Models for analyzing and forecasting share prices on the stock exchange
The work is devoted to the analysis and forecasting of share prices for four leading technology companies: Nvidia, Apple, Google and Netflix. These companies are leaders in their fields and have a significant impact on the global economy. The goal is to study the dependencies affecting the share pr...
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| Format: | Article |
| Language: | English |
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Igor Sikorsky Kyiv Polytechnic Institute
2024-10-01
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| Series: | Adaptivni Sistemi Avtomatičnogo Upravlinnâ |
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| Online Access: | https://asac.kpi.ua/article/view/313196 |
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| author | Р. Пізнак Т. Ліхоузова |
| author_facet | Р. Пізнак Т. Ліхоузова |
| author_sort | Р. Пізнак |
| collection | DOAJ |
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The work is devoted to the analysis and forecasting of share prices for four leading technology companies: Nvidia, Apple, Google and Netflix. These companies are leaders in their fields and have a significant impact on the global economy. The goal is to study the dependencies affecting the share prices of companies, as well as to develop models for forecasting future trends. In the work, a thorough analysis of historical data on company share prices and their macroeconomic indicators was carried out. The study was based on the fundamental concepts of economic science. For the task of forecasting the share price on the stock market, the following methods were chosen: LSTM, decision trees, and ARIMA. These methods complement each other and allow you to get a comprehensive approach to the analysis and forecasting of financial data. The results showed that the LSTM model showed the best performance for forecasting stock prices, especially for companies with relatively stable dynamics like Google. Decision trees also showed acceptable results for some companies, but were inferior to LSTMs for more volatile time series. The ARIMA model proved ineffective for this task due to its linear nature and inability to capture complex nonlinear effects in financial data. The obtained results can be used both by investors and by the companies themselves to make more informed decisions and develop effective strategies. The results of the study are expected to provide a deeper understanding of the future prospects of these companies.
Ref. 11, pic. 7, tabl. 1.
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| format | Article |
| id | doaj-art-cb15a5f321484fbbaccd948ee39dfeea |
| institution | Matheson Library |
| issn | 1560-8956 2522-9575 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Igor Sikorsky Kyiv Polytechnic Institute |
| record_format | Article |
| series | Adaptivni Sistemi Avtomatičnogo Upravlinnâ |
| spelling | doaj-art-cb15a5f321484fbbaccd948ee39dfeea2025-07-06T10:48:14ZengIgor Sikorsky Kyiv Polytechnic InstituteAdaptivni Sistemi Avtomatičnogo Upravlinnâ1560-89562522-95752024-10-0124510.20535/1560-8956.45.2024.313196351730Models for analyzing and forecasting share prices on the stock exchangeР. Пізнак0Т. Ліхоузова1Igor Sikorsky Kyiv Polytechnic InstituteIgor Sikorsky Kyiv Polytechnic Institute The work is devoted to the analysis and forecasting of share prices for four leading technology companies: Nvidia, Apple, Google and Netflix. These companies are leaders in their fields and have a significant impact on the global economy. The goal is to study the dependencies affecting the share prices of companies, as well as to develop models for forecasting future trends. In the work, a thorough analysis of historical data on company share prices and their macroeconomic indicators was carried out. The study was based on the fundamental concepts of economic science. For the task of forecasting the share price on the stock market, the following methods were chosen: LSTM, decision trees, and ARIMA. These methods complement each other and allow you to get a comprehensive approach to the analysis and forecasting of financial data. The results showed that the LSTM model showed the best performance for forecasting stock prices, especially for companies with relatively stable dynamics like Google. Decision trees also showed acceptable results for some companies, but were inferior to LSTMs for more volatile time series. The ARIMA model proved ineffective for this task due to its linear nature and inability to capture complex nonlinear effects in financial data. The obtained results can be used both by investors and by the companies themselves to make more informed decisions and develop effective strategies. The results of the study are expected to provide a deeper understanding of the future prospects of these companies. Ref. 11, pic. 7, tabl. 1. https://asac.kpi.ua/article/view/313196intelligent data analysisprediction modeltime seriesLSTMdecision treeARIMA |
| spellingShingle | Р. Пізнак Т. Ліхоузова Models for analyzing and forecasting share prices on the stock exchange Adaptivni Sistemi Avtomatičnogo Upravlinnâ intelligent data analysis prediction model time series LSTM decision tree ARIMA |
| title | Models for analyzing and forecasting share prices on the stock exchange |
| title_full | Models for analyzing and forecasting share prices on the stock exchange |
| title_fullStr | Models for analyzing and forecasting share prices on the stock exchange |
| title_full_unstemmed | Models for analyzing and forecasting share prices on the stock exchange |
| title_short | Models for analyzing and forecasting share prices on the stock exchange |
| title_sort | models for analyzing and forecasting share prices on the stock exchange |
| topic | intelligent data analysis prediction model time series LSTM decision tree ARIMA |
| url | https://asac.kpi.ua/article/view/313196 |
| work_keys_str_mv | AT rpíznak modelsforanalyzingandforecastingsharepricesonthestockexchange AT tlíhouzova modelsforanalyzingandforecastingsharepricesonthestockexchange |