Journal of CENTRUM Cathedra. Vol. 02, Issue 01

URI permanente para esta colecciónhttp://54.81.141.168/handle/123456789/194726

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JCC Volume 2 - Issue 1 - 2009
  • Equity Market Risk Premium and Global Integration Johnson, Robert; Soenen, Luc; 1
  • Using Random Forests and Logistic Regression for Performance Prediction of Latin American ADRS and Banks Creamer, Germán G; 2
  • Entrepreneurs, Intuition, and Small-Business Performance Chaston, Ian; 3
  • Evaluation of Market Readiness of Medium and Small-Scale Nonprofit Organizations in Northern India: An Empirical Study Madan, Pankaj; 4
  • Understanding Employee Turnover Patterns in Mexican Maquiladoras Romero, Eric; Cruthirds, Kevin W; 5
  • Productivity Growth as The Predictor of Shareholders' Wealth Maximization: An Empirical Investigation Kumar, Mukesh; Charles, Dr Vincent; 6

    • Doctoral Research Proposal
  • Competitive Advantage of Operational and Dynamic Information Technology Capabilities Bullon, Luis A; 1
  • Managerial Capabilities in Peruvian Family Companies: An Exploratory Study Salomon, Jaime O; 2

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      Using Random Forests and Logistic Regression for Performance Prediction of Latin American ADRS and Banks
      (Pontificia Universidad Católica del Perú. CENTRUM, 2009) Creamer, Germán G.
      In the paper, random forests and logistic regressions’ support of financial analysis functions’ predictive tool to forecast corporate performance and rank accounting and corporate variables according to their impact on performance is demonstrated. Ten-fold cross-validation experiments are conducted on one sample each of Latin American depository receipts (ADRs) and Latin American banks. Random forests indicate that the most important variables that affect ADRs performance are size and the law-and-order tradition; the most important variables that affect banks are size, long-term assets to deposits, number of directors, and efficiency of the legal system. The interpretation of predictive models for a small sample improved when the capacity of random forests to rank and predict with the parameters of a logistic regression were combined.