Following established theories from about the effects of signals on asset prices, through discerning of sentiments from social media and news, we propose and develop a causal filtering approach for deep learning-based predictions for cryptocurrency prices. Using time-series data about cryptocurrency prices and approximately 24 sentiment indices measured in time, we develop a two-stage process to predict prices. In the first stage, we apply time-series causality derived from information theory we filter out signals from noise. In the second stage using the signals with the highest causal scores, we use the Long-term Short Term Memory (LSTM) model of recurrent neural networks (RNNs). Our results depict very high predictability with extremely low loss functions for both day-to-day predictions, and for short-term interval-based predictions for Ethereum and Bitcoin.
Subramanian, Hemang; Angle, Patricia; and Nagaraj, Nithin, "Do Sentiment Indices Outperform Quantitative Indicators As Predictors For Cryptocurrency Prices?" (2022). PACIS 2022 Proceedings. 293.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.