Forecasting Cryptocurrency Returns Using Search Engines
This article focuses on using search engine data as a tool for forecasting cryptocurrency returns and volume. By analyzing the volume of search queries related to cryptocurrency on search engines such as Google, the authors aim to gain insight into the level of interest in cryptocurrency and how it relates to price movements.
The authors use various machine learning techniques, including linear regression and time-series analysis, to model the relationship between search engine data and cryptocurrency returns and volume. They find that search engine data is a significant predictor of cryptocurrency returns and volume, suggesting that it can be used to help forecast future price movements.
The authors also examine the relationship between search engine data and various other factors that are commonly considered to impact cryptocurrency prices, such as news articles, market volatility, and market sentiment. They find that the relationship between search engine data and cryptocurrency prices is relatively stable over time, indicating that it may be a useful tool for long-term investment decisions.
Overall, this article highlights the potential of using search engine data as a tool for forecasting cryptocurrency prices and volume. The results suggest that search engine data may provide valuable insights into market sentiment and demand, and could be a useful tool for cryptocurrency traders and investors looking to make informed investment decisions.