Journal Articles
Permanent URI for this collectionhttp://10.0.100.92:4000/handle/123456789/21
Browse
39 results
Search Results
Item Public and scholarly interest in social robots: An investigation through Google Trends, bibliometric analysis, and systematic literature review(Technological Forecasting and Social Change, 2024-09) Mishra, Nidhi; Bharti, Teena; Tiwari, Aviral Kumar; Pfajfar, GregorThe COVID-19 pandemic has expedited the integration of social robots within the healthcare sector. This research employs a tripartite methodology, combining Google Trends analysis, bibliometric analysis, and a systematic literature review, to gauge both public and research interest in social robots within the healthcare domain. In the Google Trends analysis, search query data for “Social Robots” was retrieved, encompassing both “all categories” and the specific “health” category. Seasonal effects on relative search volumes (RSV) were assessed through the cosinor model. The analysis confirmed statistically significant seasonal patterns in RSV for “social robots” within the “health” category. Conversely, for the broader “all categories,” only the intercept showed significance, while sinw and cosw were deemed insignificant. For bibliometric analysis, the global literature on “robotics” and “healthcare” was examined in the SCOPUS database. From the extensive pool of publications, 144 relevant studies were identified out of 4037 publications. These studies were further analyzed using VOSviewer, providing insights into recent trends and hot topics concerning social robots in healthcare. The systematic literature review focused on 46 articles published from 2019 to the end of 2023. The findings revealed a lack of consensus on the drivers, barriers, and outcomes associated with social robot acceptance and human-robot interaction (HRI). The study systematically maps the existing research on these aspects, introducing a novel categorization and presenting the concept of a “robot user's ecosystem.” This concept emphasizes the imperative involvement of all stakeholders in the development and understanding of social robots. Ultimately, this methodological approach not only identifies nine research gaps in the current literature but also formulates numerous research questions to guide future researchers in this domain.Item Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning(Management Research Review, 2024-07) Jaiswal, Rachana; Gupta, Shashank; Tiwari, Aviral KumarPurpose Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022. Design/methodology/approach Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment. Findings Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years. Research limitations/implications This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook. Practical implications Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing. Social implications By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies. Originality/value This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.Item Asymmetric spillover effects in energy markets(International Review of Economics & Finance, 2024-04) Tiwari, Aviral Kumar; Abakah, Emmanuel Joel Aikins; Doğan, Buhari; Adekoya, Oluwasegun B.; Wohar, MarkThis paper explores the asymmetric relationship between clean and dirty energy markets. The study uses the time-varying and frequency-domain spillover approaches, while accounting for asymmetries. We use natural gas, gasoline, gas oil, heating oil, crude oil, coal, petroleum, kerosene, propane, and diesel to denote dirty energy markets and wind, solar and clean energy markets to denote clean energy markets. We use daily data running from May 18, 2011, to August 12, 2020. According to the results obtained, good news in fluctuations in global energy market indices increases the integration of international energy markets in the long run compared to bad news. Our result show that transmission of good and bad volatilities in global energy market indices are dispersed with different time-varying intensities. Empirical evidence further reveals that good news increases integration of international energy markets in the long run compared to bad news. Additionally, markets transmit more bad volatility on average than good volatility during global events. According to the results of the research, we foresee that portfolio managers and investors may experience difficulties in diversifying opportunities in financial volatility periods in the short term. Overall, our findings reveal asymmetric risk effects in investment opportunities between clean and dirty energy. As a result of this information, investors can diversify their investments in the clean energy sector in the long term by using the asymmetry in good and bad fluctuations.Item The Dynamic Relationship Between Gas and Crude Oil Markets and the Causal Impact of US Shale Gas(Computational Economics, 2024-06) Ghosh, Sudeshna; Tiwari, Aviral Kumar; Doğan, Buhari; Abakah, Emmanuel Joel AikinsAlthough the recent debate in energy economics on the importance of oil price indexation versus shale gases suggest that big data can be used in predictive analysis in energy economics, little is known particularly in the context of shale gas and oil price interlinkages. Grounding our investigations in such directions we investigate in this paper the relationship between gas and crude oil markets and the impact US shale gas by employing time-varying causality method by Shi et al. (J Time Ser Anal 39(6):966–987, 2018; J Financ Econom 18(1):158–180, 2020) and cross-quantilogram correlation approach by Han et al. (J Econom 193(1):251–270, 2016). In particular, as a representative of the crude oil market, we use OPEC oil; WTI; Crude oil Oman; Crude oil Dubai while for the gas market, we use natural gas prices of UK NBP (National Balancing Point), NYMEX HH (Henry Hub) and US shale gas prices. Data period is from 11th January 2013 to 8th September 2020. We find significant negative spillovers from crude oil markets to natural gas markets particularly during moderate market conditions. The results suggest crucial implications in energy economics literature, to diversify assets to hedge against risks. We further find strong causality association between oil markets, natural gas markets and further oil markets and shale gas markets. Our findings describe that aftermath of the shale-gas boom the predictability nexus between oil and natural gas increased. Once we condition for shale gas the significant negative spill overs from oil markets to natural gas markets increases in the long-run. We suggest important policy prescriptions which have interconnected market repercussions.Item A cross-quantile correlation and causality-in-quantile analysis on the relationship between green investments and energy commodities during the COVID-19 pandemic period(Studies in Economics and Finance, 2024) Sharma, Aarzoo; Tiwari, Aviral Kumar; Abakah, Emmanuel Joel Aikins; Owusu, Freeman BrobbeyPurpose This paper aims to examine the cross-quantile correlation and causality-in-quantiles between green investments and energy commodities during the outbreak of COVID-19. To be specific, the authors aim to address the following questions: Is there any distributional predictability among green bonds and energy commodities during COVID-19? Is there exist any directional predictability between green investments and energy commodities during the global pandemic? Can green bonds hedge the risk of energy commodities during a period of the financial crisis. Design/methodology/approach The authors use the nonparametric causality in quantile and cross-quantilogram (CQ) correlation approaches as the estimation techniques to investigate the distributional and directional predictability between green investments and energy commodities respectively using daily spot prices from January 1, 2020, to March 26, 2021. The study uses daily closing price indices S&P Green Bond Index as a representative of the green bond market. In the case of energy commodities, the authors use S&P GSCI Natural Gas Spot, S&P GSCI Biofuel Spot, S&P GSCI Unleaded Gasoline Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI, OPEC Oil Basket Price, Crude Oil Oman, Crude Oil Dubai Cash, S&P GSCI Heating Oil Spot, S&P Global Clean Energy, US Gulf Coast Kerosene and Los Angeles Low Sulfur CARB Diesel Spot. Findings From the CQ correlation results, there exists an overall negative directional predictability between green bonds and natural gas. The authors find that the directional predictability between green bonds and S&P GSCI Biofuel Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI Spot, OPEC Oil Basket Spot, Crude Oil Oman Spot, Crude Oil Dubai Cash Spot, S&P GSCI Heating Oil Spot, US Gulf Coast Kerosene-Type Jet Fuel Spot Price and Los Angeles Low Sulfur CARB Diesel Spot Price is negative during normal market conditions and positive during extreme market conditions. Results from the non-parametric causality in the quantile approach show strong evidence of asymmetry in causality across quantiles and strong variations across markets. Practical implications The quantile time-varying dependence and predictability results documented in this paper can help market participants with different investment targets and horizons adopt better hedging strategies and portfolio diversification to aid optimal policy measures during volatile market conditions. Social implications The outcome of this study will promote awareness regarding the environment and also increase investor’s participation in the green bond market. Further, it allows corporate institutions to fulfill their social commitment through the issuance of green bonds. Originality/value This paper differs from these previous studies in several aspects. First, the authors have included a wide range of energy commodities, comprising three green bond indices and 14 energy commodity indices. Second, the authors have explored the dependency between the two markets, particularly during COVID-19 pandemic. Third, the authors have applied CQ and causality-in-quantile methods on the given data set. Since the market of green and sustainable finance is growing drastically and the world is transmitting toward environment-friendly practices, it is essential and vital to understand the impact of green bonds on other financial markets. In this regard, the study contributes to the literature by documenting an in-depth connectedness between green bonds and crude oil, natural gas, petrol, kerosene, diesel, crude, heating oil, biofuels and other energy commodities.Item The conditional impact of market conditions, volatility and liquidity shocks on the arbitrage opportunities during pre-COVID and COVID periods(International Journal of Finance & Economics, 2024-07) Lakshmi, VDMV; Sisodia, Garima; Joseph, Anto; Tiwari, Aviral KumarThe study examines the effects of market conditions, volatility and liquidity shocks on the arbitrage profits during pre-COVID and COVID periods. The study uses a conditional quantile regression and finds no significant difference in the impact of market conditions on the arbitrage profits during pre-COVID and COVID crisis periods. The increase in volatility combined with low liquidity during the COVID period makes arbitrage non-viable. However, the decline in volatility during the COVID period encourages investors to initiate arbitrage. The results are useful to fund managers and market analysts to develop suitable trading strategies and stock market regulators to take necessary steps to improve price discovery mechanisms and market efficiency.Item Analyzing time-varying tail dependence between leveraged loan and debt markets in the U.S. economy(International Review of Finance, 2024-06) Tiwari, Aviral Kumar; Trabelsi, Nader; Abakah, Emmanuel Joel Aikins; Lee, Chi-ChuanThis study analyzes the time-varying dependence between U.S. leveraged loan and debt markets within a highly linked financial system using a quantile-based time-varying connectedness framework to determine the hedging benefits of leveraged loans for financial investors at various quantiles. Based on daily closing price data from November 28, 2008 to October 3, 2023, the evidence demonstrates considerable (moderate) spillovers across the leveraged loan and debt markets for severe (normal) occurrences, with additional results indicating symmetric interaction. In terms of risk spillover, we also affirm the dominance of short-term fixed-income instruments over leveraged loans and long-term bonds. These findings indicate that no hedging or diversification occurred among the investigated markets.Item Real-world asset tokens and commodities: static and dynamic linkages(China Accounting and Finance Review, 2025-08) Tiwari, Aviral Kumar; Abdullah, Mohammad; Sarker, Provash Kumer; Abakah, Emmanuel Joel AikinsPurpose – This study explores the static and dynamic interconnectedness between real world asset (RWA) tokens and traditional commodities. Additionally, the study examines the role of uncertainty factors in explaining the interconnectedness. Finally, the study examines portfolio diversification opportunities. Design/methodology/approach – A novel R-squared based time-frequency connectedness approach is used to examine interconnectedness using data from March 14, 2018, to June 9, 2023. To compute optimal portfolio weights and hedging ratios for each pair, the DCC-GARCH model is utilized and the best weights and hedge ratios are estimated. Findings – The static connectedness result shows that RWA tokens and commodities demonstrate a relatively lower level of interconnectedness. The dynamic connectedness measures unveil time-varying interconnectedness, particularly heightened during economic events. Moreover, global uncertainty factors are positively associated with connectedness, emphasizing the multifaceted channels through which shock is transmitted. Portfolio analysis underscores potential diversification opportunities between RWAs and commodities, offering insights for informed decision-making in navigating the evolving landscape of blockchain-based assets and traditional commodities. Originality/value – The main novelty of this manuscript is the exploration of RWA tokens, an emerging asset class that has received limited academic attention compared to cryptocurrencies, NFTs and DeFi. Unlike prior studies, this research employs a novel R-Squared-based time-frequency connectedness approach to analyze the static and dynamic linkages betweenRWA and traditional commodities.It also examines global uncertainty factors and incorporates portfolio backtesting, providing insightsfor investorsseeking diversification in tokenized assets.Item Tracing the ties that bind: navigating the static and dynamic connectedness between NFTs and equity markets in ASEAN based on QVAR-approach(Financial Innovation, 2025-01-10) Naveed, Muhammad; Ali, Shoaib; Tiwari, Aviral KumarBased on market integration theory, we investigate the static and dynamic connectedness between nonfungible tokens (NFTs) and the Association of Southeast Asian Nations (ASEAN) equity markets using the Quantile Vector Auto Regressive model. We also compute optimal weights and hedge ratios for our variable of interest to establish their diversification and hedging potential. Our analysis infers a moderate level of return transmission at the median quantile, where equity markets evolved as the net recipients of return spillover from the system, while NFTs emerge as key transmitters. In extreme market conditions, transmission between variables is amplified, but the increase is symmetrical across extreme quantiles, suggesting a similar impact. However, the interlinkage among assets is symmetric across conditional quantiles. The dynamic analysis demonstrates that the system integration amplifies during uncertain times (e.g., COVID-19 and the Russia–Ukraine conflict). Our portfolio analysis shows that NFTs provide diversification and hedging in all market conditions. However, the period of turmoil dampened the diversification potential, and hedging became expensive. Our study offers detailed and insightful information about the transmission mechanism and enables the participants of financial markets to diversify and hedge their portfolio.Item Asymmetry in returns and volatility between green financial assets, sustainable investments, clean energy, and international stock markets(Research in International Business and Finance, 2025-01) Doğan, Buhari; Jabeur, Sami Ben; Tiwari, Aviral Kumar; Abakah, Emmanuel Joel AikinsThis paper presents empirical evidence on the asymmetric relationship between green investments and international stock markets. We employ the asymmetric versions of Diebold and Yilmaz (2012) and Barunik and Krehlik (2018) for time-frequency connectedness, analyzing daily returns and volatilities from June 23, 2009, to June 23, 2022. Our study reveals significant time-frequency asymmetries in returns and volatility spillovers between green investments and developed equity markets in the short and long term. Regarding net directional spillovers, the equity markets in the United States, the United Kingdom, Italy, Germany, and France emerge as net transmitters of shocks. In contrast, green investments, notably those in sustainability and the environment, act primarily as net emitters of shocks. China and Japan are the primary recipients of these shocks. Meanwhile, green bonds generally function as net receivers of shocks, with occasional exceptions.