Market Turbulence and Investment Strategy: A Comparative study of Cryptocurrencies and Traditional Assets with Machine Learning Predictions.

Technological advancements of recent years have shown us a high volatility due to global economic shifts and rising popularity of new asset classes. These new asset classes are cryptocurrencies and some other unique digital tools like non fungible tokens (NFT), which created a lot of attention among investors and regular people, who wanted to make easy and fast money. This hype made digital assets challenge the dominance of traditional assets. As market turbulence becomes increasingly more common, investors face the complex task of navigating these fluctuations to ensure profitability of their portfolios. The aim of this work is to address this challenge by conducting a comparative study between new assets and traditional ones, with emphasis on performance during turbulent times and crises. For this purpose, machine learning techniques will be used to analyze market trends, the performance of assets and to predict their behavior in future. By integrating financial analysis with advanced computational tools, this research aims to provide insights into how investors can better manage risk and optimize returns in a dynamic financial landscape. Fast growth of cryptocurrencies popularity can be compared to a gold rush. As people left everything behind to try their luck and get rich a century ago, the modern digital gold awakened people's interest in getting rich fast. This created a fast growing market, but it is also extremely volatile. It’s almost impossible to know for sure where the market is going and how fast the situation is going to change. Contrary to this unpredictable market, investors have time-tested traditional assets. They are much more predictable and if you know the environment, your risks are relatively small, but you won’t get a lot of money fast, traditional assets are about time and patience. Both of these different investment areas are subject to shocks. Some turbulences are caused by the asset’s nature itself, other by the global shocks and crises. By integrating financial analysis with advanced computational tools, this research aims to provide insights into how investors can better manage risk and optimize returns in a dynamic financial landscape. This thesis aims to provide a comprehensive understanding of how different asset classes respond to market shocks and how advanced predictive tools can enhance decision making in investment management.