Valencia, Andrés MoraPrado, MelissaNeupert, Paul2024-11-262024-11-262023-10-182023-09-12http://hdl.handle.net/10362/175812This thesis aims to investigate the factors influencing daily cryptocurrency returns and assesses the feasibility of forecasting these returns using a diverse set of 25 globally traded assets as predictors between 2017 and 2022. Inspired by a New York Times article on cryptocurrency bubbles and market volatility, the investigation undergoes several statistical computations such as the Principal Component Analysis and the Complete Subset Regression to observe the data from distinct angles. Notably, the FX-rate USD/CNY and the Japanese NIKKEI225 index emerge as consistently influential predictors within the out-of sample forecasting periods, however, the significance of the Out-of-Sample R-squared remains low. This research contributes to the understanding of cryptocurrency market dynamics by examining the impact of a wide range of predictors and aligns with recent academic findings regarding the challenges in forecasting cryptocurrencies. Further research should focus on market sentiments regarding potential price movements in the crypto space.engForecastingCryptocurrency marketComplete subset regressionTrading strategyPrincipal component analysismaster thesis203684281