We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.

Until the Supreme Court’s landmark decision in National Collegiate Athletics Association (NCAA) vs. Alston, student-athletes were not allowed to be compensated for the millions of dollars in revenue they generate for universities. While universities cannot directly pay student-athletes, student-athletes can now make money based off their name, image, and likeness (NIL). NIL legislation has the potential (and has begun to) change college recruiting with the transfer portal and free agency landscape. Now, schools can bake NIL connections into their recruiting pitch, creating a recruiting renaissance. This research is an empirical study to determine the factors that contribute to an athlete’s NIL valuation and earnings. A hierarchical mixed-model analysis run in SAS also is used to analyze the data. The significance of this study includes providing schools and athletes with vital information pertaining to their fiscal valuation during the recruiting process. The findings can help families and student athletes to better estimate expected NIL earnings.