Using Twitter Attribute Information to Predict Stock Prices

• URL: http://arxiv.org/abs/2105.01402v1
• Date: Tue, 4 May 2021 10:27:37 GMT
• Title: Using Twitter Attribute Information to Predict Stock Prices
• Authors: Roderick Karlemstrand, Ebba Leckstr\"om
• Abstract summary: The model is based on a neural network with several layers of LSTM and fully connected layers. It is trained with historical stock values, technical indicators and Twitter attribute information retrieved. The results show that by adding more Twitter attributes, the MSE between the predicted prices and the actual prices improved by 3%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Being able to predict stock prices might be the unspoken wish of stock investors. Although stock prices are complicated to predict, there are many theories about what affects their movements, including interest rates, news and social media. With the help of Machine Learning, complex patterns in data can be identified beyond the human intellect. In this thesis, a Machine Learning model for time series forecasting is created and tested to predict stock prices. The model is based on a neural network with several layers of LSTM and fully connected layers. It is trained with historical stock values, technical indicators and Twitter attribute information retrieved, extracted and calculated from posts on the social media platform Twitter. These attributes are sentiment score, favourites, followers, retweets and if an account is verified. To collect data from Twitter, Twitter's API is used. Sentiment analysis is conducted with VADER. The results show that by adding more Twitter attributes, the MSE between the predicted prices and the actual prices improved by 3%. With technical analysis taken into account, MSE decreases from 0.1617 to 0.1437, which is an improvement of around 11%. The restrictions of this study include that the selected stock has to be publicly listed on the stock market and popular on Twitter and among individual investors. Besides, the stock markets' opening hours differ from Twitter, which constantly available. It may therefore introduce noises in the model.

Related papers

• Beyond Trend Following: Deep Learning for Market Trend Prediction [49.89480853499917]
  We advocate for the use of Artificial Intelligence and Machine Learning techniques to predict future market trends. These predictions, when done properly, can improve the performance of asset managers by increasing returns and reducing drawdowns.
  arXiv  Detail & Related papers  (2024-06-10T11:42:30Z)
• Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages [8.504685056067144]
  We propose a novel Targeted Aspect-Based Emotion Analysis (TABEA) system that can individually discern the financial emotions (positive and negative forecasts) on the different stock market assets in the same tweet. It is based on Natural Language Processing (NLP) techniques and Machine Learning streaming algorithms. It achieves over 90% precision for the target emotions, financial opportunity, and precaution on Twitter.
  arXiv  Detail & Related papers  (2024-03-30T16:46:25Z)
• Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
  The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
  arXiv  Detail & Related papers  (2023-07-05T20:16:20Z)
• BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline [47.61306219245444]
  Twitter has become a target for bots and fake accounts, resulting in the spread of false information and manipulation. This paper introduces a semi-automatic machine learning pipeline (SAMLP) designed to address the challenges correlated with machine learning model development. We develop a comprehensive bot detection model named BotArtist, based on user profile features.
  arXiv  Detail & Related papers  (2023-05-31T09:12:35Z)
• Taureau: A Stock Market Movement Inference Framework Based on Twitter Sentiment Analysis [0.0]
  We propose Taureau, a framework that leverages Twitter sentiment analysis for predicting stock market movement. We first utilize Tweepy and getOldTweets to obtain historical tweets indicating public opinions for a set of top companies. We correlate the temporal dimensions of the obtained sentiment scores with monthly stock price movement data.
  arXiv  Detail & Related papers  (2023-03-30T19:12:08Z)
• The Impact of Twitter Sentiments on Stock Market Trends [0.0]
  We analyze the volume, sentiment, and mentions of the top five stock symbols in the S&P 500 index on Twitter over three months. Our study revealed a significant correlation between stock prices and Twitter sentiment.
  arXiv  Detail & Related papers  (2023-02-14T18:43:20Z)
• Evaluating Impact of Social Media Posts by Executives on Stock Prices [0.5429166905724048]
  Social media like Twitter, Reddit have become hotspots of such influences. This paper investigates the impact of social media posts on close price prediction of stocks using Twitter and Reddit posts.
  arXiv  Detail & Related papers  (2022-11-01T03:45:17Z)
• 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)
• Identification of Twitter Bots based on an Explainable ML Framework: the US 2020 Elections Case Study [72.61531092316092]
  This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
  arXiv  Detail & Related papers  (2021-12-08T14:12:24Z)
• 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)
• A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
  We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
  arXiv  Detail & Related papers  (2020-12-10T01:15:48Z)

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.