کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
385108 660860 2011 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction
چکیده انگلیسی

Precise prediction of stock prices is difficult chiefly because of the many intervening factors. Unpredictability is particularly notable in the aftermath of the global financial crisis. Data mining may however be used to discover highly correlated estimation models. This study looks at artificial neural networks (ANN), decision trees and the hybrid model of ANN and decision trees (hybrid model), the three common algorithm methods used for numerical analysis, to forecast stock prices. The author compared the stock price forecasting models derived from the three methods, and applied the models on 10 different stocks in 320 data sets in an empirical forecast. Average accuracy of ANN is 15.31%, the highest, in terms of match with real market stock prices, followed by decision trees, at 14.06%; hybrid model is 13.75%. The study also discovers that compared to the other two methods, ANN is a more stable method for predicting stock prices in the volatile post-crisis stock market.


► The study use ANN, decision trees and the hybrid model of ANN and decision trees, to forecast stock prices.
► By using digital game content stocks in Taiwan as the sample.
► ANN is a more stable method for predicting stock prices in the volatile post-crisis stock market.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 38, Issue 12, November–December 2011, Pages 14846–14851
نویسندگان
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