Cryptocurrency market trend forecasting and economic value assessment based on GA-SVM algorithm

With the rapid development of the cryptocurrency market (CM), accurately predicting market trends and rationally assessing their economic value has become an urgent need for investors and the market. However, current market forecasting methods still need to be improved in terms of economic accuracy. To improve the accuracy of CM trend prediction, the study uses genetic algorithm to optimize support vector machine (SVM) and constructs a CM trend prediction model based on the improved SVM. Comparative experiments on the improved SVM revealed that the accuracy and loss value of the improved algorithm were 96.4% and 0.031, respectively. Subsequent validation of the proposed CM trend prediction model found that the model predicted the CM with an accuracy of 97.8%, which is better than the comparison model, and the economic value assessment of it as an indicator of the rate of return, the average rate of return is 12.9%. The above results illustrate that the forecasting model proposed in the study not only provides investors with more accurate predictions of market trends, but also offers new perspectives and methods for assessing the economic value of cryptocurrencies. This study not only lays the foundation for the further development of the cryptocurrency sector, but also promises to further promote the development of the virtual currency economy.