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Digital Signal Processing for Predicting Stock Prices
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 26 |
| Year of Publication: 2020 |
| Authors: Okpor Margaret Dumebi |
10.5120/ijca2020920762
|
Okpor Margaret Dumebi . Digital Signal Processing for Predicting Stock Prices. International Journal of Computer Applications. 175, 26 ( Oct 2020), 15-19. DOI=10.5120/ijca2020920762
Abstract
With the substantial increase in the amount of data collected all over the world form various stock markets, it has become impossible to use traditional statistic and mathematical calculation for analyzing the data and using it for predicting trends like the opening and closing prices or highs and lows of the day for a company stock. Another crucial factor is the speed at which the data is generated. Hence it has become important to use concepts of digital signal processing for carrying out this type of analysis and prediction. Over the years many diverse types of filter and prediction algorithms have been developed which can, with a high amount of accuracy, predict stock market trends. This work will involve studying some of the popular filters and prediction algorithms used for stock market analysis and how they have been modified over the years to improve performance. This includes evaluating the techniques based on performance parameters such as speed, complexity and accuracy of prediction. In the next stage, some of the above algorithms will be implemented in Python and their performance will be analyzed using recent stock market data of Google finance. In the last section, an overall analysis of the results achieved will be discussed followed by conclusion
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