The cryptocurrency market has experienced rapid growth over the past decade, with a significant increase in market capitalization from $7 billion to nearly $3 trillion. However, this growth has also been accompanied by volatility and complexity, making it challenging to model and analyze the market. Many cryptocurrency assets lack clear fundamental value or cash flows, which can lead to fragmentation and vulnerability to market manipulation. The existing literature offers various explanations for these dynamics, but our understanding of the primary factors driving crypto asset prices remains limited.
To address this challenge, we propose a new approach that integrates machine learning techniques with traditional financial modeling methods. Our approach can handle complex data structures and provide more accurate predictions by accounting for the non-linear relationships between variables. By combining these techniques, we aim to improve our understanding of the cryptocurrency market and help investors make more informed decisions.
In this article, we demystify complex concepts by using everyday language and engaging metaphors or analogies to explain the concepts in a simple and comprehensible way. We also provide a balance between simplicity and thoroughness to capture the essence of the article without oversimplifying.
Overall, our proposed approach has the potential to significantly improve the accuracy of cryptocurrency market predictions and help investors navigate this rapidly evolving market with more confidence.
Learning Causal Networks via Model Selection and Wiener Filtering
