ABSTRACT
Prediction and analysis of the stock
market data is playing a significant role in today's economy. The process in
the stock market is obviously with lot of uncertainty so it is highly affected
by lot of many factors. This became an important endeavoring business and
finance. The objective of this project is to make a literature search and
identify algorithms use for stock exchange prediction. Two deep learning
algorithms were used in this work to predict the stock exchange price which are
CNN and BiLSTM , and the raw data was collected from Tata Global Beverage from
19/07/2010 to 4/01/2019 is used including 2260 trading days, and our statically
results shows clearly that BiLSTM model produced Superior results compared to
the CNN model, therefore it can be deduced that it is better to predict future
stock exchange price by means of BiLSTM.
Stock
is a financial product characterized by high risk, high return and flexible
trading, which is favored by many investors. Investors can get abundant returns
by accurately estimating stock price trends. However, the stock price is
influenced by many factors such as macroeconomic situation, market condition,
major social and economic events, investors’ preferences and companies’
managerial decisions. Therefore, prediction of the stock price has always been
the focus and difficult research topic. Statistical and econometric models are
generally used in traditional stock price prediction, but these methods cannot
deal with the dynamic and complex environment of the stock market. Since 1970,
with the rapid development of computer technology, researchers have begun using
machine learning to predict stock prices and fluctuations, helping investors
determine investment strategies to reduce risk and increase returns.
The
stock market is a highly complex time series scenario and has typical dynamic
characteristics. There will be a lot of stock dynamic trading after the opening
of the market and stock price will change accordingly. Moreover, the stock
price is affected by many unpredicted factors, which results in a typical
nonstationary stock price time-series data. Therefore, stock price prediction
is one of the most challenging problems in all kinds of prediction research. In
the past decades, scholars have studied stock price prediction from many
perspectives, where the improvement of prediction models and the selection of
model features are the two most important directions among them. Most of the
early studies used econometric models, such as autoregressive integrated moving
average (ARIMA) and autoregressive conditional heteroskedastic autoregressive
integrated moving average (ARCH-ARIMA) (Booth et al., 2001), to predict stock
price. However, it is difficult for econometric models to consider the impact
of other factors on stock price fluctuations and they have strong assumptions
about the data, which are often difficult to meet (Le and Xie, 2018). Therefore,
machine learning has been widely used in stock price prediction in recent years
and many more suitable models for stock prediction have been proposed. Many
studies have shown that deep learning has superior efficiency than other models
(Marmer, 2008) and neural network models excel regression and discriminant
models (Refenes et al., 1994). In terms of feature selection, some scholars
explore the correlation between new features and stock price and some new
features, including political factors, macroeconomic factors and investors’
sentiment, etc., have been incorporated into the prediction model
(Cervello-Royo et al., 2015).
Predicting
the accurate stock price has been the aim of investors ever since the beginning
of the stock market. Millions
of dollars’ worth of trading
happens every single day, and every trader hopes to earn profit from his/her
investments. Investors who can make right buy and sell decisions
will end up in profits.
To make right decisions, investors have to judge based on technical analysis, such as company’s charts,
stock market indices
and information from newspapers and microblogs.
However, it is difficult for investors to analyze and forecast the market by
churning all this information. Therefore, to predict the trends automatically,
many Artificial Intelligence (AI) techniques have been investigated. (Vanaga
and Sloka, 2020). Some of the first research in prediction of stock prices dates
back to 1994, in which
a comparative study
(Sousa et al., 2019), with machine learning
regression. models were performed. Since then, many researchers were
investing resources to devise strategies for forecasting the price of the
stock.
Financial
analysts investing in stock market usually are not aware of the stock market behavior.
They are facing the problem of trading as they do not properly understand which
stocks to buy or which stocks to sell in order to get more profits. In today’s
world, all the information pertaining to stock market is available but
analyzing all this information individually or manually is difficult due to the
sporadic data. Automation of the process
is required to achieve the solution to the stock trading problem. Many
technologies and techniques have been deployed with achievable results to help investors
in analyzing stock exchange trading. To automate the understanding and analysis
of such numerical time series, machine learning might provide a good result
with intelligent decision. This will allow financial analysts to foresee the
behavior of the stock that they are interested in and thus act accordingly.
The aim of this project is to design and
implement a deep learning model that can predict stock exchange
The objectives are; prediction models for stock markets, which is also the scope of
this survey.
i.
To make a literature
search and identify algorithms to use for stock exchange prediction.
ii.
To build a prediction model for stock markets.
iii.
To evaluate the
performance of the built model
Stock exchange
prediction is very tedious for financial analyst due to huge number of
information to understand and apply in order to gain profit in the investment.
Despite that it consumes lots of time, dealing with volume of information
always leads to inaccuracy.
This
project work will add to the literature on the stock exchange prediction using
Artificial Intelligence (AI) technique. Extracting and analysing huge number of
information for the prediction by human is always prone to errors, whereas
applying the AI technique appropriately not only produce accurate result, but
also saves time and energy. Above all, more precision and timeliness are always
achieved with the AI techniques applied.
1.5
Scope of the Study
The
scope of this research focused on financial analysis of stock exchange market
to predict the price of stock exchange based on 10-year dataset obtained from
TATA GLOBAL BEVERAGES LIMITED.
Chapter
One introduces the study of stock market, machine learning in line with the
project topic, including background of the study, statement of the problem,
aims and objectives, significance of the study and project organization.
Chapter Two deals with literature review of stock market, machine learning,
support vector machine, deep learning, Convolutional Neural Network (CNN) and
Long Short Time Memory (LSTM) and highlighted related works were discussed.
Chapter Three presents the methodology adopted for ensuring and carrying out
this project and the development of a model that predicts a dataset of stock
market. Chapter Four presents the results and discussions of the models used in
the prediction of stock market while Chapter Five provides the summary of the
results, recommendations, and conclusion.
Click “DOWNLOAD NOW” below to get the complete Projects
FOR QUICK HELP CHAT WITH US NOW!
+(234) 0814 780 1594
Buyers has the right to create
dispute within seven (7) days of purchase for 100% refund request when
you experience issue with the file received.
Dispute can only be created when
you receive a corrupt file, a wrong file or irregularities in the table of
contents and content of the file you received.
ProjectShelve.com shall either
provide the appropriate file within 48hrs or
send refund excluding your bank transaction charges. Term and
Conditions are applied.
Buyers are expected to confirm
that the material you are paying for is available on our website
ProjectShelve.com and you have selected the right material, you have also gone
through the preliminary pages and it interests you before payment. DO NOT MAKE
BANK PAYMENT IF YOUR TOPIC IS NOT ON THE WEBSITE.
In case of payment for a
material not available on ProjectShelve.com, the management of
ProjectShelve.com has the right to keep your money until you send a topic that
is available on our website within 48 hours.
You cannot change topic after
receiving material of the topic you ordered and paid for.
Login To Comment