Apr 07 2019

Keynote – National Institute of Applied Science and Technology

Dr. Schumaker was keynote at NIT Day for the National Institute of Applied Science and Technology (INSAT) on April 7, 2019 on “Becoming a Data Scientist.” This lecture is part of the ACM’s Distinguished Speakers Program.

Apr 06 2019

Invited Talk – ESPIRIT (Tunis, Tunisia)

Dr. Schumaker provided a motivational talk on getting into the field of Data Science to students at Ecole Supérieure Privée d’Ingénierie et de Technologies (ESPIRIT) on April 6,2019. This lecture is part of the ACM’s Distinguished Speakers Program.

Feb 22 2019

Invited Talk – Western Washington University

Dr. Schumaker was invited to present to Western Washington University in Bellingham, WA on May 16, 2019 on “Becoming a Data Scientist” and “Prediction from Regional Angst – A Study of NFL Sentiment in Twitter Using Technical Stock Market Charting.”

Feb 07 2019

Invited Talk – Seattle Pacific University

Dr. Schumaker was invited to present to Seattle Pacific University in Seattle, WA on May 15, 2019 on “Becoming a Data Scientist” and “Lessons from the Future: Predictions in Finance, Sports and Medicine.”

Jan 31 2019

UT Tyler Professor Explains How Sports Analytics Could Predict Super Bowl 53 Winner

Dr. Schumaker was featured in a segment by Jeff Wright on “UT Tyler Professor Explains How Sports Analytics Could Predict Super Bowl 53 Winner” for KLTV.com.

The article can be accessed here.

Jan 30 2019

Interview – KYTX The Noon Show

Dr. Schumaker was interviewed live by Jen Moynihan on “The Science of Sports Analytics” for The Noon Show.

Jan 30 2019

Interview – KLTV East Texas Now

Dr. Schumaker was interviewed live by Jeremy Butler on “Analytics and Superbowl LIII” for East Texas Now.

Jan 28 2019

Keynote for National Institute of Applied Science and Technology through ACM Distinguished Speakers Program

Dr. Schumaker was invited to present to the ACM Student Chapter of National Institute of Applied Science and Technology in Tunis, Tunisia on April 7, 2019 on “Becoming a Data Scientist.”

Dec 31 2018

JITIM Paper – Machine Learning the Harness Track: A Temporal Investigation of Race History on Prediction

New publication available here.

Machine learning techniques have shown their usefulness in accurately predicting greyhound races. Many of the studies within this domain focus on two things; win-only wagers and using a very particular combination of race history. Our study investigates altering these properties and studying the results. In particular we found a race history combination that optimizes our S&C Racing system’s predictions on seven different wager types. From this, S&C Racing posted an impressive 50.44% accuracy in selecting winning wagers with a payout of $609.34 and a betting return of $10.06 per dollar wagered.

Oct 09 2018

CIIMA paper – A Descriptive Analysis of Abnormal Stock Price Movement Following Financial News Article Release

A new publication is available.

What effect does a financial news article have on stock price? To answer this question we investigate stock price movements within the minutes following financial news releases, broken down by media outlet, time of release and article sentiment. Our data shown a Sharpe ratio (a measure for calculating risk-adjusted return) of 1.81 versus a random dataset of ‑0.06, indicating significant price movement immediately following article release. Second, we found that articles released through WSJ, Reuters – UK Focus, NYT and FT all experienced significant positive returns, whereas articles in Barrons, MarketWatch, Forbes and Bloomberg experienced significant negative returns. Third, we found that articles released at certain times had abnormally high price movements associated with them, more so than random chance. Lastly we discovered a minority of positive news articles trending upwards and suddenly reversing direction following a financial news article release. In one particular case there was a period of several days where the release of IBM articles triggered large price declines with steady prices otherwise. We believe these findings could be used by companies as a form of stock price management.

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