[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120756-en":3,"doc-seo-120756-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120756,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms","Accurate short-term stock price prediction remains difficult due to the many factors that drive market movements. Leveraging machine learning, the study compares four models—XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression—on three NYSE-traded stocks over March 2020 to May 2022. Exogenous variables include short-term interest rate proxies, inflation measures, market index movements, and longer-term treasury rates. Evaluation via RMSE, MAPE, MTT, and MPE shows XGBoost achieves the highest accuracy, though with longer runtime, and suggests further tuning or additional exogenous features.","Short-Term Stock Price Forecasting using exogenous variables and Machine Learning  \nAlgorithms  \narXiv :2309 .00618v1 [ q-fin .TR] 17 May 2023  \nAlbert Wong  \nMathematics and Statistics Langara College Vancouver, Canada 0000-0002-0669-4352  \nNiha Sachin  \nMathematics and Statistics Langara College Vancouver, Canada [nsachin00@mylangara.ca](nsachin00@mylangara.ca)  \nSteven Whang  \nMathematics and Statistics Langara College Vancouver, Canada [swhang00@mylangara.ca](swhang00@mylangara.ca)  \nGustavo Dutra  \nMathematics and Statistics Langara College Vancouver, Canada [gdutra01@mylangara.ca](gdutra01@mylangara.ca)  \nEmilio Sagre  \nMathematics and Statistics Langara College Vancouver, Canada [esagre00@mylangara.ca](esagre00@mylangara.ca)  \nYew-Wei Lim  \nMathematics and Statistics Langara College Vancouver, Canada [ywlim@langara.ca](ywlim@langara.ca)  \nGatan Hains  \nLACL Universite´ Paris-Est Crteil, France 0000-0002-1687-8091  \nYoury Khmelevsky  \nComputer Science Okanagan College Kelowna, Canada 0000-0002-6837-3490  \nFrank Zhang  \nSchool of Computing University of the Fraser Valley Abbotsford, Canada 0000-0001-7570-9805  \nAbstract—Creating accurate predictions in the stock market has always been a great challenge in the finance world. With the rise of machine learning as the next level in the forecasting area, this research paper compares four machine learning models and their accuracy in forecasting three wellknown stocks traded in the NYSE in the short term over the period from March 2020 to May 2022. We deploy, develop, and tune 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, MTT, and MPE. Using a training data set of 240 trading days, we find that XGBoost gives the highest accuracy despite taking longer (up to 10 seconds) to run. Results from this study may improve with the further tuning of the individual parameters or introducing of more exogenous variables.  \nIndex Terms—Stock Price Predictions, Exogenous variables, Support Vector Regression, Multilayer Perceptron, Random Forest, XGBoost, Machine Learning, Algorithmic Trading.  \nI. INTRODUCTION  \nMachine learning models and algorithms have become increasingly popular over the past few years and will continue to be used even more in the future. Researchers, analysts, and other professionals have used machine learning and incorporated it into our daily lives. From corporations to individuals, machine learning can be applied in a wide range of areas. In this research paper, we explore the use of machine learning models to predict stock market price  \nforecasts. This work extended those completed in [1]–[5] and built on the ideas used in [6], [7] . A more detailed survey of these works is presented in Section II.  \nMachine learning algorithms use past data to create short-term forecasts of the movement of the chosen stock prices. However, predicting stock market prices is known to be difficult because many factors contribute to the movement of these prices. Well-known theories such asthe Efficient Market Hypothesis and Random Walk theory suggest that it is impossible to beat the market consistently. By comparing four different machine learning algorithms, this research aims to determine the model that produces the most accurate prediction of the movement of the chosen stocks.  \nThere are many possibilities of exogenous variables that can be used for a machine learning algorithm. For the purpose of this research, we have built on our previous study [8] and include variables that represent short-term interest rate movement (2-year treasury bonds) as well as inflation (the price of gold and the price of crude oil) . This is in addition to variables that we believe are central to the prediction of the price of a stock: movements of the overall market (Dow Jones, SI&P, and NASDAQ indexes) and longer-term interest rate (5 and 10-year treasury bonds) .  \nNot","cbCaifAKMTEHyXXB","https://ap.wps.com/l/cbCaifAKMTEHyXXB","pdf",510623,1,6,"English","en",105,"# Abstract\n# Introduction\n# Existing Work","[{\"question\":\"Which machine learning models are compared for short-term stock price forecasting?\",\"answer\":\"The study evaluates XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression, selecting performance based on RMSE, MAPE, MTT, and MPE.\"},{\"question\":\"What exogenous variables are included in the forecasting features?\",\"answer\":\"The research includes variables representing short-term interest rate movement (2-year treasury bonds) and inflation (gold and crude oil prices), plus broad market index movements and longer-term treasury bonds (5 and 10-year).\"},{\"question\":\"What dataset and time period are used to evaluate the models?\",\"answer\":\"The models are tested on short-term data covering March 2020 to May 2022, using a training dataset of 240 trading days and assessing performance with multiple error metrics.\"}]","Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms | 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machine learning models are compared for short-term stock price forecasting?","Question",{"text":75,"@type":76},"The study evaluates XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression, selecting performance based on RMSE, MAPE, MTT, and MPE.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What exogenous variables are included in the forecasting features?",{"text":80,"@type":76},"The research includes variables representing short-term interest rate movement (2-year treasury bonds) and inflation (gold and crude oil prices), plus broad market index movements and longer-term treasury bonds (5 and 10-year).",{"name":82,"@type":73,"acceptedAnswer":83},"What dataset and time period are used to evaluate the models?",{"text":84,"@type":76},"The models are tested on short-term data covering March 2020 to May 2022, using a training dataset of 240 trading days and assessing performance with multiple error 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