Filtering by
- All Subjects: Regression
- Creators: School of Mathematical and Statistical Sciences
- Resource Type: Text
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.
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.
This investigation evaluates the most effective time series model to forecast the stock price for companies that started trading during the COVID-19 stock market crash. My research involved the analysis of five companies in the technology industry. I was able to create three different machine-learning models for each company. Each model contained various criteria to determine the efficacy of the model. The AIC and SBC are common metrics among Autoregressive, autoregressive moving averages, and cross-correlation input models. Lower AIC and SBC values indicated better-fitted models. Additionally, I conducted a white-noise test to determine stationarity. This yielded an Auto-correlation graph determining whether the data was non-stationary or stationary. This paper is supplemented by a project plan, exploratory data analysis, methodology, data, results, and challenges section. This has relevance in understanding the overall stock market trend when impacted by a global pandemic.