Author
Listed:
- Rizwan Ali
- Jin Xu
- Mushahid Hussain Baig
- Hafiz Saif Ur Rehman
- Muhammad Waqas Aslam
- Kaleem Ullah Qasim
Abstract
Purpose - This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators. Design/methodology/approach - In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR. Findings - This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy. Originality/value - According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.
Suggested Citation
Rizwan Ali & Jin Xu & Mushahid Hussain Baig & Hafiz Saif Ur Rehman & Muhammad Waqas Aslam & Kaleem Ullah Qasim, 2024.
"From data to decisions: enhancing financial forecasts with LSTM for AI token prices,"
Journal of Economic Studies, Emerald Group Publishing Limited, vol. 51(8), pages 1677-1693, April.
Handle:
RePEc:eme:jespps:jes-01-2024-0022
DOI: 10.1108/JES-01-2024-0022
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eme:jespps:jes-01-2024-0022. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Emerald Support (email available below). General contact details of provider: .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.