A Novel Market Sentiment Analysis Model for Forecasting Stock and Cryptocurrency Returns(Article)
- aUniversity of Novi Sad, Faculty of Technical Sciences, Novi Sad, 21000, Serbia
- bUniversity of Guelph, Department of Economics and Finance, Guelph, ON N1G 2W1, Canada
- cIÉSEG School of Management, LEM-CNRS, Department of Finance, Lille, 59000, France
- dIÉSEG School of Management, Department of Finance, Lille, 59000, France
Abstract
This article develops a method for extracting information related to the underlying stock and cryptocurrency market sentiment from European put and call option prices. We study the evolution of market sentiment and predictability of prices in the S&P 500 index and Bitcoin (BTC/USD) futures markets during the 2020-2022 period. Several innovative temporal entropic and nonentropic measures of market sentiment based on a pessimistic, a market consensus, and an optimistic view are proposed in our nonlinear forecasting models. We show that these measures have significant predictive power for future spot prices at longer forecast horizons, where they statistically and economically outperform alternative models. We also find that the BTC/USD market is more susceptible to extreme sentiments reflected in demand-based shocks, while the information regarding the degree of pessimism in relation to the market consensus is more useful in forecasting the spot S&P 500 index movements in the presence of systemic shocks. © 2013 IEEE.
Indexed keywords
| Engineering controlled terms: | CostsDecision support systemsFinancial marketsForecastingLearning systemsRisk assessment |
|---|---|
| Engineering uncontrolled terms | Analysis modelsDecision supportsElectric shockExtracting informationIndexMachine-learningMarket consensusPredictive modelsSentiment analysisSystemic risks |
| Engineering main heading: | Sentiment analysis |
- ISSN: 21682216
- Source Type: Journal
- Original language: English
- DOI: 10.1109/TSMC.2024.3402160
- Document Type: Article
- Publisher: Institute of Electrical and Electronics Engineers Inc.
Gradojevic, N.; University of Novi Sad, Faculty of Technical Sciences, Novi Sad, Serbia;
© Copyright 2024 Elsevier B.V., All rights reserved.
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