3.1 Norwegian Mutual Funds
The data on mutual funds are obtained from the Morningstar Direct database. The final sample includes 99 open-end equity funds that have ever existed from 1996 to 2019 and invested at least 80 percent in the Norwegian stock market. The restriction is required to examine the performance of Norwegian funds exclusively. The sample excludes index funds since actively managed funds are the ones that we are interested in investigating. The actively managed funds pursue an active investment strategy and claim to deliver returns above the specified market benchmark.
The number of monthly returns for the selected mutual funds varies from 13 to 288 observations. The monthly return on Morningstar is constructed using the following formula:
𝑟𝑖,𝑡= 𝑁𝐴𝑉𝑡− 𝑁𝐴𝑉𝑡−1
𝑁𝐴𝑉𝑡−1 In which NAV is the monthly net asset value of the fund. All income and capital gain distributions during the period are assumed to be reinvested, while management, administrative, distribution, and other costs are deducted (Morningstar, 2020).
3.2 Risk Factor Loadings
We extract the Norwegian stock market's risk pricing factors from Bernt Arne Ødergaard's website to run Fama and French (1996) and Carhart (1997) benchmark models. Table 1 shows which particular factors are used in the study and their descriptive statistics.
Table 1: Descriptive Statistics of Risk Factors
Both tables display statistics on the risk factors constructed by Bernt Ødegaard that are used in the regression analysis. Panel A shows the average monthly values of the factors in different time periods. The numbers in brackets are p values against the null hypothesis of observations being equal to zero. Panel B shows correlation statistics among the factors throughout the whole time period. OSEFX index is Rm, which is the proxy for market return. The choice of OSEFX as the market return is discussed in section 3.4. The returns are reported in percent.
Panel A: Average values
1996-2019 1996-2003 2004-2011 2012-2019 SMB 0.62 (0.01) 1.13 (0.00) 0.30 (0.50) 0.44 (0.14) HML -0.12 (0.64) 0.05 (0.92) -0.17 (0.66) -0.24 (0.49) PR1YR 1.04 (0.00) 0.45 (0.41) 1.04 (0.02) 1.63 (0.00)
Panel B: Correlations
Rm SMB HML PR1YR
Rm 1
SMB -0.47 1 HML -0.2 -0.05 1
PR1YR -0.22 0.15 -0.04 1
3.3 Risk-free rate
As a proxy for the risk-free rate, we use the Norwegian Interbank Offered Rate (NIBOR)1. NIBOR reflects the interest rate required by lenders for unsecured money within two days of delivery. The rate is calculated as a simple average of submitted interest rates by NIBOR panel banks for each maturity (2020). Historical NIBOR rates are taken from the Norges Bank website for the period from 1995 to 2013, and from the Oslo Børs website for the period 2014-2019 since Norges Bank and Oslo Børs were the official authorities responsible for calculating the money market interest rate. To transform the NIBOR rate to a monthly risk-free interest rate, we apply the following formula:
(1 + 𝑁𝐼𝐵𝑂𝑅)1/12− 1
1 Bernt Ødegaard also used NIBOR as a proxy for the interest rate in his studies of Norwegian stock market.
3.4 Market Return
The market return can be typically obtained by getting the return of a value-weighted portfolio of all listed stocks. Although in case of the Norwegian stock market, the value-weighted portfolio might be formed only by a handful of big companies. For instance, Telenor, Statoil, and Norsk Hydro constituted 53% of the total stock market in 2006 (Næs et al., 2009). Hence, a choice of performance benchmark as a market portfolio proxy is crucial with the Norwegian stock market because depending on the choice, mutual funds' performance may vary drastically.
Oslo Børs created a capped version of the benchmark investible index - OSEFX. The uncapped version of the index, OSEBX, represents all the shares listed on the Oslo stock exchange.
OSEBX exhibits the problem mentioned above – some stocks can skew the overall performance of the index, which makes the index performance less representative of all stocks listed on the stock exchange. As shown in Table 2, OSEFX and OSEBX are highly correlated since OSEFX is constructed based on the OSEBX index. What is noticeable is that OSEFX is highly correlated with the value-weighted index. OSEFX index is like a version of the VW index that is constructed to comply with the restrictions for regulating investments in Norwegian mutual funds. We use OSEFX as the market proxy in our study since it is the most fitting index approximating the market return in Norway. OSEFX allows a maximum weight of the security to be 10% of the total market value of the index, and securities that exceed 5%
cannot exceed 40% combined. The monthly returns of the OSEFX index for the period 1996-2019 are obtained from the Oslo Børs website (Oslo Børs, 2020).
Table 2: Descriptive Statistics of Market Indexes
The table illustrates statistics on different market indexes of the Oslo stock exchange. Panel A shows monthly returns in percentage terms for the overall and split time periods. Mean is the average value of monthly returns. St.d is the standard deviation in the sample of monthly returns. Min is the minimum value, while max represents the maximum value in the sample of monthly returns. Med denotes the median value in the sample of monthly returns. EW and VW are equal-weighted and value-weighted indexes constructed by Ødegaard using Norwegian market data. EW and VW indexes are presented in the table to compare the constructed indexes with OSEBX and OSEFX indexes. Panel B shows correlations among the Norwegian market indexes.
3.5 Survivorship Bias
The sample data includes all the funds that have ever existed for at least 12 months, from 1996 to 2019. The inclusion of dead funds is crucial to conduct an accurate analysis of the mutual funds' performance in the specified time period. If a sample has only survived mutual funds, that sample's overall performance will be positively skewed and not entirely representative of the reality (Brown, Goetzmann, Ibbotson & Ross, 1992). Additionally, the studies conducted on the samples that exclude dead funds indicate predictability in the funds' returns (Brown et al., 1992; Carpenter & Lynch, 1999). Those mutual funds indicate performance persistence mainly due to survivorship bias rather than managerial ability to generate excess risk-adjusted returns (Malkiel, 1995). Therefore, we use survivorship bias-free data to avoid adverse effects when dead funds are excluded.