International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
Stock Market Prediction using Long Short-Term Memory Shubhodh Amaravadi1, Pulluri Anudeep2, Yeramalla Uttam3, K Nagendra Chary4 1,2,3B. Tech Scholars, Department of Computer Science and Engineering, SNIST, Hyderabad-501301, India
4Assistant Professor, Department of Computer Science and Engineering, SNIST, Hyderabad-501301, India
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Abstract - It has never been straightforward to invest in
The model created by the authors uses the support vector machine (SVM) method and the mean variance (MV) method for portfolio selection. Others then go on to forecast stock returns using this unique decision-making model for day trading investments on the stock market. Deep learning techniques for smart indexing were discussed in another work. In addition, numerous studies have examined a wide range of trends and Applications of Machine Learning in Quantitative Finance. The literature review covered in this paper includes return forecasting, portfolio construction, ethics, fraud detection, decision making, language processing, and sentiment analysis. Since these models don't rely on long-term memory (passed-down data sequences), a class of machine learning techniques based on recurrent neural networks has shown to be particularly beneficial in financial market price prediction. The vanishing gradient problem, which arises as a result of the RNN blocks' repetitive usage of the same parameters at each step, is one of the primary problems with RNN.
a portfolio of assets since the abnormalities of the financial market prevent simple models from accurately forecasting future asset values. The current main trend in scientific study is machine learning, which consists of teaching computers to execute activities that ordinarily need human intelligence. In order to forecast future stock market values, this article uses recurrent neural networks (RNN), particularly the Long-Short Term Memory model (LSTM). This paper's major goal is to determine how accurately a machine learning algorithm can predict and how much epochs can enhance our model. Time series data are used to represent stock prices, and neural networks are trained to identify patterns from trends in the historical data. To increase the accuracy of stock price prediction, this system used an LSTM algorithm. Key Words: Machine Learning, RNN, LSTM, Price Prediction, SVM, Regression.
1.INTRODUCTION
While generalising variable-length sequences and maintaining a fixed number of learnable parameters overall, we introduce unique parameters at each step. Internal variables known as Gates are stored in gated cells. Every time step's information, including early states, determines the value of each gate. The many variables of interest are then multiplied by the gate's value in order to affect them. Data collected in a time-series format over a period allows us to track changes over time. Time-series data can monitor development over a period of seconds, days, or even years. This overall increases the accuracy in predicting the stock price.
Several research have investigated the use of machine learning in the quantitative financial sector. Machine learning algorithms can be used to predict prices for managing and restricting a full portfolio of assets, as well as for the investing process and many other processes. Machine learning, in its broadest sense, refers to all algorithmic techniques that enable computers to identify patterns based solely on data and without the use of programming instructions. Many models provide a variety of techniques that can be combined with machine learning in quantitative finance, particularly when choosing assets, to predict future asset value. This class of models provides a technique that combines weak sources of information to create an odd tool that can be applied effectively. A critical neural network, gradient boosted regression trees, support vector machines, and random forecast are just a few of the machine learning methods that have recently benefited from the integration of statistics and learning models. These algorithms can make complicated patterns with nonlinear properties and certain relations that are challenging to find with linear methods. Additionally, compared to linear regression methods, these ones demonstrate greater efficacy and multicollinearity. The application of machine learning techniques in finance is the topic of numerous research some of which utilised tree-based models to forecast portfolio returns and others which employed deep learning to create future.
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2. LITERATURE SURVEY The stock exchange has grown to be one of the most important events in today's financial world. The current state of the stock market has a significant impact on the global economy. People from many walks of life, whether they come from business or academic backgrounds, have been drawn to the stock market with great success. The stock market's nonlinear character has made research on it one of the most important and popular topics worldwide. People choose to invest in the stock market based on their predictions or knowledge from earlier studies. In terms of forecasting, people frequently seek out instruments or strategies that would reduce their risks and maximize their earnings; as a result, stock price forecasting assumes a
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