MANAGING INVESTMENT RISKS: INSIGHTS FROM UNCERTAINTY AND VOLATILITY

Investment risks in IT project development are heightened by uncertainty, incomplete information, and fluctuating projected cash flows. These challenges are exacerbated by the lack of robust statistical data, leaving stakeholders with limited tools for making informed decisions. This research addresses these issues by proposing a novel methodology for optimizing risk management in investment processes using advanced deep learning techniques. The study aims to develop and validate an algorithm that quantifies and mitigates investment risks through the integration of machine learning models and convolutional neural networks. A key component of this work is the Risk, Investment, and Compliance (RIC) method, which combines multiple financial indicators into a composite scoring system. The methodology was validated using five years of historical financial datasets from reputable sources, and applied to ten companies across diverse industries to analyse financial performance, market behaviour, and consumer sentiment. Key datasets include Kaggle’s Twitter Dataset, encompassing 1.5 million tweets to assess market sentiment, McKinsey’s dataset of 500 million consumer interactions, and daily updates from Yahoo Finance. The findings demonstrate that the RIC methodology effectively distinguishes between high-risk and secure investments. Companies scoring above 60% were identified as strong investment opportunities, while those below 30% were flagged as high-risk ventures. These results provides a robust framework for managing risks in IT investment projects, enabling more reliable decision-making under uncertainty and offering broad applications across industries.

1. 1 Gampfer Fabian, Andreas Jürgens, Markus Müller and Ruediger Buchkremer. Past, Current, and Future Trends in Enterprise Architecture: A View Beyond the Horizon. Computers in Industry, 2018, vol. 100, pp. 70–84. https://doi.org/10.1016/j.compind.2018.03.006.

2. Ilin V., Levina A., Dubgorn A. and A. Abran Investment Models for Enterprise Architecture and IT Architecture Projects Within the Open Innovation Concept. Journal of Open Innovation: Technology, Market, and Complexity, 2021, vol. 7, pp. 1–18. https://doi.org/10.3390/joitmc7010069.

3. Purwita W. and A.P. Subriadi. Information Technology Investment: In Search of the Closest Accurate Method. Procedia Computer Science, 2019, vol. 161, pp. 300–307. https://doi.org/10.1016/j.procs.2019.11.127.

4. Meyer R. and P. Degoulet. Choosing the Right Amount of Healthcare Information Technologies Investments. International Journal of Medical Informatics, 2010, vol. 79, pp. 225–231. https://doi.org/10.1016/j.ijmedinf.2010.01.001.

5. Ali S., Green P., and A. Robb. Information Technology Investment Governance: What Is It and Does It Matter? Inter. Journ. of Accounting Information Systems, 2015, vol. 18, pp. 1–25. https://doi.org/10.1016/j.accinf.2015.04.002.

6. Berghout E., and C.W. Tan. Understanding the Impact of Business Cases on IT Investment Decisions: An Analysis of Municipal E-Government Projects. Information and Management, 2013, vol. 50, pp. 489–506. https://doi.org/10.1016/j.im.2013.07.010.

7. Chen P.S., Yen D.C., Lin S.C., and C.S. Chou. Toward an IT Investment Decision Support Model for Global Enterprises. Computer Standards and Interfaces, 2018, vol. 59, pp. 130–140. http://dx.doi.org/10.1016/j.csi.2018.04.001.

8. Witra W.P.P., and A.P. Subriadi. Gender and Information Technology (IT) Investment Decision-Making. Procedia Computer Science, 2021, vol. 197, pp. 583–590. https://doi.org/10.1016/j.procs.2021.12.176.

9. Shin Y.Z., Chang J.Y., Jeon K., et al. Female Directors on the Board and Investment Efficiency: Evidence from Korea. Asian Business & Management, 2020, vol. 19, pp. 438–479. https://doi.org/10.1057/s41291-019-00066-2.

10. Lee H., Choi H., Lee J., Min J., and H. Lee. Impact of IT Investment on Firm Performance Based on Technology IT Architecture. Telematics and Informatics, 2016, vol. 91, pp. 652–661. https://doi.org/10.1016/j.procs.2016.07.164

11. Grekul V., Isaev E., Korovkina N., and T. Lisienkova. Developing an Approach to Ranking Innovative IT Projects. Business Informatics, 2019, vol. 13, pp. 43–58. https://doi.org/10.17323/1998-0663.2019.2.43.58.

12. Gao Y., Zhao C., Sun B., and W. Zhao. Effects of Investor on Stock Volatility: New Evidence from Multi-Source Data in China's Green Stock Markets. Financial Innovation, 2020, vol. 8, no. 1, pp. 1–30. https://doi.org/10.1186/s40854-022-00381-2.

13. Smatov N., Kalashnikov R., and A. Kartbayev. Development of Context-Based Sentiment Classification for Intelligent Stock Market Prediction. Big Data and Cognitive Computing, 2024, vol. 8, no. 6, p. 51. https://doi.org/10.3390/bdcc8060051.

14. Sonkavde G., Dharrao D.S., Bongale A.M., Deokate S.T., Doreswamy D., and S.K. Bhat. Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis, and Discussion of Implications. International Journal of Financial Studies, 2023, vol. 11, p. 94. https://doi.org/10.3390/ijfs11030094.

15. Van den Berg M., Slot R., van Steenbergen M., Faasse P., and H. van Vliet. How Enterprise Architecture Improves the Quality of IT Investment Decisions. Journal of Systems and Software, 2019, vol. 152, pp. 134–150. https://doi.org/10.1016/j.jss.2019.02.053.

16. Gong X., Wang C., Zhang W., and J. Wang. Investor Sentiment and Stock Volatility: New Evidence. International Review of Financial Analysis, 2022, vol. 81, p. 102008. http://dx.doi.org/10.1016/j.irfa.2022.102028.

17. Rouatbi W., Demir E., Kizys R., and A. Zaremba. Immunizing Markets Against the Pandemic: COVID-19 Vaccinations and Stock Volatility Around the World. International Review of Financial Analysis, 2021, vol. 76, p. 101785. https://doi.org/10.1016/j.irfa.2021.101819.

18. Chevallier J., Ma F., Guo Y., and D. Huang. Macroeconomic Attention, Economic Policy Uncertainty, and Stock Volatility Predictability. International Review of Financial Analysis, 2022, vol. 81, p. 102037. https://doi.org/10.1016/j.irfa.2022.102339.

19. Macrotrends LLC. Macrotrends – Website with Financial Data. Accessed 2024. https://www.macrotrends.net/.

20. Infront Analytics. Infront Analytics – Website with Financial Data. Accessed 2024. https://infrontanalytics.com.

21. Comparably. Comparably – Website with Brand and Company Data. Accessed 2024. https://www.comparably.com/brands/.

22. Kalashnikov R., and A. Kartbayev. Assessment of the Impact of Big Data Analysis on Decision-Making in Stock Trading Processes. Procedia Comp. Sci., 2024, vol. 231, pp. 786–791. https://doi.org/10.1016/j.procs.2023.12.137.

23. Maqbool B., and S. Herold. Potential Effectiveness and Efficiency Issues in Usability Evaluation Within Digital Health: A Systematic Literature Review. Journal of Systems and Software, February 2024, vol. 208, p. 111881. https://doi.org/10.1016/j.jss.2023.111881.

24. Venugopal J.P., Subramanian A.A.V., Sundaram G., Rivera M., and P. Wheeler. A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data. Applied Sciences, 2024, vol. 14, p. 11471. https://doi.org/10.3390/app142311471.

25. Asif M., and Z. Gouqing. Innovative Application of Artificial Intelligence in a Multi-Dimensional Communication Research Analysis: A Critical Review. Discover Artificial Intelligence, 2024, vol. 4, p. 37. https://doi.org/10.1007/s44163-024-00134-3.