An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning

Abstract

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.

Forecasting mid-price movement of Bitcoin futures using machine learning

“…The second is based on the development of cryptocurrencies with an emphasis on futures pricing behaviour, while finally, the third area through which we develop our work is based on several pieces that have examined the predictability of cryptocurrency spot prices. Primarily, machine learning has been used across a variety of areas such as that of stock markets (Wittkemper and Steiner 1996;Ntakaris et al 2018;Sirignano 2019;Huck 2019;Sirignano and Cont 2019;Huang and Liu 2020;Philip 2020); currency markets during crises (El Shazly and El Shazly 1999;Zimmermann et al 2001;Auld and Linton 2019); energy markets such as West Texas Intermediate (Chai et al 2018), crude oil markets (Fan et al 2016), Cushing oil and gasoline markets (Wang et al 2018), gold markets (Chen et al 2020); gas markets (Ftiti et al 2020), agricultural futures (Fang et al 2020); copper markets (Sánchez Lasheras et al 2015); and coal markets (Matyjaszek et al 2019;Alameer et al 2020); cryptocurrency spot markets Chowdhury et al 2020;Chen et al 2021) options markets (Lajbcygier 2004;De Spiegeleer et al 2018); and futures markets (Kim et al 2020).…”

Review on Bitcoin Price Prediction Using Machine Learning and Statistical Methods

“…* ARIMA yields the best results among the statistical methods [7]. * ARIMA remains weak compared to RNN, LSTM [12], [30] and SVM, MLP [6], [11]. *As a result, it was proved that ML methods produced better results compared to statistical methods.…”

“…Based on reference [30], the researchers used methods that covered ARIMA, LSTM, RNN, Gradient boosted trees (GBDT), Ensemble learning method, K-NN. Unlike other models, the K-NN model did not work rather effectively.…”

Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods

“…This is based on the WHO declaring COVID-19 a global pandemic on that date (WHO 2020). The CCI30 served as a market proxy following Chowdhury et al ( 2020 ), and the 1-month USD London Interbank Offered Rate (LIBOR) interest rate served as the risk-free rate proxy following Anyfantaki and Topaloglou ( 2018 ). For both time periods, 30 days forward are examined using 1-week and 7-day ahead predictions.…”