Faculty Publications

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Publications by NITK Faculty

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    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.
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    Diagnostic classification of undifferentiated fevers using artificial neural network
    (American Institute of Physics Inc. subs@aip.org, 2020) Vasudeva, S.T.; Rao, S.S.; Karanth P, N.K.; Mahabala, C.; Dakappa, P.H.; Prasad, K.
    Accurate diagnosis of undifferentiated fever case at the earliest is a challenging effort, which needs extensive diagnostic tests. Prediction of undifferentiated fever cases at an early stage will help in diagnosing the disease in comparatively lesser time and more effectively. The aim of the present study was to apply Artificial Intelligence (AI) algorithm using temperature information for the prediction of major categories of diseases among undifferentiated fever cases. This was an observational study carried out in tertiary care hospital. Total of 103 patients were involved in the study and 24-hour continuous temperature recording was done. Analysis was done using Artificial Neural Network (ANN) model based on the temperature data of each patients and its statistical parameters. Temperature datasets were labeled with the help of experienced physicians. Levenberg Marquardt error back-propagation algorithm was used to train the network. A good relation was found between the target data set and output data set, purely based on the observed 24 hr continuous tympanic temperature of the patients. An accuracy of 98.1% was obtained from ANN prediction model. The study concluded that a single noninvasive temperature parameter is sufficient to predict the major categories of diseases using ANN algorithms, from the undifferentiated fever cases. © 2020 Author(s).
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    Predictive Intelligent System Development for Disease Classification in Diagnostic Applications
    (Springer Science and Business Media Deutschland GmbH, 2024) Shrivathsa, T.V.; Rao, S.S.; Karanth, P.N.; Adiga, K.; Mahabala, M.; Dakappa, P.H.; Prasad, K.
    With ever increasing explosion in information domain and demand for highest accuracy in medical diagnosis, the existence of a reliable, accurate prediction system is the need of the hour. In this work, an effective prediction system has been developed for accurate classification of undifferentiated ailments using a unique approach. Prediction of undifferentiated diseases at an early stage always helps in better diagnosis. Illnesses like tuberculosis, non-tubercular bacterial infection, dengue fever, non-infectious diseases have regular manifestation of fever. In present work, the uniqueness lies in the use of only temperature data of the patient being referred in predicting the nature of fever, with highest degree of accuracy, instead of several self-defined parameters over limited interval of time. The system has been developed based on artificial intelligent technique, and optimization has been achieved by assessing the performance of different classifiers available. Using prediction model with classifiers, decision can take over comparative results between different classifier algorithms. A result of predictive system defines the combination of good classifier and system developed. Accuracy score and other salient parameters describe the complete picture of the system. Predictive model development in this work proved to be one of the best assistant tools to a doctor to take call over the disease crucial period. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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    Influence of financial distress on exchange rate exposure: Evidence from India
    (Inderscience Publishers, 2018) Prasad, K.; Suprabha, K.R.; Devji, S.
    This paper investigates the relationship between exchange rate exposure and level of financial distress. We argue that the exchange rate movements have a higher effect on the value of the firms with higher level of financial distress. The effect of other firm level variables such as profitability, size of the firm, foreign sales and expenses and liquidity on exchange rate exposure were also studied. We use Merton's (1974) structural default model to estimate firms' distance to default as a proxy for their probability of financial distress. A sample 387 firms listed in National Stock Exchange (NSE) is studied for a period of 2012-2016. We find that the level of firms' exchange rate exposure is significantly positively related to distance to default, indicating that firms that have a greater probability of financial distress are more affected by exchange rate movements. © © 2018 Inderscience Enterprises Ltd.
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    Does GRI compliance moderate the impact of sustainability disclosure on firm value?
    (Emerald Publishing, 2023) Sreepriya, J.; Suprabha, K.R.; Prasad, K.
    Purpose: This paper aims to examine the moderating role of global reporting initiative (GRI) compliance in the association between sustainability reporting and firm value. Design/methodology/approach: This study investigates a sample of 223 manufacturing firms, encompassing 11 industries from 2010 to 2019. Using GRI compliance as a moderator, the authors employed a generalized method of moments model to study how sustainability disclosure impacts firm value. Findings: The results indicate a positive and significant association between sustainability disclosure and firm value. This study reveals that GRI compliance moderates the relationship between sustainability disclosure and firm value, such that firm value increases when the firm adopts GRI in sustainability reporting. Originality/value: No prior studies have examined GRI compliance's direct and moderating effects on the association between sustainability disclosures and firm value in the Indian manufacturing sector. This study is also valuable for the managers and industry to understand the significance of implementing voluntary sustainability disclosure practices and being GRI compliant. © 2022, Emerald Publishing Limited.
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    An iron(iii) oxide-anchored conductive polymer-graphene ternary nanocomposite decorated disposable paper electrode for non-enzymatic detection of serotonin
    (Royal Society of Chemistry, 2024) Prashanth, S.; Aziz, R.A.; Raghu, S.V.; Shim, Y.-B.; Prasad, K.; Vasudeva Adhikari, A.V.
    Serotonin, also known as 5-hydroxytryptamine (5-HT), is an important neurotransmitter that regulates many physiological processes. Both low and high concentrations of 5-HT in the body are associated with several neurological disorders. Hence, there is an urgent need to develop fast, accurate, reliable, and cost-effective disposable sensors for 5-HT detection. Herein, we report the sensing of 5-HT using a disposable paper-based electrode (PPE) modified with a ternary nanocomposite comprising poly(pyrrole) (P(py)), reduced graphene oxide (rGO), and iron oxide (Fe2O3). The sensor material was well characterized in terms of its structural, morphological, and chemical attributes using electron microscopy, spectral techniques, and electrochemical studies to prove the robust formation of the electroactive ternary nanocomposite and its suitability for 5-HT detection. The developed sensor exhibited an impressive limit of detection (LOD) of 22 nM with a wide linear range of 0.01 to 500 ?M, which falls in the recommended clinically relevant range. The analytical recovery, spike sample analysis, and interference studies with ascorbic acid (AA), uric acid (UA), and epinephrine (E) showed satisfactory results, wherein the sensor could detect simultaneously both 5-HT and dopamine (DA). The potential practical utility of the developed sensor was further assessed by quantifying the concentration of 5-HT in the brain samples of Drosophila melanogaster, a versatile genetic model organism employed for modeling different neural disorders in humans, and validated by gold-standard HPLC-UV experiments. The as-fabricated single-run disposable sensor with a ternary nanocomposite exhibits excellent stability with good reproducibility and is a promising platform for identifying clinically relevant concentrations of 5-HT. © 2024 RSC.
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    The impact of ESG disclosure on mitigating financial distress: exploring the moderating role of firm life cycle
    (Palgrave Macmillan, 2025) Suprabha, K.R.; Sreepriya, J.; Prasad, K.
    Globally, businesses are increasingly gaining recognition for their non-financial performance due to heightened stakeholder demands about ethical and environmental responsibilities. This noteworthy shift in perspective has catalyzed the inception of this research, which seeks to scrutinize the influence of environmental, social, and governance disclosure (ESGD) on financial distress. To investigate the potential capacity of ESGD in mitigating financial distress, the researchers have employed the Generalized Method of Moments. Additionally, the study takes into account the moderating role played by the firm’s life cycle in this relationship. The findings underscore that the adoption of ESGD is associated with a decreased likelihood of default, thus highlighting its effectiveness as a risk management strategy. Moreover, this investigation emphasizes the impact of the firm’s life cycle on the link between ESGD and corporate financial distress. Rooted in signalling theory as the theoretical framework, the research posits that a wide spectrum of Environmental, Social, and Governance (ESG) initiatives not only enhances the consistency of signals but also amplifies the associated signal costs. Consequently, in alignment with this theoretical perspective and substantiated by empirical evidence, our study confirms a multifaceted influence of ESGD on financial distress, contingent upon the distinctive phases of a firm's life cycle. In consequence, this study offers valuable insights for managerial decision-making, guiding the development of tailored disclosure policies that align with the specific characteristics of a firm’s life cycle. © The Author(s), under exclusive licence to Springer Nature Limited 2024.