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Implementation of Hidden Markov Model For Profit, Loss and Return rate
Prediction
Research Proposal · August 2020
DOI: 10.13140/RG.2.2.16683.46886
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Ateeq Ur Rehman
University of Engineering and Technology, Peshawar
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Implementation of Hidden Markov Model For Profit, Loss and Return rate Prediction
UNIVERSITY OF ENGINEERING AND TECHNOLOGY
PESHAWAR, PAKISTAN
POSTGRADUATE RESEARCH PROPOSAL
Implementation of Hidden Markov Model For Profit, Loss
PROJECT TITLE
and Return rate Prediction
Computer System Engineering
DEPARTMENT AND
SPECIALIZTAION
STUDENT NAME
Ateeq ur rehman
FATHER’S NAME
Saeed akhtar
CONTACT NO.
03159539026
EMAIL
ateequrrehman941@gmail.com
Registration No
12PWCSE0976
Date of Regn.
07-09-2017
RESEARCH SUPERVISOR
CO-SUPERVISOR (IF ANY)
COURSES STUDIED
S. NO.
COURSE NO. AND TITLE
GRADE
1.
Advance Artificial Intelligence
A-
2.
Wireless Networks
B
3.
Advanced Software Engineering
B+
4.
Network Modelling
5.
6.
7.
8.
9.
CGPA 3.35
Implementation of Hidden Markov Model For Profit, Loss and Return rate Prediction
1.
INTRODUCTION
The stock exchange is a network that provides a platform for pretty much all major
economic transactions within the world at a dynamic rate known as the stock worth that
relies on market equilibrium. Predicting this stock worth offers huge profit opportunities
that area unit a large motivation for analysis during this space. Information of a stock worth
beforehand by even a fraction of a second may result in high profits. Similarly, a
probabilistically correct prediction will be very profitable.
A hidden Markov model (abbreviated HMM) is, loosely speaking, a Markov chain
observed in noise. Indeed, the model comprises a Markov chain, which we will denote by
{Xk}k≥0, where k is an integer index. This Markov chain is often assumed to take values in
a finite set, but we will not make this restriction in general, thus allowing for a quite
arbitrary state space. Now, the Markov chain is hidden, that is, states are not observable.
Recall In a regular Markov model, the state is directly visible to the observer, and therefore
the state transition probabilities are the only parameters. In a hidden Markov model, the
state is not directly visible, but output, dependent on the state, is visible. Each state has a
probability distribution over the possible output tokens. Therefore the sequence of tokens
generated by an HMM gives some information about the sequence of states. Note that the
adjective 'hidden' refers to the state sequence through which the model passes, not to the
parameters of the model; even if the model parameters are known exactly, the model is still
'hidden'.
1.1 The research problem
HMM are extensively used for pattern recognition and classification issues due to its welltried quality for modelling dynamic systems. However, victimisation HMM for predicting
future events isn't easy. Here we tend to use only 1 HMM that's trained on the dataset of the
chosen window. The trained HMM is employed to look for the variable of interest activity
information pattern from the dataset. By interpolating the close values of those datasets
forecasts measure ready. The results obtained victimisation HMM square measure
encouraging and HMM offers a brand new paradigm for securities market prediction, a part
that has been of abundant analysis interest recently
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Implementation of Hidden Markov Model For Profit, Loss and Return rate Prediction
2.
LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK
In Recent years, a variety of forecasting methods have been proposed and implemented for
the stock market analysis. A brief study on the literature survey is presented. Markov
Process is a stochastic process where the probability at one time is only conditioned on a
finite history, being in a certain state at a certain time. Markov chain is “Given the present,
the future is independent of the past”. HMM is a form of probabilistic finite state system
where the actual states are not directly observable, to get an idea about the trend analysis
of stock market behaviour using Hidden Markov Model (HMM). The estimate once
followed over a particular period will sure repeat in future. his paper presents Hidden
Markov Models (HMM) approach for forecasting stock price for interrelated markets. We
apply HMM to forecast some of the airlines stock.
3.
APPROACH AND METHODOLOGY
The main purpose of this part is to introduce the methodology we use in this project. In
order to apply the Hidden Markov Chain, we divide the total historical data of 1400 data
points into two parts. The first part consisted of the first 1000 numbers and is used to
construct a well enough model while the second part of the rest 400 numbers is used to
implement our trading strategy to see how well it works. In finance, stock prices are often
assumed to follow a Markov process. This means that the present value is all that is needed
for predicting the future, and that the past history and the taken path to today’s value is
irrelevant. Considering that equity and currency are both financial assets, traded under
more or less the same conditions, it would not seem farfetched assuming that currency
prices also follow a Markov process.

Data Collection:
The complete set of data for the proposed study has been taken from yahoofinance.com.
The 400 days, 1000 values of the stock market trading.
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Implementation of Hidden Markov Model For Profit, Loss and Return rate Prediction
4.
REFERENCE LIST
1. Md. Rafiul Hassan and Baikunth Nath, “Stock Market forecasting using Hidden
Markov Model: A New Approach,” Proceeding of the 2005 5th international conference on
intelligent Systems Design and Application 0-7695-2286-06/05, IEEE, 2005.
2. L.R Rabiner, “A tutorial on HMM and Selected Applications in Speech Recognition,”
In:[WL], proceedings of the IEEE,Vol. 77 (2), pp. 267-296,199
3. Kavitha G, Udhayakumar A and Nagarajan D, Stock Market Trend Analysis Using
Hidden Markov Models, , 2011, 5, 11-18
4. Md. Rafiul Hassan, Baikunth Nath and Michael Kirley, “ A fusion model of HMM,
ANN and GA for stock market forecasting,” Expert systems with Applications., pp. 171180,2007.
5. A. S. Weigend A. D. Back, “What Drives Stock Returns?-An IndependentComponent
Analysis,” In Proceedings of the IEEE/IAFE/INFORMS 1998 Conference on
Computational Intelligence for Financial Engineering, IEEE, New York., pp. 141156,1998.
6
H.White, “Economic prediction using neural networks: the case of IBM daily stock
returns,” In Proceedings of the second IEEE annual conference on neural networks., II,
pp. 451–458,1988.
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