Testing the Application of Support Vector Machine (SVM) to Technical Trading Rules

Fonseca, André; Leles, Michel C. R.; Moreira, Mariana Geny; Vale-Cardoso, Adriano S.; Pereira, Marcos Vinicius Lopes; Sbruzzi, Elton F.; Nascimento, Cairo Lúcio

doi:10.1109/syscon48628.2021.9447068

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Grillenzoni (2012) compared the performance of the exponential smoothing with time-varying parameters and prediction error statistics, in the process of detecting financial TPs, and reported the satisfactory performance of the entire applied methods. In this regard, the research papers authored by Fior and Cagliero (2020), Chou and Hung (2021) and Fonseca et al (2021) can be referred for the application of the TIs for detecting financial TPs.…”

Section: Turning Points (Tps) Detection Methodsmentioning

confidence: 99%

A binary integer programming (BIP) model for optimal financial turning points detection

Yazdani

Khashei

Hejazi

2022

JM2

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Purpose This paper aims to detect the most profitable, i.e. optimal turning points (TPs), from the history of time series using a binary integer programming (BIP) model. TPs prediction problem is one of the most popular yet challenging topics in financial planning. Predicting profitable TPs results in earning profit by offering the opportunity to buy at low and selling at high. TPs detected from the history of time series will be used as the prediction model’s input. According to the literature, the predicted TPs’ profitability depends on the detected TPs’ profitability. Therefore, research for improving the profitability of detection methods has been never given up. Nevertheless, to the best of our knowledge, none of the existing methods can detect the optimal TPs. Design/methodology/approach The objective function of our model maximizes the profit of adopting all the trading strategies. The decision variables represent whether or not to detect the breakpoints as TPs. The assumptions of the model are as follows. Short-selling is possible. The time value for the money is not considered. Detection of consecutive buying (selling) TPs is not possible. Findings Empirical results with 20 data sets from Shanghai Stock Exchange indicate that the model detects the optimal TPs. Originality/value The proposed model, in contrast to the other methods, can detect the optimal TPs. Additionally, the proposed model, in contrast to the other methods, requires transaction cost as its only input parameter. This advantage reduces the process’ calculations.

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Section: Turning Points (Tps) Detection Methodsmentioning

confidence: 99%

A binary integer programming (BIP) model for optimal financial turning points detection

Yazdani

Khashei

Hejazi

2022

JM2

View full text Add to dashboard Cite

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Predicting stock market direction in South African banking sector using ensemble machine learning techniques

Mcwera,

Mba

2023

DSFE

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<abstract> <p>The ability to accurately predict stock price direction is important for investors and policymakers. We aim to predict the direction of daily stock returns for five major South African banks using ensemble machine learning techniques. Financial ratios were used as predictors in single classifier and ensemble models. The key findings were that the support vector machine performed best among single classifiers, with the highest accuracy for 4 banks ranging from 54% to 99% and produces fewer wrong classifications compared to its peer single classifiers. More importantly, the heterogeneous ensemble classifier, combining support vector machines, decision trees and k- (KNN) nearest neighbors, achieved average accuracy rates above 95% and outperformed all other models. This confirms that ensemble methods that combine multiple models can generate more accurate predictions compared to single classifiers. The results suggest that the heterogeneous ensemble is a suitable approach for predicting stock price direction in the South African banking sector. The findings imply that investing in banks may be a good decision and can assist investors. However, further research could expand the models to incorporate macroeconomic and other external factors that influence stock prices. Overall, we demonstrate the value of ensemble learning for a complex forecasting problem. The heterogeneous ensemble approach achieved high accuracy and outperformed single classifiers. However, future research incorporating additional factors and policy implications could build on these findings.</p> </abstract>

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Testing the Application of Support Vector Machine (SVM) to Technical Trading Rules

Cited by 2 publications

References 16 publications

A binary integer programming (BIP) model for optimal financial turning points detection

A binary integer programming (BIP) model for optimal financial turning points detection

Predicting stock market direction in South African banking sector using ensemble machine learning techniques

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