Forecasting stock market movement direction with support vector machine

Huang, Wei; Nakamori, Yoshiteru; Wang, Shouyang

doi:10.1016/j.cor.2004.03.016

Cited by 910 publications

(432 citation statements)

References 22 publications

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“…Initially, SVMs have been expanded for categorization tasks (Burges, 1998). SVMs have been expanded to resolve time series prediction and nonlinear regression problems, with the introduction of Vapnik's ε-insensitive loss function and they show excellent performance (Huang et al, 2005;Muller et al, 1997). Derived from this standard, SVMs will ultimately produce better simplification performance in comparison with other neural networks.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Daily Crude Oil Price Forecasting Model Using Arima, Generalized Autoregressive Conditional Heteroscedastic and Support Vector Machines

Ahmed¹

2014

American Journal of Applied Sciences

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Crude oil price forecasting is gaining increased interest globally. This interest is due mainly to the economic value attached to the product. For this reason, new forecasting methods are proposed in the literature. This paper proposes a novel technique for forecasting crude oil price based on Support Vector Machines (SVM). The study adopts the data on crude oil price of West Texas Intermediate (WTI) for its experimental purposes. This is because many studies have previously used this same data and it will afford a common basis for assessment. To evaluate the performance of the model, the study employs two measures, RMSE and MAE. These are used to compare the performance of the proposed technique and that of ARIMA and GARCH methods for the most efficient in crude oil price forecasting. The results reveal that the proposed method outperforms the other two in terms of forecast accuracy while it achieved a forecast error of 0.8684 that of ARIMA and GARCH were 0.9856 and 1.0134 respectively judging by their RMSE.

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Section: Review Of Related Literaturementioning

confidence: 99%

Daily Crude Oil Price Forecasting Model Using Arima, Generalized Autoregressive Conditional Heteroscedastic and Support Vector Machines

Ahmed¹

2014

American Journal of Applied Sciences

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“…The experimental results show that SVMs outperform the other methods and that they should be considered as a promising methodology for financial time-series forecasting. In [9], a SVM Classifier is ised to predict the directional movement of the Nikkei225 index with extremely promising results. In this study, we apply SVM in the classification way.…”

Section: Literature Review Of Svmmentioning

confidence: 99%

Different Stock Market Models Using Support Vector Machines

Rosillo¹,

Giner²,

Puente³

et al. 2013

IJTEF

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Abstract-The goal of this research is to analyse the different results that can be achieved using Support Vector Machines to forecast the weekly change movement of the different simulated markets. The data cover 3000 daily close for each simulated market. The main characteristic of these markets are: high volatility, bearish movement, bullish movement and low volatility. The inputs of the SVM are the Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD). SVM-KM is used by Matlab in order to design the algorithm. The outputs of the SVM are the degree of set membership and the market movement (bullish or bearish).The configuration for the SVM shows that results are better in high volatility markets or low volatility markets than trend markets.Index Terms-Support vector machines, quantitative trading, stock market models, technical analysis. I. INTRODUCTIONDifferent market situations such us high volatility, low volatility, bullish movements and bearish movements are shown in this paper. The SVM helps to investor in the quantitative decision making choosing a weekly forecast (bullish or bearish). We analyse in which market situation the SVM can achieve the best results.The rest of the paper is structured as follows. Section II, the literature review of SVM is presented. Section III explains the design of the trading rule. The results are shown in section IV. Finally section V provides some concluding remarks. with extremely promising results. In this study, we apply SVM in the classification way. II. LITERATURE REVIEW OF SVM III. TRADING RULEThe design of the trading rule is presented in this section. A. Simulated MarketsThe simulated markets are 4 in this experiment. An especially characteristic has been chosen in order to differentiate each series. We want to analyse how SVM help to investors in bullish trend (Fig. 2), bearish trend (Fig. 3), high volatility (Fig. 4) and low volatility (Fig. 5). B. InputsThe inputs of the SVM are the quantitative analysis indicators RSI and MACD. In [10] it is explained that RSI gets good profits in blue chips and Momentum indicator gets good profits in Small Caps, MACD and Stochastic indicators have been analysed over the Spanish Continuous Market too.  Relative Strength Index (RSI)It was designed by [11]. A brief explanation of this indicator is shown below in equation 1. If more details are needed it can be seen in [11].The RSI is an oscillator that shows the strength or speed of the asset price by means of the comparison of the individual upward or downward movements of the consecutive closing prices.For each day, an upward change (U) or downward change (D) is calculated. "Up days" are characterised by the daily close St being higher than the close of previous day St-1 (Ut = St -St-1, Dt = 0). "Down days" are characterised by the daily close being lower than the close of the previous day (Ut = 0, Dt = St-1 -St).The average Ut and Dt are calculated using an n-period exponential moving average (EMAn).Relative Strength Index at time t (RSIt) is t...

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“…Accordingly, a variety of predicting models have been proposed and developed, including these famous artificial intelligence methods such as artificial neural network (ANN) and genetic algorithms (GA), the widely used machine learning method-support vector machine (SVM), and the two widespread methods based on the stochastic process, namely autoregressive moving average model (ARMA) and hidden markov model (HMM). A number of literatures have applied the above methods to make forecasting in the stock market, for example, Faria, et al (2009) 1 studied the principal index of the Brazilian stock market using ANN and the adaptive exponential smoothing method; Huang, et al (2005) 2 forecasted the weekly movement direction of NIKKEI 225 index with SVM; Hassan and Nath (2005) 3 proposed a new approach to predict the local stock market based on HMM. Also, the similar literatures can also be found in the newly published literatures such as Cao, et al (2005) 4 , Du, et al (2013) 5 , Ni, et al (2011) 6 , and Golob, et al (2012) 7 .…”

Section: Introductionmentioning

confidence: 99%

A Combination Prediction Model of Stock Composite Index Based on Artificial Intelligent Methods and Multi-Agent Simulation

Li¹,

Wu²,

Liu³

et al. 2014

IJCIS

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Predicting stock composite index is useful, which can raise the interest of both the investors and the corresponding researchers. This paper presented a new combination prediction model based on the technique of artificial intelligence and the principle of combination forecast. The principle of combination forecast, as a valid foundation of the new model, was strictly proved and carefully illustrated in this paper. Given the predicting rules, the new combination model was established by synthesizing three commonly used prediction models based on the principle of combination forecast. The comprehensive usage of qualitative forecast and quantitative forecast is also a feature of the new model. To valid the new model, comparison analysis and multi-agent simulation were both applied. Besides, the application of multi-agent simulation made the new model able to guide the investors' operations in a real stock market. According to the theoretical proof, the comparison analysis and the simulation experiment, the new combination prediction model tends to be a powerful and applicable tool in making the investment decisions.

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Forecasting stock market movement direction with support vector machine

Cited by 910 publications

References 22 publications

Daily Crude Oil Price Forecasting Model Using Arima, Generalized Autoregressive Conditional Heteroscedastic and Support Vector Machines

Daily Crude Oil Price Forecasting Model Using Arima, Generalized Autoregressive Conditional Heteroscedastic and Support Vector Machines

Different Stock Market Models Using Support Vector Machines

A Combination Prediction Model of Stock Composite Index Based on Artificial Intelligent Methods and Multi-Agent Simulation

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