Optimal Feature Selection of Technical Indicator and Stock Prediction Using Machine Learning Technique

Naik, Nagaraj; Mohan, Biju R.

doi:10.1007/978-981-13-8300-7_22

Cited by 35 publications

(12 citation statements)

References 15 publications

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“…These shadow variables are recreated in each iteration, and the algorithm stops when only the essential variables are left in the test. Naik and Mohan [2019] utilized Boruta in finding out the appropriate feature set for a stock prediction problem. Hallmark and Dong [2020], meanwhile, leveraged this algorithm to work on roadway safety models in an LRM setup.…”

Section: Tree-based Methodsmentioning

confidence: 99%

A review and recommendations on variable selection methods in regression models for binary data

Bag¹,

Gupta²,

Deb³

2022

Preprint

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The selection of essential variables in logistic regression is vital because of its extensive use in medical studies, finance, economics and related fields. In this paper, we explore four main typologies (test-based, penalty-based, screening-based, and tree-based) of frequentist variable selection methods in logistic regression setup. Primary objective of this work is to give a comprehensive overview of the existing literature for practitioners. Underlying assumptions and theory, along with the specifics of their implementations, are detailed as well. Next, we conduct a thorough simulation study to explore the performances of fifteen different methods in terms of variable selection, estimation of coefficients, prediction accuracy as well as time complexity under various settings. We take low, moderate and high dimensional setups and consider different correlation structures for the covariates. A real-life application, using a highdimensional gene expression data, is also included in this study to further understand the efficacy and consistency of the methods. Finally, based on our findings in the simulated data and in the real data, we provide recommendations for practitioners on the choice of variable selection methods under various contexts.

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Section: Tree-based Methodsmentioning

confidence: 99%

A review and recommendations on variable selection methods in regression models for binary data

Bag¹,

Gupta²,

Deb³

2022

Preprint

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“…Zhang et al [21] proposed a model that performs feature selection using minimum redundancy maximum relevance [22,23] for stock index data. Nalk et al [24] improved the performance of stock index prediction using the Boruta feature selection algorithm [25] with an artificial neural network [26]. Yuan et al [27] compared the performance of the stock index prediction models such as a support vector machine (SVM) [28], random forest [29], and an artificial neural network.…”

Section: Stock Index Predictionmentioning

confidence: 99%

Genetic Feature Selection Applied to KOSPI and Cryptocurrency Price Prediction

2021

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Feature selection reduces the dimension of input variables by eliminating irrelevant features. We propose feature selection techniques based on a genetic algorithm, which is a metaheuristic inspired by a natural selection process. We compare two types of feature selection for predicting a stock market index and cryptocurrency price. The first method is a newly devised genetic filter involving a fitness function designed to increase the relevance between the target and the selected features and decrease the redundancy between the selected features. The second method is a genetic wrapper, whereby we can find the better feature subsets related to KOPSI by exploring the solution space more thoroughly. Both genetic feature selection methods improved the predictive performance of various regression functions. Our best model was applied to predict the KOSPI, cryptocurrency price, and their respective trends after COVID-19.

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“…Fundamental analysis looks at the indicators that affect market supply and demand [14]. The idea is to collect and process information before it reflects its consequences on the market.…”

Section: A Stock Price Predictionmentioning

confidence: 99%

Applying LSTM for Stock Price Prediction with Sentiment Analysis

Heiden¹,

Parpinelli²

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Financial news has been proven to be valuable source of information for the evaluation of stock market volatility. Most of the attention has been given to social media platforms, while news from vehicles such as newspapers are not as widely explored. Newspapers provide, although in a smaller volume, more reliable information than social media platforms. In this context, this research aims to examine the influence of financial news within the stock price prediction problem, by using the VADER sentiment analysis model to process the news and feed the sentiments as a feature into a LSTM-based stock price prediction model, along with the historical data of the assets. Experiments indicate that the model has better results when the news’ sentiments are considered, and the model demonstrates potential to accurately predict stock prices up to around 60 days into the future.

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Optimal Feature Selection of Technical Indicator and Stock Prediction Using Machine Learning Technique

Cited by 35 publications

References 15 publications

A review and recommendations on variable selection methods in regression models for binary data

A review and recommendations on variable selection methods in regression models for binary data

Genetic Feature Selection Applied to KOSPI and Cryptocurrency Price Prediction

Applying LSTM for Stock Price Prediction with Sentiment Analysis

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