THE ROLE OF STOCKBROKERS IN THE DEVELOPMENT OF THE STOCK MARKET AND THE PROMOTION OF GLOBAL INVESTMENT

Serhii Zadvornykh Private Higher Education Institution “Rauf Ablyazov East European University”, 16, Nechui-Levytskyi Str., Cherkasy, 18036, Ukraine https://orcid.org/0000-0002-7025-4116

Keywords: Stockbrokers, Stock Market, Financial Analysts, Investments, Financial Intermediaries

Abstract

This study explores the evolving role of stockbrokers in the context of global financial market transformation and the rapid expansion of digital technologies. We examine how the traditional function of brokers as mere transaction intermediaries has shifted toward more complex roles, including analytical support, strategic advisory, and information intermediation, particularly in cross-border investment contexts. We analyze empirical data from diverse regulatory environments – including the United States, Canada, the European Union, Hong Kong, Japan, and South Africa – highlighting a global trend: while the number of brokerage firms is declining, the number of individual brokers is increasing, and so is the demand for analytical services. We argue that in regions where financial information is fragmented, regulatory frameworks are underdeveloped, or linguistic and cultural barriers impede investor decisions, brokers act as crucial facilitators of market transparency and information access. Our findings show that brokers, unlike financial analysts, are often better positioned to interpret local business conditions, communicate context-specific insights, and reduce informational asymmetries that discourage foreign capital inflows. This is especially significant for emerging and frontier markets. We further evaluate the challenges that brokers face as they assume analytical roles. These include the necessity of mastering digital tools, maintaining objectivity in financial reporting, and enhancing cybersecurity practices. Based on a mixed-methods approach that integrates content analysis, comparative market review, and basic statistical correlation, we have built a nuanced understanding of how the broker profession is adapting to fintech disruption and regulatory evolution. Our results suggest that brokers who successfully integrate traditional brokerage services with analytical competencies can enhance the quality of investment decisions, foster trust among international investors. We also analyzed the differences between the activities of classical financial analysts and stockbrokers engaged in analytical activities, as well as the gaps that the latter can fill, thereby improving the availability of necessary information for global investors.

Downloads

Download data is not yet available.

Author Biography

Serhii Zadvornykh, Private Higher Education Institution “Rauf Ablyazov East European University”, 16, Nechui-Levytskyi Str., Cherkasy, 18036, Ukraine

PhD (Economics), Associate Professor

References

Bhuiyan, MD S. M. et al. (2025). Deep learning for algorithmic trading: A systematic review of predictive models and optimization strategies. Array, 26, 100390. doi: https://doi.org/10.1016/j.array.2025.100390

Roy, G., Fiaidhi, J., & Mohammed, S. (2022). Multi-Timeframe Algorithmic Trading Bots Using Thick Data Heuristics with Deep Reinforcement Learning. Artificial Intelligence Evolution, 3(2), 107–159. doi: https://doi.org/10.37256/aie.3220221722

Adegboye, A., Kampouridis, M., & Otero, F. E. B. (2022). Algorithmic trading with directional changes. Artificial Intelligence Review, 56(6), 5619-5644. doi: https://doi.org/10.1007/s10462-022-10307-0

Frattini, A., Bianchini, I., Garzonio, A., & Mercuri, L. (2022). Financial Technical Indicator and Algorithmic Trading Strategy Based on Machine Learning and Alternative Data. Risks, 10(12), 225. doi: https://doi.org/10.3390/risks10120225

Jeong, Y.-C., Yu, J., and Ryu, W. (2024). Connecting Cross-Border Market Participants: The Intermediary Role of International Analysts in Global Capital Markets. Journal of Management Studies, 61(6), 2535-2569. doi: https://doi.org/10.1111/joms.12988

Khanna, T., Palepu, K. G., & Sinha, J. (2025). Strategies that fit emerging markets. Harvard Business Review, 83, 63-76.

Döring, S. et al. (2021). Institutional investment horizons and firm valuation around the world. Journal of International Business Studies, 52, 212-244. doi: 10.1057/s41267-020-00351-9

Lundholm, R., Rahman, N., & Rogo, R. (2017). The foreign investor bias and its linguistic origins. Management Science, 64, 4433-4450. doi: https://doi.org/10.1287/mnsc.2017.2812

Busenbark, J. R., Semadeni, M., Arrfelt, M., & Withers, M. C. (2022). Corporate-level influences on internal capital allocation: the role of financial analyst performance projections. Strategic Management Journal, 43, 180-209. doi: https://doi.org/10.1002/smj.3331

Sharkey, A., Kovács, B., & Hsu, G. (2023). Expert critics, rankings, and review aggregators: the changing nature of intermediation and the rise of markets with multiple intermediaries. Academy of Management Annals, 17, 1-36. doi: https://doi.org/10.5465/annals.2021.0025

Havrysh, О. М., & Fadiulin, S. А. (2020). The role of a broker in trading on the stock exchange. Scientific economic journal “Intellect XXI”, 2, 50-54. doi: https://doi.org/10.32782/2415-8801/2020-2.9 (in Ukrainian)

Kovaliuk, А. О., & Ulych, О. М. (2024). Foreign experience of stock markets functioning and their impact on the economy. Scientific Bulletin of Odesa National Economic University, 5-6 (318-319), 58-68. DOI: doi: https://doi.org/10.32680/2409-9260-2024-5-6-318-319-58-68 (in Ukrainian)

Krupka, А., & Pyrih, H. (2023). Stock exchanges in the development of the country’s financial sector: theoretical and applied aspects. Economic Analysis, 33(4), 156-163. doi: https://doi.org/10.35774/econa2023.04.156 (in Ukrainian)

Cao, Z. et al. (2022). Optimizing Stock Option Forecasting with the Assembly of Machine Learning Models and Improved Trading Strategies. arXiv.Org, abs/2211.15912. doi: https://doi.org/10.48550/arXiv.2211.15912

Mahfooz, S. Z., Ali, I., & Khan, M. N. (2022). Improving Stock Trend Prediction using LSTM Neural Network Trained on a Complex Trading Strategy. International Journal For Science Technology And Engineering, 10(7), 4361-4371. doi: https://doi.org/10.22214/ijraset.2022.45961

Majidi, N., Shamsi, M., & Marvasti, F. (2022). Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning. Social Science Research Network, abs/2210.03469. doi: https://doi.org/10.48550/arXiv.2210.03469

Medarhri, I. et al. (2022). Predicting Stock Market Price Movement using Machine Learning Techniques. 2022 8th International Conference on Optimization and Applications (ICOA), Genoa, Italy (pp. 1–5). doi: https://doi.org/10.1109/ICOA55659.2022.9934252

Saud, A. S., & Shakyam, S. (2022) Directional movement index based machine learning strategy for predicting stock trading signals. International Journal of Electrical and Computer Engineering, 12(4), 4185. doi: https://doi.org/10.11591/ijece.v12i4.pp4185-4194

Sazu, M. H. (2022). How Machine Learning Can Drive High Frequency Algorithmic Trading for Technology Stocks. International Journal of Data Science and Advanced Analytics, 4 (4), 84-93. doi: https://doi.org/10.69511/ijdsaa.v4i4.97

Soltero, F. J., Fern’andez-Blanco, P., & Hidalgo, J. I. (2022). Collaborative Multiobjective Evolutionary Algorithms in search of better Pareto Fronts. An application to trading systems. arXiv.Org, abs/2211.02451. doi: https://doi.org/10.48550/arXiv.2211.02451

Stern, E. J. (2022). Machine Learning and the Stock Market. Journal of Financial and Quantitative Analysis, 58(4), 1431-1472. doi: https://doi.org/10.1017/s0022109022001120

Tirumala, A. et al. (2022). Designing a novel and high performance algorithmic trading model using evolutionary AutoML and technical analysis. Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 312-315). doi: https://doi.org/10.1145/3520304.3529038

Ahearne, A. G., Griever, W. L., & Warnock, F. E. (2004). Information costs and home bias: an analysis of US holdings of foreign equities. Journal of International Economics, 62, 313–336.

Engels, B. (2023). Künstliche Intelligenz in der deutschen Wirtschaft: Ohne Digitalisierung und Daten geht nichts. Wirtschaftsdienst, 103(8), 525-529. doi: https://doi.org/10.2478/wd-2023-0151

Hüther, M. (2020). Potenziale und Umsetzung der Digitalisierung auf Unternehmensebene. Wirtschaftsdienst, 100(13), 12-19. doi: https://doi.org/10.1007/s10273-020-2610-x

Baik, B., Kang, J.-K., Kim, J.-M., & Lee, J. (2013). The liability of foreignness in international equity investments: evidence from the US stock market. Journal of International Business Studies, 44, 391-411. doi: https://doi.org/10.1057/jibs.2013.13

Barber, B., Lehavy, R., McNichols, M., & Trueman, B. (2001). Can investors profit from the prophets? Security analyst recommendations and stock returns. Journal of Finance, 56, 531-563.

Twedt, B., & Rees, L. (2012). Reading between the lines: an empirical examination of qualitative attributes of financial analysts’ reports. Journal of Accounting and Public Policy, 31, 1-21. doi: https://doi.org/10.1016/j.jaccpubpol.2011.10.010

Brown, L. D., Call, A. C., Clement, M. B., & Sharp, N. Y. (2015). Inside the “black box” of sell-side financial analysts. Journal of Accounting Research, 53, 1-47. DOI: https://doi.org/10.1111/1475-679X.12067

Derrien, F., & Kecskés, A. (2013). The real effects of financial shocks: evidence from exogenous changes in analyst coverage. Journal of Finance, 68, 1407-1440. doi: https://doi.org/10.1111/jofi.12042

Bell, R. G., Filatotchev, I., & Rasheed, A. A. (2012). The liability of foreignness in capital markets: sources and remedies. Journal of International Business Studies, 43, 107-122. doi: https://doi.org/10.1057/jibs.2011.55

Lang, M. H., Lins, K. V., & Miller, D. P. (2004). Concentrated control, analyst following, and valuation: do analysts matter most when investors are protected least? Journal of Accounting Research, 42, 3, 589-623.

Demary, V., & Schaefer, T. (2023). Mit Digitalisierung zu klimaneutralem Wachstum und Wohlstand. Wirtschaftsdienst, 103(7), 450-453. doi: https://doi.org/10.2478/wd-2023-0132