Explainable stock prices prediction from financial news articles using sentiment analysis

Abstract

The stock market is very complex and volatile. It is impacted by positive and negative sentiments which are based on media releases. The scope of the stock price analysis relies upon ability to recognise the stock movements. It is based on technical fundamentals and understanding the hidden trends which the market follows. Stock price prediction has consistently been an extremely dynamic field of exploration and research work. However, arriving at the ideal degree of precision is still an enticing challenge. In this paper, we are proposing a combined effort of using efficient machine learning techniques coupled with a deep learning technique—Long Short Term Memory (LSTM)—to use them to predict the stock prices with a high level of accuracy. Sentiments derived by users from news headlines have a tremendous effect on the buying and selling patterns of the traders as they easily get influenced by what they read. Hence, fusing one more dimension of sentiments along with technical analysis should improve the prediction accuracy. LSTM networks have proved to be a very useful tool to learn and predict temporal data having long term dependencies. In our work, the LSTM model uses historical stock data along with sentiments from news items to create a better predictive model.

KL-MOB: automated COVID-19 recognition using a novel approach based on image enhancement and a modified MobileNet CNN

“…Besides, this work lacked in applying some of the techniques such as progressive resizing ( Bhatt, Ganatra & Kotecha, 2021a ), which can be applied to CNNs to carry out imaging-based diagnostics. Furthermore, visual ablation studies ( Bhatt, Ganatra & Kotecha, 2021b ; Joshi, Walambe & Kotecha, 2021 ; Gite et al, 2021 ) can be performed along with deep learning, which will significantly improve the detection of COVID-19 manifestations in the CXR images. Since only a limited number of CXR images are available for COVID-19 infection, out-of-distribution issues may arise, so more data from related distributions is needed for further evaluation.…”

Explainable Sentiment Analysis: A Hierarchical Transformer-Based Extractive Summarization Approach

“…It is particularly true due to the advent of blogs and social media, and, thus, the impressive growth that shared content has shown. Organizations are currently using these kind of data for their decision making processes instead of conduct surveys, for example, to rank products or services from the users' reviews [10] and provide recommendations to the users [11], to predict changes in the stock prices [12], or, to give an example closer to the domain of our work, to predict incomes from movies at the box-office basing the prediction on the online movies' reviews [13]. In particular, sentiment analysis tasks may be performed at the word, sentence, and document levels.…”

COVID19-HPSMP: COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction

“…Recent advancements and developments in the field of ML and AI, in particular, Deep Neural Networks (DNNs), have motivated different research works to incorporate such advanced modeling techniques for prediction and forecasting tasks in stock markets ( Tetlock, 2017 ). In particular, there has been a recent surge of interest in information fusion ( Narkhede et al, 2021 ) and sentiment analysis ( Choudrie et al, 2021 ) based on stock market data ( Gite et al, 2021 , Patel et al, 2015 ) and their effects on stock markets. On the one hand, Ref.…”

“…On the other hand, Ref. Gite et al (2021) focused on predicting stock prices from sentiment analysis with use of ML/DL approaches. News headlines have a tremendous effect on the buying and selling patterns as traders easily get influenced by what they read.…”