A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Roy, Shuvashish
- Application of Topsis Method for Financial Performance Evaluation:A Study of Selected Scheduled Banks in Bangladesh
Authors
1 K. B Group of Industries, Bokshigonj, Jamalpur, Mymensingh, BD
2 Department of Commerce, The University of Burdwan, Golapbag, Burdwan, West Bengal, IN
Source
Journal of Commerce and Accounting Research, Vol 7, No 1 (2018), Pagination: 24-29Abstract
The main objective of our study is to analyze the financial performance of select scheduled banks (namely, the state-owned commercial banks, private commercial banks and foreign commercial banks) in Bangladesh during the period 2000-2013 with the help of TOPSIS (i.e., Technique for Order Preference by Similarity to an Ideal Solution). In the context of banking sector in Bangladesh, the broad classification of ratios/indicators has been done with the help of the methodology of Bangladesh Institute of Bank Management [Bank-Reviews, (2012-2013)]. After computing all category wise ratios/indicators (i.e., profitability and efficiency ratios, size and growth indicators, strength and soundness ratios and asset quality ratios) for all the select nineteen banks during the study period, the weights of the selected ratios/indicators have been calculated with the help of Shannon entropy method. Composite index values of the select banks have been determined on the basis of TOPSIS and from major findings of the present research work it can be concluded that the profitability, efficiency, strength and soundness, size and growth and asset quality positions of foreign commercial banks and private commercial banks are better than those of state-owned commercial banks.Keywords
Financial Performance, Shannon Entropy, TOPSIS.- Clustering Mid-Cap Stocks in Indian Market using Multi-Variate Data Analysis Technique
Authors
1 Hazrat Khajar Bashir Unani Ayurvedic Medical College & Hospital Foundation, Jamalpur, BD
2 Institute of Business Management, National Council of Education Bengal, Kolkata, West Bengal, IN
Source
Indian Journal of Economics and Development, Vol 7, No 6 (2019), Pagination: 1-10Abstract
Objectives: This study attempts to identify homogeneous clusters of constituent companies of the CNX NIFTY Mid Cap 50 Index in the Indian markets based on valuation ratios.
Methods: Nine selected valuation ratios of the fifty constituent companies of the CNX NIFTY Mid Cap 50 Index have been considered for the three consecutive years from 2015-16 to 2017-18. The values were standardized to facilitate cluster analysis. Hierarchical and K-Means cluster analysis have been done to identify the clusters of homogeneous stocks in terms of valuation ratios.
Findings: It has been observed that the stocks in all the three years under study, showed two clusters. Mostly there were clear groupings of stocks into the two clusters. A few occasional events have been observed where companies from one sector have been distributed in both the clusters. On an overall basis, considering all the three years under study, Banking, Chemicals, Power & Iron & Steel Industries have been found to have homogeneous valuation ratios. On the other hand, Automobiles, Information Technology, Industrial Gas & Fuels, Healthcare, Agriculture Construction Materials constitute the other cluster. The findings of the study leads to the conclusion that valuation ratios can be used as categorizing factors in clustering of companies across sectors in the mid cap segment of the Indian market.
Applications: Investors in equity shares may use the information about cluster membership based on valuation ratios in deciding the constitution of their portfolios.
Keywords
Cluster Analysis, Midcap Stocks, CNX NIFTY Midcap 50 Index, Valuation Ratio.References
- T. Ekrem, H. Bahattin. Clustering of financial ratios of the quoted companies through fuzzy logic method. Journal of Naval Science and Engineering. 2003; 1(2), 123-140.
- Costa Da Jr. Newton, Cunha Jefferson, Silva Sergio Da. Stock selection based on cluster analysis. Economics Bulletin. 2005; 13 (1), 1−9.
- C.A.A. Lemos, M. P. E. Lins, N.F.F. Ebecken. DEA implementation and clustering analysis using the K-Means algorithm. WIT Transactions on Information and Communication Technologies 2005; 35, 321-329.
- W. Yu-Jie, L. Hsuan-Shih. A clustering method to identify representative financial ratios. Information Sciences. 2008; 178, 1087–1097.
- S.D. Venugopal, T.M. Rangaswamy, A.V. Suresh. Analysis and clustering of Nifty companies of share market using data mining tools. Journal of Engineering Research and Studies. 2010; 1(1), 152-164.
- Li Hui, Sun Jie. Mining business failure predictive knowledge using two-step clustering. African Journal of Business Management. 2011; 5(11), 4107-4120.
- B.M. Suresh, N. Geethanjali, B. Satyanarayana. Clustering approach to stock market prediction. International Journal of Advanced Networking and Applications. 2012 03 (04) 1281-1291.
- Ferst Robert, Seres David. Clustering Austrian Banks’ Business Models and Peer Groups in the European Banking Sector. Financial Stability Report. 2012; 1-24.
- SetyaningsihSanti. Using cluster analysis study to examine the successful performance entrepreneur in Indonesia. Procedia Economics and Finance. 2012; 4, 286 – 298.
- Y. Temouri. The cluster scoreboard: measuring the performance of local business clusters in the knowledge economy. OECD Local Economic and Employment Development (LEED) Working Papers 2012. 2012/13 OECD Publishing. 2012.
- Aghabozorgi Saeed, Teh, Ying Wah. Stock market co-movement assessment using a three-phase clustering method. Expert Systems with Applications. 2014; 41(2014), 1301–1314.
- G. Andreas, S. Fabian. Risk Cluster Framework – How to analyse Companies by Operating Leverage. 2015.
- Marvin Karina. Creating Diversified Portfolios Using Cluster Analysis 2015.
- MomeniMansoor, Mohseni Maryam, Soofi, Mansour. Clustering Stock Market Companies via K- Means Algorithm. Arabian Journal of Business and Management Review. 2015; 4(5).
- G. Szucs. The Financial analysis of the hungarian automotive industry based on profitability and capital structure ratios. Central European Business Review. 2015; 4(1).
- Cai Fan, Le-KhacNhien-An, Kechadi M-Tahar. Clustering approaches for financial data analysis: a survey. 2016; 1-7.
- Dias Antonio, Pinto Carlos, Batista Joao, Neves Elisabete. Signaling tax evasion, financial ratios & cluster analysis. BIS Quarterly Review. 2016; 1-34.
- B. Hou. Financial distress prediction of k-means clustering based on genetic algorithm and rough set theory. Chemical Engineering Transactions. 2016; 51.
- GoudarziSiamak, Jafari Mohammad Javad, Afsar Amir. A hybrid model for portfolio optimization based on stock clustering and different investment strategies. International Journal of Economics and Financial. 2017; 7(3).
- Perisa Ana, Kurnoga Natasa, Sopta Martina. Multivariate analysis of profitability indicators for selected companies of Croatian market. UTMS Journal of Economics. 2017; 8(3), 231–242.
- Ding Kexing, Hoogduin Lucas, PengXuan, Vasarhelyi Miklos A., Wang Yunsen. Clustering Based Peer Selection with Financial Ratios. Rutgers, State University of New Jersey.
- Banerjee Ryan, Hofmann Boris. The rise of zombie firms: causes and consequences. BIS Quarterly Review. 2018; 67 – 78.
- Ferrando Annalisa, LekpekSenad. Access to finance and innovative activity of EU firms: a cluster analysis. European Investment Bank. 2018.
- Fodor Andy, Jorgensen Randy D., Stowe John D. Forming Stock Groups with a Cluster Analysis of Common Size Statements. Southwestern Finance Association Annual Conference. 2015; 1-36.
- Alexandra Horobet, Joldes Cosmin, Gabriel Dan Dumitrescu. A cluster analysis of financial performance in central and eastern Europe. 2019; 289-294.
- Identifying Homogeneity of Small-Cap Stocks in Indian Market:A Data Mining Approach
Authors
1 Hazrat Khajar Bashir Unani Ayurvedic Medical College & Hospital Foundation, Jamalpur, BD
2 Institute of Business Management, The National Council of Education Bengal, Kolkata, West Bengal, IN
Source
International Journal of Business Analytics and Intelligence, Vol 7, No 1 (2019), Pagination: 53-63Abstract
Investors in equity shares look for two basic aspects while investing i.e. consistently rising returns with a decreasing or at least stabilized level of risk involved. Amidst the numerous stocks available in the market which differ widely on the basis of different aspects i.e. segment, sector, industry, market capitalization etc. it becomes a challenge for the investor to form a diversified portfolio where heterogeneity of the constituent stocks is the main criterion. Thus it is imperative that the basis be finalized on which the heterogeneity of the stocks shall be determined. Traditionally portfolios have been constituted on the basis of low coefficient of correlation of returns from the constituent stocks. The dissimilarity of co-movement of returns from stocks has traditionally been attempted to be maximized in portfolios. Another method of grouping similar stocks by using data mining approach is fast gaining popularity. This approach uses clustering technique to group homogeneous stocks on the basis of a set of selected criteria. These criteria can be financial ratios, indices or any other related matrices. Advanced versions of this technique can group homogeneous time series data as well. This paper attempts to identify homogeneous clusters of companies in the Indian small-cap segment based on valuation ratios. Valuation ratios have been selected to be the grouping criteria as these were not been used in earlier studies by researchers and scholars. The small cap companies in India have been chosen for this study for its better resilience and recovering potential compared to mid cap and large cap companies. The companies constituting the CNX NIFTY Small Cap 50 Index have been considered in the study.Keywords
Cluster Analysis, Valuation Ratios, Small Cap Sector, CNX NIFTY Mid Cap 50 Index.References
- Alexandra, H., Joldes, C., & Gabriel, D. D. (2019). A cluster analysis of financial performance in central and Eastern Europe, 289–294. Retrieved from https://www.researchgate.net/publication/237262963
- Aghabozorgi, S., & Teh, Y. W. (2014). Stock market comovement assessment using a three-phase clustering method. Expert Systems with Applications, 41(2014), 1301–1314.
- Babu, M. S., Geethanjali, N., & Satyanarayana, B. (2012). Clustering approach to stock market prediction. International Journal of Advanced Networking and Applications, 3(4), 1281–1291.
- Banerjee, R., & Hofmann, B. (2018). The rise of zombie firms: Causes and consequences. BIS Quarterly Review, 67–78.
- Cai, F., Le-Khac, N.-A., & Kechadi, M.-T. (2016). Clustering approaches for financial data analysis: A survey. Retrieved from https://arxiv.org/ftp/arxiv/ papers/1609/1609.08520.pdf
- Costa Jr., N., Cunha, J., & Silva, S. D. (2005). Stock selection based on cluster analysis. Economics Bulletin, 13(1), 1−9.
- Dias, A., Pinto, C., Batista, J., & Neves, E. (2016). Signaling tax evasion, financial ratios & cluster analysis. BIS Quarterly Review. Working Paper No. 51, 2016.
- Ding, K., Hoogduin, L., Peng, X., Vasarhelyi, M. A., & Wang, Y. (n.d.). Clustering based peer selection with financial ratios. Rutgers, The State University of New Jersey. Retrieved from http://raw.rutgers.edu/docs/wcars/40wcars/Presentations/KexingXuan Yunsen.pdf
- Ferrando, A., & Lekpek, S. (2018). Access to finance and innovative activity of EU firms: A cluster analysis. European Investment Bank: Working Papers, 2018/02.
- Ferst, R., & Seres, D. (2012). Clustering austrian banks’ business models and peer groups in the European Banking Sector. Financial Stability Report, 24 December 2012.
- Fodor, A., Jorgensen, Randy, D., & Stowe, J. D. (2015). Forming stock groups with a cluster analysis of common size statements. Southwestern Finance Association Annual Conference.
- Goudarzi, S., Jafari, M. J., & Afsar, A. (2017). A hybrid model for portfolio optimization based on stock clustering and different investment strategies. International Journal of Economics and Financial Issues, 7(3).
- Gruener, A., & Schoenenberger, F. (2015). Risk cluster framework: How to analyse companies by operating leverage. Retrieved from https://efmaefm.org/0efmameetings/efma%20annual%20meetings/2015-Amsterdam/papers/efma2015_0219_ fullpaper.pdf
- Hou, B. (2016). Financial distress prediction of k-means clustering based on genetic algorithm and rough set theory. Chemical Engineering Transactions, 51.
- Lemos, C. A. A., Lins, M. P. E., & Ebecken, N. F. F. (2005). DEA implementation and clustering analysis using the K-Means algorithm. WIT Transactions on Information and Communication Technologies, 35, 321–329.
- Li, H., & Sun, J. (2011). Mining business failure predictive knowledge using two-step clustering. African Journal of Business Management, 5(11), 4107–4120.
- Marvin, K. (2015). Creating diversified portfolios using cluster analysis.
- Momeni, M., Mohseni, M., & Soofi, M. (2015). Clustering stock market companies via K-means algorithm. Arabian Journal of Business and Management Review, 4(5).
- Perisa, A., Kurnoga, N., & Sopta, M. (2017). Multivariate analysis of profitability indicators for selected companies of Croatian market. UTMS Journal of Economics, 8(3), 231–242.
- Setty, D. V., Rangaswamy, T. M., & Suresh, A. V. (2010). Analysis and clustering of nifty companies of share market using data mining tools. Journal of Engineering Research and Studies, 1(1), 152–164.
- Setyaningsih, S. (2012). Using cluster analysis study to examine the successful performance entrepreneur in Indonesia. Procedia Economics and Finance, 4, 286–298.
- Szucs, G. (2015). The financial analysis of the hungarian automotive industry based on profitability and capital structure ratios. Central European Business Review, 4(1).
- Temouri, Y. (2012). The cluster scoreboard: Measuring the performance of local business clusters in the knowledge economy. OECD Local Economic and Employment Development (LEED) Working Papers. 2012/13, OECD Publishing.
- Tufan, E., & Hamarat, B. (2003). Clustering of financial ratios of the quoted companies through fuzzy logic method. Journal of Naval Science and Engineering, 1(2), 123–140.
- Wang, Y.-J., & Lee, H.-S. (2008). A clustering method to identify representative financial ratios. Information Sciences, 178(2008), 1087–1097.
- An Assessment of the Role of National Culture as a Determinant of Entrepreneurial Orientation
Authors
1 Department of Finance, International School of Business and Media, Kolkata, IN
2 Department of Finance, Hazrat Khajar Bashir Unani Ayurvedic Medical College and Hospital Foundation, BD
3 Department of Management Studies, Institute of Innovation in Technology and Management, IN
Source
ICTACT Journal on Management Studies, Vol 6, No 2 (2020), Pagination: 1197-1203Abstract
Entrepreneurship is an important factor of production. It is considered as a source of innovative change. Thus, it catalyzes enhancement in sustainable economic development of a nation. Entrepreneurship is inseparably interlinked with flexibility and knowledge. These two factors have gained importance as a source of competitive edge in the present globalized & interconnected economy. Entrepreneurship prevents concentration of economic activities, income and wealth and promotes decentralized development of commerce, trade and industry. This in turn, leads to removal of regional and industrial imbalance. Development of entrepreneurial activities and sustainable development in entrepreneurship have gained priority in the national agenda across the world. Entrepreneurship is even more crucial for developing countries as it has high employment elasticity and potential for earning foreign exchange. However, entrepreneurship is essentially a behavioural aspect. Hence culture has a causal relationship with entrepreneurship. This paper aims at assessing the role of Hofstede’s dimensions of culture in developing entrepreneurship in nations by using the technique of linear multi-variate regression.Keywords
Entrepreneurship, Hofstede’s Dimensions of National Culture, Linear Multivariate Regression.- A Comparative Study of Intention to Use Agent Banking Vis-a-Vis Traditional Bank Branches in Bangladesh
Authors
1 Senior Lecturer, Faculty of Business Administration, North South University, Dhaka, BD
2 Associate Professor, Faculty of Business Administration, American International University, Dhaka, BD
3 Financial Advisor, Hazrat Khajar Bashir Unani Ayurvedic Medical College & Hospital Foundation, Jamalpur, BD
4 Professor, Institute of Innovation in Technology and Management, GGSIP University, Delhi, IN
Source
Journal of Commerce and Accounting Research, Vol 10, No 1 (2021), Pagination: 33-40Abstract
Information technology has signaled a paradigm shift in the service availability to the customers. Banking services have grown in phenomenal dimensions and agent banking is much to be credited for this increase in market reach. Researchers conducted a review of literature which brought about numerous advantages and challenges of agent banking services. This research paper has used the social exchange theory (SET) as the basis in trying to understand and analyze the case of Bangladesh in identifying the factors relating to adoption of agent banking compared to traditional banking systems. The study is corroborated from data collected at Tongi area of Bangladesh, and seeks to validate the findings with the help of empirical evidence analyzed using SPSS software. The paper serves as an extension to prior literature on intention to use services. The findings can be used as a foundation to design marketing strategies for developing markets with similar demographics.Keywords
Agent Banking, Social Exchange Theory (SET), Banks, Bangladesh.References
- Bangladesh Bank (2017). Guidelines on agent banking for the banks. Retrieved October 19, 2019, from https://www.bb.org.bd/aboutus/regulationguideline/psd/agentbanking_banks_v13.pdf
- Bangladesh Bank. (n.d.). Regulation, policy and licensing. Retrieved October 19, 2019, from https://www.bb.org.bd/fnansys/paymentsys/paysystems.php
- Blau, P. (1964). Exchange, and power in social life. New York: John Wiley & Sons.
- Chang, M. K., Cheung, W., & Lai, V. S. (2005). Literature derived reference models for the adoption of online shopping. Information & Management, 42(4), 543-559.
- Clinton, M. E., & Guest, D. E. (2014). Psychological contract breach and voluntary turnover: Testing a multiple mediation model. Journal of Occupational and Organizational Psychology, 87(1), 200-207.
- Daily Star. (2019). Deposit through agent banking rises 122pc. Retrieved October 19, 2019, from https://www.thedailystar.net/business/banking/news/deposit-through-agent-banking-rises-122pc-1701760
- Dangolani, S. K. (2011). The Impact of information technology in banking system: A case study in Bank Keshavarzi IRAN. Procedia-Social and Behavioral Sciences, 30, 13-16.
- Deci, E. L. (1975). Intrinsic motivation. New York, NY: Plenum Press.
- Dhir, S., Aniruddha, & Mital, A. (2014). Alliance network heterogeneity, absorptive capacity and innovation performance: A framework for mediation and moderation effects. International Journal of Strategic Business Alliances, 3(2-3), 168-178.
- Emerson, R. M. (1962). Power-dependence relations. American Sociological Review, 27(1), 31-41.
- Fishbein, M., & Ajzen, I. (1975). Belief, attitude. Intention and Behavior: An Introduction to Theory and Research.
- Flaherty, K. E., & Pappas, J. M. (2009). Expanding the sales professional’s role: A strategic re-orientation? Industrial Marketing Management, 38(7), 806-813.
- Fu, F. Q., Bolander, W., & Jones, E. (2009). Managing the drivers of organizational commitment and salesperson effort: An application of Meyer and Allen’s three-component model. Journal of Marketing Theory and Practice, 17(4), 335-350.
- Grimmer, M., & Oddy, M. (2007). Violation of the psychological contract: The mediating effect of relational versus transactional beliefs. Australian Journal of Management, 32(1), 153-175.
- Helm, S. (2007). The role of corporate reputation in determining investor satisfaction, and loyalty. Corporate Reputation Review, 10(1), 22-37.
- Helm, S. (2011). Employees’ awareness of their impact on corporate reputation. Journal of Business Research, 64(7), 657-663.
- Homans, G. (1958). Social behavior as exchange. American Journal of Sociology, 63(6), 597-606.
- Hsu, C., & Lin, J. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence, and knowledge sharing motivation. Information & Management, 45(1), 65-74.
- Kankanhalli, A., Tan, B., & Wei, K. (2005). Contributing knowledge to electronic knowledge repositories: An empirical investigation. MIS Quarterly, 29(1), 113-143.
- Klapper L., & Singer, D. (2014). The opportunities of digitizing payments: How digitization of payments, transfers, and remittances contributes to the G20 goals of broad-based economic growth, financial inclusion, and women’s economic empowerment. Report by the World Bank Development Research Group, the Better Than Cash Alliance, and the Bill & Melinda Gates Foundation to the G20 Global Partnership for Financial Inclusion. Retrieved from http://www.gpfi.org/sites/default/files/documents/FINAL_The%20Opportunities%20of%20Digitizing%20Payments.pdf
- Kumar Behera, A., Nayak, N. C., Das, H. C., & Mohapatra, R. N. (2015). An empirical study of the impact of IT on performance in Indian service industries. Global Business and Organizational Excellence, 34(3), 67-78.
- Lewis, B. R. (1989). Quality in the service sector: A review. International Journal of Bank Marketing, 7(5), 4-12.
- Mas, I. (2008). Realizing the potential of branchless banking: Challenges ahead. CGAP Focus Note, (50).
- Mas, I., & Siediek, H. (2008). Banking through networks of retail agents. CGAP Focus Note, (47).
- Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709-734.
- Metzger, M. J. (2004). Privacy, trust, and disclosure: Exploring barriers to electronic commerce. Journal of Computer-Mediated Communication, 9(4), JCMC942.
- Mungai, E. (2016). Factors influencing the adoption of agent banking by commercial banks in Kenya (Doctoral dissertation, United States International University-Africa).
- Pappas, J. M., & Flaherty, K. E. (2008). The effect of trust on customer contact personnel strategic behavior and sales performance in a service environment. Journal of Business Research, 61(9), 894-902.
- Parameswar, N., Dhir, S., & Dhir, S. (2017). Banking on innovation, innovation in banking at ICICI bank. Global Business and Organizational Excellence, 36(2), 6-16.
- Pervan, S. J., Bove, L. L., & Johnson, L. W. (2009). Reciprocity as a key stabilizing norm of interpersonal marketing relationships: Scale development and validation. Industrial Marketing Management, 38(1), 60-70.
- Salam, A., Rao, R., & Pegels, C. (1998). An investigation of consumer-perceived risk on electronic commerce transactions: The role of institutional trust and economic incentive in a social exchange framework. AMCIS 1998 Proceedings, 114.
- Shankaran, S., & Roy, A. (2009). RBI permits corporations to work as rural agents of banks. Wall Street Journal.
- Shimp, T. A., & Kavas, A. (1984). The theory of reasoned action a lied to coupon usage. Journal of Consumer Research, 11(3), 795-809.
- Tamjidyamcholo, A., Baba, M. S. B., Tamjid, H., & Gholipour, R. (2013). Information security - Professional perceptions of knowledge-sharing intention under self-efficacy, trust, reciprocity, and shared-language. Computers & Education, 68, 223-232.
- Villasenor, J., West, D., & Lewis, R. (2015). The 2015 Brookings Financial and Digital Inclusion Project Report.
- Wairi, D. (2011). Factors influencing the adoption of agent banking innovation among commercial banks in Kenya (Unpublished MBA research project). University of Nairobi, Kenya.
- Wasko, M. M., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly, 29(1), 35-57.
- Young-Ybarra, C., & Wiersema, M. (1999). Strategic flexibility in information technology alliances: The influence of transaction cost economics and social exchange theory. Organization Science, 10(4), 439-459.
- Asymmetric Volatility and Volatility Spillover: A Study of Major Cryptocurrencies
Authors
1 Associate Professor, Globsyn Business School, Kolkata, West Bengal,, IN
2 Professor, Department of Commerce, The University of Burdwan, Golapbag, Burdwan, West Bengal, IN
3 Financial Advisor, Hazrat Khajar Bashir Unani Ayurvedic Medical College & Hospital Foundation, Jamalpur, BD
Source
Journal of Commerce and Accounting Research, Vol 11, No 1 (2022), Pagination: 69-86Abstract
Cryptocurrencies have recently emerged as a popular asset class, with investors having high risk appetite and speculative attributes. They are not backed by physical assets, such as commodities or real currencies; they are purely speculative assets having high volatility. Regulatory authorities across the globe have conflicting rules regarding cryptocurrencies. Recent studies on volatility of cryptocurrencies have primarily addressed univariate volatility analysis and volatility spillover between cryptocurrencies and other asset classes, mostly stocks and commodities. This study has three objectives. Firstly, it considers six prominent cryptocurrencies, i.e., Bitcoin, Ethereum, Binance Coin, Cardano, Tether, and Ripple, and examines the nature of asymmetrical volatility in them using EGARCH and TGARCH techniques. Secondly, it examines whether there are volatility spillovers between the cryptocurrencies as well as from one of the most popular global fear indices, i.e., CBOE volatility index, using dynamic conditional correlation (DCC). Thirdly, it further measures the total and directional volatility spillover among the cryptocurrencies using the Diebold-Yilmaz index. This study has found that Ethereum and Ripple may be used to construct a portfolio. There exists long-term volatility spillover among all the cryptocurrencies; however, there is no short-term spillover of volatility. Volatility of Binance Coin, Cardano, and Ripple influence and are influenced the most by volatilities of other cryptocurrencies.Keywords
Cryptocurrency, Volatility Spillover, EGARCH, TGARCH, Dynamic Conditional Correlation (DCC), Diebold-Yilmaz IndexReferences
- Abakah, E. J. A., Gil-Alana, L. A., Madigu, G., & Romero-Rojo, F. (2020). Volatility persistence in cryptocurrency markets under structural breaks. International Review of Economics & Finance, 69, 680-691. doi:https://doi.org/10.1016/j.iref.2020.06.035
- Ali, Ghulam. (2013). EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH Models for Pathogens at Marine Recreational Sites. Journal of Statistical and Econometric Methods, 2(3), 57-73.
- Allen, D., McAleer, M., Powell, R., & Singh, A. (2017). Volatility spillover and multivariate volatility impulse response analysis of GFC news events. Proceedings of the 2017 International Conference on Economics, Finance and Statistics (ICEFS 2017). doi:https://doi.org/10.2991/icefs-17.2017.9
- Baur, D. G., & Dimpfl, T. (2018). Asymmetric volatility in cryptocurrencies. Economics Letters, 173, 148-151. doi:https//doi.org/10.1016/j.econlet.2018.10.008
- Bigmore, R. (2018). A decade of cryptocurrency: From bitcoin to mining chip. Retrieved July 16, 2021, from https://www.telegraph.co.uk/technology/digital-money/the-history-of-cryptocurrency/
- Bouri, E., Das, M., Gupta, R., & Roubaud, D. (2018). Spillovers between Bitcoin and other assets during bear and bull markets. Applied Economics, 50(55), 5935-5949. doi:https//doi.org/10.1080/00036846.2018.1488075
- Carnero, M. A., Pena, D., & Ruiz, E. (2004). Persistence of Kurtosis in GARCH and stochastic volatility models. doi:10.1093/JJFINEC/NBH012. Retrieved September 3, 2021, from https://core.ac.uk/download/pdf/29429136.pdf
- Dangi, V. (2020). Volatility dynamics of cryptocurrencies’ returns: An econometric study. IUP Journal of Applied Finance, 26(1), 5-30.
- Diebold, F., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. Economic Journal, 119(534), 158-171.
- Diebold, F., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57-66.
- Engle, Robert F., & Ng, Victor K. (1993). Measuring and testing the impact of news on volatility. The Journal of Finance, 48(5), 1749-1778.
- ERTUĞRUL, H. M. (2019). Kripto Paralarin Volatilite Dinamiklerinin İncelenmesi: Garch Modelleri Üzerine Bir Uygulama. Journal of Management & Economics Research, 17(4), 59-71. doi:https//doi.org/10.11611/yead.555713
- Fasanya, I. O., Oyewole, O., & Odudu, T. (2021). Returns and volatility spillovers among cryptocurrency portfolios. International Journal of Managerial Finance, 17(2), 327-341. doi:https//doi.org/10.1108/IJMF-02-2019-0074
- Field, A. (2000). Discovering statistics using SPSS for windows. London-Thousand Oaks-New Delhi: Sage publications.
- Field, A. (2009). Discovering statistics using SPSS. London: SAGE.
- Ftiti, Z., Louhichi, W., & Ben Ameur, H. (2021). Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak? Annals of Operations Research, 1-26. doi:https://doi.org/10.1007/s10479-021-04116-x
- George, D., & Mallery, M. (2010). SPSS for windows step by step: A simple guide and reference, 17.0 update (10a ed.). Boston: Pearson.
- Gradojevic, N., & Tsiakas, I. (2021). Volatility cascades in cryptocurrency trading. Journal of Empirical Finance, 62, 252-265. doi:https://doi.org/10.1016/j.jempfin.2021.04.005
- Gravetter, F., & Wallnau, L. (2014). Essentials of statistics for the behavioral sciences (8th ed.). Belmont, CA: Wadsworth.
- Hsu, S. H., Sheu, C., & Yoon, J. (2021). Risk spillovers between cryptocurrencies and traditional currencies and gold under different global economic conditions. North American Journal of Economics & Finance, 57(C). doi:https://doi.org/10.1016/j.najef.2021.101443
- Huang, J.-Z., Huang, Z. J., & Xu, L. (2021). Sequential Learning of cryptocurrency volatility dynamics: Evidence based on a stochastic volatility model with jumps in returns and volatility. Quarterly Journal of Finance, 11(2), 1-37. doi:https://doi.org/10.1142/S2010139221500105
- Jimoh, S. O., & Benjamin, O. O. (2020). The effect of cryptocurrency returns volatility on stock prices and exchange rate returns volatility in Nigeria. Acta Universitatis Danubius: Oeconomica, 16(3), 200-213.
- Katsiampa, P., Corbet, S., & Lucey, B. (2019). High frequency volatility co-movements in cryptocurrency markets. Journal of International Financial Markets, Institutions & Money, 62, 35-52. doi:https://doi.org/10.1016/j.intfin.2019.05.003
- Kayral, İ. E. (2020). En Yüksek Piyasa Değerine Sahip Üç Kripto Paranin Volatilitelerinin Tahmin Edilmesi. Journal of Financial Researches & Studies / Finansal Arastirmalar ve Calismalar Dergisi, 11(22), 152-168. doi:https://doi.org/10.14784/marufacd.688447
- Koutmos, D. (2018). Return and volatility spillovers among cryptocurrencies. Economics Letters, 173, 122-127. doi:https://doi.org/10.1016/j.econlet.2018.10.004
- Lobato, I., & Velasco, C. (2004). A simple test of normality for time series. Econometric Theory, 20, 671-689. doi:10.1017/S026646664204030
- Lundbergh, S., & Terasvirta, T. (2002). Evaluating GARCH models. Journal of Econometrics, 110, 417-435.
- Ma, F., Liang, C., Ma, Y., & Wahab, M. I. M. (2020). Cryptocurrency volatility forecasting: A Markov regime-switching MIDAS approach. Journal of Forecasting, 39(8), 1277-1290. doi:https://doi.org/10.1002/for.2691
- Malladi, R. K., & Dheeriya, P. L. (2021). Time series analysis of cryptocurrency returns and volatilities. Journal of Economics & Finance, 45(1), 75-94. doi:https://doi.org/10.1007/s12197-020-09526-4
- Palamalai, S., & Maity, B. (2019). Return and volatility spillover effects in leading cryptocurrencies. Global Economy Journal, 19(3), 1-20. doi:https://doi.org/10.1142/S2194565919500179
- Sensoy, A., Silva, T. C., Corbet, S., & Tabak, B. M. (2021). High-frequency return and volatility spillovers among cryptocurrencies. Applied Economics, 53(37), 4310-4328. doi:https://doi.org/10.1080/00036846.2021.1899119
- Siu, T. K. (2021). The risks of cryptocurrencies with long memory in volatility, non-normality and behavioural insights. Applied Economics, 53(17), 1991-2014. doi:https://doi.org/10.1080/00036846.2020.1854669
- Trentina, K., & Schmidt, J. (2021, August). Top 10 cryptocurrencies. Retrieved August 20, 2021, from https://forbes.com/advisors/investing/top-10-cryptocurrencies
- Trochim, W. M., & Donnelly, J. P. (2006). The research methods knowledge base (3rd ed.). Cincinnati, OH: Atomic Dog.
- Urbina, J. (2013). Financial spillovers across countries: Measuring shock transmissions. Munich Personal RePEc Archive Paper No. 75756. Retrieved from https://mpra.ub.uni-muenchen.de/75756/
- Xiao, X. & Huang, J. (2018). Dynamic connectedness of international crude oil prices: The Diebold-Yilmaz approach. Sustainability, 10, 3298. doi:https://doi.org/10.3390/su10093298
- Yaya, O. S., Ogbonna, A. E., Mudida, R., & Abu, N. (2021). Market efficiency and volatility persistence of cryptocurrency during pre- and post-crash periods of Bitcoin: Evidence based on fractional integration. International Journal of Finance & Economics, 26(1), 1318-1335. doi:https://doi.org/10.1002/ijfe.1851
- Yi, S., Xu, Z., & Wang, G.-J. (2018). Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency? International Review of Financial Analysis, 60, 98-114. doi:https://doi.org/10.1016/j.irfa.2018.08.012
- Yin, L., Nie, J., & Han, L. (2021). Understanding cryptocurrency volatility: The role of oil market shocks. International Review of Economics & Finance, 72, 233-253. doi:https://doi.org/10.1016/j.iref.2020.11.013
- Addressing the Investors Dilemma Using Pairs Trading - Co-Integrational Study of Indian Stocks
Authors
1 Department of Finance, International School of Business and Media, IN
2 Department of Finance, Hazrat Khajar Bashir Unani Ayurvedic Medical College and Hospital Foundation, BD
3 Department of Management, Institute of Innovation in Technology and Management, IN
Source
ICTACT Journal on Management Studies, Vol 7, No 2 (2021), Pagination: 1382-1387Abstract
The increasing volatility in stock, commodities and foreign exchange markets compel investors and scholars to look for strategies which would immunize the investors against the unprecedented movement of the markets. Investors are often at dilemma to take correct positions to offset the risks in the market. This effort to offset market risk led to discovery of several market-neutral investment strategies of which a very popular one is Pairs Trading. It essentially involves taking opposite positions in two highly correlated assets. This study is on identifying pairs of stocks in the National Stock Exchange (NSE) which are suitable for pairs trading. The method of cointegration, both in long and short run, have been utilized in this study. Related statistical concepts of autocorrelation and stationarity have also been used in the study.Keywords
Pairs Trading, NSE, Cointegration, Autocorrelation, StationarityReferences
- Bernhard Pfaff, “VAR, SVAR and SVEC Models: Implementation within R Package Vars”, Journal of Statistical Software, Vol. 27, No. 4, pp. 1-13, 2008.
- Do Binh Do, &Robert Faff, “Does Simple Pairs Trading Still Work?”, Financial Analysts Journal, Vol. 66, No. 4, pp. 83-95, 2018.
- Christian L. Dunis, Jason Laws and Adam Shone, “Cointegration-Based Optimisation of Currency Portfolios”, Journal of Derivatives and Hedge Funds, Vol. 17, No. 2, pp. 86-117, 2011.
- Evren Bolgun, Engin Kurun and Serhat Guven, “Dynamic Pairs Trading Strategy for the Companies Listed in the Istanbul Stock Exchange”, Munich Personal RePEc Archive, Vol. 23, No. 3, pp. 1-15, 2009.
- R.S. Tsay, “Analysis of Financial Time Series”, Wiley, 2005.
- Franco Ho Ting Lin, “Dynamic Asset Allocation for Pairs Trading”, Available at https://francohtlin.github.io, Accessed at 2018.
- Jan Broel Plater and Khurram Nisar, “A Wider Perspective on Pairs Trading, A Trading Application with Non-Equity Assets”, Master Thesis, Department of Economics, School of Economics and Management Lund University, pp. 1-145, 2010.
- João F. Caldeira and Guilherme V. Moura, “Selection of a Portfolio of Pairs Based on Cointegration: A Statistical Arbitrage Strategy”, SSRN, Vol. 12, No. 2, pp. 1-13, 2013.
- Jose Balarezo, “International Diversification Using Cointegration and Modern Portfolio Theory”, Master Thesis, Department of Management Studies, Copenhagen Business School, pp. 1-145, 2010.
- Markus Harlacher, “Cointegration Based Algorithmic Pairs Trading”, Ph.D. Dissertation, School of Management, University of St. Gallen, pp. 1-207, 2016.
- B. Pfaff, “Analysis of Integrated and Cointegrated Time Series with R”, 2nd Edition, Springer, 2008.
- Ravi Bansa and Dana Kiku, “Cointegration and Long-Run Asset Allocation”, Journal of Business and Economic Statistics, Vol. 29, No. 1, pp. 1-11, 2011.
- RStudio Team, “RStudio: Integrated Development for R. RStudio”, Available at http://www.rstudio.com/, Accessed at 2005.
- Samar Habibi and Kamran Pakizeh, “Profitability of the Pair Trading Strategy across Different Asset Classes”, International Research Journal of Finance and Economics, Vol. 13, No. 2, pp. 1-17, 2017.