A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers

Caley, Jeffrey A.

doi:10.15760/etd.2000

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…Traders in any part of the world are interested in a market that is profitable and uses multiple technical indicators, macroeconomic factors and stock market indexes to study the market. This diverse market drivers' information reflects existing market price characteristics and facilitates prediction of future market price characteristics (Caley, 2013;Yang et al, 2019). As a result, we can prevent anticipated negative changes in the market due to new information about the market.…”

Section: Stock Market Forecastmentioning

confidence: 99%

“…As a result, we can prevent anticipated negative changes in the market due to new information about the market. However, market analysis is not a common practice and is often carried out using traditional tools and manual practices making the processing time consuming and prone to errors (Caley, 2013). Although other countries have carried out various studies on market prediction, a direct implementation of their findings is not practical.…”

Section: Stock Market Forecastmentioning

confidence: 99%

See 1 more Smart Citation

Ultimate Prediction of Stock Market Price Movement

Nabi¹,

Saeed²,

Harron³

et al. 2019

Journal of Computer Science

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Investment in the stock market is currently very popular due to its economic gain. Numerous researchers' and academicians' work is focused on financial time series prediction due to its data availability and profitability. Therefore, this study presents the design and implementation of a novel binary classification framework to predict stock market trends. The framework is composed of data preprocessing, feature engineering, feature selection and classification algorithms. The model is built on multiple sector stock market companies' data collected from NASDAQ over a period of ten years. Various feature selection algorithms are applied in combination with several machine learning algorithms. Furthermore, as the new contribution, we have constructed two new features which have been found to be promising in terms of improving overall performance. Ultimately, a collaboration of feature selection and classification techniques is employed. The application of Principal Component Analysis (PCA) with Multilayer Perceptron and Support Vector Machine (SVM) to added featured datasets shows 100% accuracy on the majority of datasets. In summary, an intensive comparison is presented among the various feature selection and classification algorithms.

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Section: Stock Market Forecastmentioning

confidence: 99%

Section: Stock Market Forecastmentioning

confidence: 99%

Ultimate Prediction of Stock Market Price Movement

Nabi¹,

Saeed²,

Harron³

et al. 2019

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…Technicians are usually the ones who perform these techniques. They use past price and volume data, which is graphically displayed, to decide where future financial markets will move (30). Technicians believe that all stock information is reflected in the stock price.…”

Section: Technical-based Stock Predictionmentioning

confidence: 99%

Department of Computer Science and Information Technology, College of Computer Science & Information Technology, Firat University, turkey

Alman¹

2020

QAJ

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This paper provides uses a new Ant Colony based algorithms called U-Turning Ant colony optimization (U-TACO) for solving one of NP-Hard problems which is widely used in computer science field called Traveling Salesman Problem (TSP). U-Turning Ant colony Optimization based on making partial tour as an initial state for the basic Ant Colony algorithm. This paper provides tables and charts for the results obtained by U-Turning Ant colony Optimization for various TSP problems from the TSPLIB95.

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A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers

Cited by 2 publications

References 11 publications

Ultimate Prediction of Stock Market Price Movement

Ultimate Prediction of Stock Market Price Movement

Department of Computer Science and Information Technology, College of Computer Science & Information Technology, Firat University, turkey

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