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Predicting Net Asset Value Discounts/Premiums
of Closed-End Funds
Data Analysis: Prof. Simonoff
The first investment decision I ever made was to buy the Asia Pacific Fund, a closed end fund that invests in companies located in the Asia Pacific region. Unlike open-end, mutual funds, closed end funds do not allow redemption of their shares at the underlying value of the stocks in which the fund invests (Net Asset Value). Rather, the shares of closed end funds trade on an exchange like a normal equity and thus a discount, or premium, can emerge if the trading price of the fund’s shares diverges from the fund’s net asset value.
When I bought the Asia Pacific Fund, in 1993, it was trading at a premium to its net asset value (i.e., the fund’s market price was higher than the underlying value of the fund’s investments). The Asia Pacific markets were very popular with investors at the time and had drummed up lots of interest in the financial media. However, shortly after I purchased the fund, the premium that I paid for the shares turned into a discount and has not recovered since. I have since learned that this is a common occurrence with closed end funds. However I have never heard a good explanation for this puzzling aspect of market demand.
If a fund trades at a discount, it means that the shareholders would gain more value if the fund disbanded and liquidated its shares. Since the underlying investments of the fund are usually market-listed stocks or bonds, the underlying net-asset value could tangibly be realized. When a fund trades at a discount for a long period of time, major investors often try to have the fund either i) liquidated and disbanded or ii) converted into an open-end fund so that shares of the fund could be redeemed on a daily basis at the net asset value per share. Given the large number of closed end funds in the market trading at a discount and the rare occurrence of liquidation, it is unlikely that discounts can be generally explained by investor sentiment.
I chose this issue as the subject for my project to try to gain a better understanding of this enigma. All the data I used for my analysis is readily available on Bloomberg and on the Internet at Morningstar’s web site1. Thus the funds selected for this project are limited to those funds rated by Morningstar. I am not aware of any bias that may exist in Morningstar-rated funds versus funds not rated by the company, yet in any case the results of this study are limited to Morningstar-rated funds. To obtain my data, I looked at the 192, alphabetically listed, closed-end funds available on the web page and selected every fourth fund, leaving a data set of 48 funds. Obviously studying all 192 would have been preferable but the data had to be manually entered into Minitab and a good portion of the data had to be searched for, individually for each fund, on Bloomberg. I chose the week ending October 24, 1997 as the ending date for the data in my study since at the time of gathering, it was the most recent. Thus the information I am trying to predict is the Net Asset Value Discount of a Morningstar-rated fund on October 24, 1997. However, to the best of my knowledge, there were no market events that made this week any different from any other,2 and thus the results may be extrapolated to cover any weekly period. To build the model for this prediction, I selected the following information to study:
1-Year Average Share Price: This represents the average market price of each fund over a 365 day period ending October 24, 1997. I chose this variable due to the fact that most companies pursue a share price above $10 for no clear economic reason. It is simply more attractive to investors to purchase a stock above $10. I would expect this to be the case with closed end funds as well. Thus it is possible that funds with lower share prices might attract less interest than those with higher share prices, and therefore could account for a deeper discount. I chose a one-year average to avoid temporary movements in prices. Below is a scatter plot showing 1-Year Average Share Price on the x-axis and Net Asset Value Discount on October 24, 1997 on the y-axis.
While there appears to be a positive linear relationship in the lower left corner of the graph, there are many outliers and influence points. Yet without looking at all the other variables, it is difficult to tell whether any of these points should be excluded.
M orningstar Rating: Morningstar rates funds on a scale from 1-5 stars based on its performance, risk profile and expenses. One would think that lower rated funds would have less market interest and would therefore trade at larger discounts to NAV. The scatter plot below doesn’t seem to support this.
There are a considerable number of 4 and 5-star funds trading at discounts, which seems to disprove the strength of Morningstar’s ratings as a predictor of NAV discounts/premiums. There is a noticeable outlier on this graph, a 1-star fund with a NAV premium over 40%. This represents the Thai Fund, a fund that invests exclusively in Thai equities. It is difficult to tell exactly why the premium is so large, but it is clearly a function of the Southeast Asian turmoil. I would have expected an unusually large discount but perhaps there is not a lot of liquidity in the fund and investors were forced to hold the fund’s shares despite drastic declines in the underlying equities. In any case, the abnormality of this fund and the special circumstances of Thai equities justify its removal from the data set before a regression is performed.
5-Year Return: Many investors look at 5-year return to determine fund performance. This can be measured in terms of NAV or in terms of stock price. Both of these show the absolute return of the fund over the 5-year period ending October 24, 1997. The difference between the 5-year NAV return and the 5-year market return, is a measure of the 5-year NAV discount/premium. All three variables are shown in scatterplots against current NAV discount below.
 
Judging from these three scatterplots, the strongest relationship seems to exist with the difference between 5-year NAV and 5-year market return, shown above as 5-year discount. Once again, the Thai Fund is a noticeable leverage point. This finding seems to imply that past performance is the best measure of current performance. It will be interesting to see how this plays out in a regression. Significantly, there are 11 funds that have not been around long enough to have a 5-year history, thus the actually number of funds in the data set will be reduced to 37 in the regression.
1 -Year Average NAV Discount: This measures the average discount over the one-year period ending October 24, 1997. The scatterplot below shows a strong relationship. It is not surprising that the best predictor of current NAV discount is the average discount over the past year. The Thai Fund again is a noticeable leverage point.
Type of Fund / Investment Goal: There are two yes or no variables being measured here. The first measures whether the fund invests in bonds (1) or equities (0). If a fund invests in both, I marked it as an equity fund. Since equities have a higher expected return and standard deviation, they have more of an effect on a portfolio’s risk and return than bonds. It would be ideal to measure the percentage of equities in the fund as a variable, but that information was not available. The second variable measures whether the fund invests in foreign (1) or domestic (0) securities. If a fund invests in both, I labeled it as foreign with the same argument used in the equity / bond case. Side by side boxplots show the distribution of current NAV broken down by the two variables.
 
In the case of bond funds, there appears to be a smaller median discount and a narrower distribution than equity funds. Given the higher variability of equities, this is not surprising. Foreign funds seem to have a larger discount than domestic funds with a slightly wider distribution. This is also unsurprising given the inherent political risk in many foreign funds. The Thai Fund is once again noticeably different than all other funds.
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