Requirement Analysis and Modelling for Stock Price Predictor

This article discusses the requirement analysis and modelling process for developing a stock price predictor. It covers steps such as understanding the objective, data collection, data preprocessing, data processing, plot visualization, and analyzing results. The article also mentions the statistical tools and techniques used, limitations, recommendations, and possible applications.

Running head:REQUIREMENT ANALYSIS AND MODELLING 1
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REQUIREMENT ANALYSIS AND MODELLING 2
Analytical approach
This is a breakdown of the process or the analytic process of our group project(stock price
predictor) aim at coming up with a stock price predictor that can be implemented across all
sectors on any stock to assess the future trend movement that can help potential investors to stay
invested, join or exit from the stock and also help in the provision of inputs in to the financial
institutions on predictive analysis to increase or decrease exposure in funding and future
movement of interest rates in these sectors based on performance and other trends in the market
and economy. (Vishwanath & Srikantaiah, 2013)This is a problem solving analytical approach.
In coming up with a model or a system that can be used for stock price prediction, it’s very
important that the following processes are applicable;1
1. Understanding the objective
This is the first and most crucial step for developing our project on stock price prediction. It
entails the understanding of the intentions and the requirements of the model of the system to be
developed. This comprehension usability helps in describing problems and a system preparatory
as a tool for accomplishing expectations. Our objective is the development of a system that can
help in determining the directions and future of stock price changes indices based on the
prevailing stock prices. in addition to that, it will help potential investors within a stock market
business in decision making on if to buy or sell stocks by the provision of the results in terms of
visualizations. 2
2. Data collection
1 Mahantesh C. Angadi; Amogh P. Kulkarni, International Journal of Advanced Research in Computer Science: vol 6 pg 21 sixth
edition
2 Ibid

REQUIREMENT ANALYSIS AND MODELLING 3
In our analytical approach breakdown concerning our project, another step is collecting data.
The data collection will involve us in the understanding of the initial data observations as a way
of identifying the needful subsets from hypotheses of the unknown information. There are
several data sources and methods that we have employed in our project. They include quant mod
and model estimation for the proposed system or stock price predictor. (Angadi & Kulkarni ,
2015)
3. Data preprocessing/ data wrangling
Data preprocessing entails all the events and activities carried out for the preparation of the
final dataset from the collected raw data. Since in our project we don’t have a specified order,
the data preparation can be conducted in a number of times. Some of these task as per our project
on stock price predictor will involve a section of records, tables, attributes, and cleaning of data
for modeling tools. There are a variety of ways to deal with time arrangement cross approval, for
example, moving estimates with and without refitting or more intricate ideas, for example, time
arrangement bootstrap resampling. The last includes rehashed tests from the rest of the
occasional disintegration of the time arrangement keeping in mind the end goal to reproduce tests
that take after an indistinguishable regular example from the first run through arrangement yet
are not precise of its qualities.
4. Data processing: data training
In order to process data, we will have to make use of a given data model such as ARIMA. In
this one, the investors make use of the autoregressive and moving average models to predict the
trends being portrayed by the stocks. The man steps that we employ here are; the identification,
estimations of parameters and forecasting. To ensure appropriate identification of the best model

REQUIREMENT ANALYSIS AND MODELLING 4
or best project to use for stock price prediction, these steps have to be repeated for several times.
For instance, the R function in the ARIMA model provides a method to forecast the time series
data. (Xing, 2013)
5. Plot visualization
This will involve plotting and use of graphs to represent numerical data. With our
methodology, we concern our self with visualizing the outcome of the results for short-term
investment and long-term investments with the use of line charts, bar charts, histograms and line
charts after considering forecast on trends in stock markets.
6. Viewing and analyzing results
This comes after plotting results whereby we can find out the correlations that exist so as to
come up with short termed predictions. Record keeping in terms of ways like use of screenshot
can be utilized whereby the investors will use them to make analysis and predict future stock
trends of one given company at a given period. With the use of this to help them in decision
making concerning selling, buying or even holding the shares in a stock market.
The Statistical tools and techniques to be used
The statistical techniques applicable in this project include3
 Designing
 Data collection
 Data analysis
 Drawing of meaningful interpretation
3 Winters R, Winters A, Amedee RG. Statistics: A brief overview. Ochsner J. 2010;10:213–6.

REQUIREMENT ANALYSIS AND MODELLING 5
 Reporting of the results of findings4
The statistical tools applicable in this project include
 Microsoft Excel
 Statistical package for the social sciences (SPSS)
 MATLAB
 R (foundation for statistical computing 5
The rationale for using the above statistical tools
1. Most of these tools offer lots of user control and flexibility, for instance, the Microsoft
Excel and the SPSS
2. They are readily and widely available alongside being cheap to acquire, some a free like
the foundation for statistical computing
3. Resulting data and figure from using most of these tools can be exported easily and also
imported easily to other tools.
4. Most of them offer graphical user interface thus the presentation of data used for stock
price prediction can easily be interpreted by potential investors
5. Makes use of scripting language that enables generation of scripts and templates for bulk
processing of datasets and parameters for instance SPSS.
6. They produce high-quality figures and plots when used for presentation.
Limitations
1. Most of these tools require expert knowledge to be applied in their usage.
4 Satake EB. Statistical Methods and Reasoning for the Clinical Sciences Evidence-Based Practice. Ist ed. San Diego: Plural
Publishing, Inc; 2015. pp. 1–19.
5 Imotions ; top 4 statistical tools you need to make your data shine pp 4

REQUIREMENT ANALYSIS AND MODELLING 6
Recommendation from the analysis
Its recommendable that the attempt made in the development of a stock price prediction
model for forecasting the trends in the stock market based on the technical analysis majorly
by the use of historical stock market data and data mining techniques be enhanced and
extended. We can achieve the implementation of the above-mentioned recommendation in
the future of our project, by the integration of the technical analysis and the fundamental
analysis techniques.
Evaluation of the social opinions by the use of the social media platform can be used to
get better results in terms of response. This can be a better implementation plan and strategy
in respect to the aforesaid recommendation to help provide improved results for potential
investors in the stock market whereby they can make choices on better timing for profitable
investment decisions
Possible applications
 This model can be used for a financial trading system using a combination of textual
and numeric data
 It can be used by analyst and industries to predict prices using data mining techniques
 It can be applicable for foreign exchange projects for GDP -USD currency pair
exchanges 6
Bibliography
Angad, M. C., & Kulkarni, A. P. (2015). International Journal of Advanced Research in Computer Science.
Research gate, 13.
6 Mahantesh Angadi; Foreign Exchange project for GBP_USD currency pair. Pg

REQUIREMENT ANALYSIS AND MODELLING 7
Vishwanath, R. H., & Srikantaiah, K. C. (2013). Forecasting Stock Time-Series using Data Approximation
and Pattern Sequence Similarity. the
stock exchange, 14.
Xing, T. (2013). The analysis and prediction of the stock price. the
stock market, 11.