Abstract
The booming applications of bitcoin Blockchain technologies made investors concerned about the return and risk of financial products. So, the return rate of bitcoin must be foreseen in prior. This research article devises an effective return rate prediction technique for Blockchain financial products based on Optimal Least Square Support Vector Machine (OLS-SVM) model. The parameter optimization of the LS-SVM model was performed using hybridization of Grey Wolf Optimization (GWO) with Differential Evolution (DE), called optimal GWO (OGWO) algorithm. The hybridization process is performed to eliminate the local optima problem of GWO and enhance the diversity of the population. To verify the goodness of the proposed model, the Ethereum (ETH) return rate was chosen as the target and experimental analysis was performed on it to verify the predictive results on the time series. The experimental outcome was analyzed in terms of two performance measures namely Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The obtained simulation outcome infers that the OLS-SVM model yielded better predictive outcome of the return rate of financial products.
Highlights
In recent times, the global economy has progressed rapidly and in parallel, several factors which hinder economic growth have been overwhelmed by the faster-growing economy
To verify the goodness of the proposed model, the Ethereum (ETH) return rate was chosen as the target and the experimental analysis was performed on it to verify the predictive results on the time series
From the above-mentioned table and figures, it can be inferred that the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values of Optimal Least Square Support Vector Machine (OLS-Support Vector Machine (SVM)) model were the least in training set followed by GA-SVM and ANN respectively
Summary
The global economy has progressed rapidly and in parallel, several factors which hinder economic growth have been overwhelmed by the faster-growing economy. Blockchain is referred as a data structure, which records the transactions in a sequential manner, facilitated as a distributed set of records Followed by, it is classified into two sections namely, header as well as transaction, and it saves the data regarding transaction details. With the application of Neural Network (NN) in forecasting the economic data, several authors have identified the potentials of optimal learning for nonlinear function It employs NN in detection and is suitable for financial data like stock markets. By leveraging the features and merits of predefined Blockchain method, the financial industry has emerged into higher enhancement space and created numerous opportunities Since it is an effective application of bitcoin Blockchain method, the developers are heavily worried about the risk factors associated with it. This research article presents a new return rate prediction model for Blockchain financial products based on Optimal Least Square Support Vector Machine (OLS-SVM) model.
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