A SOFTWARE TOOL FOR VALUE STOCK IDENTIFICATION

Authors

  • ADONU SUNDAY EJIYIME Department of Computer Science, the Federal, Polytechnic Bauchi, Bauchi state, Nigeria. Author
  • ABUBAKAR MUHAMMAD Department of Computer Science, the Federal, Polytechnic Bauchi, Bauchi state, Nigeria. Author
  • IKENNA C ONUORAH Department of Computer Science, the Federal, Polytechnic Bauchi, Bauchi state, Nigeria. Author
  • AHMED ABDULRAZAQ BELLO Department of Computer Science, the Federal, Polytechnic Bauchi, Bauchi state, Nigeria. Author

Abstract

The stock market is popular among investment options for wealth creation in the financial market. Investors in the stock market constantly strive to identify equities that promise high return potentials. This can come in the form of equities with the capacity to reward investors through regular dividend payment but sell at a discount  price or equities that may be trading at low price at the point of analysis but have the potential for price appreciation in the near future. The capacity to identify value stock is a rare skill which only few investors can boast of, with evidence of consistent performance in their previous equity selections for investment in the past.  It even become more difficult when done manually. The problem is further complicated by the need to regularly do the evaluation of the  numerous listed equities in the market to avoid missing out on those that present opportunities for entry through every day price changes. The computer is efficient at handling such mundane tasks and this software tool developed to search for value stocks in the market using data from the Nigerian Stock Exchange (NGX) does such great job of finding equities of value (based on dividend payment history) that sold at high price in the recent past but have dropped sharply in price, thereby presenting investors with equities with high probability for profitability for further investment decision analysis.

Keywords:

Value Stock, Dividend Yield, Equities, Investor, Margin of Safety, Herd Effect

Downloads

Download data is not yet available.

Downloads

Article Stats

Viewed: 125 times
Downloaded: 61 times

Published

2024-06-30

Issue

Section

Articles

How to Cite

ADONU SUNDAY EJIYIME, ABUBAKAR MUHAMMAD, IKENNA C ONUORAH, & AHMED ABDULRAZAQ BELLO. (2024). A SOFTWARE TOOL FOR VALUE STOCK IDENTIFICATION. Journal of Systematic and Modern Science Research, 4(9). https://berkeleypublications.com/bjsmsr/article/view/170

Share

Most read articles by the same author(s)

1 2 3 4 5 > >> 

Similar Articles

1-10 of 14

You may also start an advanced similarity search for this article.