![]() ![]() In the meantime, researchers from financial domains were applying conventional statistical methods and signal processing techniques on analyzing stock market data. Their main contribution is performing a comparison between multi-layer perceptron (MLP) and SVM then found that most of the scenarios SVM outperformed MLP, while the result was also affected by different trading strategies. Ince and Trafalis in targeted short-term forecasting and applied support vector machine (SVM) model on the stock price prediction. One of the key findings by them was that the volume was not found to be effective in improving the forecasting performance on the datasets they used, which was S&P 500 and DJI. in already applied artificial neural networks on stock market price prediction and focused on volume, as a specific feature of stock market. In this research, our objective is to build a state-of-art prediction model for price trend prediction, which focuses on short-term price trend prediction.Īs concluded by Fama in, financial time series prediction is known to be a notoriously difficult task due to the generally accepted, semi-strong form of market efficiency and the high level of noise. Stock market is one of the major fields that investors are dedicated to, thus stock market price trend prediction is always a hot topic for researchers from both financial and technical domains. With the detailed design and evaluation of prediction term lengths, feature engineering, and data pre-processing methods, this work contributes to the stock analysis research community both in the financial and technical domains. The system achieves overall high accuracy for stock market trend prediction. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The proposed solution is comprehensive as it includes pre-processing of the stock market dataset, utilization of multiple feature engineering techniques, combined with a customized deep learning based system for stock market price trend prediction. We collected 2 years of data from Chinese stock market and proposed a comprehensive customization of feature engineering and deep learning-based model for predicting price trend of stock markets. In the era of big data, deep learning for predicting stock market prices and trends has become even more popular than before. ![]()
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