Understanding Cryptocurrency Price via Contextual Information
In today's economic market, the determination to purchase or trade cryptocurrency is an exciting challenge encountered by day traders. The prices of cryptocurrencies depend on technical progress, internal contest, market intimidation, financial problems, safety issues, political elements, etc. Therefore, their increased volatility guides the great possibility of high profit if intelligent inventing methods are taken. Unfortunately, compared to conventional financial projections like stock market predictions, cryptocurrencies are relatively challenging to predict because they lack indexes. In the past, the research community only considered cryptocurrencies for predicting crypto prices. A systematic publications review procedure is used to identify relevant peer-reviewed journal articles from the past twenty years, evaluate and categorize studies with identical strategies and contexts, and then resemble the reflections in each class to specify common findings, unique findings, constraints, and areas that need further investigation. This will provide artificial intelligence and finance researchers with guidance for future research into using ML techniques to predict stock market index importance and trends. In this work, I am taking other stocks, i.e., Gasoline, Oil, Gold, Silver, and all Big IT firms, along with the ten cryptocurrencies, into our consideration for predicting the crypto prices.I am building a model which will give insight to the investors and traders not only in the cryptocurrency market but in others contextual market also. I have introduced a few new methods in building the machine learning model for the forecasting of cryptocurrency. This model can incorporate other stocks, i.e., Gasoline, Oil, Gold, Silver, and all Big IT firms' stocks.
Primary Advisor's Department
Stander Symposium, College of Arts and Sciences
Institutional Learning Goals
Diversity; Scholarship; Community
"Understanding Cryptocurrency Price via Contextual Information" (2023). Stander Symposium Projects. 3022.