Investigation of financial applications with blockchain technology

This thesis focusing on utilizing machine learning (ML) and deep learning (DL) methodologies for forecasting cryptocurrency prices. The research concentrates on interpreting historical price data of prime cryptocurrencies, including Bitcoin, Ethereum, Dogecoin, USD Coin, Binance Coin, and Cardano. These virtual currencies demonstrate unique and unpredictable behavior; therefore, the need to understand their price dynamics is vital for making informed financial decisions. The essential objective of this study is to utilize various ML and DL techniques to construct tailored prediction models for each of these cryptocurrencies, aiding investors, traders, and financial institutions in making more precise and lucrative decisions in the rapidly evolving cryptocurrency market. The central tenet of this study is the use of diverse ML and DL algorithms to develop specialized prediction models for each cryptocurrency. Several techniques are deployed, such as LASSO (Least Absolute Shrinkage and Selection Operator), Linear Regression, Ridge Regression, Decision Tree, AstroML, Convolutional Neural Networks, Support Vector Machines, K-Nearest Neighbors, LSM (Least Squares Method), XGBoost Regression, and Gaussian Process Regression. These techniques were chosen based on their ability to handle complex non-linear relationships, missing values, and large data volumes. The approach was to model each cryptocurrency individually, recognizing that each might display unique characteristics and behaviors influenced by different external factors. The efficacy of these prediction models was evaluated using robust performance metrics like root mean square error (RMSE) and R-square. RMSE measures the average magnitude of the prediction error, providing an understanding of how accurately the model forecasts the prices, while R-square represents the proportion of the variance for the dependent variable that's explained by the independent variables in the model. These metrics allowed for a comprehensive and rigorous evaluation of the prediction models. The findings of the research provide a wealth of insights for various stakeholders in the cryptocurrency market. By demonstrating the effectiveness of ML and DL algorithms in predicting cryptocurrency prices, the research offers investors, traders, and financial institutions valuable tools to aid their decision-making processes. Furthermore, by comparing the performance of various models, it provides insights into which ML techniques are most suitable for cryptocurrency price prediction. Of particular interest is the outstanding performance of the Gaussian Process Regression in predicting cryptocurrency prices. This technique, known for its capability to handle a large number of predictors and complex relationships among variables, outperformed the other models tested. This highlights the potential of Gaussian Process Regression in predicting cryptocurrency prices, providing a promising direction for further research in the field.