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%.

This is a composition of information derived from the research conducted to produce each episode of Nefarious. As part of my final thesis project for my undergraduate degrees in criminal justice and forensic psychology, I created a podcast on the topic of true crime with an academic approach titled Nefarious. Each episode of Nefarious examines a particular case in relation to some academic theme learned throughout my educational career at Arizona State University. Episode one examines the life and crimes of Richard Ramirez and different criminological theories that could be used to explain his behavior. Episode two looks at the case of Brock Turner and Chanel Miller and focuses on the larger problem of rape culture in America and sexual assault on college campuses. In episode three the case of the Central Park Five is detailed in relation to false confessions and wrongful convictions within the legal system. Episode four is centered around Kyle Rittenhouse and the use of self-defense as a legal defense in the justice system as well as the political atmosphere surrounding this case. The fifth episode explores three cases of police brutality against racial minorities and the history of police in America. The final episode of Nefarious covers the life and crimes of Jeffrey Dahmer and analyzes such actions from a forensic psychology approach.