Stock Market Prediction Using Machine Learning(ML)Algorithms

Umer, Muhammad; Awais, Muhammad; Muzammul, Muhammad

doi:10.14201/adcaij20198497116

Cited by 51 publications

(17 citation statements)

References 12 publications

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“…Clustering, abnormal sample identification, missing value filling, and data presentation are all possible using the matrix. e similarity matrix might be considered one of the most useful random forest tools [10][11][12][13]. For the financial dataset of listed enterprises with N samples, first generate an N × N zero matrix, which is recorded as P � p ij 􏽮 􏽯(i, j � 1, 2, .…”

Section: Financial Data Extraction Of Listed Enterprises Based Onmentioning

confidence: 99%

Research on Intelligent Prediction Method of Financial Crisis of Listed Enterprises Based on Random Forest Algorithm

Jiang¹,

Wang²

2021

Security and Communication Networks

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Traditional financial crisis prediction approaches have a tough time extracting the properties of financial data, resulting in financial crisis prediction with insufficient accuracy. As a result, based on the random forest algorithm, an intelligent financial crisis prediction approach for listed enterprises is proposed. The random forest method is used to mine the characteristics of financial data based on financial index data from publicly traded companies. This research develops a financial crisis prediction index system based on the findings of data feature mining. The CCR model is used to assess the efficiency of listed firms’ decision-making units with more input and output, and the efficiency index of each decision-making unit is calculated. The efficiency evaluation index of publicly traded companies is used to divide the severity of the financial crisis. The experimental results reveal that, when compared to standard prediction methods, this method’s forecast accuracy is commensurate with the actual state of businesses, and it can reduce the time it takes to predict financial crises.

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Section: Financial Data Extraction Of Listed Enterprises Based Onmentioning

confidence: 99%

Research on Intelligent Prediction Method of Financial Crisis of Listed Enterprises Based on Random Forest Algorithm

Jiang¹,

Wang²

2021

Security and Communication Networks

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“…Ghani et al stated the volatility in the global stock market and wished to utilize ML algorithms to help people to take advantage of the stock market with minimum effort [2]. The authors predicted Amazon, Apple, and Google historical stock data using LR, where the close price is the independent variable…”

Section: Lr Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of the Application of Machine Learning Algorithms and Econometric Models in Stock Market Prediction

Ni¹,

Chen²

2022

BCPBM

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Forecasting the future price trend of a stock traded on a financial exchange is the aim of stock market prediction. In recent decades, stock market prediction has been a fascinating topic in the domain of Data Science and Finance. In reality, the stock movement is ambiguous and chaotic due to various influencing factors such as government policy, current events, interest rates Etc. At the same time, accurate enough forecasting of stock price movement leads to substantial benefits for investors. This paper provides a comprehensive review of the application and comparison of Machine Learning (ML) algorithms and Econometric Models in stock market prediction. The mentioned models are categorized into (i) ML algorithms, including Linear Regression (LR), K-nearest neighbors (KNN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM). (ii) Econometric Models, including Autoregressive Integrated Moving Average (ARIMA) Model, Capital Asset Pricing Model (CAPM), and Fama-French (FF) Factor Model.

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“…Much research has been conducted about linear regression. M Umer Ghania, et al, (2019) successfully predicted the future stock price of amazon and apple using linear regression and improved the accuracy of their model by using a three-month moving average of their data and implementing exponential smoothing [1]. Vaishnavi Gururaj, et al, (2019) compared linear regression with support vector machines and provided pros and cons for both models [2].…”

Section: Introductionmentioning

confidence: 99%

Predicting ETF prices using linear regression

Li¹

2023

BCPBM

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Machine learning has allowed computers to analyze data and make future predictions based on those dates. One of the most common and easiest to implement machine learning algorithms used to do this is simple linear regression. Simple linear regression finds trends in a data set by graphing a line that shows the relationship between two variables. This paper will show how Simple linear regression can predict future ETF prices by finding linear trends in two particular exchange-traded funds: Invesco QQQ and vanguard VGT, predict their value six months later using their five-year closing price in yahoo finance and compare their respective predicted growth rate.

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Stock Market Prediction Using Machine Learning(ML)Algorithms

Cited by 51 publications

References 12 publications

Research on Intelligent Prediction Method of Financial Crisis of Listed Enterprises Based on Random Forest Algorithm

Research on Intelligent Prediction Method of Financial Crisis of Listed Enterprises Based on Random Forest Algorithm

A Comparative Analysis of the Application of Machine Learning Algorithms and Econometric Models in Stock Market Prediction

Predicting ETF prices using linear regression

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