Optimizing Portfolios with Machine Learning: A Bibliometric Analysis Using VOS Viewer

Syeda Fizza Abbas; Muhammad Usman

doi:10.69565/jems.v3i3.338

Authors

Syeda Fizza Abbas University of the Punjab
Muhammad Usman Hailey College of Commerce, University of the Punjab

DOI:

https://doi.org/10.69565/jems.v3i3.338

Keywords:

Machine learning, portfolio optimization, cryptocurrencies, traditional assets, bibliometric analysis

Abstract

This research aims to underscore the application of machine learning in achieving an optimal portfolio that incorporates both cryptocurrencies and traditional assets by mapping and analyzing the academic literature through bibliometric analysis. This study encompasses the Preferred Reporting and Items for Systematic Reviews and Meta-Analysis (PRISMA) Framework to exclude duplicate, missing information, and irrelevant studies. After a rigorous analysis based on inclusion/exclusion criteria, the bibliometric analysis, facilitated by VOSviewer, encompasses Performance Analysis and Science Mapping. The former involves the publication and citation-related metrics, while the latter incorporates co-authorship and co-occurrence analyses. The study offers the probability to improve the efficiency of a portfolio with the help of machine learning with a focus on cryptocurrencies. The value of this study lies in emphasizing publication growth, collaborative efforts, and influential keywords in the connection between machine learning, portfolio optimization, cryptocurrencies, and traditional assets. An emerging concern in this field is evident, yet very few studies of bibliometric analysis could be found. This study provides an evaluation of recent findings and emerging trends. Being an efficient method used to summarize literature; it also has some limitations as well. Thus, the study highlights the areas for scholars and professionals in further research based on the identified research gaps.

References

Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of informetrics, 5(4), 594-607.

Adams, J. D., Black, G. C., Clemmons, J. R., & Stephan, P. E. (2005). Scientific teams and institutional collaborations: Evidence from US universities, 1981–1999. Research policy, 34(3), 259-285.

Alam, K. S., Kaif, A. D., & Das, S. K. (2024). A blockchain-based optimal peer-to-peer energy trading framework for decentralized energy management with in a virtual power plant: Lab scale studies and large scale proposal. Applied Energy, 365, 123243.

Almeida, J., & Gonçalves, T. C. (2022). Portfolio diversification, hedge and safe-haven properties in cryptocurrency investments and financial economics: a systematic literature review. Journal of Risk and Financial Management, 16(1), 3.

Bozeman, B., Fay, D., & Slade, C. P. (2013). Research collaboration in universities and academic entrepreneurship: the-state-of-the-art. The journal of technology transfer, 38(1), 1-67.

Carbone, F., Contreras, J., Hernández, J. Z., & Gomez-Perez, J. M. (2012). Open Innovation in an Enterprise 3.0 framework: Three case studies. Expert Systems with Applications, 39(10), 8929-8939.

Chaweewanchon, A., & Chaysiri, R. J. I. J. o. F. S. (2022). Markowitz mean-variance portfolio optimization with predictive stock selection using machine learning. 10(3), 64.

Cui, B., Sun, R., & Su, J. (2022). A Novel Deep Reinforcement Learning Strategy in Financial Portfolio Management. 2022 7th International Conference on Big Data Analytics (ICBDA),

Ding, Y., Chowdhury, G. G., Foo, S. J. I. p., & management. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. 37(6), 817-842.

Dong, Z., Huang, S., Ma, S., & Qian, Y. J. a. p. a. (2021). Factor Representation and Decision Making in Stock Markets Using Deep Reinforcement Learning.

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296.

Economics, F. Machine Learning and Data Sciences for Financial Markets.

Fan, J., Peng, S. J. C. I., & Neuroscience. (2022). Financial stock investment management using deep learning algorithm in the Internet of Things. 2022(1), 4514300.

Fang, F., & Polukarov, M. (2023). Novel Machine Learning Pipelines with Applications to Finance King’s College London].

Ferreira, A., & Sandner, P. (2021). Eu search for regulatory answers to crypto assets and their place in the financial markets’ infrastructure. Computer Law & Security Review, 43, 105632.

Garg, G., Shamshad, M., Gauhar, N., Tabash, M. I., Hamouri, B., & Daniel, L. N. J. I. J. o. F. S. (2023). A bibliometric analysis of fintech trends: An empirical investigation. 11(2), 79.

Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577.

Goyal, S., Chauhan, S., & Mishra, P. (2021). Circular economy research: A bibliometric analysis (2000–2019) and future research insights. Journal of cleaner production, 287, 125011.

Guo, Y.-M., Huang, Z.-L., Guo, J., Li, H., Guo, X.-R., & Nkeli, M. J. J. S. (2019). Bibliometric analysis on smart cities research. 11(13), 3606.

Habib Agahi, M. R., Kermani, M. A. M. A., & Maghsoudi, M. (2022). On the Co-authorship network analysis in the Process Mining research Community: A social network analysis perspective. Expert Systems with Applications, 206, 117853.

Hairudin, A., Sifat, I. M., Mohamad, A., & Yusof, Y. (2022). Cryptocurrencies: A survey on acceptance, governance and market dynamics. International Journal of Finance & Economics, 27(4), 4633-4659.

Hali, N. A., & Yuliati, A. (2020). Markowitz model investment portfolio optimization: a review theory. International Journal of Research in Community Services, 1(3), 14-18.

Hou, H., Kretschmer, H., & Liu, Z. (2008). The structure of scientific collaboration networks in Scientometrics. Scientometrics, 75, 189-202.

Huynh, T. L. D., Hille, E., & Nasir, M. A. (2020). Diversification in the age of the 4th industrial revolution: The role of artificial intelligence, green bonds and cryptocurrencies. Technological Forecasting and Social Change, 159, 120188.

Jason, E. K. J. N. P. S., Monterey, California, USA. (1998). Forecasting financial markets using neural networks: An analysis of methods and accuracy.

Jun, S.-P., Yoo, H. S., & Hwang, J. (2021). A hybrid recommendation model for successful R&D collaboration: Mixing machine learning and discriminant analysis. Technological Forecasting and Social Change, 170, 120871.

Kapsis, I. (2020). Blockchain and cryptocurrencies: essential tools in a two-tier financial system. Capital Markets Law Journal, 15(1), 18-47.

Kemeç, A., & Altınay, A. T. (2023). Sustainable energy research trend: A bibliometric analysis using VOSviewer, RStudio bibliometrix, and CiteSpace software tools. Sustainability, 15(4), 3618.

Khurana, K. J. J. o. E. T. S. (2023). A Bibliometric Study of Publications on Adoption of FinTech Using VOSviewer. 51(4), 503-512.

Koseoglu, M. A. (2016). Growth and structure of authorship and co-authorship network in the strategic management realm: Evidence from the Strategic Management Journal. BRQ Business Research Quarterly, 19(3), 153-170.

Kovásznai, G., Gajdár, K., & Narodytska, N. (2021). Portfolio solver for verifying binarized neural networks. Annales Mathematicae et Informaticae,

Landherr, A., Friedl, B., & Heidemann, J. (2010). A critical review of centrality measures in social networks. Business & Information Systems Engineering, 2, 371-385.

Liesiö, J., Salo, A., Keisler, J. M., & Morton, A. (2021). Portfolio decision analysis: Recent developments and future prospects. European journal of operational research, 293(3), 811-825.

Lim, W. M., Kumar, S. J. G. B., & Excellence, O. (2024). Guidelines for interpreting the results of bibliometric analysis: A sensemaking approach. 43(2), 17-26.

Luo, D., Liao, W., & Yan, R. (2023). Lower Risks, Better Choices: Stock Correlation Based Portfolio Selection in Stock Markets. Companion Proceedings of the ACM Web Conference 2023,

Majumder, S., Biswas, D. J. I. J. o. I. S., & Management, S. C. (2023). A Bibliometric and Co-Occurrence Analysis of Work-Life Balance: Related Literature Published Pre-and During COVID-19 Pandemic. 16(1).

Maknickienė, N., & Sabaliauskas, D. (2019). Investment portfolio analysis by using neural networks. International Scientific Conference „Contemporary Issues in Business, Management and Economics Engineering",

Marchesi, C. (2021). THE IMPACT OF DIGITAL REVOLUTION ON ENTREPRENEURSHIP AND FINANCIAL SERVICES.

Markowitz, H. M. (2009). Harry Markowitz: selected works (Vol. 1). World Scientific.

Mashrur, A., Luo, W., Zaidi, N. A., & Robles-Kelly, A. (2020). Machine learning for financial risk management: a survey. IEEE Access, 8, 203203-203223.

Mejia, C., Wu, M., Zhang, Y., & Kajikawa, Y. (2021). Exploring topics in bibliometric research through citation networks and semantic analysis. Frontiers in Research Metrics and Analytics, 6, 742311.

Mirete-Ferrer, P. M., Garcia-Garcia, A., Baixauli-Soler, J. S., & Prats, M. A. (2022). A Review on Machine Learning for Asset Management. Risks, 10(4), 84.

Mosavi, A., Faghan, Y., Ghamisi, P., Duan, P., Ardabili, S. F., Salwana, E., & Band, S. S. (2020). Comprehensive review of deep reinforcement learning methods and applications in economics. Mathematics, 8(10), 1640.

Mundi, H. S., & Kumar, D. (2023). The potential of alternative investments as an asset class: a thematic and bibliometric review. Qualitative Research in Financial Markets, 15(1), 119-141.

Newman, M. E. (2001). The structure of scientific collaboration networks. Proceedings of the national academy of sciences, 98(2), 404-409.

Palmatier, R. W., Houston, M. B., & Hulland, J. J. J. o. t. A. o. M. S. (2018). Review articles: Purpose, process, and structure. In (Vol. 46, pp. 1-5): Springer.

Puaschunder, J. M. (2023). Resilient Finance in Responsible Investment. In The Future of Resilient Finance: Finance Politics in the Age of Sustainable Development (pp. 55-102). Springer.

Qudah, H., Malahim, S., Airout, R., Alomari, M., Hamour, A. A., & Alqudah, M. (2023). Islamic finance in the era of financial technology: A bibliometric review of future trends. International Journal of Financial Studies, 11(2), 76.

Raban, D. R., & Gordon, A. (2020). The evolution of data science and big data research: A bibliometric analysis. Scientometrics, 122(3), 1563-1581.

Rouf, N., Malik, M. B., Arif, T., Sharma, S., Singh, S., Aich, S., & Kim, H.-C. (2021). Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions. Electronics, 10(21), 2717.

Sabry, F., Labda, W., Erbad, A., & Malluhi, Q. (2020). Cryptocurrencies and artificial intelligence: Challenges and opportunities. IEEE Access, 8, 175840-175858.

Seabe, P. L., Moutsinga, C. R. B., & Pindza, E. (2024). Optimizing Cryptocurrency Returns: A Quantitative Study on Factor-Based Investing. Mathematics, 12(9), 1351.

Sen, J. (2023). Portfolio optimization using reinforcement learning and hierarchical risk parity approach. In Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications (pp. 509-554). Springer.

Shah, J., Vaidya, D., & Shah, M. (2022). A comprehensive review on multiple hybrid deep learning approaches for stock prediction. Intelligent Systems with Applications, 16, 200111.

Shemahonge, A. H., Mosha, G. E., & Siyao, P. (2022). A 14-Years Mapping of Research Productivity of the School of Business Academicians at Mzumbe University, Tanzania: A Bibliometrics Analysis.

Simar, J. (2023). Enhancing estimation of expected returns in modern portfolio theory through machine learning.

Singh, V. K., Singh, P., Karmakar, M., Leta, J., & Mayr, P. J. S. (2021). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. 126, 5113-5142.

Snow, D. (2020). Machine learning in asset management—Part 1: Portfolio construction—Trading strategies. The Journal of Financial Data Science, 2(1), 10-23.

Sutiene, K., Schwendner, P., Sipos, C., Lorenzo, L., Mirchev, M., Lameski, P., . . . Cerneviciene, J. (2024). Enhancing portfolio management using artificial intelligence: literature review. Frontiers in Artificial Intelligence, 7, 1371502.

Tepe, G., Geyikci, U. B., & Sancak, F. M. (2021). FinTech companies: a bibliometric analysis. International Journal of Financial Studies, 10(1), 2.

Ullah, M., Shahid, A., Din, I. U., Roman, M., Assam, M., Fayaz, M., . . . Aljuaid, H. J. C. (2022). Analyzing Interdisciplinary Research Using Co‐Authorship Networks. 2022(1), 2524491.

Verbeeck, N., Caprioli, R. M., & Van de Plas, R. (2020). Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry. Mass spectrometry reviews, 39(3), 245-291.

Wales, W. J., Kraus, S., Filser, M., Stöckmann, C., & Covin, J. G. (2021). The status quo of research on entrepreneurial orientation: Conversational landmarks and theoretical scaffolding. Journal of Business Research, 128, 564-577.

Young, D. G., & Bunting, B. D. (2024). Rethinking College Transitions: Legitimate Peripheral Participation as a Pathway to Becoming. AERA Open, 10, 23328584241255631.

Zhao, T., Ma, X., Li, X., & Zhang, C. J. E. S. w. A. (2023). Asset correlation based deep reinforcement learning for the portfolio selection. 221, 119707.