Short-term stock price forecasting using kernel principal component analysis and support vector machines: the case of Casablanca stock exchange

Nahil, Anass; Lyhyaoui, Abdelouahid

doi:10.1016/j.procs.2018.01.111

Cited by 41 publications

(17 citation statements)

References 14 publications

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“…Zhing and Enke [15] forecast the daily direction of the S&P 500 Index ETF (SPY) return and show that DNNs using two PCA-represented datasets give slightly higher classification accuracy than the entire untransformed dataset. Nahil and Lyhyaoui [16] show that the structure of the investment decision system can be simplified through the application of kernel PCA. Berradi and Lazaar [17], using both PCA and recurrent neural network model, reduce the number of features from eight to six, giving a good prediction of total Maroc stock price.…”

Section: Pca and The Stock Marketmentioning

confidence: 99%

Measuring the Systematic Risk of Sectors within the US Market via Principal Components Analysis: Before and during the COVID-19 Pandemic

Jiménez¹,

Santiago²

2023

Psychosocial, Educational, and Economic Impacts of COVID-19

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This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

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Section: Pca and The Stock Marketmentioning

confidence: 99%

Measuring the Systematic Risk of Sectors within the US Market via Principal Components Analysis: Before and during the COVID-19 Pandemic

Jiménez¹,

Santiago²

2023

Psychosocial, Educational, and Economic Impacts of COVID-19

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“…e performance of the MFNN was found to be better than other NN approaches, SVM, and random forests [44]. In [45], the authors combined support vector machines for regression (SVR) and kernel principal component analysis (KPCA) to enhance prediction accuracy that may help investors for short-term decisions. However, the high dimension of input variables makes the learning process long, and the final model computational complexity becomes very large.…”

Section: Related Workmentioning

confidence: 99%

Multivariate and Online Prediction of Closing Price Using Kernel Adaptive Filtering

Mishra

Ahmed

Mishra

et al. 2021

Computational Intelligence and Neuroscience

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This paper proposes a multivariate and online prediction of stock prices via the paradigm of kernel adaptive filtering (KAF). The prediction of stock prices in traditional classification and regression problems needs independent and batch-oriented nature of training. In this article, we challenge this existing notion of the literature and propose an online kernel adaptive filtering-based approach to predict stock prices. We experiment with ten different KAF algorithms to analyze stocks’ performance and show the efficacy of the work presented here. In addition to this, and in contrast to the current literature, we look at granular level data. The experiments are performed with quotes gathered at the window of one minute, five minutes, ten minutes, fifteen minutes, twenty minutes, thirty minutes, one hour, and one day. These time windows represent some of the common windows frequently used by traders. The proposed framework is tested on 50 different stocks making up the Indian stock index: Nifty-50. The experimental results show that online learning and KAF is not only a good option, but practically speaking, they can be deployed in high-frequency trading as well.

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“…Huberman [71] compares functional time series and grouped multivariate forecasting on annuity pricing using the functional time series model with Lee-Carter model their result inferred that combination of both model has improved the error performance, although they conclude that, the Lee-Carter method does not surpass functional time series method in terms of forecasting. Tran, Iosifidi [72] used neural network with a layer architecture which integrates the concept of a bilinear forecast along with an interested mechanism that aids the layer to identify and concentrate on spatial information. Their studies reveal that two-hidden-layer network with the proposed layer network surpasses most of the existing state-of-the-art network results.…”

Section: Parametersmentioning

confidence: 99%

“…On their study attention was concentrated on the model performance ability living data and forecasting accuracy unpredicted. [65], [66], [67], [68], [69], [70] [71], [73], [76], [79], [80], [81] Linear model 2 [62], [72] Non-linear model 13 [65], [66], [68], [69], [70], [71], [72], [73], [76], [79], [80], [81], [82], Heteroscedasticity 9 [69], [70], [71], [73], [76], [79], [80], [81] Table 2 reveals the overview of the studied literature on stock exchange index. With over 80% of the literatures on stock exchange index utilized nonlinear model for forecasting stock price index with consideration for heteroscedasticity in the data.…”

Section: Parametersmentioning

confidence: 99%

Dynamics of Heteroscedasticity Modelling and Forecasting of Tax Revenue in a Developing Economy: A Review

Alhassan¹,

Yusof²,

Norrulashikin³

et al. 2020

IJMH

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Tax revenue modelling and forecasting is very crucial for revenue collection and tax administration management. The dynamics of heteroscedasticity in the financial time series (tax revenue) in the domain of technique used to model and predict tax revenue in the emerging economy threw us to this investigation. The reviews are categorized into two the tax revenue and stock exchange index. Five factors were considered in this studies modelling, forecasting, linear model, nonlinear model and heteroscedasticity, it is on this note that we syntheses over 75 studies from the literature to consider the pattern of reporting tax revenue and stock market index. Thus, from the reviewed literature, we inferred that the pattern of reporting tax revenue data and the analytical techniques employed by most of these studies are responsible for the instability (volatility) in the financial time series forecasting. Also, results revealed that linear models are mostly applied to tax revenue data with fewer non-linear models, while combination and single non-linear models were mostly used for stock exchange data. Thus, we recommend the combination of linear and nonlinear models for both tax revenue and stock exchange data which can minimize the error of heteroscedasticity in the forecasting of tax revenue in a developing economy.

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Short-term stock price forecasting using kernel principal component analysis and support vector machines: the case of Casablanca stock exchange

Cited by 41 publications

References 14 publications

Measuring the Systematic Risk of Sectors within the US Market via Principal Components Analysis: Before and during the COVID-19 Pandemic

Measuring the Systematic Risk of Sectors within the US Market via Principal Components Analysis: Before and during the COVID-19 Pandemic

Multivariate and Online Prediction of Closing Price Using Kernel Adaptive Filtering

Dynamics of Heteroscedasticity Modelling and Forecasting of Tax Revenue in a Developing Economy: A Review

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