Transformer-based approach for Ethereum Price Prediction Using Crosscurrency correlation and Sentiment Analysis

• URL: http://arxiv.org/abs/2401.08077v1
• Date: Tue, 16 Jan 2024 03:03:39 GMT
• Title: Transformer-based approach for Ethereum Price Prediction Using Crosscurrency correlation and Sentiment Analysis
• Authors: Shubham Singh, Mayur Bhat
• Abstract summary: The research delves into the capabilities of a transformer-based neural network for cryptocurrency price forecasting. The experiment runs around the hypothesis that cryptocurrency prices are strongly correlated with other cryptocurrencies and the sentiments around the cryptocurrency.
• Score: 4.641297430032214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The research delves into the capabilities of a transformer-based neural network for Ethereum cryptocurrency price forecasting. The experiment runs around the hypothesis that cryptocurrency prices are strongly correlated with other cryptocurrencies and the sentiments around the cryptocurrency. The model employs a transformer architecture for several setups from single-feature scenarios to complex configurations incorporating volume, sentiment, and correlated cryptocurrency prices. Despite a smaller dataset and less complex architecture, the transformer model surpasses ANN and MLP counterparts on some parameters. The conclusion presents a hypothesis on the illusion of causality in cryptocurrency price movements driven by sentiments.

Related papers

• Can Transformers Learn $n$-gram Language Models? [77.35809823602307]
  We study transformers' ability to learn random $n$-gram LMs of two kinds. We find that classic estimation techniques for $n$-gram LMs such as add-$lambda$ smoothing outperform transformers.
  arXiv  Detail & Related papers  (2024-10-03T21:21:02Z)
• Multi-Source Hard and Soft Information Fusion Approach for Accurate Cryptocurrency Price Movement Prediction [5.885853464728419]
  We introduce a novel approach termed hard and soft information fusion (HSIF) to enhance the accuracy of cryptocurrency price movement forecasts. Our model has about 96.8% accuracy in predicting price movement. incorporating information enables our model to grasp the influence of social sentiment on price fluctuations.
  arXiv  Detail & Related papers  (2024-09-27T16:32:57Z)
• Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers [54.20763128054692]
  We study how a two-attention-layer transformer is trained to perform ICL on $n$-gram Markov chain data. We prove that the gradient flow with respect to a cross-entropy ICL loss converges to a limiting model.
  arXiv  Detail & Related papers  (2024-09-09T18:10:26Z)
• IT Strategic alignment in the decentralized finance (DeFi): CBDC and digital currencies [49.1574468325115]
  Decentralized finance (DeFi) is a disruptive-based financial infrastructure. This paper seeks to answer two main questions 1) What are the common IT elements in the DeFi? And 2) How the elements to the IT strategic alignment in DeFi?
  arXiv  Detail & Related papers  (2024-05-17T10:19:20Z)
• AI-Assisted Investigation of On-Chain Parameters: Risky Cryptocurrencies and Price Factors [0.9831489366502302]
  This paper focuses on analyzing historical data and using artificial intelligence algorithms on on-chain parameters. We conducted an analysis of historical cryptocurrencies' on-chain data and measured the correlation between the price and other parameters. Our analysis revealed a significant negative correlation between cryptocurrency price and maximum and total supply, as well as a weak positive correlation between price and 24-hour trading volume.
  arXiv  Detail & Related papers  (2023-08-11T09:20:28Z)
• Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships [1.4356611205757077]
  Six popular cryptocurrencies, Bitcoin, Coin, Litecoin, Ripple, and Tether are studied in this work. We propose a dynamic Bayesian network (DBN)-based approach to uncover potential causal relationships among various features including social media data, traditional financial market factors, and technical indicators. The results show that while DBN performance varies across cryptocurrencies, some cryptocurrencies exhibiting higher predictive accuracy than others, the DBN significantly outperforms the baseline models.
  arXiv  Detail & Related papers  (2023-06-13T22:07:51Z)
• 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)
• Social Media Sentiment Analysis for Cryptocurrency Market Prediction [0.0]
  We study how the different sentiment metrics are correlated with the price movements of Bitcoin. One of the models outperforms more than 20 other public ones.
  arXiv  Detail & Related papers  (2022-04-19T03:27:29Z)
• The Doge of Wall Street: Analysis and Detection of Pump and Dump Cryptocurrency Manipulations [50.521292491613224]
  This paper performs an in-depth analysis of two market manipulations organized by communities over the Internet: The pump and dump and the crowd pump. The pump and dump scheme is a fraud as old as the stock market. Now, it got new vitality in the loosely regulated market of cryptocurrencies. We report on three case studies related to pump and dump groups.
  arXiv  Detail & Related papers  (2021-05-03T10:20:47Z)
• LSTM Based Sentiment Analysis for Cryptocurrency Prediction [11.811501670389935]
  This research is directed to predicting the volatile price movement of cryptocurrency by analyzing the sentiment in social media. We propose a method to identify the sentiment of the Chinese social media posts from the most popular Chinese social media platform Sina-Weibo. We develop the pipeline to capture Weibo posts, describe the creation of the crypto-specific sentiment dictionary, and propose a long short-term memory (LSTM) based recurrent neural network.
  arXiv  Detail & Related papers  (2021-03-27T04:08:37Z)
• Quantum Multi-Solution Bernoulli Search with Applications to Bitcoin's Post-Quantum Security [67.06003361150228]
  A proof of work (PoW) is an important cryptographic construct enabling a party to convince others that they invested some effort in solving a computational task. In this work, we examine the hardness of finding such chain of PoWs against quantum strategies. We prove that the chain of PoWs problem reduces to a problem we call multi-solution Bernoulli search, for which we establish its quantum query complexity.
  arXiv  Detail & Related papers  (2020-12-30T18:03:56Z)

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.