Economics, Econometrics and Finance, Decision Sciences
16
Scopus Publications
Scopus Publications
Prediction of cryptocurrency’s price using ensemble machine learning algorithms N.S.S. Kiranmai Balijepalli, Viswanathan Thangaraj European Journal of Management and Business Economics, 2025 PurposeCryptocurrency markets are gaining popularity, with over 23,000 cryptocurrencies in 2023 and a total market valuation of 870.81 billion USD in 2023. With its increasing popularity, cryptocurrencies are also susceptible to volatility. Predicting the price with the least fallacy or more accuracy has become the need of the hour as it significantly influences investment decisions.Design/methodology/approachThis study aims to create a dynamic forecasting model using the ensemble method and test the forecasting accuracy of top 15 cryptocurrencies’ prices. Statistical and econometric model prediction accuracy is examined after hyper tuning the parameters. Drawing inferences from the statistical model, an ensemble model using machine learning (ML) algorithms is developed using gradient-boosted regressor (GBR), random forest regressor (RFR), support vector regression (SVR) and multi-layer perceptron (MLP). Validation curves are utilized to optimize model parameters and boost prediction accuracy.FindingsIt is found that when the price movement exhibits autocorrelation, the autoregressive integrated moving average (ARIMA) model and the ensemble model performed better. ARIMA, simple linear regression (SLR), random forest (RF), decision tree (DT), gradient boosting (GB) and multi-model regression (MLR) ensemble models performed well with coins, showing that trends, seasonality and historical price patterns are prominent. Furthermore, the MLR approach produces more accurate predictions for coins with higher volatility and irregular price patterns.Research limitations/implicationsAlthough the dataset includes crisis period data, anomalies or outliers are yet to be explicitly excluded from the analysis. The models employed in this study still demonstrate high accuracy in predicting cryptocurrency prices despite these outliers, suggesting that the models are robust enough to handle unexpected fluctuations or extreme events in the market. However, the lack of specific analysis on the impact of outliers on model performance is a limitation of the study, as it needs to fully explore the resilience of the forecasting models under adverse market conditions.Practical implicationsThe present study contributes to the body of literature on ensemble methods in forecasting crypto price in general, potentially influencing future studies on price forecasting. The study motivates the researchers on empirical testing of our framework on various asset classes. As a result, on the prediction ability of ensemble model, the study will significantly influence the decision-making process of traders and investors. The research benefits the traders and investors to effectively develop a model to forecast cryptocurrency price. The findings highlight the potential of ensemble model in predicting high volatile cryptocurrencies and other financial assets. Investors can design the investment strategies and asset allocation decisions by understanding the relationship between market trends and consumer behavior. Investors can enhance portfolio performance and mitigate risk by incorporating these insights into their decision-making processes. Policymakers can use this information to design more effective regulations and policies promoting economic stability and consumer welfare. The study emphasizes the need for using diversified model to understand the market dynamics and improving trading strategies.Originality/valueThis research, to the best of our knowledge, is the first to use the above models to develop an ensemble model on the data for which the outliers have not been adjusted, and the model still outperformed the other statistical, econometric, ML and deep learning (DL) models.
Geopolitical shockwaves: the Russia-Ukraine war’s impact on BRICS financial markets Suresh Gopal, Viswanathan Thangaraj, Naveen Kumara R., Rupa R. Cogent Economics and Finance, 2025 The Russia-Ukraine War triggered global financial market turmoil and disrupted the global supply chain, including agriculture and energy. This study explores the impact of the Russia-Ukraine war on BRICS nations’ stock markets, highlighting varying degrees of volatility and contagion effects. It examines the extent of contagion in the BRICS stock markets and their financial linkages by employing the multivariate DCC-GARCH model. The study reveals sensitive turbulence in Russian markets post-crisis, influenced by its direct involvement in the conflict. Brazil and China experienced higher market volatility after the event, and Brazil shifted its financial linkages with the global market. Conversely, the Indian market experienced eased overall volatility, but its financial linkage with Russia has increased due to its trade partnership. In the post-event period, China and South African markets indicate structural market decoupling. The long-term volatility persists over the short-term volatility of BRICS market dynamics. This study underscores the implications for investors and policymakers, emphasising the need for adjustments in monetary and fiscal policies to stabilise financial markets amid geopolitical uncertainties. This study examines the contagion effects of the ongoing Russia-Ukraine war on BRICS stock markets and highlights how geopolitical risk impacts the financial stability of interconnected and interdependent countries. This study provides a significant understanding of financial vulnerabilities during geopolitical issues by differentiating contagion from spillover. The findings offer important insights for the stakeholders, emphasising the need for strategic risk management through proper policy interventions and diversification to enhance financial resilience even during geopolitical issues.
Risk perception as a barrier to renewable energy finance – a study of debt investors in the Indian context Swarnalakshmi Umamaheswaran, Vandita Dar, John Ben Prince, Viswanathan Thangaraj International Journal of Energy Sector Management, 2024 Purpose This study aims to explore the perceptions of investors regarding the risks associated with funding renewable energy projects in India, as well as the various factors that influence these perceptions. The investigation is limited to debt providers and seeks to pinpoint the primary risks that bankers perceive and the drivers that shape these perceptions. Design/methodology/approach This study draws on interviews and surveys of Indian bank executives, investigating how finance providers perceive risks in the Indian context and the factors driving such perceptions. Qualitative interviews have been used for operationalizing “risk perception” within the renewable energy domain, followed by a quantitative survey and exploratory factor analysis. Findings The authors find that experience and capacity are the most important factors that account for 30% of the overall variance. The second factor, which accounts for 15% of the variance, includes the perceived risks in funding renewable energy projects as compared to infrastructure projects. Among individual risks, the authors find that bankers perceive technological risk to be the lowest (5%) and contractual and regulatory risks as the highest (66%) in renewable energy projects. Research limitations/implications The study contextualizes risk perception toward renewable energy investments in the Indian context by drawing from the risk perception literature and qualitative interviews with senior bankers. It presents empirical evidence on the decision-making behavior of bankers, who are important stakeholders of the renewable energy ecosystem. The main limitation of the study is the relatively small sample, and generalizing the results to the broader population might require a larger sample. This will facilitate the use of confirmatory factor analysis and structural equation modeling, which can facilitate a more comprehensive understanding of risk perceptions in renewables financing. Originality/value Insights gained can be used to provide policy recommendations for improving the financing ecosystem of renewable energy projects. The research significantly contributes to the extant literature within the renewable energy financing domain for emerging economies.
Foreign direct investment and macroeconomic factors: evidence from Indian economy Asha Nadig, T. Viswanathan International Journal of Public Sector Performance Management, 2023 The World Investment Report for 2017 released by UNCTAD projected that FDI inflows to developing Asian countries will increase by 15% in 2017, to $515 billion. With a boost in investor confidence in the economic outlook in major Asian economies, India is the second most preferred destination in the world as regards FDI inflows. This paper examines the relationship between FDI and macroeconomic factors, like, GDP, FER, employment, exports, and GCF for the period 1978-1979 to 2016-2017. Techniques like augmented Dickey-Fuller test, multivariate regression analysis Johansen's co-integration test, impulse response analysis and Chow breakpoint test, multiple breakpoint test and Bai-Perron sequentially determined breaks test are used. The findings suggest that there is a significant correlation between FDI and the macroeconomic factors. The results of Johansen's co-integration test reveal that there is a long run causal relationship between FDI and other variables.
Dynamic interaction of urban development and rural–urban migration: an application of integrated urban metabolism analysis tool (IUMAT) for sustainable city planning Neha Chhabra Roy, Viswanathan Thangaraj Digital Policy Regulation and Governance, 2022 Purpose This study aims to gauge the effect of rural–urban migration and its challenges on the urban development of Bengaluru. This study examines the driving forces behind urbanization and its impact on social, economic and environment areas. The research helps to establish the sustainable city planning, after evaluation of specific challenges of zones, and this mitigation will optimize government investment and reduce cost. Design/methodology/approach Bengaluru is used as a study area to examine the interaction of migration and urban development. The study covers the period between 2011 and 2019. Panel data analysis is applied to measure the effect of urban development indicators on urban migration. The authors applied the integrated urban metabolism analysis tool to quantify the urban development indicators and identified the most critical areas for migrants. Later, authors proposed mitigation measures based on alternate scenario approach. Findings The authors found that there is a mixed effect of urban migration on urban development across various zones in Bengaluru. Besides, the authors suggest how planned collaboration should play a significant role in urban planning and optimize city planning judiciously. Effective mitigation measures should be developed based on zone-specific core issues, and practical trainings, research, public awareness campaigns and skill up-gradation of migrants would enhance the socio-economic and environmental conditions. Research limitations/implications The study will support the ongoing and upcoming initiatives launched by the Government of India i.e. Awas Yojna, Atmanirbhar Bharat and Swach Bharat. Practical implications The structured city planning suggested in the study will help to save wastage of resources and cost and time of developers and policymakers. This will also help to upgrade the status of migrants and enhance the ambience of city around social, economic and environment fronts. Originality/value The first of its kind of innovative model mapped in the study area establishes a link between strategic city planning under rural–urban migration and urban development.
Price discovery and volatility transmission in the spot and futures market of pepper: An empirical analysis Asha Nadig, T. Viswanathan International Journal of Intelligent Enterprise, 2022 Pepper, the 'king of spices', is one of the oldest and widely traded spices across the world over many centuries. As a commodity traded in the spot, futures and export market, global demand and supply play a crucial role in shaping pepper price and volatility. As price risks are integral to farmers and traders, forecasting successive prices will be of great help to them. The price risk can be minimised through effective hedging. The futures market provides a platform for both hedging and speculation. Hence understanding the relationship between spot and future market is essential for the traders of commodities. This paper examines the price discovery mechanism and volatility transmission between the spot and futures prices of pepper. Applying the statistical, seasonal variation and econometric models for forecasting, forecasting accuracy is tested. The Holt-Winter's model gives biased estimate of future prices. The ARIMA model is the appropriate model to forecast the price of pepper.
