International Journal Of Advanced Research Essay

Discuss About The International Journal Of Advanced Research In Computer Science.

Answer:

Introduction

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. 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.

  1. Data collection

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)

  1. 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.

  1. 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 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)

  1. 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.

  1. 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 include

  • Designing
  • Data collection
  • Data analysis
  • Drawing of meaningful interpretation
  • Reporting of the results of findings

The statistical tools applicable in this project include

  • Microsoft Excel
  • Statistical package for the social sciences (SPSS)
  • MATLAB
  • R (foundation for statistical computing

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.

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

References

Angad, M. C., & Kulkarni, A. P. (2015). International Journal of Advanced Research in Computer Science. Research gate, 13.

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.

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