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The support vector machine (SVM) is a machine learning method developed based on statistical learning theory. The SVM is widely used in classification and prediction. Since the financial time series is complex, the traditional forecasting methods are less reliable. In this paper, we research on financial time series forecasting based on the support vector machine. Although the speed of prediction...
In order to predict stock index, an empirical mode decomposition (EMD) based on support vector machine (SVM) ensemble learning paradigm was proposed. Firstly, the original stock index series were first decomposed into a finite number of independent intrinsic mode functions (IMFs), with different frequencies. Then the IMFs were composed into high-frequency sequence, low-frequency sequence, trend series...
Securities investment is a high risk, high-yield financial management, while the good operation of the market trends help investors avoid risk and seize the opportunity. In this paper, we propose a method based on several moving average lines slope and support vector machine algorithm to determine the current market trends in order to take the appropriate investment strategy. Simulations show that...
This paper compares the performance in financial market prediction of a Neural Network approach and an approach using the regression feature of SVM. The historical values used are those of the Hang Sang Index (HSI) from 2002 to 2007 and data for January 2007 and January 2008. SVM performs well in the short term forecast.
Technical indicators are very important tools in the analysis of securities investment. Closing prices and volume of business are basic index, and they compose many complex technical index. In this paper, we represent the daily closing prices and daily volume of business as input vector, and construct 9 projects according different input vector. After 9 contrast experiments with support vector machines,...
We analyze the relations between the stock market and a stock bulletin board system (BBS) in Japan. Previous studies in the USA found that the characteristics of messages posted on stock BBSs can predict market volatility and trading volume. We develop hypotheses based on the results of those analyses and apply statistical analysis to the data about companies mentioned in a large number of messages...
In order to evaluate the performance of several forecasts, the paper firstly uses three forecasting methods, namely grey model (GM (1,1)), BP neural networks and support vector machines (SVM), to forecast the Shanghai Industrial Index, the Shanghai Commercial Index, the Shanghai Real Estate Index, the Shanghai Public Utilities Index. Through evaluating the results of these forecasting methods, it...
In order to evaluate the performance of several combining forecasts, the paper firstly uses three single forecasting methods, namely grey model(GM (1,1)), BP neural networks and support vector machines (SVM), to forecast the Shanghai Industrial Index, the Shanghai Commercial Index, the Shanghai Real Estate Index, the Shanghai Public Utilities Index. Then it uses optimal weight linear combining forecasts...
In this paper, a hybrid prediction model based on rough set (RS) and support vector machine (SVM), RSS prediction model, is proposed to explore the stock index futures tendency. In this approach, RS is used for feature vectors selection to reduce the computation complexity of SVM and then the SVM is used to identify stock index futures movement direction. To evaluate the prediction ability of RSS...
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