Predicting stock market prices is one of the most difficult and important areas of research in computer science, economics, and finance. The extensive availability of historical stock data and recent technologies that were developed to use artificial intelligence have caused a large increase in the number of machine learning techniques that usage in order to predict future stock prices. There are many different internal and external factors that will affect stock prices and thus, the volatility of stock prices on the stock market. Some of those factors include, but are not limited to, investor psychology, global events, economic policies, and company earnings. Traditional statistical analysis may not always identify nonlinear trends within financial time series data; therefore, monetary indicators such as Moving Average Convergence Divergence, Relative Strength Index, and Moving Average will be used in conjunction with machine learning techniques to accurately predict whether prices will go up or down for stock prices. The data to train the different classifiers (Support Vector Machine, Random Forest, Logistic Regression) will come from past National Stock Exchange data. The accuracy and other classification model evaluation metrics (Precision, Recall, F1 Score) will be measured. Overall, ensemble classification techniques provide better prediction accuracy compared to other traditional techniques. Predicting the future stock price direction continues to be a strong challenge because financial markets are nonlinear, dynamic, and chaotic in basis. The development of additional historical stock price data and the use of artificial intelligence are enabling stock price prediction to be more widely adopted, through the use of machine learning. Because there are numerous internal and external factors that affect the volatility of the stock market (i.e., investor sentiment, world events, economic policy, and firm performance), financial time series data often contains nonlinear trends that traditional statistical methodologies cannot detect. This study will suggest a way to determine whether stock prices have the probability of moving up or down by utilizing technical indicators such as MACD, RSI, and MA, along with machine learning algorithms. Financial data (historical stock price data) will be collected through the National Stock Exchange of India (NSE) to create a model to determine whether stock prices will continue moving up or move down. In order to create a model to determine the future movement of stock prices using machine learning algorithms, we will use three different types of algorithms, support vector machines (SVM), logistic regression, and long short term memory (LSTM) networks, in order to categorize future stock price movements as either increasing or decreasing. Due to the highly dynamic, non-linear, and volatile nature of financial markets, it has always been very difficult for investors and traders to accurately predict price movements in the stock market. However, accurate predictions regarding stock price movements, whether prices will rise or fall, are critical in risk management, portfolio optimization, and investment decisions.
Technical indicators, stock market, machine learning, logistic regression, RSI, MACD, Predictive modeling, data base decision making, Algorithmic Trading, Quantitative Finance, Ensemble Learning, feature engineering, Technical indicators, moving averages, RSI, MACD, Historical stock data, Market trend analysis, Volatility prediction, Financial data mining
International Journal of Trend in Scientific Research and Development - IJTSRD having
online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International
Journal which provides rapid publication of your research articles and aims to promote
the theory and practice along with knowledge sharing between researchers, developers,
engineers, students, and practitioners working in and around the world in many areas
like Sciences, Technology, Innovation, Engineering, Agriculture, Management and
many more and it is recommended by all Universities, review articles and short communications
in all subjects. IJTSRD running an International Journal who are proving quality
publication of peer reviewed and refereed international journals from diverse fields
that emphasizes new research, development and their applications. IJTSRD provides
an online access to exchange your research work, technical notes & surveying results
among professionals throughout the world in e-journals. IJTSRD is a fastest growing
and dynamic professional organization. The aim of this organization is to provide
access not only to world class research resources, but through its professionals
aim to bring in a significant transformation in the real of open access journals
and online publishing.