Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Exchange Rate Exposure and Usage of Foreign Currency Derivatives by Indian Nonfinancial Firms(Springer Science and Business Media B.V., 2018) Prasad, K.; Suprabha, K.R.This paper investigates the usage of currency derivatives and its impact on the exchange rate exposure. A sample of 387 nonfinancial Indian firms listed in the National Stock Exchange of India were studied for a period of 5 years from 2011–2012 to 2015–2016. The currency derivatives data was collected from the annual reports of the sample firms, and the stock price data was collected from Ace Equity and Centre for Monitoring Indian Economy (CMIE) database Prowess. The results of the study indicate that the currency forward contract is the most preferred hedging instrument among the sample firms. The Indian firms showed the lower interest in exchange-traded instruments especially currency futures. This is in spite of the growth in the third-generation innovative and low-cost derivative instruments. This study also provided evidence that hedging using currency derivatives decreased the firms’ foreign exchange exposure level, while the use of foreign currency borrowing was found insignificant in decreasing the firm’s level of currency exposure. © 2018, Springer International Publishing AG.Item 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.Item 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.Item Impact of Contextual Factors on Adaptive Performance: A Study on Indian PSBs Using ANN Analysis(Springer Science and Business Media Deutschland GmbH, 2025) Shetty, D.S.; Suprabha, K.R.The Indian Public Sector Banks (PSBs) are at the cusp of significant structural changes and technological shifts. These radical changes have enormously altered the world of work by endlessly spinning due to the environment’s volatility, uncertainty, complexity, and ambiguity (VUCA). The maladaptive conditions created during these circumstances have increased the need for employees with stronger and steadier adaptive performance. Human capital is the asset of any organisation; thus, attracting, retaining, and maintaining these assets becomes the focal function of financial institutions like banks. Training and development activities are ongoing and crucial organizational processes to manage talents. The vitality of these activities is easily traceable in the institute’s growth graph. Thus, identifying the contextual competencies and training the employees to enhance these competencies is a challenge to any organization. This study proposes the application of Artificial Neural Network (ANN) as a tool to explore the competency in demand during disruptions in the Indian public sector banks. The use of ANN in accruing answers to research questions about employee competency management to enhance adaptive performance is a rare and inevitable study while dealing with disruptions in the banking sector. This study investigates the influence of Work Autonomy (WA), Emotional Stability (ES), Workplace Spirituality (WS), and Career Adaptability (CA) on Adaptive Performance (AP). The current study results are convincingly satisfactory and are very close to the Structural Equational Modelling (SEM) outcomes when compared with the same. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.