By: Indu Eswar Shivani Nayuni, Department Of Computer Science & Engineering(Data Science), Student Of Computer Science & Engineering(Data Science) ,Madanapalle Institute of Technology and Science, Angallu(517325),Andhra Pradesh . indunayuni1607@gmail.com
Abstract:
Based on the information, it is concluded that there are many applications in which generative AI models can help, whereas in the case of stock market prediction, there are some problems. Although generative AI is presented with influential stock price prediction methodologies, this paper brings out its demerits and difficulties. Nevertheless, there are some limitations, mainly, stock markets that are very dynamic and non-stationary, and that is why it is impossible to use generative models, moreover, the overfitting is high here. Also, the indicators that have been tested might be more suitable for use in the analysis of historical financial statements and can barely incorporate for sudden one-off market events or changes in the nature of the economy. The third and last concern that have been mentioned is the explanation of generative AI models because while making given predictions, they are difficult to be explained. This article also discuss about the problem in applying generative AI with other traditional financial models as well as the idea of applying some hybrid of the best features with the aim of increasing the forecast’s reliability. Solving these issues is important for improving the examined approaches of AI generated stock market prediction.
Keywords: Gen AI models, stock market prediction, their problems and solutions, indicators, financial models, improving in the solving issues.
Introduction
The area of stock trading remains a severe and rapidly evolving field that attracted much attention in developing better methods of forecasting and prediction. Lately, generative AI, a category of AI, that involves creating new data samples from the obtained patterns, became an object of interest with regard to innovative approaches to financial forecasting. derived from large data sets, and ability to find rich structures within data, generative AI holds great potential for improving market forecasting.
Therefore, while generative AI has noticeable social impact, applied to stock market prediction it has its own set of problems. They are affected by high fluctuations, non-stationary characteristics and numerous factors that comprise historical analysis. These characteristics present a significant amount of challenge for the generative AI models that primarily depend on pattern recognition and which could be less responsive to any abrupt change in the market or other conditions that are unexpected.
Also, generative AI models for same are deemed as ‘black boxes’ that are difficult to understand the working and decision making principles of the model. This lack of transparency can thereby hence reduce confidence in the generated predictions through these models and hence hamper practical use of these model in investment strategies. [1]
Thus, the use of generative AI along with the conventional financial techniques has its merits and demerits. In contrast to the theory-driven starting points, where econometric or technical analysis tools come from well-established financial theories, mixing them with generative AI requires some efforts to be properly incorporated in order to avoid both worsening the complexity and delivering non-useful suggestions that do not fit the market’s characteristics. [2]
In the context of applying generative AI to predict stock markets, this article focuses on difficulties like data reliance, overlearning, explainability, and the combination with traditional approaches. Through addressing these issues we want to contribute to defining the role and prospects of generative AI in the field of financial forecasting and identify the ways to design methods and tools that could be stronger and more efficient.
As the financial industry moves forward and become more open and inclusive towards the newer technologies, it is necessary to analyse the specific interactions between the generative AI and stock market prediction to use all the advantages of the technology and address its potential shortcomings.
Challenges faced by Gen Ai in Stock Market Prediction
Some of the difficulties related to generative AI in the context of the study of the stock exchange include the following and represented in the following fig 1.
This paper has concerned with generative AI since it is an emerging field that attracts much attention due to its capability to design and synthesize patterns in the data to enhance the stock market prediction. Still, the use of such models in forecasting the financial environment is accompanied by certain challenges that may influence their applicability and validity[3]. This part covers the main difficulties that may be encountered when employing generative AI to analyze stock prices.
1. High Volatility and Non-Stationarity
Description: Financial markets can be described as rather unstable and changing their behavior in a short and an unpredictable manner. Seasonal and trend are the components of non-stationarity that show the mean and the variance differ from one period to the other.
Challenges: In the case of generative AI models, it often takes previous data to train the application and make forecasts. These models may not be as useful due to high volatility and non-stationarities of financial markets, which can derive from still unexplored trends that drastically differ from the previously observed patterns. It can also cause over fitting, or give good results for past data, but bad results for contemporaneous data.[4]
2. Data Dependency and Overfitting
Description: Most of the generative AI models are highly dependent on large amounts of data for them to be trained properly. These models uses the history data to make prediction.
Challenges: However, historical information, although important, cannot always provide information about the state of the market, today and in the future. Generative AI models using historical data may also be vulnerable to the issue of overfitting to historical data, thus the model will embrace new market data in a highly specific way and may not work with data that the model did not develop with. This relativity to historical data can weaken the model to immensely depend on it and thus mispredicting unprecedented market occurrences.
3 Interpretability and Transparency
Description: With many generative AI models and especially those that use deep learning algorithms, one cannot really interrogate the processes that the model is following to make its decision.
Challenges: This disadvantage is particularly relevant to generative AI models, as they often do not make their results easily interpretable and this is especially acute in the financial forecasting when it is crucial to recognize the reason of making a specific forecast. If the specifics of a model are opaque, the investors and analysts cannot trust and act on the model’s predictions and therefore dismiss the models as having little real-world application.
4 Interconnectedness with Conventional Financial Frameworks
Description: The earlier financial models are based on either econometric model, and technical analysis tools where DM is derived based on the past movement of financial markets or based on theories.
Challenges: Regarding the issues with applying generative AI in these traditional models, there are always issues when integrating this new AI method with these models that is based on different methodological principles. The disparate conventional models are helpful in split tendency detection and analysis provided by such finance theories as the balance sheet or the income statement; their interaction with the generative AI poses certain risks connected with the aggravation of the mentioned discrepancies and the scope of the conception ambit. The main opposition that results from the hybrid models is the assertion that both benefit types are maximized, and no new additional errors or anomalies appear.
5. A key consideration in uncertainty, and the handling of the limit situations: hazards.
Description: The financial market always has a condition that it is dependent of many factors such as geopolitical regimes, economical insecurity, other events hence the past records are not very useful in predicting operations in the market currently.
Challenges: Thus, the models of generative AI might have the issue of low resilience to the effects of the outliers, such as, for example, the emergence of the event which was previously impossible. Thus, there may be times that some situations or events cannot be explained by the models or there are understatements of the produce that may stem from it; thus delivering the nice forecasts a deadly blow during the booms or during the major movements in the markets.
6. Algorithms and their Space and Time Complexity
Description: Some categories of generative AI are the deep learning AI models, and these are capable of learning and processing more resources.
Challenges: They also have another relative disadvantage that is; in an attempt to apply generative AI in stock market prediction then a lot of computation is required. These models are limited to high-performance servers that are specifically designated for such computation and may take a long time to train; thus, it may only be appropriate for major financial institutions that are likely to cough out huge amounts of cash for the same; the other institutions may face some challenges when it comes to integrating and implementing such complex systems.
Future solution and directions for prediction of stock market
As far as the application of generative AI in predicting the movements of stock market for the future is concerned, Further Directions & Solutions. And some of the directions are showed in the following fig2.
Nevertheless, with the generative Artificial Intelligence in place, it will be very crucial to reduce the deficit in using the same to predict the stock market, with a view of enhancing the effectiveness of the system and reliability. Here are some key future directions and potential solutions to overcome the obstacles currently faced:Here following are the probably future direction and solutions for coping up with the present outcomes or barriers:
1.AI Integrated Models or HMMs:
Using the conventional marketing strategies, when applying AI, there are so many complex techniques that have been devised:
Description: It is also appropriate to incorporate generative AI in other traditional works that have been conducted in finance since the latter has its speciality.
Solutions:
Develop Hybrid Models: The currently acknowledged attributes of the generative AI pattern recognition of the generative AI as well as the data gathered with the help of econometric and technical analyses tactics should be expanded. Its application can enrich the pure incorporative perspective and obtain improved prognosis from the appraisal of the history data applied in the identification of a pattern.
2. Cross-Validation: It should have been used as the bench mark which is shown above in the drama components forming the outcomes of the task concerning the existent generative AI models.
Description: It should be noted that historical dependency situation can be removed by increasing the amount of input information sources and calculating the Feature Extraction issue.
Solutions:
Incorporate Alternative Data: Therefore, depend on other externality characteristics/ information like social networking attitude, satellite and real economic indicators for prediction. They can contribute to additions of more specifics and to the changes connected to new tendencies and circumstances, the latter being pretty common.
Dynamic Data Integration: Ways of using the stream data, and altering it to generative AI to make it relavant in the current market environment.
3. Advancements in Model Interpretability
Description: There would be overall three more objectives, which would be useful for the further development of the generative AI model, and they are: fifth – the model should be explainable; sixth – understandable; and last but not least, the model should progress the usage of the generative AI models[5].
Solutions:
Explainable AI Techniques: Such measures should be used together with methods of interpretability model-agnostic, attention mechanisms and other tools that could shed the light on the process through which generative models come up with their predictions.
Simplify Model Architectures: In fact some of the model with lower dimension of complexity can be evaluated with a view of optimizing the understanding of the model especially with with many layers.
4. Enhanced Computational Efficiency
Description: Thus, in generic forms, AI can increase efficiency and general availability if it addresses the issue of high energy consumption[6].
Solutions:
- Optimize Algorithms: It requires many resources and time to accomplish the task of taking a sample from the generative models; thus, the algorithm should be improved for better performance[5].
- Leverage Cloud Computing: Use clouds programming to acquire computation facilities to work on and reduce the loads and tasks of different organizations.
5. Cross-Disciplinary Collaboration
Description: Repository from AI researcher’s technical background, conventional financial gurus and qualified economists can lead to a combined effort hence foster the invention of better solutions that are not only innovative, but also realistic.
Solutions:
- Interdisciplinary Research: Accomplish effective teamwork between the specialists in artificial intelligence technologies and financial analysts, who will guarantee that the developed models promote practice-based applications of theoretical assumptions[7]. Thus, the specific nonlinear terms of interactive preprocessing can solve the problems, which are connected with the financial prediction of the research cooperation[8].
- Feedback Loops: Create applications and techniques in which data obtained by pattern recognition from such financial specialists will be valuable for the enhancement of the algorithms applied in AI model construction on one hand, while data from conclusions of the constructed models on the other hand will be valuable to the specialists
Conclusion
Therefore, one can conclude that generative AI has the potential to bring a major change to the stock market prediction by incorporating sophisticated modeling and pattern recognition, yet it is significant to solve the key problems for AI’s successful implementation. Various factors like the market fluctuation, depen- dency on data, interpretability, and computation complexity needs to be addressed properly for its actual potential to be harnessed. As a result, a more balanced approach, where hybrid models are adopted, new data sources are utilized, and the models’ transparency is improved, will be required. Moreover, proper coordination between the AI specialists, financial specialists, and economists will also be an important part to build AI’s future. Thus, addressing these challenges by establishing new solutions and further cooperation, the financial industry will be able to unleash the potential of generative AI and provide a higher level of market prediction, which will help gain a better understanding of financial markets’ nature and tendencies.
References:
- P. Pappachan, Sreerakuvandana, and M. Rahaman, “Conceptualising the Role of Intellectual Property and Ethical Behaviour in Artificial Intelligence,” in Handbook of Research on AI and ML for Intelligent Machines and Systems, IGI Global, 2024, pp. 1–26. doi: 10.4018/978-1-6684-9999-3.ch001.
- S. Feuerriegel, J. Hartmann, C. Janiesch, and P. Zschech, “Generative AI,” Bus. Inf. Syst. Eng., vol. 66, no. 1, pp. 111–126, Feb. 2024, doi: 10.1007/s12599-023-00834-7.
- M. Moslehpour, A. Khoirul, and P.-K. Lin, “What do Indonesian Facebook Advertisers Want? The Impact of E-Service Quality on E-Loyalty,” in 2018 15th International Conference on Service Systems and Service Management (ICSSSM), Jul. 2018, pp. 1–6. doi: 10.1109/ICSSSM.2018.8465074.
- M. Betaubun, D. E. L. Rokhmah, and J. Budiasto, “Personalized Pathways to Proficiency: Exploring the Synergy of Adaptive Learning and Artificial Intelligence in English Language Learning,” Tech. Romanian J. Appl. Sci. Technol., vol. 17, pp. 60–66, Nov. 2023, doi: 10.47577/technium.v17i.10047.
- M. Rahaman, F. Tabassum, V. Arya, and R. Bansal, “Secure and sustainable food processing supply chain framework based on Hyperledger Fabric technology,” Cyber Secur. Appl., vol. 2, p. 100045, Jan. 2024, doi: 10.1016/j.csa.2024.100045.
- B. Do, A. Dadvari, and M. Moslehpour, “Exploring the mediation effect of social media acceptance on the relationship between entrepreneurial personality and entrepreneurial intention,” Manag. Sci. Lett., vol. 10, no. 16, pp. 3801–3810, 2020.
- S. Mishra and A. R. Tripathi, “AI business model: an integrative business approach,” J. Innov. Entrep., vol. 10, no. 1, p. 18, Jul. 2021, doi: 10.1186/s13731-021-00157-5.
- N. Rane, “Role and Challenges of ChatGPT and Similar Generative Artificial Intelligence in Business Management,” Jul. 26, 2023, Rochester, NY: 4603227. doi: 10.2139/ssrn.4603227.
- Gaurav, A., Arya, V., Chui, K. T., Gupta, B. B., Choi, C., & Lee, O. J. (2023, August). Long Short-Term Memory Network (LSTM) based Stock Price Prediction. In Proceedings of the 2023 International Conference on Research in Adaptive and Convergent Systems (pp. 1-6).
- AlZu’bi, S., Hawashin, B., Mujahed, M., Jararweh, Y., & Gupta, B. B. (2019). An efficient employment of internet of multimedia things in smart and future agriculture. Multimedia Tools and Applications, 78, 29581-29605.
Cite As
Nayuni I.E.S. (2024) Challenges in Using Generative AI for Stock Market Prediction, Insights2Techinfo, pp.1