Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets

Chen, Wun-Hua; Shih, Jen-Ying; Wu, Shuo‐Jye

doi:10.1504/ijef.2006.008837

Cited by 109 publications

(68 citation statements)

References 31 publications

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“…Data not present in Table 2 showed that the average return per trade is 0.06537% for the long trades and 0.2691% for short trades, which means that the OneR classifier was extremely profitable in downwards periods for this particular experiment. For the other classifiers, while prediction accuracies may be similar to those observed in the literature (Chen & Shih, 2006;Eng et al, 2008;Kim, 2003;Lee et al, 2007; S. Li & Kuo, 2008;Tenti, 1996), the average return per trade does not allow the models to have a positive return at the end of the trading period. These initial results suggest that it is possible to generate positive return with modest middle range accuracy.…”

Section: Single Training Experimentsmentioning

confidence: 53%

“…On average, the SVM approach obtained better accuracy than the back propagation NN, but also in middle-range levels: 47.7% by SVM against 45.0% obtained by back propagation NN. SVM also outperformed a back propagation NN in the work presented by Chen and Shih (2006), where these techniques were used to predict the value of six Asian indices, obtaining 57.2% level of accuracy with SVM and 56.7% with NN models. In addition to simple models, Barbosa and Belo (2008b) also analysed the performance of SVM in the Forex market reporting cumulative accuracy 53.4% with a positive cumulative return of 56.0% trading a currency pair over a two year period.…”

Section: Accuracy Comparisonmentioning

confidence: 99%

Section: Machine Learning In Financial Forecastingmentioning

confidence: 99%

“…Maggini et al (Maggini, Giles, & Horne, 1997) had pointed out that there is an inherent difficulty in generating statistically reliable technical indicators, due to the fact that the rules inferred to produce accurate predictions are changing continually in financial time series, and that it is even possible to evidence the presence of a high number of contradictory instances in the training sets due to the fact that market data exhibit statistical characteristics found in other types of time series. This situation is reflected in the large volume of papers (Chen & Shih, 2006;Eng, Li, Wang, & Lee, 2008;Kim, 2003;Lee, Park, O, Lee, & Hong, 2007;S. Li & Kuo, 2008;Tenti, 1996) that have reported accuracies under 60% with ML models which have shown impressive performance in areas other than financial prediction.…”

Section: Machine Learning In Financial Forecastingmentioning

confidence: 99%

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Evaluating machine learning classification for financial trading: An empirical approach

Gerlein

McGinnity

Belatreche

et al. 2016

Expert Systems with Applications

133

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Technical and quantitative analysis in financial trading use mathematical and statistical tools to help investors decide on the optimum moment to initiate and close orders. While these traditional approaches have served their purpose to some extent, new techniques arising from the field of computational intelligence such as machine learning and data mining have emerged to analyse financial information. While the main financial engineering research has focused on complex computational models such as Neural Networks and Support Vector Machines, there are also simpler models that have demonstrated their usefulness in applications other than financial trading, and are worth considering to determine their advantages and inherent limitations when used as trading analysis tools. This paper analyses the role of simple machine learning models to achieve profitable trading through a series of trading simulations in the FOREX market. It assesses the performance of the models and how particular setups of the models produce systematic and consistent predictions for profitable trading. Due to the inherent complexities of financial time series the role of attribute selection, periodic retraining and training set size are discussed in order to obtain a combination of those parameters not only capable of generating positive cumulative returns for each one of the machine learning models but also to demonstrate how simple algorithms traditionally precluded from financial forecasting for trading applications presents similar performances as their more complex counterparts. The paper discusses how a combination of attributes in addition to technical indicators that has been used as inputs of the machine learning-based predictors such as price related features, seasonality features and lagged values used in classical time series analysis are used to enhance the classification capabilities that impacts directly into the final profitability.

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Section: Single Training Experimentsmentioning

confidence: 53%

Section: Accuracy Comparisonmentioning

confidence: 99%

Section: Machine Learning In Financial Forecastingmentioning

confidence: 99%

Section: Machine Learning In Financial Forecastingmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating machine learning classification for financial trading: An empirical approach

Gerlein

McGinnity

Belatreche

et al. 2016

Expert Systems with Applications

133

View full text Add to dashboard Cite

show abstract

“…The reported results indicated that augmented ANN models with trading volumes can improve forecasting performance in both medium-and long-term horizons. A comparison between SVM and Backpropagation (BP) ANN in forecasting six major Asian stock markets was reported in [22]. Other soft computing techniques such as Fuzzy Logic (FL) have been used to solve many stock market forecasting problems [23], [24].…”

Section: Introductionmentioning

confidence: 99%

A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index

Sheta¹,

Ahmed²,

Faris³

2015

ijarai

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Abstract-Obtaining accurate prediction of stock index significantly helps decision maker to take correct actions to develop a better economy. The inability to predict fluctuation of the stock market might cause serious profit loss. The challenge is that we always deal with dynamic market which is influenced by many factors. They include political, financial and reserve occasions. Thus, stable, robust and adaptive approaches which can provide models have the capability to accurately predict stock index are urgently needed. In this paper, we explore the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVM) to build prediction models for the S&P 500 stock index. We will also show how traditional models such as multiple linear regression (MLR) behave in this case. The developed models will be evaluated and compared based on a number of evaluation criteria.

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Application and performance of data mining techniques in stock market: A review

Kaur

Dharni

2022

Intell Sys Acc Fin Mgmt

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Prediction and the stock market go hand in hand. Due to the inherent limitations of traditional forecasting methods and the pursuit to uncover the hidden patterns in stock market data, stock market prediction using data mining techniques has caught the fancy of academicians, researchers, and investors. Based on a systematic review of more than 143 research studies spanning 25 years, the present paper brings to light the major issues concerning forecasting of stock markets based on data mining techniques, such as usage of data mining techniques in the stock market, input data types, single versus hybrid techniques, instruments and stock markets researched, types of software and algorithms used, measures of forecast accuracy, and performance of various data mining techniques. Emerging patterns related to various dimensions have been critically analyzed by highlighting the existing limitations and suggesting future research paradigms. This analysis can be useful for academicians, researchers and investors looking for futuristic directions in a given research domain.

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Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets

Cited by 109 publications

References 31 publications

Evaluating machine learning classification for financial trading: An empirical approach

Evaluating machine learning classification for financial trading: An empirical approach

A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index

Application and performance of data mining techniques in stock market: A review

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