Browse Articles

The authors looked the ability to predict future stock prices using various machine learning models.

Algorithmic trading has been increasingly used by Americans. In this work, we tested whether including the opening, closing, and highest prices in three supervised learning models affected their performance. Indeed, we found that including all three prices decreased the error of the prediction significantly.

The authors developed a quantum inspired model for stock market fluctuations.

Here, seeking to understand the correlation of 50 of the most important economic indicators with inflation, the authors used a rolling linear regression to identify indicators with the most significant correlation with the Month over Month Consumer Price Index Seasonally Adjusted (CPI). Ultimately the concluded that the average gasoline price, U.S. import price index, and 5-year market expected inflation had the most significant correlation with the CPI.

Advertising platforms like Google Ads use AI to drive the algorithms used to maximize advertisers benefits. This study shows that AI does not adjust it strategy based on auction type and highlights the limitations of AI running without explicit guidance.

In this article the authors looked at different attributes of apps within the Google Play store to determine how those may impact the overall app rating out of five stars. They found that review count, amount of storage needed and when the app was last updated to be the most influential factors on an app's rating.

The authors assessed the correlation between the stock, commodity, and consumer markets with the housing market.

Classical financial forecasting models often fail to capture the complex, nonlinear dynamics of the stock market. This study demonstrates that incorporating a single variable to represent the 'geometric curvature' of a time series dramatically improves the accuracy of standard econometric forecasts. Our findings highlight that geometric properties are a significant predictive factor, opening new avenues for more powerful financial modeling.

In this study, the authors conduct a survey of high school students to evaluate the effects of screen time and habits on motivation.

This study used machine learning models to examine which factors most influenced U.S. household energy consumption in 2020 using data from 18,496 households.