A comprehensive evaluation of ensemble learning for stock-market prediction

Nti, Isaac Kofi; Adekoya, Adebayo Felix; Weyori, Benjamin Asubam

doi:10.1186/s40537-020-00299-5

Cited by 162 publications

(106 citation statements)

References 53 publications

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“…ML focuses on prediction and can make data-analysis resourceful by looking at huge amounts of data simultaneously. Several ML algorithms are applicable for this study; however, we adopted the decision tree (DT) algorithm due to its simplicity but highly efficient, faster training and testing time, which results in low computational cost (2) . The "DT is a flow-chart-like tree structure that uses a branching technique to clarify every single likely result of a decision" (2) .…”

Section: Machine Learning Modelmentioning

confidence: 99%

“…Several ML algorithms are applicable for this study; however, we adopted the decision tree (DT) algorithm due to its simplicity but highly efficient, faster training and testing time, which results in low computational cost (2) . The "DT is a flow-chart-like tree structure that uses a branching technique to clarify every single likely result of a decision" (2) . Algorithm 1 explains the operations of the DT algorithm (2,50) .…”

Section: Machine Learning Modelmentioning

confidence: 99%

“…The "DT is a flow-chart-like tree structure that uses a branching technique to clarify every single likely result of a decision" (2) . Algorithm 1 explains the operations of the DT algorithm (2,50) . The settings of our DT model was, max_depth = 4 and criterion = entropy.…”

Section: Machine Learning Modelmentioning

confidence: 99%

“…The stock market is one of the financial markets that receive several investments daily due to its associated benefits and gains (1)(2)(3)(4) . Trillions of dollars are invested in the stock market globally by investors to make a substantial profit, despite its stochastic nature (5) .…”

Section: Introductionmentioning

confidence: 99%

“…Trillions of dollars are invested in the stock market globally by investors to make a substantial profit, despite its stochastic nature (5) . Its stochastic nature is believed to be influenced by several factors ranging from political, economic instability, investors and public sentiments, the board of directors of firms, firms financial status and economic standing (1,2) .…”

Section: Introductionmentioning

confidence: 99%

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The COVID-19 outbreak and effects on major stock market indices across the globe: A machine learning approach

Adekoya¹,

Nti²

2020

IJST

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Background/Objectives: The Coronavirus disease in 2019 (COVID-19) was first seen in Wuhan (capital of Hubei, Chain), and has since spread throughout the world, resulting in the World Health Organisation (WHO) declaring the 2019-20 coronavirus a pandemic. Its ongoing spread has resulted in a standstill of the world's economy, total lockdown in some counties, closedown of business and firms globally. As of 25 th Mar 2020, 11:00 am GMT, a total of 491,280 cases were reported globally. The evolution of COVID-19 and its economic impact on the regional financial markets are highly uncertain, which makes it difficult for legislators to formulate a suitable macroeconomic strategy response. Methods: To ascertain the possible economic impact of COVID-19 globally, this study examines the effect of COVID-19 on major stocks indices across the globe. Using a random sampling technique, we selected thirty (30) world stock market indices in different countries infected with COVID-19 from 31 st Dec 2019 to 25 th Mar 2020. However, not like a high percentage of previous studies that focus on the regional stock market, we examine the information on daily reported COVID-19 cases and stock market fluctuation over thirty stock market indices that houses the stock prices of several countries around the globe. Also, we estimate the monetary loss within the period, project the future surge of this pandemic on the stock market and outline some portfolio allocation strategies to help the investor hedge against investment risk. Findings: The experimental results in this study show that even a controlled outbreak of the COVID-19 can significantly influence the world's economy in both the short-term and longterm. Our obtained results of COVID-19 associated economic loss echoed in stock prices movements advise that the cost can escalate severely and quickly into global economic stress. Hence, we conclude by outlining some measure that might help investors hedge against such risk using portfolio allocation strategies.

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Section: Machine Learning Modelmentioning

confidence: 99%

Section: Machine Learning Modelmentioning

confidence: 99%

Section: Machine Learning Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The COVID-19 outbreak and effects on major stock market indices across the globe: A machine learning approach

Adekoya¹,

Nti²

2020

IJST

Self Cite

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An Extensive Survey on the Prediction of Bankruptcy

Nayak

Rout

2022

The New Advanced Society

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Application and performance of data mining techniques in stock market: A review

Kaur

Dharni

2022

Intell Sys Acc Fin Mgmt

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Prediction and the stock market go hand in hand. Due to the inherent limitations of traditional forecasting methods and the pursuit to uncover the hidden patterns in stock market data, stock market prediction using data mining techniques has caught the fancy of academicians, researchers, and investors. Based on a systematic review of more than 143 research studies spanning 25 years, the present paper brings to light the major issues concerning forecasting of stock markets based on data mining techniques, such as usage of data mining techniques in the stock market, input data types, single versus hybrid techniques, instruments and stock markets researched, types of software and algorithms used, measures of forecast accuracy, and performance of various data mining techniques. Emerging patterns related to various dimensions have been critically analyzed by highlighting the existing limitations and suggesting future research paradigms. This analysis can be useful for academicians, researchers and investors looking for futuristic directions in a given research domain.

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A comprehensive evaluation of ensemble learning for stock-market prediction

Cited by 162 publications

References 53 publications

The COVID-19 outbreak and effects on major stock market indices across the globe: A machine learning approach

The COVID-19 outbreak and effects on major stock market indices across the globe: A machine learning approach

An Extensive Survey on the Prediction of Bankruptcy

Application and performance of data mining techniques in stock market: A review

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