A Comparative Analysis of the Effect of Liquidity on the Price of Bitcoin

Abstract

This paper delves into the intricate relationship between liquidity indicators and the price dynamics of Bitcoin, a prominent cryptocurrency. Liquidity, a fundamental aspect of financial markets, profoundly influences market stability and efficiency. Leveraging statistical analysis and AI modeling techniques, my study explores various liquidity metrics—including trading volume, bid-ask spread, volatility, number of transactions, and bid and ask sums as separate indicators—to assess their impact on the price of Bitcoin. The findings offer valuable insights into the factors driving Bitcoin price movements and shed light on the role of liquidity in cryptocurrency markets. Through correlation analysis as well as three different machine learning models – random forests, XGBoost, and linear regression – , my study evaluates the significance of individual liquidity factors and their relationships with Bitcoin prices. The best performing model was the random forest regressor and XGBoost where I identified that the volatility was the feature that was the most informative of the model’s performance. My research contributes to advancing our understanding of liquidity and price discovery in cryptocurrency markets and underscores the need for future studies to explore alternative factors and mechanisms shaping cryptocurrency prices. By embracing the findings and continuously refining analytical approaches, researchers can navigate the evolving landscape of cryptocurrency trading, ultimately enhancing market efficiency and informing regulatory decisions.