Machine Learning for Forecasting Cryptocurrency Index and Constituent Prices
Summary
The paper applies machine-learning methods to forecast closing prices for a cryptocurrency index and nine constituent cryptocurrencies. It says that several algorithms and models are compared to identify the best-performing approach, and that the work is also compared with related studies. The stated motivation is to study price fluctuations in a volatile market and explore possible uses for trading and cryptocurrency portfolio management.
The authors report that their best approach produces results they describe as better than, or competitive with, prior work. However, the supplied description does not name the algorithms, data sources, forecast horizon, evaluation measures, or specific results. It also does not explain whether forecasts are evaluated as trading signals after costs or how they perform across changing market conditions. The text therefore establishes the forecasting task and a broad comparative claim, but offers too little detail to judge predictive reliability or practical trading value.
Key ideas
- The study forecasts closing prices for a cryptocurrency index and nine constituent assets.
- It compares several machine-learning models to select a preferred forecasting approach.
- The authors compare their reported results with related forecasting studies.
- The supplied description omits model details, data, evaluation measures, and trading tests.
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Full text
# Predicting and Forecasting the Price of Constituents and Index of Cryptocurrency Using Machine Learning # Predicting and Forecasting the Price of Constituents and Index of Cryptocurrency Using Machine Learning At present, cryptocurrencies have become a global phenomenon in financial sectors as it is one of the most traded financial instruments worldwide. Cryptocurrency is not only one of the most complicated and abstruse fields among financial instruments, but it is also deemed as a perplexing problem in finance due to its high volatility. This paper makes an attempt to apply machine learning techniques on the index and constituents of cryptocurrency with a goal to predict and forecast prices thereof. In particular, the purpose of this paper is to predict and forecast the close (closing) price of the cryptocurrency index 30 and nine constituents of cryptocurrencies using machine learning algorithms and models so that, it becomes easier for people to trade these currencies. We have used several machine learning techniques and algorithms and compared the models with each other to get the best output. We believe that our work will help reduce the challenges and difficulties faced by people, who invest in cryptocurrencies. Moreover, the obtained results can play a major role in cryptocurrency portfolio management and in observing the fluctuations in the prices of constituents of cryptocurrency market. We have also compared our approach with similar state of the art works from the literature, where machine learning approaches are considered for predicting and forecasting the prices of these currencies. In the sequel, we have found that our best approach presents better and competitive results than the best works from the literature thereby advancing the state of the art. Using such prediction and forecasting methods, people can easily understand the trend and it would be even easier for them to trade in a difficult and challenging financial instrument like cryptocurrency.
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