Stock Trend Prediction with Technical Indicators using SVM
Short-term prediction of stock price trend has potential application for personal investment without high-frequency-trading infrastructure. Unlike predicting market index (as explored by previous years’ projects), a single stock price tends to be affected by large noise and long-term trend inherently converges to the company’s market performance. So this project focuses on short-term (1-10 days) prediction of stock price trend and takes the approach of analyzing the time series indicators as features to classify trend (Raise or Down). The validation model is chosen so that the testing set always follows the training set in the time span to simulate real prediction. Cross-validated Grid Search on parameters of RBF-kernelized SVM is performed to fit the training data to balance the bias and variances. Although the efficient-market hypothesis suggests that stock price movements are governed by the random walk hypothesis and thus are inherently unpredictable, the experiment shows that with 1000 transaction days as training data, we are able to predict AAPL’s next day actual close price trend with 56% accuracy, better than the random walk; and more than 70% accuracy on next 3-day, 5-day, 7-day, 10-day price trend. In the end, we conclude that stock technical indicators are very effective and efficient features without any sentiment data in predicting a short-term stock trend.
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