Mutual Fund Flows:
A study of variables influencing investment
Ted Morrissey
November 20, 2001
QTM7010-61
Professor Sharpe
Introduction
Investment activities occur for many reasons. Companies invest their money into assets which they hope will generate revenue. Individuals invest their money, hoping to earn a return on their investment and obtain a more secure future. This analysis will examine the flow of money into/out of US stock market mutual funds during the period of 1984 to 1996. The objective of this case discussion is to examine what factors determine the flow of money into and out of the US mutual funds.
Data
The Investment Company Institute tracked the money flowing into and out of US stock market mutual funds on a monthly basis, from April 1984 until December 1996. Other economic variables recorded during this period were: the stock market’s return % on investment, interest rates on one-year certificates of deposits (CDs), US disposable per capita income and gold prices/oz.. These economic variables were also recorded on a monthly basis. (Sharpe, Ali and Potter, 2001)
Data Analysis
Scatter plot graphs were conducted to determine if any relationships appeared present between these variables and mutual fund investment flows (Figures 1-4). Stock market return % and disposable income appeared to have linear relationships with fund flows. There appeared to be a curvilinear relationship between CD interest rates and fund flows. A relationship between gold prices/oz. and fund flows was not visible during the exploratory data analysis.
A correlation chart was constructed to determine the relationships, if any, which these variables had with mutual fund investment flows (Table 1). Market return %, CD interest rates and disposable income had p-values (less than a significance level of .05) which pointed to a significant relationship between these variables and mutual fund flows. Disposable income had the highest correlation value (.665) of these variables, with CD interest rates having the second highest correlation value (.629) and market return % in third (.204). Gold prices/oz. did not appear to have a significant relationship with mutual fund flows. A potential area of concern was observed during the examination of the correlation table. It appeared as though collinearity existed between CD interest rates and disposable income, which in turn would lead to unstable coefficients in the multiple regression model. Figure 5 suggested a negative linear relationship between the two variables, indicating that when included in a multiple regression equation together, they may cancel each other out, negating the other’s effect on mutual fund flows.
Further analysis of these variables consisted of regression equation tables (Tables 2,3,4 &5). US disposable income (Table 4) had the highest coefficient of determination (44.3), with a similar adjusted value (43.9). The relationship between fund flows and US disposable income was as follows: For every dollar of US disposable income, 5.64 million dollars was invested into US stock market mutual funds (Table 4). CD interest rates (Table 3) also had a high coefficient of determination in respect to its relationship with fund flows (39.5). In examining the relationship of this equation’s residuals versus fits (Figure 7), there appeared to be a pattern in the form of a curve. A linear equation model did not sufficiently describe this relationship, for the residuals did not vary randomly with each “x” value. This variable was initially hypothesized to have a curvilinear relationship with fund flows. A transformation of this variable was conducted (Table 8). The plot of residuals versus fits for this transformation (Figure 11) indicated that this description satisfied an assumption necessary to describe the relationship between fund flows and CD interest rate as curvilinear.
Multiple regression analysis (Table 6) for all the variables brought the coefficient of determination to 57.7%, but collinearity was of concern, as well as the issue that the CD interest rates variable, in original form, was not sufficient to provide description of its relationship with fund flows (which also effected the plot of residuals vs. fits for this equation: Figure 9). Market return % and disposable income (Table 7) provided a coefficient of determination (48.6%) that was lower than other multiple regression equations, yet more conservative in terms of collinearity. Therefore, this model was the most reliable.
Due to previous data analysis (Mutual Fund Flows Case A: Sharpe, Ali and Potter, 2001) in which the market return % provided different patterns on mutual fund flows based upon time frame (i.e. 1984 – 1989 and 1990-1996), it was hypothesized that different variables may have been either more or less significant, based upon the time period that fund flows were analyzed. Therefore, regression analysis was conducted for each time period (Tables 10-18).
From 1984 until 1989, the market return % appeared to be the most significant factor in determining mutual fund flows, with the highest coefficient of determination (38.7%; Table 11), as well as the highest correlation factor (.622; Table 10). An interesting discovery made was that during this time period, an increase in disposable income meant a decrease in money invested in mutual fund flows (Tables 10 & 12). Once again, CD interest rates and disposable income appeared to be related (Table 10). From 1984-1989, gold prices appeared to have a significant relationship with mutual fund flows (Tables 10 & 13). In order to achieve a better coefficient of determination, multiple regression analysis was conducted (Table 14). When all variables were taken into consideration, gold price/oz. did not have as significant a relationship with fund flow (Table 14).
These variables played a much different role in the mutual fund flows from 1990-1996 (Table 15). Disposable income had the highest correlation value (.655), while the correlation of market return % to fund flows during this period (.282) was similar to that of gold prices/oz. (.260). Collinearity between CD interest rates and disposable income did not appear to be of concern for this period (Table 15; Figure 15). A multiple regression equation involving all variables (Table 17) had a high coefficient of determination (71.8), but once again, collinearity between the variables (disposable income and gold prices/oz.) raised concern. To gain a more reliable model, gold prices were excluded from the multiple regression analysis (Table 18), and a high coefficient of determination was still obtained (68.2).
Conclusion
It makes sense that disposable income and market return % were the most influential variables in fund flows. If people have money, they will want to watch it grow, so they will invest it. If the market is providing a good return %, then they will invest their money. Put these two variables together (Table 7), and one has money to invest and a place to earn on investment. If other investment opportunities, such as CDs, provide a better return on investment, then it would be wise to withdraw money out of the mutual fund market and invest in a better return opportunity (Table 8). Another relationship that would be interesting to examine would be that of the actual worth of a mutual fund to the money flowing into it.
It was interesting to see the difference between the investment practices of two different decades. The 1980s were more conservative, and the market return % had the heaviest influence on money flowing into a mutual fund. The 1990s saw a booming economy, and people were much more willing to invest their money in the market, hoping to watch it grow. With the economy in its current state, it would be interesting to examine mutual fund flows for this decade. Combined with the variable of a mutual fund’s worth, a full analysis of mutual fund investment could be completed for the 1980s, 1990s and 2000s, providing a full realm of economic conditions and a better understanding of when to invest in a mutual fund, and when to take that investment elsewhere.
Bibliography:
McClave, James T., Benson, P. George and Sincich, Terry (2001),
Statistics for Business and Economics, 8th Edition. New Jersey: Prentice Hall.
Sharpe, N., Ali, A., and Potter, M.E. (2001), A Casebook for Business Statistics:
Laboratories for Decision Making. NY: John Wiley & sons, Inc.
· All tables and graphs were constructed in the Minitab Program
Figure 1: Mutual Fund flows to Market Return % (Entire Sample)
Figure 2: Mutual Fund Flows to CD Interest Rate (Entire Sample)
Figure 3: Mutual Fund Flows to US Disposable Income per Capita (Entire Sample)
Figure 4: Mutual Fund Flows to Gold Price/oz. (Entire Sample)
Table 1: Correlation Chart (Entire Sample)
Fund Flows by: Market% CD Rate Dispos Inc.
Market Return % 0.204
0.011
CD Interest Rate -0.629 0.049
0.000 0.546
US Disposable Income 0.665 -0.006 -0.595
0.000 0.941 0.000
Gold Price/oz. -0.071 -0.067 -0.005 0.188
0.386 0.412 0.955 0.020
Cell Contents: Correlation
P-Value
Figure 5: CD Interest Rate to US Disposable Income (Entire Sample)
Table 2: Regression Analysis of Fund Flows by Market Return % (Entire Sample)
The regression equation is
Fund Flows ($millions) = 4589 + 310 Market Return (%)
Predictor Coef StDev T P
Constant 4589.1 523.3 8.77 0.000
Market % 310.2 121.1 2.56 0.011
S = 6145 R-Sq = 4.2% R-Sq(adj) = 3.5%
Figure 6: Residuals of Market Return % v. Fits (Entire Sample)
Table 3: Regression Analysis of Fund Flows by CD Interest Rate (Entire Sample)
The regression equation is
Fund Flows ($millions) = 16470 - 1761 CD Interest Rate
Predictor Coef StDev T P
Constant 16470 1219 13.51 0.000
CD Rate -1761.2 177.3 -9.93 0.000
S = 4882 R-Sq = 39.5% R-Sq(adj) = 39.1%
Figure 7: Residuals of CD Interest Rate vs. Fits (Entire Sample)
Table 4: Regression Analysis of Fund Flows by US Disposable Income (Entire Sample)
The regression equation is
Fund Flows ($millions) = - 94972 + 5.64 US Disposable Income
Predictor Coef StDev T P
Constant -94972 9140 -10.39 0.000
Disposable Income 5.6359 0.5148 10.95 0.000
S = 4686 R-Sq = 44.3% R-Sq(adj) = 43.9%
Figure 8: Residuals of US Disposable Income vs. Fits (Entire Sample)
Table 5: Regression Analysis of Fund Flows by Gold Price/oz. (Entire Sample)
The regression equation is
Fund Flows ($millions) = 9384 - 11.6 Gold price/oz.
Predictor Coef StDev T P
Constant 9384 5058 1.86 0.065
Gold price -11.58 13.32 -0.87 0.386
S = 6261 R-Sq = 0.5% R-Sq(adj) = 0.0%
Table 6: Mutliple Regression Equation for Fund Flows (Entire Sample)
The regression equation is
Fund Flows ($millions) = - 55144 + 342 Market Return (%)
- 1054 CD Interest Rate + 3.75 US Disposable Income
Predictor Coef StDev T P
Constant -55144 10732 -5.14 0.000
Market % 342.41 81.10 4.22 0.000
CD Rate -1054.3 186.0 -5.67 0.000
Disposable income 3.7513 0.5617 6.68 0.000
S = 4109 R-Sq = 57.7% R-Sq(adj) = 56.9%
Figure 9: Residuals vs. Fits of Multiple Regression Model (Entire Sample)
Table 7: Multiple Regression Model for Market Return & Disposable Income
(Entire Sample)
The regression equation is
Fund Flows ($millions) = - 95591 + 316 Market Return (%)
+ 5.65 US_Disposable_Income_per_Capita
Predictor Coef StDev T P
Constant -95591 8809 -10.85 0.000
Market % 316.35 88.98 3.56 0.001
Disposable income 5.6466 0.4960 11.38 0.000
S = 4516 R-Sq = 48.6% R-Sq(adj) = 47.9%
Figure 10: Residuals vs. Fits of Market Return & Disposable Income (Entire Sample)
Table 8: Regression Analysis for CD interest Rate (Adjusted Variable) (Entire Sample)
The regression equation is
Fund Flows ($millions) = 22899 - 3837 CD Interest Rate
+ 150 cd interest rate squared
Predictor Coef StDev T P
Constant 22899 2730 8.39 0.000
CD rate -3836.7 811.1 -4.73 0.000
CD rate squ. 149.62 57.11 2.62 0.010
S = 4790 R-Sq = 42.2% R-Sq(adj) = 41.4%
Figure 11: Residuals of Adjusted CD Rate vs. Fits (Entire Sample)
Table 9: Correlation Chart (Entire Data) with Adjusted Variable
Fund Flows by: Market% CD Rate Dispos Inc.
Market Return % 0.204
0.011
CD Interest Rate -0.629 0.049
0.000 0.546
US Disposable Income 0.665 -0.006 -0.595
0.000 0.941 0.000
CD interest Rate Squared -0.579 0.038 0.977 -0.612
0.000 0.639 0.000 0.000
Table 10: Correlation Model (1984-1989)
Fund Flows by: Market% CD Rate Dispos Inc.
Market Return % 0.622
0.000
CD Rate -0.177 0.011
0.145 0.929
US Disposable Income -0.290 0.019 -0.315
0.016 0.874 0.008
Gold price -0.243 -0.121 -0.399 0.524
0.044 0.322 0.001 0.000
Table 11: Regression Analysis Fund Flows by Market Return % (1984-1989)
The regression equation is
Fund Flows ($millions) = 206 + 262 Market Return (%)
Predictor Coef StDev T P
Constant 205.9 206.2 1.00 0.322
Market % 261.50 40.23 6.50 0.000
S = 1630 R-Sq = 38.7% R-Sq(adj) = 37.8%
Figure 12: Residuals of Market Return vs. Fits (1984-1989)
Table 12: Regression Analysis of fund Flows by US Disposable Income (1984-1989)
The regression equation is
Fund Flows ($millions) = 20003 - 1.13 US Disposable Income
Predictor Coef StDev T P
Constant 20003 7825 2.56 0.013
Disposable Income -1.1324 0.4569 -2.48 0.016
S = 1992 R-Sq = 8.4% R-Sq(adj) = 7.0%
Figure 13: Residuals of Disposable Income vs. Fits (1984-1989)
Table 13: Regression analysis of Fund Flows by Gold Price/oz.
(1984-1989)
The regression equation is
Fund Flows ($millions) = 4343 - 9.70 Gold _price_per_oz
Predictor Coef StDev T P
Constant 4343 1832 2.37 0.021
Gold price -9.701 4.728 -2.05 0.044
S = 2019 R-Sq = 5.9% R-Sq(adj) = 4.5%
Figure 14: Residuals of Gold Price/oz. Vs. Fits (1984-1989)
Table 14: Multiple Regression Model for Fund Flows (1984-1989)