Stock Market Price Prediction and Analysis
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| Natura: | Recurso digital |
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Zenodo
2021
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| _version_ | 1866901308284338176 |
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| author | Ajinkya Rajkar Aayush Kumaria Aniket Raut Nilima Kulkarni |
| author_facet | Ajinkya Rajkar Aayush Kumaria Aniket Raut Nilima Kulkarni |
| contents | India's stock market is extremely variable and indeterministic, which has a limitless number of aspects that regulate the directions and trends of the stock market; therefore, predicting the uptrend and downtrend is a complicated process. This paper aims to demonstrate the use of recurrent neural networks in finance to predict the closing price of a selected stock and analyze sentiments around it in real-time. By combining both these techniques, the proposed model can give buy or sell recommendations. The proposed system has been implemented as a web app using Django and React. The React Web App displays all live prices and news received from the self-built Django Server via web scraping. Additionally, the Django server serves as a bridge between the React frontend and the machine learning algorithm built with Keras and further enhanced with Tensorflow. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18609822 |
| institution | Zenodo |
| language | |
| publishDate | 2021 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Stock Market Price Prediction and Analysis Ajinkya Rajkar Aayush Kumaria Aniket Raut Nilima Kulkarni Django Server React Web-app Recurrent Neural Networks Stock Market Prediction Time Series India's stock market is extremely variable and indeterministic, which has a limitless number of aspects that regulate the directions and trends of the stock market; therefore, predicting the uptrend and downtrend is a complicated process. This paper aims to demonstrate the use of recurrent neural networks in finance to predict the closing price of a selected stock and analyze sentiments around it in real-time. By combining both these techniques, the proposed model can give buy or sell recommendations. The proposed system has been implemented as a web app using Django and React. The React Web App displays all live prices and news received from the self-built Django Server via web scraping. Additionally, the Django server serves as a bridge between the React frontend and the machine learning algorithm built with Keras and further enhanced with Tensorflow. |
| title | Stock Market Price Prediction and Analysis |
| topic | Django Server React Web-app Recurrent Neural Networks Stock Market Prediction Time Series |
| url | https://doi.org/10.5281/zenodo.18609822 |