Do Sentiment Indices Outperform Quantitative Indicators As Predictors For Cryptocurrency Prices?
Conference
Subramanian, HC, Angle, P, Nagaraj, N. (2022). Do Sentiment Indices Outperform Quantitative Indicators As Predictors For Cryptocurrency Prices?
. Pacific Asia Conference on Information Systems
Subramanian, HC, Angle, P, Nagaraj, N. (2022). Do Sentiment Indices Outperform Quantitative Indicators As Predictors For Cryptocurrency Prices?
. Pacific Asia Conference on Information Systems
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. Next using the signals with the highest causal scores, we use the Long-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. However, compared to the baseline of the 5-Day average and relative strength indicator (RSI), our methods are less performant in predicting prices.
