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Author: search.filters.author.Arjun, R.Has files: No

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    Modeling hybrid indicators for stock index prediction
    (Springer Verlag service@springer.de, 2020) Arjun, R.; Suprabha, K.R.
    The study aims to assess the major predictors of stock index closing using select set of technical and fundamental indicators from market data. Here two of major service sector specific indices of Bombay stock exchange (BSE) and National stock exchange (NSE) with historical data from 2004 up to 2016 are considered. By experimental simulation, the predictive estimates of index closing using automatic linear modeling, time-series based forecasting, and also artificial neural network models are analyzed. While linear models show better performance for BSE, artificial neural network based models exhibit higher predictive modeling accuracy for NSE. The design aspects are outlined for augmenting intelligent market prediction systems. © Springer Nature Switzerland AG 2020.
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    Deep Learning for Stock Index Tracking: Bank Sector Case
    (Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Arjun, R.; Suprabha, K.R.; Majhi, R.
    The current study explores the efficacy of deep learning models in stock market prediction specific to banking sector. The secondary data of major fundamental indicators and technical variables during 2004–2019 periods of two banking indices, BSE BANKEX and NIFTY Bank of Bombay stock exchange and National stock exchange, respectively, are collected. The factors impacting market index prices were analyzed using nonlinear autoregressive neural network. Preliminary findings contradict the general random walk hypothesis theory and model improvement over previous studies. The implications from practical and theoretical perspective for stakeholders are discussed. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Forecasting banking sectors in Indian stock markets using machine intelligence
    (IOS Press BV, 2019) Arjun, R.; Suprabha, K.R.
    The study analyze stock index closing from myriad set of technical and fundamental analysis variables extracted from real market data to assist forecast of market closing. For this, major service sector indices of Bombay stock exchange (BSE) and National stock exchange (NSE) with historical data were taken from banking industry. The predictive model performance of index closing using statistical procedures like automatic linear modeling, time-series based econometric forecasting, vector auto regression as with artificial neural network based models were simulated and analyzed. The results indicate that BSE had higher forecast accuracy using autoregressive models and market volatility factor had major influence. Whereas, NSE was impacted by quarterly performance that can be modeled using neural networks. The empirical results were contrasted with latest state-of-art research theories to provide agenda and future research challenges of market forecast systems. © 2019 - IOS Press and the authors. All rights reserved.
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    Innovation and challenges of blockchain in banking: A scientometric view
    (Universidad Internacional de la Rioja, 2020) Arjun, R.; Suprabha, K.R.
    Blockchain has been gaining focus in research and development for diverse industries in recent years. Nevertheless, innovations that impact to the banking nurture a potential for disruptive impact globally for economic reasons; however it has received less scholarly attention. Hence the effect of blockchain technologies on banking industry is systematically reviewed. The relevant literature is extracted from Scopus, Web of Science and bibliometric techniques are applied. While a bulk of earlier papers focuses only on bit coins, a broader framework is envisaged that synthesizes interdisciplinary thematic areas for advancement; hence novelty in current work. A few practical and theoretical implications for stakeholders in view of technology, law and management are discussed. © 2020, Universidad Internacional de la Rioja. All rights reserved.