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Agostino Capponi is a professor of Industrial Engineering and Operations Research at Columbia University, where he is also the director of the Center for Digital Finance and Technologies, and a member of the Data Science Institute. His research interests are in market microstructure, financial technology, economic networks, and machine learning in finance.
Website: http://www.columbia.edu/~ac3827/
Paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5031617
Abstract
We propose a nonparametric method to aggregate rich firm characteristics over a large supply chain network to explain the cross-section of expected returns. Each target firm receives a nonlinearly constructed pricing signal passed from neighboring firms that are within d-hops on the supply chain network. Analyzing all US-listed stocks with supply chain data, our model achieves over 50% higher out-of-sample Sharpe ratios compared to models using only direct suppliers and consumers, outperforming Fama-French five-factor and principal component models. Through a graph-Monte Carlo experiment, we demonstrate the interplay between d and degree centrality, showing that the most central firms are twice as sensitive as peripheral firms. Our recommended d = 6 balances bias-variance and ensures robustness.
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