Prediction of Stock Market by Principal Component Analysis

Waqar, Muhammad; Dawood, Hassan; Guo, Ping; Shahnawaz, Muhammad Bilal; Ghazanfar, Mustansar Ali

doi:10.1109/cis.2017.00139

Cited by 40 publications

(16 citation statements)

References 6 publications

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“…Waqar et al [14] analyze three stock exchanges and show how PCA can help to improve the predictive performance of machine learning methods while reducing the redundancy among the data. 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.…”

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

View full text Add to dashboard Cite

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“…A number of researchers have been (Waqar et al, 2017) applied principal component analysis (PCA) and linear regression to predict stock market patterns. PCA improves machine learning predictions and reduces data redundancy.…”

Section: Ai and Stock Market Prediction: Background Informationmentioning

confidence: 99%

A Bibliometric Analysis of Artificial Intelligence-Based Stock Market Prediction

ALI¹,

SURI²

2022

EPESS

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The primary purpose of this study is to conduct a scientometrics analysis of stock market forecasts based on artificial intelligence. This research examined 1,301 publications that were published between January 2002 and June 2022. We investigated 183 journal articles among 1,329 papers. In addition to entering the keywords into Scopus, a comprehensive dataset of relevant research papers was compiled. These papers discussed the optimization of investment portfolios, artificial intelligence-based stock market forecasts, investor emotions, and market monitoring. We found the most prolific documents by affiliation, the most prolific author, the most cited papers, nations, institutions, co-authorship maps, inter-country co-authorship maps, and keywords occurrences in this study. Co-authorship analysis network maps and keyword occurrence linkages are generated using the VOS-viewer software. According to our findings, it is evident from the review that the body of literature is becoming more specific and extensive. Primarily, neural networks, support vector machines, and neuro-fuzzy systems are employed to predict the future price of a stock market index based on the composite index's historical prices. Artificial intelligence techniques are able to consider challenges facing financial systems when forecasting time series. Our findings provide actionable guidance on how artificial intelligence can be used to predict stock market movements for market participants, including traders, investors, and financial institutions.

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“…In summary, the earlier methods are mostly based on machine learning, data mining or deep learning alone. In our proposed work therefore, PCA is used to minimize dimensions of different variables in the dataset [19]. PCA is used to handle high dimensionality and avoid issues like over-fitting in high dimensional space.…”

Section: Related Workmentioning

confidence: 99%

AHybrid Artificial Intelligence Approach to Predict Flight Delay

Alharbi¹,

Prince²

2020

International Journal of Engineering Research and Technology

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Commercial airlines and the passengers suffer from flight delay. Flight delay causes huge loss for the airlines and unsatisfied passengers. The researchers attempt to solve this problem through prediction extensively by machine learning approach and data mining tools. Accurate and robust performance is still to get through existing models. Our proposed hybrid approach is intended to use the power of machine learning as data mining tool and to predict the delay using classification algorithm of deep learning. An extensive evaluation of the proposed method is carried out by comparing the performance by using two data sets: one is local and the other is benchmark from Kaggle to obtain the best performing classifier. Three predictive models were applied on the datasets: logistic regression, decision tree and the proposed approach. The result shows that the proposed method performed well as comparing to the existing state-ofthe art.

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Prediction of Stock Market by Principal Component Analysis

Cited by 40 publications

References 6 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

A Bibliometric Analysis of Artificial Intelligence-Based Stock Market Prediction

AHybrid Artificial Intelligence Approach to Predict Flight Delay

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