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Prediction of Related Party Transactions Using Artificial Neural Network

Author

Listed:
  • Sayed Ali Vaez

    (Department of Accounting, Shahid Chamran University of Ahvaz, Iran)

  • Mohammad Banaf

    (Department of Accounting, Shahid Chamran University of Ahvaz, Iran.)

Abstract

Recent scandals of companies in America (Adelfia, Enron) and Europe (Parmalt) have magnified transactions with related parties. Experience has shown that transactions with related parties not only can disrupt in create value for shareholders, but also can provide caused of the collapse of firms. In this line, the aim of this study is to predict the amount of transactions with related parties using artificial neural network in companies listed in the Tehran Stock Exchange. Multi-layer artificial perceptron neural network with backwards propagation algorithm, the duality of the board of directors, the independence of the board of directors, financial leverage, institutional ownership, the ratio of market value to book value of assets, company size and profitability were used to predict the amount of transactions with related parties, the predictor variables of board size. Finally, a network with the mean square error 0.229, 0.424, 0.299, 0.268 were chosen respectively for educational data, validation, test and total data, and coefficient of determination more than 76%, as the best network to predict the amount of transactions with related people were selected.

Suggested Citation

  • Sayed Ali Vaez & Mohammad Banaf, 2017. "Prediction of Related Party Transactions Using Artificial Neural Network," International Journal of Economics and Financial Issues, Econjournals, vol. 7(4), pages 207-213.
    • Handle: RePEc:eco:journ1:2017-04-28
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Forecast Transactions with Related Parties; Propagation Algorithm; Related Parties Transaction;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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