
Research on the Synthesis of Hong Kong NFT Index Using Principal Component Analysis and Index Prediction Based on LSTM-Modified ARMA-GARCH Model
- 1 Minzu University of China
- 2 Minzu University of China
* Author to whom correspondence should be addressed.
Abstract
With the advent of the Web3.0 era, virtual assets have gained prominence in individuals’ asset portfolios, making Non-Fungible Tokens (NFTs) increasingly significant within the financial trading landscape. To address the issue of multicollinearity in regression analysis, this paper employs Principal Component Analysis (PCA) to perform dimensionality reduction on five correlated foundational sectors. Moreover, to enhance the accuracy and reliability of predictive outcomes, the study combines the Long Short-Term Memory (LSTM) model with the Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) model. Through the application of these methods and practical implementation, the study forecasts the NFT index of the Hong Kong stock market for the next 30 days. This forecasting of return volatility contributes vital insights for investment decision-making. The research complements and offers application recommendations in financial innovation, deepening, and regulation. By devising novel products and tools to meet investor demands, providing risk management and investment opportunities, the model’s predictive outcomes can be utilized in regulatory and risk management strategies within the national financial trading market. This study provides regulatory guidance, policy formulation insights, and envisions further refinements of the research methodology by integrating information shock effects.
Keywords
NFT, principal component analysis, LSTM model, ARMA-GARCH model
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Cite this article
He,W.;Yu,J. (2023). Research on the Synthesis of Hong Kong NFT Index Using Principal Component Analysis and Index Prediction Based on LSTM-Modified ARMA-GARCH Model. Advances in Economics, Management and Political Sciences,55,59-76.
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