Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Business and Policy Studies
Series Vol. 14 , 13 September 2023
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Access | Article
Forecasting NFTI Index Model: An Univariate Multivariable Regression Approach
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Abstract
This paper proposed a forecasting model of the NFT index (NFTI) in the following year through repeating simulations applied to the univariate multivariable regression model. Beginning by choosing suitable predictors for the regression model which might affect NFTI through the best subset regression method, the team creates the multivariate regression model consisting of four dependent variables which are the log return of BTC, BTC/NFTI, NFTI spread, and NFTI volume/spread with respect to the independent variable log return of NFTI. Application of the regression model to the simulations based on the historical data generates 1000 pairs of data of log return of NFTI and log return of BTC as well as the corresponding predicted price of NFTI and BTC which are moderately correlated. Results are summarized in the cross-tabulation to quantitatively analyze the relationship between two variables which provides information for the investors about how they should formulate their own investment strategy. The results suggest that NFTI outperforms BTC unless NFTI crashes. Therefore, investment strategies can be made depending on the trend of BTC in the following year.
Keywords
forecasting model, univariate multivariable regression, NFT index
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Business and Policy Studies
- ISBN (Print)
- 978-1-915371-71-3
- ISBN (Online)
- 978-1-915371-72-0
- Published Date
- 13 September 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/14/20230801
- Copyright
- 13 September 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited