Enhancing Financial Market Analysis Bitcoin vs Gold through Machine Learning Algorithms: A Study on Risk Assessment and Portfolio Management

Abstract

Financial markets exhibit complex, nonlinear dynamic behavior that is often challenging to interpret using traditional linear analysis methods. Risk assessment and, accordingly, portfolio management are the most important tasks for investors in order to avoid big losses. Gold trading is considered a safe haven, but it shows slow growth. In contrast, bitcoin or cryptocurrency trading is being proliferated. Many established financial institutions, like PayPal and Fidelity, are making it easier than ever for individuals to buy and sell Bitcoin. While Bitcoin trading can be potentially lucrative, it’s crucial to understand the significant risks involved before diving The volatile nature of Bitcoin can be emotionally stressful, leading to anxiety and poor decision-making. High-risk activity like stock trading requires a strong understanding of the market, particularly in high-risk assets like Bitcoin and gold, which demands a deep comprehension of market dynamics and the ability to tolerate volatility. Through this study, we delve into the application of machine learning algorithms to comprehend correlations, causality, and predict volatility in stock trading. Specifically, we focus on analyzing the trading behavior of cryptocurrency (bitcoin) vs. physical assets (gold) in terms of their volatility and comparing them in correlation with the stock index of crude-oil, S&P 500, and the dollar. This structured study aims to shed light on the effectiveness of machine learning algorithms in understanding and predicting volatility in stock trading, with a specific focus on Bitcoin and gold as high-risk assets.