RT Journal Article SR Electronic T1 Mean-ETL Portfolio Selection under Maximum Weight and Turnover Constraints Based on Fundamental Security Factors JF The Journal of Investing FD Institutional Investor Journals SP 14 OP 24 DO 10.3905/joi.2012.21.1.014 VO 21 IS 1 A1 Naoshi Tsuchida A1 Xiaoping Zhou A1 Svetlozar Rachev YR 2012 UL https://pm-research.com/content/21/1/14.abstract AB In this article, we model stock returns using fundamental data and minimizing average value at risk (AVaR) and multiperiod portfolio selection with weight and turnover constraints. Equity returns are decomposed into returns explained by fundamental and nonfundamental factors. While the former are found to be independent, the latter are found to be highly dependent among various stocks. Then, we construct models to forecast returns using several ARMA–GARCH models with different innovation distributions and simulate scenarios of future returns. Based on these scenarios, we examine various approaches of portfolio optimization. By comparing actual portfolios based on real data, we find that 1) the ARMA–GARCH model with classical tempered stable distribution provides a superior prediction of equity prices than the normal and Student’s t-distribution and 2) AVaR provides a better risk measure than variance. We also see how portfolio performance changes under weight and turnover constraints and suggest that it is effective to reduce the stock universe and trade large-capitalization securities.TOPICS: VAR and use of alternative risk measures of trading risk, analysis of individual factors/risk premia, portfolio construction