Pattern graph tracking-based stock price prediction using big data

By Seungwoo Jeon, Bonghee Hong, Victor I. Chang. 2018

73 citations recorded by the source corpus.

Stock price forecasting is the most difficult field owing to irregularities. However, because stock prices sometimes show similar patterns and are determined by a variety of factors, we propose determining similar patterns in historical stock data to achieve daily stock prices with high prediction accuracy and potential rules for selecting the main factors that significantly affect the price, while simultaneously considering all factors. This study is intended at suggesting a new complex methodology that finds the optimal historical dataset with similar patterns according to various algorithms for each stock item and provides a more accurate prediction of daily stock price. First, we use a Dynamic Time Warping algorithm to find patterns with the most similar situation adjacent to a current pattern. Second, we select the determinants most affected by the stock price using feature selection based on Stepwise Regression Analysis. Moreover, we generate an artificial neural network model with selected features as training data for predicting the best stock price. Finally, we use JaroWinkler distance with Symbolic Aggregate approXimation (SAX) as a prediction accuracy measure to verify the accuracy of our model. We propose a new methodology combined Dynamic Time Warping, Stepwise Regression Analysis, and Artificial Neural Network.We use JaroWinkler distance with Symbolic Aggregate approXimation (SAX) as prediction accuracy measure to verify our model.We construct a big data processing framework to handle the overall processes using big data open sources.

Subfields: Economics and Econometrics, Management Science and Operations Research, Signal Processing

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In Management Science and Operations Research

1. Abolghasem Sadeghi-Niaraki
2. Weiwei Jiang

Citations in the corpus

• Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network (2018, methodology): “Researchers have applied many algorithms such as multiple kernel learning [3], deep learning [5], [18], [17], stepwise regression analysis [4], etc.”