Forthcoming Articles

International Journal of Financial Engineering and Risk Management

International Journal of Financial Engineering and Risk Management (IJFERM)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Financial Engineering and Risk Management (2 papers in press)

Regular Issues

  • Portfolio Optimisation in Modular Neural and Hybrid Models   Order a copy of this article
    by Nikolaos Loukeris, G. Gikas 
    Abstract: The Modular (MDN) Neural Nets with Hybrids that convolute them to GAs elaborated under the portfolio optimisation problem. A series of sub-problems were analysed in terms of the: a) patterns of behaviour on speculators, b) effect of Chaos Entropy to express investor patterns in SDEs over the returns fluctuations in Fractal Market Hypothesis, c) identification of the best MDN classifier among 40 MDN neural and hybrids fit for portfolio selection. The portfolio selection is advanced implementing novel approaches to the market metrics that represent objectively the investors' preferences, the herding phenomena, and the optimal evaluation of assets in the markets.
    Keywords: Modular Neural Networks; Genetic Algorithms; Portfolio Selection; Chaos; Entropy; Epicurus; Aristotle; Logic; Free Will; Eudaimonia.
    DOI: 10.1504/IJFERM.2025.10074188
     
  • Impact of Bank Mergers & Acquisitions on Bank Performance Calculated on a Year Basis Using Python and Neural Networks for Predictive Modeling   Order a copy of this article
    by Nikolaos Panozachos, Simeon Papadopoulos 
    Abstract: This study evaluates the post-merger performance of UBS, JP Morgan, and First Citizens BancShares, analyzing financial and operational health through key performance indicators such as Return on Equity, Return on Assets, and the Cost-to-Income ratio. Additionally, liquidity metrics like the Liquidity Coverage Ratio were examined to assess the banks' funding structure and stability. A machine learning approach, specifically a Long Short-Term Memory model, was used to forecast stock prices, leveraging historical data from 2008 to 2023. The model employed a 365-day lookback period and preprocessing techniques to predict daily closing prices, with Mean Absolute Percentage Error, Root Mean Square Error, and R-squared values used to measure accuracy. While UBS and FCNCA forecasts achieved high accuracy, JPM predictions were less precise, highlighting challenges in accounting for unpredictable market conditions. Nevertheless, the model’s overall performance underscores its value in supporting financial analyses by providing insights into stock trends and post-merger stability.
    Keywords: finance; mergers; acquisitions; banks; bank performance; time series data; predictive modeling; neural networks; LSTM.

 

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