Enhancing Cryptocurrency Price Forecasting by Integrating Machine Learning with Social Media and Market Data

Abstract

Since the advent of Bitcoin, the cryptocurrency landscape has seen the emergence of several virtual currencies that have quickly established their presence in the global market. The dynamics of this market, influenced by a multitude of factors that are difficult to predict, pose a challenge to fully comprehend its underlying insights. This paper proposes a methodology for suggesting when it is appropriate to buy or sell cryptocurrencies, in order to maximize profits. Starting from large sets of market and social media data, our methodology combines different statistical, text analytics, and deep learning techniques to support a recommendation trading algorithm. In particular, we exploit additional information such as correlation between social media posts and price fluctuations, causal connection among prices, and the sentiment of social media users regarding cryptocurrencies. Several experiments were carried out on historical data to assess the effectiveness of the trading algorithm, achieving an overall average gain of 194% without transaction fees and 117% when considering fees. In particular, among the different types of cryptocurrencies considered (i.e., high capitalization, solid projects, and meme coins), the trading algorithm has proven to be very effective in predicting the price trends of influential meme coins, yielding considerably higher profits compared to other cryptocurrency types.

Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques

“…They then employed these two networks in conjunction with each other to predict price. In addition, Belcastro et al [129] introduced a methodology aimed at optimizing cryptocurrency trading decisions to enhance profit margins. Their approach integrates various statistical, text analytics, and deep-learning methodologies to support a recommendation trading algorithm.…”

An Out-of-Distribution Generalization Framework Based on Variational Backdoor Adjustment

“…The OOD generalization problem has been widely observed in various domains [1,5,6,9,[14][15][16][17][18]. To address this issue, researchers have proposed various algorithms from different perspectives, such as distributional robust optimization [19,20] and causal invariant learning [1,5,6,[21][22][23][24][25][26].…”