Proximal support vector machine based hybrid prediction models for trend forecasting in financial markets

Kumar, Deepak; Meghwani, Suraj S.; Thakur, Manoj

doi:10.1016/j.jocs.2016.07.006

Cited by 66 publications

(48 citation statements)

References 29 publications

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“…[ 9 ] also showed that SVMs with fractal feature selection gave the best performance among SVM with other feature selection methods in trend prediction of the Shanghai Stock Exchange Composite Index (SSECI). Similarly [ 10 ] have combined a proximal SVM with four feature selection techniques. The performances of four hybrid classifiers were better than the individual proximal SVM and the SVM with random forests was the superiority over all other prediction methods.…”

Section: Introductionmentioning

confidence: 99%

Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets

et al. 2017

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The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction.

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Section: Introductionmentioning

confidence: 99%

Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets

et al. 2017

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“…The consensual classification of the most accurate trees is combined into a single one, comprising the RF algorithm. The combination of decision trees in the RF technique can be used in regressions or classification, leading to good results for financial market prediction, as demonstrated by [2,[47][48][49][50].…”

Section: Resultsmentioning

confidence: 99%

Prediction of LQ45 Index in Indonesia Stock Exchange: A Comparative Study of Machine Learning Techniques

Syukur¹,

Istiawan²

2021

IJIES

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LQ45 is an Indonesia Stock Exchange Index (ISX) incorporate of 45 companies that meet certain criteria to target investors for selecting certain stocks. The prediction of stock price direction in the financial world is a major issue. The implementation of machine learning and other algorithms for market price analysis and forecasting is a very promising field. Different types of classification algorithms were used to predict the stock market. However, when individual studies are considered separately there is no clear consensus that algorithms work best. In this research, a comparison framework is proposed, which aims to benchmark the performance of a wide range of classification models and use them to predict the LQ45 index. The data in this research contains the transaction level and capitalization size are obtained from the Indonesian Stock Exchange (ISX). For analysis purposes, we set out 10 classifiers that can be used to build classification models and test their performance in the LQ45 dataset. The performance criterion chosen to measure this effect is accuracy, recall, and precision. The results showed that the random forest algorithm had the best performance for predicting the LQ45 index. Whilst the classification and regression trees, C4.5, support vector machine, and logistic regression algorithms also perform well. Besides, the models based on traditional statisticalbased learners that are Naïve Bayes and linear discriminant analysis seem to underperform for predicting the LQ45 index. These results are not only beneficial to enrichment the machine learning techniques literature but also have a significant influence on the stock market prediction in terms of the ability to predict the LQ45 index.

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“…Lowest price LL and HH show the support and resistance levels, respectively. Those indicators can indicate near-low and near-high price levels according to the trend of the market [27].…”

Section: Datasetmentioning

confidence: 99%

Stock daily return prediction using expanded features and feature selection

Gündüz¹,

Çataltepe²,

Yaslan³

2017

Turk J Elec Eng & Comp Sci

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Stock market prediction is a very noisy problem and the use of any additional information to increase accuracy is necessary. In this paper, for the stock daily return prediction problem, the set of features is expanded to include indicators not only for the stock to be predicted itself but also a set of other stocks and currencies. Afterwards, different feature selection and classification methods are utilized for prediction. The daily close returns of the 3 most traded stocks (GARAN, THYAO, and ISCTR) in Borsaİstanbul (BIST) are predicted using indicators computed on those stocks, indicators for all the other stocks listed in the BIST100 index, and indicators on the dollar-gold prices. Twenty-five different indicators on daily stock prices are computed to form feature vectors for each trading day. These feature vectors are assigned class labels according to the daily close returns. Expanding the feature space with BIST100 stocks features results in a high dimensional feature space, with possibly noisy or irrelevant features. Therefore, feature selection methods are utilized to select the most informative features. In order to determine relevance scores of features, fast filter-based methods, gain ratio and relief, are used. Experiments are performed based on individual stock features, dollar-gold features (DG), BIST100 stock features (BIST100), and a combination of BIST100 and DG with and without feature selection. Using the gain ratio feature selection with a gradient boosting machine (GBM), the movements of GARAN stock were predicted with an accuracy of 0.599 and an F-measure of 0.614. For THYAO, the relief feature selection with the GBM gave an accuracy of 0.558, and for ISCTR, the gain ratio feature selection with logistic regression achieved an accuracy of 0.581. It was found that using BIST100 stock features boosts classification results for all stocks in terms of accuracy.

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Proximal support vector machine based hybrid prediction models for trend forecasting in financial markets

Cited by 66 publications

References 29 publications

Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets

Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets

Prediction of LQ45 Index in Indonesia Stock Exchange: A Comparative Study of Machine Learning Techniques

Stock daily return prediction using expanded features and feature selection

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