Year: 2024
Type: article
Abstract: Stock prices prediction is interesting and challenging research topic. Developed countries'economies are measured according to their power economy. Currently, stock markets areconsidered to be an illustrious trading field because in many cases it gives easy profits with lowriskrate of return. Stock market with its huge and dynamic information sources is considered asa suitable environment for data mining and business researchers. In this project, we applied knearestneighbor algorithm and non-linear regression approach in order to predict stock prices fora company’s stock data to assist investors, management, decision makers, and users in makingcorrect and informed investments decisions. This algorithm uses the open, close, high, lowvalues of a stock in a day and volumes of that stock to train the module. Then for testing, anopening value of the stock is taken from the user and given as test variable for the module. Themodule will return the predicted closing value of that stock. The differences between actual andpredicted closing values of the stock can be interpreted using visualization graph plotted betweenthose two data. According to the results, the kNN algorithm is robust with small error ratio;consequently, the results were rational and also reasonable. In addition, depending on the actualstock prices data; the prediction results were close and almost parallel to actual stock prices.
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7217367315