PT - JOURNAL ARTICLE
AU - Zhou, Ji
AU - Kim, Hye Yeon
AU - Peirce, Jeffery
TI - Methodology Using Nonlinear Regression Models with Kelly Criterion to Determine Optimal ETF Investment Strategies: <em>An Application to the S&P 500 Index</em>
AID - 10.3905/jii.2013.3.4.049
DP - 2013 Feb 28
TA - The Journal of Index Investing
PG - 49--59
VI - 3
IP - 4
4099 - http://jii.iijournals.com/content/3/4/49.short
4100 - http://jii.iijournals.com/content/3/4/49.full
AB - How to allocate the proportion of money to invest into the stock market to earn optimal profit at lower risk? To answer the question, this study uses the Kelly Criterion to help develop a methodology to determine the optimal proportional investment strategies for ETFs, based on the forecasting results of the Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) and the Glosten-Jagannathan-Runkie GARCH (GJR-GARCH) models, which together consider the volatility of fund indices. We use six different GARCH and GJR models to forecast volatilities, and five different estimated long-term returns in Kelly Criterion to calculate final profit; then we determine the profitable investment strategies. The necessary inputs for the GARCH, GJR, and Kelly Criterion are the closing prices of the S&P 500 Index. While we use the closing prices of the S&P 500 Index from 1950 to 2011 as the inputs of the methodology, we analyze mainly the forecasting results from the representative years from 1981 to 2011. The methodology seems to be robust, as optimal investment strategies can be deduced from it.