Filtering by
- Creators: School of Mathematical and Statistical Sciences
- Creators: Middleton, James
funds, although thee differ in a crucial way. ETFs rely on a creation and redemption feature to
achieve their functionality and this mechanism is designed to minimize the deviations that occur
between the ETF’s listed price and the net asset value of the ETF’s underlying assets. However
while this does cause ETF deviations to be generally lower than their mutual fund counterparts,
as our paper explores this process does not eliminate these deviations completely. This article
builds off an earlier paper by Engle and Sarkar (2006) that investigates these properties of
premiums (discounts) of ETFs from their fair market value. And looks to see if these premia
have changed in the last 10 years. Our paper then diverges from the original and takes a deeper
look into the standard deviations of these premia specifically.
Our findings show that over 70% of an ETFs standard deviation of premia can be
explained through a linear combination consisting of two variables: a categorical (Domestic[US],
Developed, Emerging) and a discrete variable (time-difference from US). This paper also finds
that more traditional metrics such as market cap, ETF price volatility, and even 3rd party market
indicators such as the economic freedom index and investment freedom index are insignificant
predictors of an ETFs standard deviation of premia. These findings differ somewhat from
existing literature which indicate that these factors should have a significant impact on the
predictive ability of an ETFs standard deviation of premia.
This study investigated how mindset intervention in freshman engineering courses influenced students’ implicit intelligence and self-efficacy beliefs. An intervention which bolsters students’ beliefs that they possess the cognitive tools to perform well in their classes can be the deciding factor in their decision to continue in their engineering major. Treatment was administered across four sections of an introductory engineering course where two professors taught two sections. Across three survey points, one course of each professor received the intervention while the other remained neutral, but the second time point switched this condition, so all students received intervention. Robust efficacy and mindset scales quantitatively measured the strength of their beliefs in their abilities, general and engineering, and if they believed they could change their intelligence and abilities. Repeated measures ANOVA and linear regressions revealed that students who embody a growth mindset tended to have stronger and higher self-efficacy beliefs. With the introduction of intervention, the relationship between mindset and self-efficacy grew stronger and more positive over time.
In collaboration with Moog Broad Reach and Arizona State University, a<br/>team of five undergraduate students designed a hardware design solution for<br/>protecting flash memory data in a spaced-based radioactive environment. Team<br/>Aegis have been working on the research, design, and implementation of a<br/>Verilog- and Python-based error correction code using a Reed-Solomon method<br/>to identify bit changes of error code. For an additional senior design project, a<br/>Python code was implemented that runs statistical analysis to identify whether<br/>the error correction code is more effective than a triple-redundancy check as well<br/>as determining if the presence of errors can be modeled by a regression model.
This project uses SAS (Statistical Analysis Software) to create a regression model that provides a prediction for which NFL playoff team will win the Super Bowl in a given year.