Stock Market Analysis and Predictions
Diya Choube, Akshata Haldule, Ayushi Zodape, Prof. Swati Shamkuwar
Department of IT, GHRCE, Nagpur
ABSTRACT:
Given the complexity and dynamic nature of the stock market, accurately predicting stock prices is a difficult task that necessitates a comprehensive understanding of various factors that influence it, including economic conditions, political events, and investor sentiment. To achieve accurate predictions, a combination of financial analysis, market knowledge, and technical expertise is required. In recent years, deep learning techniques, including neural networks and machine learning algorithms, have become increasingly popular as a means of predicting stock prices. This study aims to explore the effectiveness of deep learning techniques in improving stock market predictions by using historical data from the S&P 500 index as a case study. Through this research,we can determine whether these techniques can enhance the accuracy of stock market predictions and contribute to better decision-making for investors.
The financial market is a constantly evolving and multifaceted system that provides opportunities for individuals to engage in buying and selling a variety of financial assets, including currencies, stocks, equities, and derivatives, all of which can be conducted through virtual platforms supported by brokers. The stock market is a prominent component of the financial market, which enables investors to own a portion of public companies through the process of trading, either by exchange or over the counter markets. By investing in stocks, investors can profit from the growth of the company and may also receive a share of its profits in the form of dividends. The stock market is a crucial aspect of the global economy as it enables businesses to raise capital for growth and expansion, and also provides a platform for investors to earn returns on their investments.
KEYWORDS:
Machine Learning Algorithms, Stock Market, Prediction, Long Short Term Memory [LSTM], Stock Price, Recurrent Neural Network [RNN].
