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Applying wrapper-based variable selection techniques to predict MFIs profitability: evidence from Peru.

In this paper, we analyse the main factors explaining the profitability (ROA) of Microfinance Institutions (MFIs) in Peru from 2011 to 2107. We apply three wrapper techniques to asample of 168 Peruvians MFIs and 69 attributes obtained from MIX Market database. After running the algorithms M5ʹ, knear...

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Published in:Journal of Development Effectiveness Vol. 13; no. 1; pp. 84 - 100
Main Authors: Pietrapiana, Fabio, Feria-Dominguez, José Manuel, Troncoso, Alicia
Format: Article
Published: Taylor & Francis Ltd Mar2021
Subjects:
Peru
Income tax
Random forest algorithms
Profitability
Profit margins
Regression trees
Online Access:View this record in EBSCOhost
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      jtl: Journal of Development Effectiveness
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      dt: Mar2021
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      pub: Taylor & Francis Ltd
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        10.1080/19439342.2021.1884119
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        atl: Applying wrapper-based variable selection techniques to predict MFIs profitability: evidence from Peru.
      aug:
        au:
          Pietrapiana, Fabio
          Feria-Dominguez, José Manuel
          Troncoso, Alicia
        affil:
          Department of Industrial Engineering, University of Lima, Peru
          Department of Financial Economics and Accounting, Pablo De Olavide University of Seville, Ctra. De Utrera, Km1, Lima, Spain
          Department of Computer Science, Pablo De Olavide University of Seville, Spain
      su:
        Peru
        Income tax
        Random forest algorithms
        Profitability
        Profit margins
        Regression trees
      sug:
        subj:
          Income tax
          Peru
          Random forest algorithms
          Profitability
          Profit margins
          Regression trees
      keyword:
        k Nearest Neighbours (KNN)
        Microfinance Institutions (MFIs)
        peru
        random Forest (RF)
        return on Assets (ROA)
        wrapper Techniques
        k Nearest Neighbours (KNN)
        Microfinance Institutions (MFIs)
        peru
        random Forest (RF)
        return on Assets (ROA)
        wrapper Techniques
      ab: In this paper, we analyse the main factors explaining the profitability (ROA) of Microfinance Institutions (MFIs) in Peru from 2011 to 2107. We apply three wrapper techniques to asample of 168 Peruvians MFIs and 69 attributes obtained from MIX Market database. After running the algorithms M5ʹ, knearest neighbours (KNN) and Random Forest, we find that the M5ʹ algorithm provides the best fit for predicting ROA. Particularly, the key variable of the regression tree is the percentage of expenses over assets and, depending on its value, it is followed by net income after taxes and before donations, or profit margins.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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