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Event Type
Research Presentation
Academic Department
Mathematics and Statistics
Location
Dana Science Building, 2nd floor
Start Date
14-4-2023 1:30 PM
End Date
14-4-2023 3:00 PM
Description
Under the direction of Dr. Julie Clark
Stock Market can be a challenging space for those wanting to invest their savings and gain some profit. Hence, it would be helpful if there was any way for the investors to predict the market accurately. Although this would sound next to impossible a few years ago, with today’s technology including artificial intelligence and machine learning, investigating this possibility is worth exploring. We explore the use of machine learning models for US stock market prediction using the S&P 500 Index. Two modes Both linear regression and the decision tree models are used to forecast the next day’s closing value of the S&P 500 companies from data from 2010 – 2021. Results from the linear regression and decision tree models are compared against each other and against the 2022 data.
Stock Market Prediction Model
Dana Science Building, 2nd floor
Under the direction of Dr. Julie Clark
Stock Market can be a challenging space for those wanting to invest their savings and gain some profit. Hence, it would be helpful if there was any way for the investors to predict the market accurately. Although this would sound next to impossible a few years ago, with today’s technology including artificial intelligence and machine learning, investigating this possibility is worth exploring. We explore the use of machine learning models for US stock market prediction using the S&P 500 Index. Two modes Both linear regression and the decision tree models are used to forecast the next day’s closing value of the S&P 500 companies from data from 2010 – 2021. Results from the linear regression and decision tree models are compared against each other and against the 2022 data.