Managerial Ability, Open-End Fund Flows, and Closed-End Fund Discounts

This empirical study investigates the rationale for the United States (US) closed-end equity fund discounts using investor sentiment approach of C. Lee, Shleifer, and Thaler (1991) for the period from 2004 to 2013. The result of this study suggests that discounts on closed-end equity funds decrease when small stocks return increase. The closed-end fund discounts have the significant stronger correlation with small capitalization as compared to large company’s stock returns. The results indicate that similar noise trading risk generated by retail traders explains the fluctuations in closed-end fund discounts and small capitalization equity returns even after controlling for fundamental factors. The results validated the existence of noise traders in market producing stochastic demand and supply based on their belief, subsequently affecting closed-end equity fund price in the market.

Possibly the earliest discussion of investor sentiment, albeit unnamed, occurred during the run-up in U.S. stocks in the spring and summer of 1929 when closed-end funds mostly sold at substantial premiums to their net asset value (see De Long and Shliefer (1991)). These early writers were involved in brokerage or portfolio management, and they were warning investors that paying premiums of 100% or more was to pay twice or more the market value for the fund’s assets. The writers were puzzled by the demand for closed-end funds and were quick to point out the superior performance that fund managers would have to achieve in order to justify the lofty premiums.

This paper investigates the impact of the onset of the COVID‐19 pandemic in the United States on closed‐end fund (CEF) discounts. I show that CEF discounts increased after the onset of the COVID‐19 pandemic in the United States, while individual investor sentiment declined. Furthermore, CEFs with higher retail ownership had a larger discount increase, which suggests that individual investor sentiment is a potential contributor to CEF discounts. This finding seems less likely to be driven by rational channels or income‐driven fire sales, as shown by further analysis. Overall, the results shed light on the CEF discount puzzle using a new setting.

Closed-End Funds: A Survey

Empirical evidence from the UK market is examined in the light of recent theories about closed-end fund discounts. Market pricing of skill, relative to the fees charged for it, accounts for a significant portion of discount variation, but cannot explain the rarity of index funds or why they trade at a discount, since fees tend to be lower than on open-end funds. Index funds have lower discount volatility, consistent with the skill hypothesis. The results imply that managerial skill, relative to the fees charged, does not wholly account for the tendency of closed-end funds to trade at a discount.

In open‐end mutual funds (unit trusts), there is a strong positive cross‐sectional relationship between net inflows to individual funds and past performance, as if investors attributed performance to managerial skill. Performance shows only very weak persistence, however, so at first sight investors do not appear to gain anything by responding to past performance information. This behaviour can be explained by the fact that past performance is effectively unpriced in the unit trust market, since management fees are unresponsive to demand. If investors believe that there is a non‐zero probability that future performance will turn out to be positively correlated with past performance (i.e. that there is an element of managerial skill in performance), but a zero probability that this correlation will be negative, it is rational to prefer funds with better past performance when performance is not priced. In other words, it costs nothing to insure against the possibility of some managerial skill effect.If this explanation of the flow–performance relationship in unit trusts is correct, one would expect the relationship between investor demand and past fund performance to be much weaker if past performance were to be priced. We test this hypothesis in the market for closed‐end funds (investment trusts). Because closed‐end funds do not trade at net asset value, but at a price determined in the market, strong demand will raise the ratio of price to net asset value (known as the premium). Since it is well established that premiums are mean‐reverting, future shareholder returns on funds currently on high premiums tend to be depressed by the reversion of the premium to the mean. In the closed‐end fund market, as for open‐end funds, there is little evidence of performance persistence, and therefore, to the extent that funds with good past performance are pushed to higher premiums, the expected return on them is less than on the average fund. This implicit pricing mechanism should mean that demand is a declining function of the premium, so that, even if demand is an increasing function of past performance for a given premium, any effect on the premium itself will be muted.We test this hypothesis for closed‐end funds traded in the US and the UK. We find that there is a statistically significant effect of past performance on the premium in both countries. However, consistent with the hypothesis, it has limited economic significance, since it represents only a small component of premium variability. Copyright © 2008 John Wiley & Sons, Ltd.

The Limits of Arbitrage and Stock Mispricing: Evidence from Decomposing the Market to Book Ratio

Prior Performance and Closed-End Fund Discounts

Investor Demand for Leverage: Evidence from Closed-End Funds

Mean-Reversion in Closed-End Fund Discount: Evidence from Half-Life

<p>Mutual Funds are the second most preferred financial investment option in India amongst households, corporate and private investors alike. Managed funds bring with them the expertise of fund managers along with the benefits of diversification and lower costs. The sensitivity of fund flows defines the ability of the fund manager in offering expected future returns. Mutual fund flows exhibit time series characteristics, it being financial data collected at regular intervals over a time period. This paper studies the dynamics of mutual fund flows by utilising time series regression modelling. Monthly fund flows data for a sample of 142 equity open-ended growth orientation across major marketcap categories – Large Cap, Large and Mid Cap, Multi Cap, Mid Cap, and Small Cap have been analysed using ARIMA Modelling in the R software package. Appropriate lag length and the presence of a unit root have been investigated with the help of established techniques coupled with suitable checks of robustness. Model of best fit has been used to forecast monthly fund flows for a lag length of 60. Our study leads us to two major outcomes. One, unlike many developed and emerging markets, fund flows in the chosen sample do not confirm to positive feedback trading hypothesis. This lends credible support to the absence of irrational exuberance in mutual fund investments. Second, equity-based funds in Large Cap, Large and Mid Cap, and Multi Cap category exhibit strong trend component while funds in Mid Cap and Small Cap category have a strong random component. Beginner investors can take advantage of alpha offered by fund managers possessing effective market -timing skills, an indicator of trend-investing strategy. Funds belonging to these categories are also lesser prone to market volatility in comparison to Mid Cap and Small Cap funds, being more suitable for experienced investors</p>

The Secondary Market for Hedge Funds and the Closed Hedge Fund Premium

To maintain and enhance the efficiency of a property portfolio the portfolio manager should select those property investment propositions which promise the maximum improvement of portfolio return and reduction of portfolio risk. To cope with this difficult task the portfolio manager needs appropriate decision criteria. This paper discusses some of the selection criteria evolved through the developments in capital market theory in order to assess their usefulness in property asset selection procedures. The use of the ‘reward‐to‐volatility’ criterion to select appropriate property portfolio projects is explored through a worked example. The reliability of such selection procedure depends on the availability of reliable historic record of the performance of the property market and of the property portfolio together with the portfolio manager's ability to perceive expected returns in different ‘states of the world’.

PurposeThis paper aims to examine the effects of managerial ability (MA) on the likelihood and the timeliness of goodwill impairment and explore whether the desirable effect of MA vary with the degree of agency problems.Design/methodology/approachThe authors propose a unified framework to simultaneously examine the effects of MA on the likelihood and the timeliness of goodwill impairment by incorporating a market-based impairment indicator (denoted as BTM), MA and the interaction of BTM with MA to this study’s regression model to account for the likelihood of goodwill impairment. BTM addresses the timeliness of goodwill impairment.FindingsThis study finds that firms with higher MA have lower likelihood of goodwill impairment, and such firms are more likely to recognize goodwill impairment in a timely manner when the underlying value of goodwill is economically impaired. This desirable effect of MA is more pronounced in non-state-owned enterprise (SOEs) and firms without chief executive officer (CEO) duality.Practical implicationsFirms can reduce the losses arising from goodwill impairment by enhancing the ability of their management teams combined with improved corporate governance structure.Originality/valueThis paper provides novel insights on understanding the role of MA in not only reducing the likelihood but also enhancing the timeliness of goodwill impairment. The findings help advance the upper echelons theory by uncovering the heterogenous effects of executives with different levels of ability.

Risk, the Pricing of Capital Assets, and the Evaluation of Investment Portfolios