Publications

Unit Interval Parking Functions and the $r$-Fubini Numbers

Published in La Matematica, 2024

We recall that unit interval parking functions of length $n$ are a subset of parking functions in which every car parks in its preference or in the spot after its preference, and Fubini rankings of length $n$ are rankings of $n$ competitors allowing for ties. We present an independent proof of a result of Hadaway, which establishes that unit interval parking functions and Fubini rankings are in bijection. We also prove that the cardinality of these sets are given by Fubini numbers. In addition, we give a complete characterization of unit interval parking functions by determining when a rearrangement of a unit interval parking function is again a unit interval parking function. This yields an identity for the Fubini numbers as a sum of multinomials over compositions. Moreover, we introduce a generalization of Fubini rankings, which we call the $r$-Fubini rankings of length $n+r$. We show that this set is in bijection with unit interval parking functions of length $n+r$ where the first $r$ cars have distinct preferences. We conclude by establishing that these sets are enumerated by the $r$-Fubini numbers.

Recommended citation: Bradt, S. A., Elder, J., Harris, P. E., Kirby, G. R., Reutercrona, E., Wang, Y., & Whidden, J. (2024). Unit Interval Parking Functions and the r-Fubini Numbers. La Matematica, 1-15

Leveraging Financial News Analysis to Predict Stock Price Movement

Published in Frontiers in Economics and Management, 2021

Stock market predictions have been prominent among scholars. Sentiment analysis applying financial news articles for predicting stock has also grown in popularity over the past few decades. Our research indicates the impact of sentiment analysis of financial news on forecasting asset prices. In this research, sentiment is culled from financial news of companies over the past three years. Subsequently the asset prices can be predicted using stock market data, alongside sentiment analysis data with several machine learning algorithms. The results show a more convincing level of precision in the aggregation of both stock market data and sentiment analysis data.

Recommended citation: Xu, M., Wang, Y., Huang, Y., & Xu, M. (2021). Leveraging Financial News Analysis to Predict Stock Price Movement. Frontiers in Economics and Management, 2(7), 265-276.