SOVEREIGN DEBT RESEARCH, INVESTMENT AND RISK MANAGEMENT
We present the internally developed framework for ex-ante analysis of the foreign exchange and sovereign bond markets based on the news sentiment in global and local media, the Global Economy and Markets Sentiment (GEMS) model. The predictive analytics from GEMS model are used to enhance the fundamental analysis to better assess risks and opportunities in the Emerging Markets sovereign debt market. We introduce the long-term and short-term trading strategies based on the produced GEMS analytics and spearhead the discussion about predictive qualities of the produced analytics.