PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin

• URL: http://arxiv.org/abs/2206.00648v2
• Date: Sat, 21 Oct 2023 10:45:31 GMT
• Title: PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin
• Authors: Yanzhao Zou, Dorien Herremans
• Abstract summary: We propose a multimodal model for predicting extreme price fluctuations in Bitcoin. This model takes as input a variety of correlated assets, technical indicators, as well as Twitter content. We show that it can be used to build a profitable trading strategy with a reduced risk over a hold' or moving average strategy.
• Score: 8.38397409405955
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculative trading as compared to more traditional assets. In this paper, we propose a multimodal model for predicting extreme price fluctuations. This model takes as input a variety of correlated assets, technical indicators, as well as Twitter content. In an in-depth study, we explore whether social media discussions from the general public on Bitcoin have predictive power for extreme price movements. A dataset of 5,000 tweets per day containing the keyword `Bitcoin' was collected from 2015 to 2021. This dataset, called PreBit, is made available online. In our hybrid model, we use sentence-level FinBERT embeddings, pretrained on financial lexicons, so as to capture the full contents of the tweets and feed it to the model in an understandable way. By combining these embeddings with a Convolutional Neural Network, we built a predictive model for significant market movements. The final multimodal ensemble model includes this NLP model together with a model based on candlestick data, technical indicators and correlated asset prices. In an ablation study, we explore the contribution of the individual modalities. Finally, we propose and backtest a trading strategy based on the predictions of our models with varying prediction threshold and show that it can used to build a profitable trading strategy with a reduced risk over a `hold' or moving average strategy.

Related papers

• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Joint Latent Topic Discovery and Expectation Modeling for Financial Markets [45.758436505779386]
  We present a groundbreaking framework for financial market analysis. This approach is the first to jointly model investor expectations and automatically mine latent stock relationships. Our model consistently achieves an annual return exceeding 10%.
  arXiv  Detail & Related papers  (2023-06-01T01:36:51Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [57.70351255180495]
  We use ChatGPT to assess whether each headline is good, bad, or neutral for firms' stock prices. We find that ChatGPT outperforms traditional sentiment analysis methods. Long-short strategies based on ChatGPT-4 deliver the highest Sharpe ratio.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models [5.88864611435337]
  We propose a deep learning Synthesizer Transformer model for forecasting volatility. Our results show that the model outperforms existing state-of-the-art models. Our findings underscore that the proposed method is a useful tool for forecasting extreme volatility movements in the Bitcoin market.
  arXiv  Detail & Related papers  (2022-10-06T05:44:29Z)
• Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models [31.690290125073197]
  We present and publicly release CryptoBubbles, a novel multi-span identification task for bubble detection. We develop a set of sequence-to-sequence hyperbolic models suited to this multi-span identification task. We show the practical applicability of CryptoBubbles and hyperbolic models on Reddit and Twitter.
  arXiv  Detail & Related papers  (2022-05-11T08:10:02Z)
• A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction [100.9772316028191]
  In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models. Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation.
  arXiv  Detail & Related papers  (2022-05-01T05:12:22Z)
• Exploration of Algorithmic Trading Strategies for the Bitcoin Market [1.933681537640272]
  This work brings an algorithmic trading approach to the Bitcoin market to exploit the variability in its price on a day-to-day basis. As an empirical test of our models, we evaluate them using a real-world trading strategy on completely unseen data collected throughout the first quarter of 2021. Using only a binary predictor, our models showed an average profit of 86%, matching the results of the more traditional buy-and-hold strategy.
  arXiv  Detail & Related papers  (2021-10-28T08:13:34Z)
• Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data [2.9223917785251285]
  We focus on volatility predictions for a relatively new asset class of cryptocurrencies (in particular, Bitcoin) using deep learning representations of public social media data from Twitter. For the field work, we extracted semantic information and user interaction statistics from over 30 million Bitcoin-related tweets. We built several deep learning architectures that utilized a combination of the gathered information.
  arXiv  Detail & Related papers  (2021-10-27T09:55:03Z)
• Stock Price Prediction Under Anomalous Circumstances [81.37657557441649]
  This paper aims to capture the movement pattern of stock prices under anomalous circumstances. We train ARIMA and LSTM models at the single-stock level, industry level, and general market level. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98%.
  arXiv  Detail & Related papers  (2021-09-14T18:50:38Z)
• Real-Time Prediction of BITCOIN Price using Machine Learning Techniques and Public Sentiment Analysis [0.0]
  The objective of this paper is to determine the predictable price direction of Bitcoin in USD by machine learning techniques and sentiment analysis. Twitter and Reddit have attracted a great deal of attention from researchers to study public sentiment. We have applied sentiment analysis and supervised machine learning principles to the extracted tweets from Twitter and Reddit posts.
  arXiv  Detail & Related papers  (2020-06-18T15:40:11Z)
• Forecasting Bitcoin closing price series using linear regression and neural networks models [4.17510581764131]
  We study how to forecast daily closing price series of Bitcoin using data prices and volumes of prior days. We followed different approaches in parallel, implementing both statistical techniques and machine learning algorithms.
  arXiv  Detail & Related papers  (2020-01-04T21:04:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.