Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision
Analyzes BERT classifiers and weak supervision for predicting cryptocurrency returns using sentiment from news and social media.
What it examines
This paper examines the impact of sentiment from news and social media on predicting cryptocurrency prices using machine learning models, specifically BERT classifiers and weak supervision. It focuses on Bitcoin and Ethereum, aiming to enhance forecast accuracy and trading decisions.
What it concludes
The research highlights the potential of weak supervision and sentiment analysis in improving cryptocurrency forecasts. Future research could explore other financial markets and refine sentiment aggregation methods. Applications include better trading strategies and financial decision-making tools.
Evidence objects
The research highlights the potential of weak supervision and sentiment analysis in improving cryptocurrency forecasts. Future research could explore other financial markets and refine sentiment aggregation methods. Applications include better trading strategies and financial decision-making tools.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: Anticipating price developments in financial markets is a topic of continued interest in forecasting. Funneled by advancements in deep learning and natural language processing (NLP) together with the availability of vast amounts of textual data in form of news articles, social media postings, etc., an increasing number of studies incorporate text-based predictors in forecasting models. We contribu… ▽ More Anticipating price developments in financial markets is a topic of continued interest in forecasting. Funneled by advancements in deep learning and natural language processing (NLP) together with the availability of vast amounts of textual data in form of news articles, social media postings, etc., an increasing number of studies incorporate text-based predictors in forecasting models. We contribute to this literature by introducing weak learning, a recently proposed NLP approach to address the problem that text data is unlabeled. Without a dependent variable, it is not possible to finetune pretrained NLP models on a custom corpus. We confirm that finetuning using weak labels enhances the predictive value of text-based features and raises forecast accuracy in the context of predicting cryptocurrency returns. More fundamentally, the modeling paradigm we present, weak labeling domain-specific text and finetuning pretrained NLP models, is universally applicable in (financial) forecasting and unlocks new ways to leverage text data. △ Less
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