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![Related Work
haithem.afli@cit.ie 8
§ Twitter [Bollen, 2011] : Sentiment Analysis of Twitter for
stock market prediction
§ Business NEWS [Hu, Ziniu, et al., 2019]: Sentiment Analysis
for stock market prediction
§ Event driven stock market forecasting [Ding, Xiao, et al.,
2015]: Finding quantifiable events in twitter and Business
NEWS.](https://image.slidesharecdn.com/praveenjoshi-haithemafliavs2020final-200610132504/85/Affective-Analytics-and-Visualization-for-Ensemble-event-driven-stock-market-forecasting-8-320.jpg)
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Affective Analytics and Visualization for Ensemble event-driven stock market forecasting
This document discusses using artificial intelligence for stock market forecasting. It proposes combining Twitter data, business news, and financial indicators to build predictive models. The approach includes preprocessing text data, feature engineering, and evaluating machine learning and deep learning models. Experimental results are presented comparing different hybrid architectures that integrate models from each data source using techniques like transfer learning, model ensembling, and engineered features. The conclusion is that combining social media, news, and financial data using artificial intelligence shows promise for predicting stock price movements.
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Affective Analytics and Visualization for Ensemble event-driven stock market forecasting
- 1. http://www.cit.ie Computer Science Department Haithem.afli@cit.ie @AfliHaithem Affective Analytics and Visualization for Ensemble event-driven Stock Market Forecasting Praveen Joshi & Haithem Afli AVI 2020 Workshop on AI, Big Data analysis and Visualization June 9th, 2020
- 2. Outline haithem.afli@cit.ie 2 Introduction andContext Stock Market and Artificial Intelligence Related work Proposed Approach System Architecture Task Description Experimental Setup Results Conclusion and Discussion
- 3. haithem.afli@cit.ie 3 The StockMarket The stock market is where you can buy, sell, and trade stocks any business day. It's also called a stock exchange, and works like an auction where investors buy and sell shares of stocks.
- 4. haithem.afli@cit.ie 4 The StockMarket Stocks allow you to own a share of a public corporation. Stock prices usually reflect investors' opinions of what the company's earnings will be.
- 5. haithem.afli@cit.ie 5 The StockMarket Traders Ø They are aware of every move that can harm the company or the sector. Ø Fundamentals of the company is also the most important part in their working. Ø Future predictions are also sometimes on purpose and without any knowing as people listen to them and they make their investments according to their saying. Ø They have contacts and the contacts are also the source for their wins to happen.
- 6. The Stock MarketTraders haithem.afli@cit.ie 6
- 7. Can we automatethe Traders Task using Artificial Intelligence? haithem.afli@cit.ie 7 Graph from Tobias Bohnhoff https://nativevideotube.blogspot.com/
- 8. Related Work haithem.afli@cit.ie 8 §Twitter [Bollen, 2011] : Sentiment Analysis of Twitter for stock market prediction § Business NEWS [Hu, Ziniu, et al., 2019]: Sentiment Analysis for stock market prediction § Event driven stock market forecasting [Ding, Xiao, et al., 2015]: Finding quantifiable events in twitter and Business NEWS.
- 9. haithem.afli@cit.ie 9 Twitter BusinessNEWS Financial Indicator Model combination Output (Rise/ Fall) X-Visualisation Contribution
- 10. Proposed Approach haithem.afli@cit.ie 10 §Build Twitter model to capture sentiments and emotions § Build Business NEWS model to capture sentiments and emotions. § Assess hybrid model architecture to incorporate multi- platform data and predict stock market. § Validate the Prediction with the help of Visualisation.
- 11. Preprocessing pipeline fortextual data haithem.afli@cit.ie 11 Scrapped Raw Text : Twitter tweets via python API and NEWS via Data Miner of Chrome Social Data Twitter Business News Financial Times Noisy Entity Removal Stop words Punctuations URL’s Text Normalization Tokenization Lemmatization Processed Textual Documents
- 12. Textual: Feature Engineered DatasetPreparation haithem.afli@cit.ie 12 Sentiment Analysis Emotion AnalysisLegends: Processed Documents Twitter Text • Tweet Length • Re-Tweets NEWS Text • NEWS Length Combining Feature Set Resultant dataset for Twitter Features Extracted from Ontology Features from Scraper Harvard IV-4 Loughran and McDonald Text Blob NRC Emotion Lexicon Feature Engineered Twitter Dataset Ontology Space
- 13. Financial Indicator: DataCuration and Feature Engineered Dataset Preparation haithem.afli@cit.ie 13 Quandl API Series Engineering • Sma1 • Sma2 • Vol increment • Open increment • Ma7 • Ma21 • 26EMA • 12EMA • MACD • 30 Day • MA 30 Day STD • Upper band • Lower band • EMA momentum • log momentum • Fourier transformed • Open • High • Close • Adj Close • Volume • Date Financial Data Feature Engineered Financial Dataset
- 14. Experimental Setup: DataOverview haithem.afli@cit.ie 14
- 15. Experimental Setup: ML/DL haithem.afli@cit.ie15 Naïve Bayes Gradient Boosting Ada Boost xGboost Voting Classifier Random Forest Machine Learning Models Dense Neural Network CNN_LSTM Network CNN BidirectionalLSTM Network CNN_GRU Network CNN_RNN Network ULMFiT Deep Learning Models Artificial Intelligence Predictive Models
- 16. haithem.afli@cit.ie 16 Hybrid Architecturebased on Best Model Selection Strategy Twitter Model Machine Learning Model ULMFiT Deep Learning Model BERT Deep Learning Model NEWS Model Machine Learning Model ULMFiT Deep Learning Model BERT Deep Learning Model Financial Model Machine Learning Model Voting Classifier Output Best Twitter model Best Financial model Best NEWS Model Input
- 17. 17 Hybrid Architecture basedon Shallow Transfer Learning Model Twitter BERT NEWS BERT Input ML/ DL Classifier Output BERT Dataset haithem.afli@cit.ie
- 18. Hybrid Architecture basedon Engineered Features 10/06/2020 haithem.afli@cit.ie 18 Twitter Model Feature Engineered NEWS Model Feature Engineered Financial Model Feature Engineered ML/ DL Classifier Output Input Feature Engineered Dataset
- 19. haithem.afli@cit.ie 19 Hybrid Architecturebased on Shallow Transfer Learning (DL Models) results Hybrid Architecture based on engineered features dataset (DL Models) results Results
- 20. haithem.afli@cit.ie 20 Results Hybrid Architecturebased on engineered features dataset (ML models) results Hybrid Architecture based on Best Model Selection Strategy results
- 21. Conclusion Haithem.afli@cit.ie § Feasibility studyof the social media platform and Business NEWS over the stock market prediction. § With the amount of data flowing in different social media platform and righteous Business NEWS, in coming future, it will be very much possible to capture the stock price movement with multiple such platforms efficiently. § Complex feature can be developed in the financial indicator as they showed prominent results as individual models.
- 22. http://www.cit.ie Computer Science Department Haithem.afli@cit.ie @AfliHaithem Thank you