Cryptocurrency price prediction based on multiple market sentiment
2020 | HICSS | Citations: 0
Authors: Wang, Yu; Chen, Runyu
Abstract: With the rapid development of the Internet, cryptocurrencies have been gaining i ...
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Abstract: With the rapid development of the Internet, cryptocurrencies have been gaining increasing amounts of attention dramatically. As a digital currency, it is not only used worldwide for online payments, but also traded as an investment tool on the market. Therefore, the ability to predict the price volatility will facilitate future investment and payment decisions. However, there are many uncertainties in the price movement of cryptocurrencies, and the prediction is extremely difficult.
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Semantic filters:
mean absolute differenceChina
Topics:
cryptocurrency social media Tether Ethereum Bitcoin
Methods:
computational algorithm experiment deep learning support vector machine machine learning
A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting
Abstract: Forecasting stock prices is a major activity of financial firms and private inve ...
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Abstract: Forecasting stock prices is a major activity of financial firms and private investors when they make investment decisions. Feature extraction is usually the first step of a stock price forecasting model development. Wavelet transform, used mainly for the extraction of information contained in signals, is a signal processing technique that can simultaneously analyze the time domain and the frequency domain. When wavelet transform is employed to construct a forecasting model, the wavelet basis functions and decomposition stages need to be determined first. However, because forecasting models constructed by different wavelet sub-series would exhibit different forecasting capabilities and yield varying forecast results, the selection of wavelet that can lead to an optimal forecast outcome is extremely critical in model construction. In this study, a new stock price forecasting model which integrates wavelet transform, multivariate adaptive regression splines (MARS), and support vector regression (SVR) (called Wavelet-MARS-SVR) is proposed to not only address the problem of wavelet sub-series selection but also improve the forecast accuracy. The performance of the proposed method is evaluated by comparing the forecasting results of Wavelet-MARS-SVR with the ones made by other five competing approaches (Wavelet-SVR, Wavelet-MARS, single ARIMA, single SVR and single ANFIS) on the stock price data of two newly emerging stock markets and two mature stock markets. The empirical study shows that the proposed approach can not only solve the problem of wavelet sub-series selection but also outperform other competing models. Moreover, according to the sub-series which are selected by the proposed approach, we can successfully identify the data of which sessions (or points in time) among past stock market prices exerted significant impact on the construction of the forecasting model.
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Semantic filters:
mean absolute differenceChina
Topics:
decision making
Methods:
autoregressive integrated moving average nonparametric test longitudinal research time series analysis Wilcoxon signed-rank test
