Chapter 3 Methodology
D. Measure of other control variables
a. Fund flow
Net fund inflows (New Money Inflow) is defined as total net asset growth in fund.
It is referenced from Sirri and Tufano (1998) and stated as following:
𝑁𝑒𝑤 𝑀𝑜𝑛𝑒𝑦 𝐼𝑛𝑓𝑙𝑜𝑤𝑖,𝑙 =𝑇𝑁𝐴𝑖,𝑙−𝑇𝑁𝐴𝑖,𝑙−1∗(1+𝑅𝑖,𝑙)
𝑇𝑁𝐴𝑖,𝑙−1 (9)
21
Where 𝑇𝑁𝐴𝑖,𝑡 is total net assets of fund i during monthly 𝑙, and 𝑅𝑖,𝑙is monthly return. 𝑇𝑁𝐴𝑖,𝑙 − 𝑇𝑁𝐴𝑖,𝑙−1∗ (1 + 𝑅𝑖,𝑙) needs to be calculated monthly first and then average them quarterly by 𝑇𝑁𝐴𝑖,𝑙−1 which is total net asset in the end of quarter. New Money Inflow reflects the percentage growth of a fund in excess of the growth that would have occurred had no new flow in and had all the dividends been invested.
b. Fund size
The average fund size is presented as the total net assets that are invested in equities. It is referenced from Wermer (1999). The data is accumulated monthly. It equals to total assets minus liabilities as of month-end, and reported in millions of dollars. We take each fund’s monthly total net assets in the end of each quarter as fund size.
c. Return
Here we obtain total monthly returns associated with given date from CRSP Survivor-Bias Free US Mutual Fund database. Monthly returns values are calculated as a change in NAV including reinvested dividends from one month to the next. All management expenses, front and rear load fees and 12b-1 fees are excluded in monthly NAVs. We examine it by averaging the amount of monthly returns in each quarter.
d. Turnover ratio
Fund turnover is defined as the minimum of aggregated sales or aggregated purchases of securities divided by the total net assets of the fund. Since the data is monthly data without change in one year, we take it in the end of each quarter as quarterly data.
22
e. Fund age
The age of fund is calculated under the number of months in which fund had been traded. We take data in the end of each quarter as quarterly fund age.
f. Volatility
The volatility of fund’s return is constructed by calculating a period time of standard deviation of monthly returns in the past. It is calculated by moving average idea. For example, if we are going to calculate the standard deviation for first quarter in 2003, we need to trace the monthly return from first end of quarter in 2002 to last end of quarter in 2002. The whole observation moving period is taken one year. This is how we work: first, we sort out each calendar time and find out calculate each fund’s total monthly return. Secondly, we trace each fund’s return back to previous year in the same quarter as its starting return. Then we calculate standard deviation by moving 12 month returns back from starting return.
g. 12b-1 fee
12b-1 fee is reported as the ratio of the total assets attributed to marketing and distribution costs. It is the actual fee represented in the most recently completed fiscal year. We take data in the end of each quarter as quarterly 12b-1 fee.
23
3-2. Multiple regression approach:
The methodology here we examine for manager’s herding behavior is through multiple regression approach. The multiple regression approach allows us to examine manager’s herding behavior when interact with managers’ tenure, and especially as career concerns dominate. We also want to examine how market competition changes as time elapses affects manager’s herding behavior.
Thus, we expect to see that when young managers face career concerns, they herd severely. Furthermore, we predict that when particular fund’s market competition becomes fierce over time, it tends to take herding decisions.
The two main incentives here we would like to take referenced form Alexander, Stefan, Tanja (2009). It takes market return as proxy for two dominant incentives, i.e., compensation concerns dominate in bull market and career concerns dominate in bear market. Although simple, but it is ideal method to directly compare with the comparative intensity of two main incentives that fund managers have. Alexander, Stefan, Tanja (2009) use the mid-year stock market return which is calculated as value-weighted index of all securities traded at the NYSE, Amex and Nasdaq as proxy of two incentives. They think mid-year market returns can represent managers might change their decision in the middle of year. However, our herding measure is mainly referenced from LSV(2002) and the observation period for herding measure is based on quarter. We cannot take this situation into consideration in our research. The data of market returns we take is from Kenneth R.
French CRSP research data which is available on the internet and mainly provided by Chicago University.4 The market returns are also calculated as value-weighted index all
4 Current Research Returns: We have revised the market return used to measure Rm-Rf in the US. It is now the value-weight return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ that have a CRSP share code of 10 or 11 at the beginning of month t, good shares and price data at the beginning of t, and good return data for t. Previously we used the CRSP NYSE/AMEX/NASDAQ Value-Weighted Market Index as the proxy for the market return. The set of firms in the new series is more consistent with the universe used to compute the other US returns.
24
CRSP firms securities traded in NYSE, Amex, and Nasdaq.
For the main purpose, we need to differentiate that career concerns dominate in bear market, while compensation concerns dominate in bull market by quarterly average market return. Next, we dummied this market return to stand for relative intensity of two main incentives every quarter to see whether manager’s tenure affects herding behaviors severely under career dominant incentive. Other control variables are also included. We then demonstrate our first regression model as following:
𝐻(𝑠,𝑖𝑡)= 𝛽0+ 𝛽1𝑅𝐼𝐷𝑡−1𝑅𝐼 + 𝛽2𝐷𝑡−1𝑦𝑜𝑢𝑛𝑔+ 𝛽3𝑅𝐼𝐷𝑡−1𝑦𝑜𝑢𝑛𝑔𝐷𝑡−1𝑅𝐼 + 𝛽4𝐷(𝑖,𝑡−1)𝑀𝐾 + 𝛽5 𝐹𝑢𝑛𝑑 𝐴𝑔𝑒(𝑖,𝑡−1)+ 𝛽6𝐹𝑢𝑛𝑑 𝑆𝑖𝑧𝑒(𝑖,𝑡−1)+ 𝛽7𝑁𝑒𝑤𝐼𝑛𝑓𝑙𝑜𝑤(𝑖,𝑡−1)+ 𝛽8𝐹𝑢𝑛𝑑 𝑇𝑢𝑟𝑛𝑜𝑣𝑒𝑟(𝑖,𝑡−1)+ +𝛽9𝑅𝑒𝑡𝑢𝑟𝑛(𝑖,𝑡−1)+ 𝛽10𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦(𝑖,𝑡−1)+ 𝛽1112𝑏1𝐹𝑒𝑒(𝑖,𝑡−1)+
𝜖(𝑖,𝑡−1) (10)
The dependent variable term, 𝐻(𝑠,𝑖𝑡)which is presented in equation (8), is what Masa (2005) defined for fund level herding which is the same concept as FHM in GTW (1995).
All the independent variables are set in previous quarter because of herding behavior is taken while considering the measure condition in previous quarter. We let dummy variable of career concerns 𝐷𝑡−1𝑅𝐼 equals to one when quarterly market return is below and equal to zero if career concerns dominate in a given quarter t; while zero otherwise. We put dummy variables 𝐷𝑡𝑅𝐼 independently in the first terms to examine and compare estimate when 𝐷𝑡𝑅𝐼 interacts with fund level herding. According to literature reviews, we here expect to see 𝛽1𝑅𝐼 > 0: when career concerns dominate, herding behaviors are more likely to occur. This should be in accordance with what we want to examine in the hypothesis.
The second and third explanatory variable term in equation (10) are the young manager’s
25
tenure. We expect to see 𝛽2 > 0, young managers are usually inclined to herd. While 𝛽3𝑅𝐼 > 𝛽2 > 0, we add career concerns factors 𝐷𝑡𝑅𝐼 to interact with manager’s tenure to examine whether the degree of herding behaviors apparently becomes severe. And we do expect so. According to Alexander, Stefan, Tanja (2009) and Gibbons and Murphy, (1992), older managers are hard to be incentivized because they already have enough reputation or close to retire in the industry.
The fourth explanatory variable term in equation (10) is dummy variable of market competition, which is defined as following:
𝑀𝐾𝑖,𝑡 = ( 𝑀𝑆𝑖,𝑡
𝑀𝑎𝑥 𝑀𝑆𝑖,𝑘) × 100 (1) 𝐷𝑖,𝑡𝑀𝐾{= 0 𝑖𝑓 𝑀𝐾𝑖,𝑡−𝑀𝐾𝑖,𝑡−1 ≥ 0
= 1 𝑖𝑓 𝑀𝐾𝑖,𝑡−𝑀𝐾𝑖,𝑡−1 < 0} (2)
As 𝐷(𝑖,𝑡)𝑀𝐾 = 0, it means the market competition for particular fund i in quarter t relative to previous quarter do not become fierce. In other word, the growth of fund’s market share means more investors are willing to invest it. Therefore, this fund’s market competition is not so fierce that it doesn’t need to take herd to attract investors. We predict market competition is negatively related to herding behaviors. As 𝐷(𝑖,𝑡)𝑀𝐾 = 1, it means the market competition for particular fund i in quarter t relative to previous quarter becomes fierce, therefore, market competition influence managers to take herd. That is market competition is positively related to herding behaviors, that is, 𝛽4 > 0.
In order to control for some other specific fund’s characteristics which are found have influences in herding in past researches, we include additional control variables as follows:
fund age since the fund began trading (Fund Age), fund inflows in dollars (New Money Inflow), turnover rate of the fund (Fund Turnover), return (Fund Return) and volatility (Fund Return Volatility), the level of its advertising and marketing fees (12b-1 Expense).
26