Bitcoin Price Forecast Using Quantitative Models

Bernardi, Daniele; Bertelli, Ruggero

doi:10.2139/ssrn.3879700

Cited by 1 publication

(1 citation statement)

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“…Prediction in Multiple Regression demonstrates the reason and use of multiple regression for predicting purposes. Moreover, in Is non-fungible token pricing driven by cryptocurrencies, the authors provide insights into the correlation between NFT pricing and other cryptocurrencies which provides ideas of selecting possible predictors associated with NFTI [3][4][5][6].…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting NFTI Index Model: An Univariate Multivariable Regression Approach

Wang,

Li,

2023

AEMPS

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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.

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Section: Literature Reviewmentioning

confidence: 99%