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Stock trend prediction using news sentiment analysis

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arxiv 1607.01958 v1 pith:4LMT2F5B submitted 2016-07-07 cs.CL cs.IRcs.LG

Joshi Kalyani , Prof. H. N. Bharathi , Prof. Rao Jyothi This is my paper · ORCID
classification cs.CLcs.IRcs.LG
keywords newsstockpredictionaccuracyarticlesmodeltrendclassification
verification ladder T0 review T1 audit T2 compute T3 formal
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Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much research has been carried in the area of prediction of stocks. This project is about taking non quantifiable data such as financial news articles about a company and predicting its future stock trend with news sentiment classification. Assuming that news articles have impact on stock market, this is an attempt to study relationship between news and stock trend. To show this, we created three different classification models which depict polarity of news articles being positive or negative. Observations show that RF and SVM perform well in all types of testing. Na\"ive Bayes gives good result but not compared to the other two. Experiments are conducted to evaluate various aspects of the proposed model and encouraging results are obtained in all of the experiments. The accuracy of the prediction model is more than 80% and in comparison with news random labeling with 50% of accuracy; the model has increased the accuracy by 30%.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Predictive Power of LLMs in Financial Markets

    q-fin.PM 2024-11 conditional novelty 5.0 of 10

    Using Beige Book text, a fine-tuned BERT predicts stock-bond correlations better than prompted GPT-3.5, which shows look-ahead bias.

  2. Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data

    cs.LG 2024-12 conditional novelty 4.0 of 10

    A factorized 'higher-order' transformer with kernelized linear attention and tweet plus price inputs reaches 72.94% accuracy and 0.516 MCC on StockNet, behind only NL-LSTM among the baselines compared.

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