Proxies and Factor Data

containing two or three conventional funds for each ethical fund, when there are only one or two matching criteria.

This study compares the performance only to one conventional fund, due to a limited number of funds available at the Scandinavian markets, which match with the ethical fund on all criteria.

Bauer et al. (2005) constructed one portfolio of ethical mutual funds and one portfolio of the matched conventional funds. The returns were then computed based on an equally weighted portfolio of the funds included in each portfolio. The performance of each portfolio was then compared. This study will replicate the method of Bauer et al. (2005). Furthermore, the analysis at the portfolio level is complemented with an analysis at the individual fund level, where the difference in performance of each individual set of funds is examined, in line with the method of Leite & Cortez, (2014).

Consequently, the MSCI regional equity indices are applied as proxies for the market-mimicking portfolio and selected for each ethical fund on the basis of the fund’s investment universe. In other words, a fund investing globally is benchmarked against a global equity index. All the MSCI indices are market capitalization weighted and consider dividends reinvested.

US

Kenneth R. French distribute the factors of the Carhart four-factor model through his website. The excess return on the market (Rm-Rf) is re-calculated every month. The market risk is denoted as the value-weighted return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX or NASDAQ. The proxy for the risk-free rate is the one-month Treasury bill rate. We employ the Fama & French calculated excess return as the market risk premium for the domestic US mutual funds.

Global

In similarity to the study of Leite & Cortez, (2014), MSCI All countries World Index (ACWI) is appointed as the most suitable indices for the ethical mutual funds included in our sample that have a global investment universe. Also, the review of mutual fund performance studies made by (Chegut et al., 2011) present MSCI All countries World (ACWI) as one of the most utilized and applicable World Index.

Scandinavia

The MSCI Nordic Equity index was employed for the Scandinavian ethical funds. The index is constructed of equities from the Finnish, Danish, Swedish and Norwegian markets and measure equity market performance in the Nordic region.

6.2.3. Risk Free Rates

The risk free rate symbolizes the return investors will receive when placing their money in a risk-free asset. The returns on zero-coupon treasury securities are commonly used as a proxy for the risk-free rate, i.e. T-bills and government bonds (Bodie, Kane & Marcus, 2014). However, several previous studies of ethical mutual fund performance use the inter bank offered rate (Bauer, Koedijk, & Otten, 2005; Renneboog et al., 2008a) . The local 3 month inter bank rate are applied as a proxy for the risk-free rate in the Scandinavian area. The Copenhagen Interbank Offered Rate (CIBOR) is applied for the Danish market, Stockholm Interbank Offered Rate (STIBOR) in Sweden

and the Norwegian Interbank Offered Rate (NIBOR) for Norwegian ethical funds. The market premium factor supplied by Fama & French is utilized for ethical mutual funds with a global or US investment focus.

For the calculations of the excess mutual fund returns are previous mentioned inter bank rates used as proxies for the risk-free rate.

6.2.4. The SMB factor

The data supplied in the Kenneth R. French data library represent the SMB factor for the ethical mutual funds that contain US concentrated holdings. Kenneth R. French data library also provides data of the SMB factor on a global level, which is used for the globally operating mutual funds.

Fama & French calculate the SMB factor for US by ranking all US stocks based on their market value. The stocks are then divided into two portfolios, the 20 % with the smallest market value into a small cap portfolio and the remaining 80 % into a mid-large cap portfolio. The large portfolio is then subtracted from the small portfolio. The portfolio is then reweighted annually. A similar approach is used to calculate the factor on a global level. Similar procedures were performed by (Bauer et al., 2005; Bauer, Otten, & Rad, 2006; Carhart, 1997). Faff (2004) introduced a substitute method of constructing the SMB factor. Faff altered the model to calculate the SMB factor by using market indices instead of composed stock portfolios. The study was made on the Australian Market, where the ASX/Russell indices were used. The average return on the large cap growth indices was subtracted from the average return of the corresponding small cap indices.

Furthermore, Faff compared his result to the findings of Fama & French and concluded that his results were consistent with the original findings. While, it was too problematic to acquire the adequate data needed to duplicate the model of Fama & French (1993), since the factors had to be constructed for every investment universe, a similar approach to the one Faff (2004) presented was utilized. Instead of applying indices averages was the SMB factor for the Scandinavian countries calculated as the difference between a portfolio of small cap stocks, proxied by the total returns of the MSCI AC Nordic Small Cap indices for the Scandinavian funds. The FTSE Nordic Large Cap Index replaced the proxy for the Nordic large cap portfolio, since the base date of the MSCI AC Nordic Large Cap was in 2007 and therefore did not cover our estimation period. The monthly data was collected from Bloomberg, and the log returns were then calculated.

6.2.5. The HML factor

Fama & French (1992) constructed the HML factor by ranking all NYSE, AMEX, and NASDAQ stocks on their market ratios. The top 30%, value stocks, are allocated in a high book-to-market portfolio and the bottom 30 %, growth stocks, to a low book-to-book-to-market portfolio. The low book-to-market portfolio is then subtracted from high book-to-market portfolio. The HML factor data for the ethical mutual funds containing US equities is extracted from the Kenneth R. French data library. However, there were restricted access to data for market-to-book ratios and beyond the scope of this paper to calculate the HML factor by using the Fama & French approach for the funds with a global and Scandinavian investment focus. Therefore, the method presented by Faff (2004), which was used for the SMB factors, will be utilized for the calculation of HML. The difference between the returns of MSCI AC World Growth and the MSCI AC World Value will be proxies for the HML for global equity mutual funds. For the Scandinavian funds will the MSCI Nordic value index represent the stocks with high book-to-market ratio, and the MSCI Nordic Growth Index represent the stocks with a low book-to-market ratio. The difference between the returns of those indices is the HML factor.

6.2.6.The MOM factor

In similarity to the SMB and the HML factors, Kenneth R. French data library provide the MOM factor for the American market and the globally investing funds. Fama & French’s MOM factor is created on the basis of the approach of Carhart (1997) by including all stocks from NYSE, Amex and Nasdaq and constructing a portfolio of the 30 % best-performing stocks over the past 11 months lagged one month. Furthermore, one portfolio of the 30 % worst performing stocks over the matching period, also lagged one month, is constructed. The portfolio is then rebalanced every month to obtain a rolling momentum factor. The Kenneth R. French website does not provide data on the MOM factor for the Nordic countries. However, the data library does provide a MOM factor for Europe, which do cover the Nordic countries, but also rest of the developed European markets. The constructing of individual MOM factors has in previous studies been regarded as the most complex factor to replicate. The portfolio is rebalanced every month, which would demand a great amount of data to be gathered. The estimation period from January 2005 to February 2016 covers 134 months.

The process of constructing the MOM factors for the markets in the Scandinavian area was considered the greatest challenge for this study, and turned out to be too time-consuming and complicated to mimic the momentum portfolio. Furthermore, it was not possible to ensure that the data collected was accurate and reliable, and thus it could not be guaranteed that no errors

occurred in the data underlying the MOM factor. Therefore, is Fama & French’s European MOM factor used for the ethical funds with a Scandinavian Investment universe, as this is considered to be the most appropriate alternative. There may exist deviations in a European MOM factor index compared to one exclusively containing data from the Scandinavian market. Consequently, this could result in an over or undervalue of the estimated fund alphas when applied in the Carhart four-factor model and the Cortez five-factor model. This should be taken into consideration when evaluating the performance outcomes of the Scandinavian-oriented funds.

6.2.7. The local factor

A fifth factor, as previous mentioned, was introduced by Cortez et al. (2012) in order to control for the likelihood of home bias, meaning that the holdings of global mutual funds tend to be concentrated in the domestic market. Cortez et al. (2012) measure this factor by the difference in returns of a local MSCI country index and the MSCI AC World Index.

In similarity to Cortez et al. (2012) is the ethical and conventional funds’ exposure to domestic stocks estimated, which capture the degree of international diversification.

The mutual funds will be matched to a local market indices based on where the fund has its domicile. The MSCI country indices will be used as proxies for the local market indices.