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@article{171070,
        author = {SHAURYA GUPTA},
        title = {METHODOLOGY FOR DEVELOPING DEEP LEARNING MODELS FOR DIFFERENT TYPES OF STOCKS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {2045-2051},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171070},
        abstract = {Stock market prediction provides an exhaustive domain of problems to be solved. The stock prices are affected by a large set of factors and their complex interrelationships. It becomes a difficult task for com- puters to generalize the trend and predict the market movement with hundred percent accuracy at all times. This project aims to develop an efficient system to predict stock price movement of a given stock. In the initial part of this project we review twenty papers from reputed journals to get the idea about the current advancement in the field of Natural Language Processing, Machine learning and Deep Learning applications in recent times. After the literature survey, we found that some statistical machine learning models are able to predict stock market data with good accuracy, mostly random forest models [12], but also found that deep learning models perform exceptionally well for stock market prediction due to their ability to understand nonlinear relation- ships. The project uses attention based Bi-LSTM with attention model[16] and different variations possible in the system to predict the stock prices for best results. Eleven different variants of LSTM based mod- els were trained and evaluated on their forecasting capabilities using Nasdaq 100 technology sector index data, and the best performing model  was  selected  to  forecast  stock  price  for the next  day  in  future.},
        keywords = {Stock Price Prediction, Machine Learning, LSTM, NLP},
        month = {December},
        }