Improves industry classification for diversified companies.
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5 results for “conglomerates”
problem Traditional industry classification struggles with multi-sector conglomerates.
method Bayesian Non-Parametrics, Markov Updating, and hierarchical modeling.
result MIS-2 provides a measurable improvement over GICS in predicting future correlations.
Study uses network analysis to examine Japanese overseas business networks.
problem Challenges the weak evidence supporting the existence of Japanese conglomerates (HK).
method Large dataset of 20,000 Japanese overseas subsidiaries analyzed using network techniques.
result Rejects Miwa-Ramseyer hypothesis (MRH) for global and regional datasets.
Study clusters Indian stocks using polyspectral means for nuanced market insights.
problem Analyzing temporal patterns and financial relationships in Indian stock market.
method k-means clustering algorithm applied to polyspectral means of stock data.
result Identified five distinctive clusters of stocks with varying ownership structures.
Develops MIS, a probabilistic model for multi-industry classification.
problem GICS's limitation of assigning each firm to exactly one industry, especially for diversified firms.
method Topic modeling to probabilistically assign firms to multiple industries based on business descriptions.
result Demonstrates MIS's ability to flexibly assign firms to multiple industries with relevance probabilities.
In this paper, we combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisions in OR/MS problems. In a departure from other work on data-driven optimization and reflecting our practic…