Course Content

Abstract:

Stock market is one of the most important sectors of a country's economy. Prediction of stock prices is not easy since it is not stationary in nature. Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock market. Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor’s gains. The recent success of the application of Artificial Intelligence in the financial sector has resulted in more firms relying on stochastic models for predicting the behavior of the market. Stock market prediction has long been a major research topic that exploits various machine learning techniques and diverse sets of data. Most existing works utilize multiple stock historical statistics as well as up-to-date data of relevant factors which could have impacted the stock price value such as oil price, gold price, etc. Very few works explore the possibility of incorporating financial news when predicting the stock price direction.

So we proposed a system with the help of machine learning techniques and LSTM (Long Short Term Memory) algorithm to predict stock price based on previous stock data.

 

Chapter 1:

Introduction:

Stock market is characterized as dynamic, unpredictable and non-linear in nature. Predicting stock prices is a challenging task as it depends on various factors including but not limited to political conditions, global economy, company’s financial reports and performance etc. Thus, to maximize the profit and minimize the losses, techniques to predict values of the stock in advance by analyzing the trend over the last few years, could prove to be highly useful for making stock market movements . Traditionally, two main approaches have been proposed for predicting the stock price of an organization. Technical analysis method uses historical price of stocks like closing and opening price, volume traded, adjacent close values etc. of the stock for predicting the future price of the stock. The second type of analysis is qualitative, which is performed on the basis of external factors like company profile, market situation, political and economic factors, textual information in the form of financial new articles, social media and even blogs by economic analyst. Stock market is an aggregation stockbrokers and traders who can buy and sell shares of stocks. Many large companies have their stocks listed on a stock market. This makes the stock liquid and thus more attractive to the investors . There is a large number of investors who invest handsome amounts in a stock market. However, it involves risk since prices of stock may rise or fall within no time . That is why predicting stock prices is not an easy task and many researchers are working on it. Stock market prediction systems have long been an essential tool for stock traders. Generally, the stock movement direction is affected by many factors, e.g., gold price, oil price, important events, and last but not least news related to companies in the stock markets. While most factors considered in stock market prediction algorithms are quantitative values, significant number of researchers has used financial news in order to achieve higher accuracy in predicting future direction of a stock. Prediction of price movements in the stock market is generally believed to be a very difficult task. A well known hypothesis amongst academics, the Efficient Market Hypothesis , suggests that prices immediately reflect all the available information and the only thing that causes security prices to change is new information. Therefore, as the arrival of new information is unpredictable, prices in the market appear to be randomly generated.

 

So we proposed a system with the help of machine learning techniques and LSTM (Long Short Term Memory) algorithm to predict stock price based on previous stock data.

 

 

 

 

 

 

 

 

 

 

Objective:

The main aim of this project to predict the stock price using machine learning techniques and LSTM (Long Short Term Memory) algorithm by considering previous stock data.

 

Problem Statement

Stock market prediction is a major challenge owing to non-stationary, blaring, and chaotic data, and thus, the prediction becomes challenging among the investors to invest the money for making profits. Several techniques are devised in the existing techniques to predict the stock market trends but none of them are giving accurate results.

 

 

Proposed System:

 

We proposed a system with the help of machine learning techniques and LSTM (Long Short Term Memory) algorithm to predict stock price based on previous stock data.