Closed-end funds (CEFs) present a unique opportunity to study finance in that the price of shares rarely matches the net value of the underlying holdings. This study investigates this phenomenon and how behavioral finance influences the discount. The study uses time-series regression analysis to examine the CNN Fear and Greed Index1 and its relationship with CEF discounts over time. Results of vector autoregression (VAR) and Granger-causality testing indicate that investor sentiment does not significantly influence the price of CEFs in relation to their net assets. Autoregressive conditional heteroskedasticity (ARCH) models show that the volatility of investor sentiment does not significantly influence the volatility of the CEF discount, but that there is an autoregressive component to movements in the discount. ARMA models show that the time series functions of investor sentiment and the CEF are cointegrated, but with no significant causation. In addition, the impact of herding on the CEF discount was evaluated using simple linear regression to show that the herding behavior of investors toward CEFs with higher distribution yields causes the fluctuation in the CEF discount. Investors may use this information to better understand the relationship between investor sentiment, herding, and the valuation inefficiencies of closed-end funds.
1 The Fear and Greed Index is an equally weighted composite index of seven investor sentiment indicators. It is computed daily by CNN on their website (http://money.cnn.com/data/fear-and-greed).
|School:||Holy Angel University (The Philippines)|
|Source:||DAI-A 79/09(E), Dissertation Abstracts International|
|Subjects:||Business administration, Management, Finance|
|Keywords:||Behavioral finance, Efficient market theory, Fear and greed index, Herding, Investor sentiment|
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