A Dual-Branch Deep Architecture Fusing Finbert Embeddings With Sequential 1ce Data For Crypto Market Prediction

Abstract

Valuation mechanics within decentralized cryptocurrency ecosystems deviate fundamentally from traditional equity asset pricing paradigms. In the absence of standardized discounted cash flows, manda-tory regulatory disclosure calendars, and audited quarterly balance sheets, decentralized token valuations fluctuate under the continuous, reflexive influence of distributed retail sentiment, narrative shifts, and microblog speculation. Traditional natural language processing pipelines and generic financial lexicons regularly fail to capture the nuances of retail crypto discourse due to domain-specific slang, pervasive sarcasm, non-standard syntax, and shifting conversational sentiment polarities.

This paper introduces an end-to-end, high-throughput multimodal framework designed to harvest, contextualize, and fuse financial social sentiment with quantitative temporal price series. We systematically evaluate FinBERT—a language representation model specialized on financial corpora—against general-purpose Transformers (BERT-base, RoBERTa) and rule-based dictionaries (VADER, Loughran-McDonald) across 36 continuous months (2021–2023) of high-frequency social feeds and market data. By modeling tabular OHLCV data through recurrent temporal networks (Long Short-Term Memory units) and integrating daily aggregated log-odds sentiment indices via intermediate late fusion, our system achieves a 61.82% directional accuracy and a 60.90% Macro F1-score for next-day Bitcoin (BTC) directional movement. This delivers an 8.7% absolute performance gain over isolated time-series baselines and surpasses general-domain NLP pipelines by 3.4%. Extensive ablations, multi-horizon evaluations (t + 1, t + 3, t + 7), hyperparameter sensitivity profiles, statistical hypothesis verifications via Diebold-Mariano tests, and simulated transaction-cost-adjusted backtests confirm that domain-adapted language modeling extracts durable alpha from microblog noise.