Investigating Timing and Impact of News on the Stock Market

Merello, Simone; Ratto, Andrea Picasso; Ma, Yukun; Oneto, Luca; Wang, Zhaoxia

doi:10.1109/icdmw.2018.00191

Cited by 10 publications

(4 citation statements)

References 23 publications

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“…Stock price prediction from news. The feasibility of predicting stock prices from news has been debated (Merello et al, 2018) as the news can affect the price before it is published. In our case this is not an issue as it only matters that the price change is reflected in the news, not the timing; news about price movements are indeed relevant.…”

Section: Related Workmentioning

confidence: 99%

From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations

Corro

Hoffart

2021

Proceedings of the Third Workshop on Economics and Natural Language Processing

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We present a method to automatically identify financially relevant news using stock price movements and news headlines as input. The method repurposes the attention weights of a neural network initially trained to predict stock prices to assign a relevance score to each headline, eliminating the need for manually labeled training data. Our experiments on the four most relevant US stock indices and 1.5M news headlines show that the method ranks relevant news highly, positively correlated with the accuracy of the initial stock price prediction task.

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Section: Related Workmentioning

confidence: 99%

From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations

Corro

Hoffart

2021

Proceedings of the Third Workshop on Economics and Natural Language Processing

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“…Nowadays, with the development of natural language processing (NLP) and the large volume of news available, sentiment analysis has been applied with relative success in the financial market [24]. Several works use news information together with historical prices for forecasts and have shown results superior to models that use only OHLCV [25]- [27].…”

Section: Introductionmentioning

confidence: 99%

Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review

Bastos

2020

IEEE Access

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Stock market forecasting is one of the biggest challenges in the financial market since its time series has a complex, noisy, chaotic, dynamic, volatile, and non-parametric nature. However, due to computing development, an intelligent model can help investors and professional analysts reduce the risk of their investments. As Deep Learning models have been extensively studied in recent years, several studies have explored these techniques to predict stock prices using historical data and technical indicators. However, as the objective is to generate forecasts for the financial market, it is essential to validate the model through profitability metrics and model performance. Therefore, this systematic review focuses on Deep Learning models implemented for stock market forecasting using technical analysis. Discussions were made based on four main points of view: predictor techniques, trading strategies, profitability metrics, and risk management. This study showed that the LSTM technique is widely applied in this scenario (73.5%). This work significant contribution is to highlight some limitations found in the literature, such as only 35.3% of the studies analysed profitability, and only two articles implemented risk management. Therefore, despite the widely explored theme, there are still interesting open areas for research and development. INDEX TERMS Deep learning, profitability metrics, risk management, stock market forecasting, systematic review, technical analysis, technical indicators.

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“…While much of the literature has focused on the prediction of stock prices (e.g., Ding et al, 2015;Xie et al, 2013), it is recognized that predicting future stock movements is a formidable challenge (see e.g. Merello et al, 2018); still, there are use-cases that might benefit from business-related event extraction from news.…”

Section: Introductionmentioning

confidence: 99%

Financial Event Extraction Using Wikipedia-Based Weak Supervision

Ein-Dor¹,

Gera²,

Toledo-Ronen³

et al. 2019

Proceedings of the Second Workshop on Economics and Natural Language Processing

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Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques. This work is in line with this latter approach, leveraging relevant Wikipedia sections to extract weak labels for sentences describing economic events. Whereas previous weakly supervised approaches required a knowledgebase of such events, or corresponding financial figures, our approach requires no such additional data, and can be employed to extract economic events related to companies which are not even mentioned in the training data.

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Investigating Timing and Impact of News on the Stock Market

Cited by 10 publications

References 23 publications

From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations

From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations

Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review

Financial Event Extraction Using Wikipedia-Based Weak Supervision

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