Forecasting Agriculture Exchange Trade Funds (ETFs): The Application of Grey Relational Analysis (GRA) and Artificial Neural Networks (ANNs)

Chen, Jo-Hui and Malinda, Maya (2015) Forecasting Agriculture Exchange Trade Funds (ETFs): The Application of Grey Relational Analysis (GRA) and Artificial Neural Networks (ANNs). SRI LANKA ECONOMICS RESEARCH CONFERENCE (SLERC) 2015 PROCEEDINGS of 4th International Conference of Sri Lanka Forum of University Economists, IV (-). pp. 357-362. ISSN -

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Abstract

ETFs and ETNs have become favoured research topics. Since their launch in 1993, ETFs have witnessed spectacular growth, with more than 4,980 ETFs currently being offered to investors. ETNs have shown rapid growth since their launch in 2006 by Barclays Bank PLC. Similar to ETFs, ETNs have many categories, such as commodity ETNs, currency ETNs, emerging market ETNs, and strategy ETNs. An example of commodity ETNs is agricultural ETN. Both agricultural ETFs and ETNs are desired by investors because of their rapid growth. However, despite many studies on ETFs, only a few have examined agricultural ETFs and ETNs. This study aims to fill this research gap and help investors select the best investment by identifying the best model to forecast agricultural ETFs and ETNs. Macroeconomic and financial variables are extracted to examine their effects on agricultural ETFs and ETNs. Several variables, namely, put/call ratio, EUR/USD exchange rate, volatility index (VIX), commodity research bureau index (CRB), short-term trading index (TRIN), New York Stock Exchange composite index (NYA), and weather index (WINX), are utilized. The weather index is added because agricultural products are influenced by weather conditions.

Item Type: Article
Subjects: H Social Sciences > HG Finance
Divisions: Fakultas Ekonomi > S-1 Manajemen
Depositing User: admin LPPM
Date Deposited: 21 Feb 2018 06:24
Last Modified: 21 Feb 2018 06:24
URI: http://repository.lppm.maranatha.edu/id/eprint/420

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