MOTIVATION

DATA MINING

Discovering Patterns Invisible to the Human Eye

Data mining is a novel concept used to intelligently read patterns from vast amounts of raw data, in this case historical data of stocks analyzed. Key uses of data mining aside from pattern discovery are pattern association, path analysis and clustering. Thus the power of data mining can be used to forecast stock performance and predict their potential future values. Using this concept with Neural Networks could result in powerful applications, not limited to the financial domain, but other industries as well.

So with the advent of Big Data in this day and age, the use of Data Mining in a plethora of industries today is at an all-time high. Essentially these techniques help to uncover hidden patterns and behaviors displayed by large data sets, which would otherwise be unperceivable by the human mind. Thus it is easy to see why Data Mining has so many applications in the financial services industry. Furthermore, any findings made through the mining of financial data can also be applied towards making accurate stock value predictions: the main goal of this project.

To read more about Data Mining click the button below, which will take you directly to Oracle's Data Mining e-book.

ARTIFICAL NEURAL NETWORKS

Making the Predictions Once Thought Impossible

An Artificial Neural Network (ANN) is a data-processing entity that is inspired by the biological nervous systems like the brain process information. ANNs are made up of a vast number of highly interconnected processing nodes (neurones) which work together to solve complex problems. A key characteristic of ANNs is that they learn by example, for example pattern recognition (sound familiar?). Thus in harnessing the remarkable ability of neural networks to derive meaning from huge repositories of data and detect trends that may be too complex for the average human to observe, lies the use of ANNs in financial forecasting.

To read more about Neural Networks, click the button below, which will take you to Michael Nielsen's online e-book which covers the topic in more detail.

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To be updated as work progresses.