BTP Report - Stock Prediction model analysis
Prof. P.M Jat
Abstract – This document report present a detailed analysis of
stock prediction and puts forth a prediction model which
facilitates the prediction. The fundamentals upon which this
research was conducted and the relevant output was produced
were strengthened by studying the previous research work
conducted in similar domain.
Keywords – stock prediction, neural networks, artificial neural
networks, trend prediction.
Beginning with formulating the problem statement, this
research aims at performing stock forecasting using neural
networks. This is the basic underlying idea of the problem
statement along lines of which, the relevant research was
conducted and ideas implemented.
1.1 Importance of the problem
This problem is primarily important because it implements
methods and produces outputs aimed at determining the future
value of stock prices. Living in a world where the global
economy spins off with an innumerable number of markets, a
tool aimed at predicting their stock values would maximize
their profits and better the economy.
1.2 General approaches
The general approaches used in stock forecasting deploy
various machine learning algorithms aimed at predicting the
prices or the price range for any upcoming day or week or
month and so on. Page Layout. Various approaches used
follow the important step of feeding the inputs to the machine
Another general approach was to work on the model of
sentimental analysis. This basically analysed the emotional
inclinations and sentiments of the investors via. their tweets
and then facilitated the prediction. In the long run, this didn't
prove to be much credible as people started getting biased.
1.3 Solution outline
My solution includes the following of a series of steps. Firstly,
I extract the data in form of excel sheets for, say a company X.
Now, for any given company, there are various factors which
contribute towards the development of its prediction model.
Opening rates for that particular period
Highest rates of that particular period
Lowest rates of that given period
The closing rates at that period
These are the primary attributes which would be considered
while applying the algorithms followed by the Artificial
neural networking tool on the data flowing into the neural
network. The time frame within which we would be doing
the simulation can be varied as per the user's interest.
data is precisely divided into sets of - training and
validation data. The simulation results are then noted down
and plots are produced as a result of before and after training
the data. Subsequently, validation is done on the (100-x)%
data where x is the %data allotted for training.
Figure 1 : Step-wise fundamentals of the initial phase
1.4 Summary of experimental results
Fine tuning different attributes results in different plots which
exhibit the different nature of outputs at different times. There
are basically 3 stages via. which the experiment has been
At first, simulation is performed before training the data. Two
different lines can be observed where one symbolizes the
feeding of data into the neural network and the other
symbolized the actual data.
After this, the data is trained with 90% of the data being
treated as training data and the rest 10% as the testing data. In
in form of
overall summary, various observations can be noted down as a
result of the plots achieved. For instance, if the highest rates
of stocks for a given company witness a declining trend over a
period of time, the predicted plot can show a profit for the
company or vice versa. Such alarming observations can be
noted down which would be discussed in depth later.
II. RELATED WORK
In the field of stock prediction, an extensive research aimed at
providing a near-accurate prediction model is underway and
has sent numerous benchmarks.
In one of the approaches, the use of 
global stock data in
correlation with the data of other financial products has been
stressed. In this very approach, the Support Vector Machine
learning algorithm has been implemented. Markets which stop
trading right before the beginning of the US markets are
studied in this approach. Specifically speaking, the world
major stock indices
are used as an input feature for the
predictor developed via. this approach.
Figure 2 : Correlation of NASDAQ stock data with other
In another approach, the importance of the Back propagation
Learning Algorithm which intends to find the
maxima/minima of the function by moving it in direction of
negative slope is stressed. There were various attributes such
as the date, time of the day, the opening price, the closing
price, the highest and the lowest prices as well as the
fractional changes in prices, of which some were taken into
account. For training, 60% of the data was used where as the
rest 40% was used for validation.
Figure 3 : Looking at the error in the second approach
Thus, in most of the related works, the application of Artificial
neural networks in order to develop a stock prediction model
are discussed. A general observation observed is that the
prediction actually is decently accurate. However, if there is a
sudden fluctuation in any of the parameters, the accuracy
III. PROBLEM STATEMENT
3.1 PROBLEM OVERVIEW
By studying the methodology of the neural networks
forecasting of the stock prices can be facilitated as was done
in this research. Motivation for conducting the theoretical
research was an important factor in developing the problem
statement for this research project. Basically, the overview of
the problem is that we need to fetch data in the form of .csv
files and then, this data needs to be fed into the neural network.
3.1.1. Precise description
Consider the stock prices data being fed for a company say
Reliance. While using the prediction model produced at the
end of this research, the user gets to choose the timeline in
which the stock price data is to be trained.
For instance, you choose to extract weekly stock price
attributes between say, 3rd January, 2005 to say, 28th
Decemeber, 2015. From a relevant source, you can mine the
data and get the csv file containing the necessary data. Of the
576 rows accumulated in the data sheet, 90% of the data is
allocated to training and 10% to testing.
The significance of this problem statement is that it
contributes a lot to the functioning of stock markets and thus
enhancing the overall functioning of national economies. A
prediction model which can predict the profit of your
company's stock at near-accurate rates happens to be a
powerful tool for the global economy.
There are various elements integral to data modelling
which form the basic underlying idea of the neural network
Figure 4 : Elements integral to NN Architecture
I have used the MATLAB tool in order to fulfil the coding
requirements and the ANN tool to train and validate the data
thereby generating the appropriate plots.
Neural networks are used to approximate functions
depending on a large number of inputs which happens to be
the underlying idea of the implementation.
The NN Architecture covers basically the types of
problems which are to be tackled by the applications. In the
architecture, stocks can be classified in different groups based
on their kind of returns. For instance, they can be classified as
either +ve or -ve or even neutral.
4.2 Individual Component
The individual components involved are the different
attributes which are considered as parameters for predicting
the stocks. For any given parameter or even for all parameters
at once, the user can simulate the input data being fed into the
neural network and make note of the predicted outputs.
The algorithm implemented has been divided into three
separate fragments of code or it can at least be considered as
Figure 5 : First fragment of the algorithm
Above is the first fragment of the algorithm in which the
testing and the training data are separated. In the 8th line,
u_train = A(:. 2:526);
the ':' implies the inclusion of all the attributes as
parameters while predicting the stock price output. In our case,
I chose 90% of the data for training which amounts to 516
rows and the last 60 rows for validation and testing which
amounts to 10% of total.
Figure 6 : Second fragment
This next fragment of the algorithm performs training on
the first 90% of the data which happens to be the first 516
rows and fine tunes the input data
being fed into the neural
network accordingly. The
function trains and simulates the data and accordingly, the
plot is generated which would be shown later.
Figure 7 : Final testing fragment of the algorithm
This is the final fragment of the algorithm which
symbolizes the part which performs testing on the remaining
10% of the data. In other words, after performing
the previous 90% of the data, the values of the last 10% were
These stock price values are now tested and compared with
the actual 10% of the values. 
That tells a great deal about the
algorithm and the nature of the stock market for the given
5.1 Objective of the experiment
The objective of the experiment is to feed a constant stream
of data into the ANN tool prior to training the data. Then, few
parameters are listed down and are considered as the prime
attributes necessary to do the simulation.
Thus, the overall objective is to train some fraction of the
input data and use rest of the data to validate the results after
5.2 Experiment setup
Setting up the experiment required the code above to be
written on Matlab. Post that, the first stage of simulation is
executed in which the below plot is produced.
It just shows the values of the stock prices before training
the data of Bombay Stock Exchange. The timeline
considered is :
From : 3rd Jan, 2005
To : 28th December, 2015
No. of rows = 576
Figure 8 : Before training
In the next stage, the training is done and the second
fragment of the algorithm is executed as shown in the
previous section. By doing this, the data is trained. 90% of the
data is trained in this stage which is roughly the 1st 516 rows
of stock prices for BSE.
We can see in the below plot that the red line which
indicates the predicted output almost coincides with the blue
line which represents the actual price. Thus, this implies that
the prediction is almost accurate while training.
Figure 9 : Post training analysis
While the experiment has been set up and the data has been
trained, based on the predicted value of the stock prices after
training the 90% of data, the predicted
value of the last 10%
which is the last 60 rows is compared with the actual value of
the stock prices of the last 10% which is represented by the
Thus, conclusions can be drawn via. this plot which shows
that the actual prices have been higher and even lower at times
than the predicted ones.
Figure 10 : After performing the testing
5.3 Results and Analysis
In a point form, the results can be drawn
as shown above
and few noteworthy points worth analysing would be :
You can choose between various parameters and
your output will be formulated accordingly.
When you consider all the parameters, a
significant different between the actual and the
predicted values can be observed at the end.
If however, one chooses to use just a single
attribute as a parameter, say closing price, the
output isn't near-accurate.
Also, the difference between the actual and the
predicted value decreases. Overall, the efficiency
of the model decreases.
Figure 11 : When using a single attribute as the parameter,
The final analysis can be concluded as saying that the user
gets the options to choose from the attributes and also gets the
option to set the parameter as output to train and validate the
The efficiency of this model varies depending upon
the input chosen.
A single input say Closing price chosen as a parameter can
produce more efficient and accurate predictions than when all
the 4 attributes are considered as parameters or even vice-
VI. DISCUSSION AND CONCLUSION
While discussing and concluding this research-based
experiment, the whole idea can be listed in terms of strengths
and drawbacks of this very model which has been presented in
The strengths of this model primarily centre around the fact
that this model enables the user to get deep insights about how
the stock of his or her firm might perform in the near future.
Accordingly, the user can corroborate with his associates and
the firm can implement measures to keep the prices or bay or
maximize its profits. This would also assist the clients who
happen to be major stockholders in one form or the other in a
great manner. A beforehand idea of how the stocks of a given
company might perform in the coming time and affect the
decision of a person investing into shares of a given company
by a great deal.
On the downside, the weakness or the drawbacks associated
with the functioning of such research-based prediction models
should also be taken into account in order to present an
unbiased thesis of the whole experiment. The true nature of
the performance of the stocks happens to be erratic. One
cannot exactly predict the future thus rendering the value of
such experiments null and void at times. For instance, a
prediction model which takes into account all the 4 attributes
as parameters is placed in front of a prediction model which is
taking into account just a single parameter to predict the stock
price. One of these models can be less accurate than the other
one and the person who is relying on the less-accurate model
unknowingly can suffer a great deal of loss in the stock
market. Thus, these experiments are trustworthy only to some
extent because post-that, it's all 'wish me luck'.
The future scope of this model can be tremendous devoid of
any bounds or limitations. Speaking in technical terms, this
model can be further expanded to develop a comparator which
would give a more direct idea of where to invest in as the user
would get much lucid insights as to which company's stock
might be performing better in the near future.
During the course of four months of this research internship, I
was able to dive deep into various domains of research
pertaining to machine learning, data mining, as well as other
technicalities associated with the field of Neural Networks and
Stock Prediction. For bestowing me with an opportunity to
pursue this research and for making the terms of research as
lucid as possible, I would like to thank my mentor, Prof. P.M.
Jat. I would also like to thank him for assisting me with
developing strategies and building ideas necessary to
overcome the roadblocks I encountered at every step during
the two phases of my internship. Also, for providing me with
the insights pertaining to all the tools and technologies
involved in my research, I would like to thank my mentor
again. All in all, this research internship was an enlightening
experience made possible only by a great guidance.
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