Creating Accurate Predictions in the Stock Market
#technology
#performance
#systems
#data
Creating accurate predictions in the stock market has always been a great challenge in finance. With the rise of machine learning as the next level in the forecasting area, this research compares four machine learning models and their accuracy for forecasting three well-known stocks traded in the NYSE in the short term over the period from March 2020 to May 2022. We deploy, develop, and hypertune XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression models and report the models that produce the highest accuracies from our evaluation metrics: RMSE, MAPE, and MPE.
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- Date: 15 Dec 2022
- Time: 01:00 AM UTC to 02:00 AM UTC
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- 1000 KLO Rd.
- Kelowna
- Kelowna, British Columbia
- Canada V1Y 4X8
- Room Number: HS301
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- Co-sponsored by Langara College
- Starts 12 December 2022 06:20 AM UTC
- Ends 15 December 2022 01:15 AM UTC
- No Admission Charge
Speakers
Steven Whang
Topic:
Creating Accurate Predictions in the Stock Market
Biography:
- Steven Whang has a Bachelor’s Degree in Financial Economics. He is finishing a PDD in Data Analytics at Langara College. He aspires to be working in the data science field in the future.
Email:
Niha Siddikha Sachin of Langara College
Topic:
Creating Accurate Predictions in the Stock Market
Biography:
- Niha Siddikha Sachin In the 5 years that Niha spent crunching numbers for the e-commerce and retail industries as a lead and a product analyst in a trillion-dollar and a Fortune 500 company, respectively, she has solved problems for various categories, created reports to track PnL and maintained servers for the banking and finance domain. Having a Bachelor of Engineering degree in IT, she became interested in data interpretation and manipulation, where data alter the perception and when the perception alters output. Niha is currently finishing her PDD in Data Analytics at Langara college.
Email:
Address:British Columbia, Canada