Application of Neural Network and Simulation Modeling to Evaluate Russian Banks’ Performance

Satish Sharma, Mikhail Shebalkov

    Research output: Contribution to journalArticlepeer-review

    3 Downloads (Pure)

    Abstract

    This paper presents an application of neural network and simulation modeling to analyze and predict the performance of 883 Russian Banks over the period 2000-2010. Correlation analysis was performed to obtain key financial indicators which reflect the leverage, liquidity, profitability and size of Banks. Neural network was trained over the entire dataset, and then simulation modeling was performed generating values which are distributed with Largest Extreme Value and Loglogistic distributions with estimated parameters providing robust results. Next, a combination of neural network and simulation modeling techniques was validated with the help of back-testing. Finally, we received nine bank clusters that describe the structural performance within the Russian Banking sector.
    Original languageEnglish
    Pages (from-to)19-37
    JournalJournal of Applied Finance and Banking
    Volume3
    Issue number5
    Publication statusPublished - 1 Sep 2013

    Fingerprint

    Dive into the research topics of 'Application of Neural Network and Simulation Modeling to Evaluate Russian Banks’ Performance'. Together they form a unique fingerprint.

    Cite this