Microsoft Word - 18970-new Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 65 A Study on the Effect of Portfolio Allocation on Mutual Funds Mihir Dash (Research Scholar of Department of Statistics, Periyar University) & Interim Associate Dean School of Applied Mathematics, Alliance University Bangalore, India - 562106 Rita Samikannu Head of Department, Department of Statistics, Periyar University Salem, India – 636011 Received: May. 8, 2023 Accepted: June 10, 2023 Published: June 10, 2023 doi:10.5296/ajfa.v15i1.18970 URL: https://doi.org/10.5296/ajfa.v15i1.18970 Abstract There are hundreds of mutual funds in the market, each offering different returns. The investors always look at funds which give high returns and have low risk. Thus while making a portfolio the asset management company should make investment allocations where returns are definite and to give justified returns for every rupee the investors pay, considering the different risks. The objective of the study was to find the short-term effects of portfolio allocation on the performance of mutual funds. The data for the study was consisted of the portfolio allocations and the performance statistics of one hundred and fifty-nine open-ended mutual funds, of which fifty were diversified debt/ income funds and one hundred and nine were diversified equity funds. These funds were further classified into different mutual fund schemes. Each of the mutual funds had a different portfolio and investments were made in different instruments like bonds, certificates of deposit, commercial papers, etc. (in case of debt) and in different sectors like technology, chemicals, services, etc. (in case of equity). The findings from the study indicate that, for debt funds, allocation in bonds and government securities tend to impact the performance of the fund, while for equity funds, allocation in Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 66 engineering, energy, and service sector stocks tend to impact the performance of the fund. Keywords: asset management company, portfolio allocations, returns, performance, debt funds, equity funds Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 67 Introduction There are hundreds of mutual funds in the market, each offering different returns. The investors always look at funds which give high returns and have low risk. Thus while making a portfolio allocation the asset management company (AMC) should make investment allocations where returns are definite, giving justified returns for every Rupee the investors pay, considering the different risks. On the other hand, there are lots of sectors and instruments in which the pool of money collected can be invested. The main aim of the AMC is to make a portfolio allocation that gives maximum returns to the investors. The problem of asset allocation for mutual funds is a long-standing field of interest for researchers. It can be traced back to the beginning of portfolio theory itself. In particular, Markowitz (1987) discussed some of the early models and approaches in portfolio construction. Sharpe (1994) studied the effect of asset allocation and management style on mutual fund performance. He proposed an asset class model for the management of mutual fund investments. Ibbotson and Kaplan (1998) examined the effect of asset allocation on returns for balanced funds and pension funds. They found that that about 90% of the variability of returns of a typical fund across time was explained by policy; about 40% of the variation of returns across funds was explained by policy; and that on average, about 100% of the return level was explained by policy return. Kadiyala (2004) studied the effect of investment in mutual funds on stock market returns. She found that stock market returns are related to contemporaneous flows into mutual funds that invest in risky stocks and bonds, but are unrelated to flows into funds that invest in safer stocks and bonds; in particular, this means that asset allocations of funds have an impact on market returns. Data & Methodology The present study examines the short-term effects of portfolio allocation on performance for open-ended mutual funds. The study was conducted with a random sample consisting of one hundred and fifty-nine different open-ended mutual funds, of which one hundred and nine diversified equity funds were used to study the allocation of funds in different sectors and fifty diversified debt/ income funds were used to study the allocation in different instruments. The sample of diversified debt/ income funds were classified as Debt: ultrashort-term funds (24%), Debt short term funds (2%), Debt: floating-rate short-term funds (6%), Debt: medium-term funds (26%), Gilt: short-term funds (6%), Gilt: medium-term funds (18%), and Hybrid funds (18%). The sample of diversified equity funds were classified as Equity: diversified funds (70.6%), Equity: index funds (13.8%) and Equity: tax planning funds (15.6%). The data for the study consisted of the portfolio allocations and the performance measures of the sample funds. The data was collected from the websites valueresearchonline.com and amfiindia.com. Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 68 The primary objective of the study was to analyze the short-term effects of the portfolio allocation on the performance of funds. In the case of diversified debt/ income funds, this involved analyzing the effect of differences in allocation of different debt/ income funds in different instruments on the differences in performance. On the other hand, in the case of diversified equity funds, this involved analyzing the effect of differences in allocation in different sectors on the difference in performance. Stepwise multiple regression analysis was used in both situations to identify statistically significant effects. Analysis & Interpretation Diversified Debt/ Income Funds The overall allocation of the sample diversified debt/ income funds is shown in Table 1: TABLE 1: Overall allocation of debt/ income funds in different instruments Descriptive Statistics 33.8808% 15.5846% 13.1120% 9.6396% 7.4802% 6.0148% 5.7076% 4.4026% 1.9400% 1.1908% 1.0470% Bonds Govt. Securities Others Cash, call & others Comm Pap Debt Cert of Deposit Tresury Bills Reverse Repo Term Deposits CP/CD Mean It was found that bonds had the highest allocation (33.88%), followed by government securities (15.58%) and others (13.11%). Amongst the least preferred instruments were reverse repos, term deposits and CP/CD’s. The descriptive statistics of the performance measures for the sample diversified debt/ income funds is shown in Table 2: Table 2. Descriptive statistics of performance measures of debt/ income funds Descriptive Statistics .3004 .73436 4.660 22.948 .5582 1.16057 4.530 21.655 .2994 .33645 1.543 1.526 .3120 .32071 .941 -.447 .4505 .57141 1.015 .423 .6491 1.17430 2.029 4.413 mean returns standard deviation of returns beta R2 Sharpe ratio Treynor ratio Mean Std. Skewnes Kurtosis The allocation in each type of debt/ income fund in the sample is shown in Table 3: Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 69 Table 3. Allocation in different instruments for different debt/ income funds Report Mean 26.4950% 40.6300% 52.2433% 53.0246% .0000% .0000% 54.3800% 33.8808% .0000% .0000% 6.2500% .8662% .0000% .0000% 2.4822% 1.0470% 19.9767% 26.4500% .0000% 1.4777% .0000% .0000% .0000% 5.7076% 20.0908% 22.6700% 14.3500% 4.2292% .0000% .0000% 1.3578% 7.4802% 5.9075% .0000% 2.3567% 4.1877% 27.4700% 23.034% 6.6511% 9.6396% 2.9025% .0000% 15.8600% 7.3231% .0000% .0000% 13.6811% 6.0148% .0000% .0000% .0000% 16.5738% 12.5100% 58.072% .3989% 15.5846% 2.5800% .0000% .0000% .0000% .0000% 3.1756% .0000% 1.1908% 2.6892% .0000% .0000% .4154% 14.8500% 15.323% .0000% 4.4026% .0000% .0000% .0000% 2.9192% 19.6833% .0000% .0000% 1.9400% 19.3583% 10.2500% 8.9400% 8.9831% 25.4867% .3944% 21.0489% 13.1120% Bonds CP/CD Cert of Deposit Comm Pap Cash, call & others Debt Govt. Securities Term Deposits Tresury Bills Reverse Repo Others Debt: ultrashort- term Debt: short-term Debt: floating-rate short-term Debt: medium term Gilt: short-term Gilt: medium- term Hybrid Total Category It was found that the Debt: ultrashort-term funds allocated primarily in bonds (26.50%), commercial papers (20.09%), certificates of deposit (19.98%), and others (19.36%). Debt short term funds showed a similar pattern, with bonds having the highest allocation (40.63%), followed by certificates of deposit (26.45%), commercial papers (22.67%), and others (10.25%). In the case of Debt Floating-rate short-term funds, bonds had the highest allocation (52.24%), followed by debt (with 15.86%), commercial papers (14.35%), and others (8.94%). Debt: Medium term funds had highest allocation in bonds (53.02%), followed by government securities (16.57%) and others (8.98%). In contrast, Gilt short term funds had highest allocation in cash, call, and others (27.47%) and others (25.48%), followed by reverse repos (19.68%), T-bills (14.85%), and government securities (12.51%). Gilt medium term funds allocated heavily in government securities (58.07%), followed by cash, call, and others (23.03%) and Treasury bills (15.32%). Finally, Hybrid funds allocated highest in bonds (54.38%), followed by others (21.04%) and debt (13.68%). The descriptive statistics of the performance measures for each type of debt/ income fund are shown in Tables 4, and the ANOVA tests for differences in performance between different types of debt/ income funds are shown in Table 5: Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 70 Table 4. descriptive statistics of performance measures for each type of debt/ income fund Report .0967 .1600 1.5333 .2585 .0900 .1322 .4756 .01303 . 2.49127 .36113 .01000 .16679 .89847 .0292 .0900 2.2933 .4469 .0733 .6711 .9467 .04814 . 3.95485 .25094 .00577 .31102 1.50889 .0492 .2100 .5733 .3408 .3867 .5200 .2422 .04660 . .45938 .23333 .11240 .46425 .38745 .0742 .0300 .3567 .5762 .0667 .5022 .1556 .09199 . .29006 .27467 .04041 .36148 .23093 1.0556 .8889 1.2104 .1959 .1310 .0006 .2660 .46782 . .70824 .45745 .12542 .19331 .13776 1.0576 .3810 1.5881 .2865 .0322 -.1281 1.3276 1.47770 . 2.49420 .79861 .03470 .30577 .83324 Mean Std. Deviation Mean Std. Deviation Mean Std. Deviation Mean Std. Deviation Mean Std. Deviation Mean Std. Deviation mean returns standard deviation of returnsbeta R2 Sharpe ratio Treynor ratio Debt: ultrashort- term Debt: short-term Debt: floating-rate short-term Debt: medium term Gilt: short-term Gilt: medium- term Hybrid Category Table 5. ANOVA tests for differences in performance between different types of debt/ income funds ANOVA Table 5.765 6 .961 2.000 .087 20.660 43 .480 26.425 49 14.949 6 2.491 2.099 .073 51.051 43 1.187 66.000 49 1.497 6 .250 2.649 .028 4.050 43 .094 5.547 49 2.398 6 .400 6.505 .000 2.642 43 .061 5.040 49 9.595 6 1.599 10.738 .000 6.404 43 .149 15.999 49 17.150 6 2.858 2.438 .041 50.420 43 1.173 67.570 49 (Combined)Between Groups Within Groups Total (Combined)Between Groups Within Groups Total (Combined)Between Groups Within Groups Total (Combined)Between Groups Within Groups Total (Combined)Between Groups Within Groups Total (Combined)Between Groups Within Groups Total mean returns * Category standard deviation of returns * Category beta * Category R2 * Category Sharpe ratio * Category Treynor ratio * Category Sum of Squares df Mean Square F Sig. It was found that there is no statistically significant difference in mean returns and standard deviation of returns between different types of debt/ income funds. Among the sample funds, the Debt floating rate short term funds had the highest mean returns, but with a lot of variation. On the other hand, there were statistically significant differences in all of the other performance measures between different types of debt/ income funds. Among the sample funds, the Debt floating rate short term funds had the highest mean beta, followed by the Gilt: medium-term funds, while the Debt: ultrashort-term funds had the lowest mean beta; the Debt medium term funds had the highest mean R2, followed by the Gilt: medium-term funds, while the Debt: ultrashort-term funds had the lowest mean R2; finally, the Debt floating rate short Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 71 term funds had the highest mean Sharpe and Treynor ratios, followed by the Debt: ultrashort-term funds, while the Gilt: medium-term funds had the lowest mean Sharpe and Treynor ratios. The correlation of the allocations of the debt/ income funds in the different instruments is shown in Table 6: Table 6. Correlation of the allocations of the debt/ income funds in different instruments Correlations 1 -.024 -.202 -.158 -.312* .195 -.439** -.226 -.391** -.113 -.148 .435 .079 .136 .014 .088 .001 .057 .003 .218 .152 50 50 50 50 50 50 50 50 50 50 50 -.024 1 -.105 -.099 .035 .213 -.155 -.058 -.087 -.049 .143 .435 .235 .246 .404 .068 .141 .343 .274 .369 .161 50 50 50 50 50 50 50 50 50 50 50 -.202 -.105 1 -.008 -.215 -.124 -.261* .200 -.044 -.085 .320* .079 .235 .479 .067 .195 .034 .082 .381 .277 .012 50 50 50 50 50 50 50 50 50 50 50 -.158 -.099 -.008 1 -.101 -.133 -.244* -.098 -.146 -.081 -.061 .136 .246 .479 .243 .178 .044 .249 .156 .287 .338 50 50 50 50 50 50 50 50 50 50 50 -.312* .035 -.215 -.101 1 -.242* .180 -.134 .048 -.111 -.267* .014 .404 .067 .243 .045 .105 .176 .370 .221 .030 50 50 50 50 50 50 50 50 50 50 50 .195 .213 -.124 -.133 -.242* 1 -.257* -.129 -.191 -.107 .053 .088 .068 .195 .178 .045 .036 .187 .092 .230 .359 50 50 50 50 50 50 50 50 50 50 50 -.439** -.155 -.261* -.244* .180 -.257* 1 .127 .136 .099 -.421** .001 .141 .034 .044 .105 .036 .190 .174 .246 .001 50 50 50 50 50 50 50 50 50 50 50 -.226 -.058 .200 -.098 -.134 -.129 .127 1 .173 -.048 -.002 .057 .343 .082 .249 .176 .187 .190 .115 .371 .494 50 50 50 50 50 50 50 50 50 50 50 -.391** -.087 -.044 -.146 .048 -.191 .136 .173 1 -.071 .030 .003 .274 .381 .156 .370 .092 .174 .115 .312 .419 50 50 50 50 50 50 50 50 50 50 50 -.113 -.049 -.085 -.081 -.111 -.107 .099 -.048 -.071 1 -.117 .218 .369 .277 .287 .221 .230 .246 .371 .312 .209 50 50 50 50 50 50 50 50 50 50 50 -.148 .143 .320* -.061 -.267* .053 -.421** -.002 .030 -.117 1 .152 .161 .012 .338 .030 .359 .001 .494 .419 .209 50 50 50 50 50 50 50 50 50 50 50 Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Bonds CP/CD Cert of Deposit Comm Pap Cash, call & others Debt Govt. Securities Term Deposits Tresury Bills Reverse Repo Others Bonds CP/CD Cert of Deposit Comm Pap Cash, call & others Debt Govt. Securities Term Deposits Tresury Bills Reverse Repo Others Correlation is significant at the 0.05 level (1-tailed).*. Correlation is significant at the 0.01 level (1-tailed).**. Correlation analysis of the allocations in the different instruments has yielded the following results:  Allocation in bonds was not positively correlated to allocation in any of the securities, and was negatively correlated to allocation in cash, call, and others, government securities, and treasury bills.  Allocation in CP/CD was uncorrelated with allocation in the other security.  Allocation in certificates of deposit was positively correlated to allocation in others, and negatively correlated to allocation in the government securities  Allocation in commercial papers was not positively correlated to allocation in any of the securities, and was negatively correlated to allocation in government securities. Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 72  Allocation in cash, call, and others was not positively correlated to allocation in any of the securities, and was negatively correlated to allocation in the bonds, debt, and others.  Allocation in debt was not positively correlated to allocation in any of the securities, and was negatively correlated to allocation in cash, call, and others and government securities.  Allocation in government securities was not positively correlated to allocation in any of the securities, and was negatively correlated to allocation in bonds, certificates of deposit, commercial papers, debt, and others.  Allocation in term deposits was uncorrelated with the allocation in any other security.  Allocation in Treasury bills was not positively correlated to allocation in any of the securities, and was negatively correlated to allocation in bonds.  Allocation in reverse repos was uncorrelated with allocation in any other security.  Allocation in others was positively correlated to allocation in certificates of deposit, and was negatively correlated to allocation in cash, call, and others and in government securities. Regression analysis was performed to analyze the effect of allocation in different instruments on the performance of debt/ income funds. The results of stepwise multiple regression of mean returns of debt/ income funds on the portfolio allocation in different instruments is shown in Table 7: Table 7. Stepwise multiple regression of mean returns on portfolio allocation in different instruments Coefficientsa .036 .153 .236 .814 .008 .003 .311 2.267 .028 (Constant) Bonds Model 1 B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: mean returnsa. It was found that variation in mean returns of debt/ income funds was explained by variation in allocation in only one instrument, viz. bonds, that the allocation in this instrument explained 9.7% of the variation in mean returns of the debt mutual funds, and that this effect was statistically significant. The results of stepwise multiple regression of standard deviation of returns of debt/ income funds on portfolio allocation in different instruments showed that variation in standard deviation of returns of debt/ income funds was not affected by allocation in any of the instruments. Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 73 The results of stepwise multiple regression of beta of debt/ income funds on the portfolio allocation in different instruments is shown in Table 8: Table 8. Stepwise multiple regression of beta on portfolio allocation in different instruments Coefficientsa .209 .052 4.054 .000 .006 .002 .424 3.246 .002 (Constant) Govt. Securities Model 1 B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: betaa. It was found that variation in beta of debt/ income funds was explained by variation in allocation in only one instrument, viz. government securities, that the allocation in this instrument explained 18% of the variation in beta of the debt/ income funds, and that this effect was statistically significant. The results of stepwise multiple regression of R2 of debt/ income funds on the portfolio allocation in different instruments is shown in Table 9: Table 9. Stepwise multiple regression of R2 on portfolio allocation in different instruments Coefficientsa .215 .048 4.510 .000 .006 .002 .477 3.762 .000 (Constant) Govt. Securities Model 1 B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: R2a. It was found that variation in R2 of debt/ income funds was explained by variation in allocation in only one instrument, viz. government securities, that the allocation in this instrument explained 22.8% of the variation in R2 of the debt/ income funds, and that this effect was statistically significant. The results of stepwise multiple regression of the Sharpe ratio of debt/ income funds on the portfolio allocation in different instruments is shown in Table 10: Table 10. Stepwise multiple regression of the Sharpe ratio on portfolio allocation in different instruments Coefficientsa .583 .090 6.482 .000 -.009 .003 -.369 -2.748 .008 (Constant) Govt. Securities Model 1 B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: Sharpe ratioa. Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 74 It was found that variation in the Sharpe ratio of debt/ income funds was explained by variation in allocation in only one instrument, viz. government securities, that the allocation in this instrument explained 13.6% of the variation in the Sharpe ratio of the debt/ income funds, and that this effect was statistically significant. The results of stepwise multiple regression of the Treynor ratio of debt/ income funds on the portfolio allocation in different instruments is shown in Table 11: Table 11. Stepwise multiple regression of the Treynor ratio on portfolio allocation in different instruments Coefficientsa .327 .207 1.583 .120 .025 .010 .330 2.420 .019 -.156 .279 -.560 .578 .028 .010 .377 2.879 .006 .013 .005 .321 2.452 .018 (Constant) Others (Constant) Others Bonds Model 1 2 B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: Treynor ratioa. It was found that variation in Treynor of debt/ income funds was explained by variation in allocation in only two instruments, viz. others and bonds. Together, allocation in these two instruments explained 21% of the variation in the Treynor ratio of the debt/ income funds, and this effect was statistically significant. Of the allocations in the two instruments, allocation in others had the greater impact on the Treynor ratio than allocation in bonds had. Diversified Equity Funds The overall allocation of the sample diversified equity funds is shown in Table 12: Table 12. Overall allocation of equity funds in different sectors Descriptive Statistics 32.0731% 17.7861% 10.2243% 9.8389% 8.2821% 5.2116% 3.9085% 3.8983% 2.2239% 2.1537% 1.9894% 1.9103% .4999% Others Technology Fin Services Energy Engineering Diversified services Metals Consu Non Dur Health Care Construction Automobile Chemicals Mean It was found that others had the highest allocation (32.07%), followed by technology (17.79%) and financial services (10.22%). Amongst the least preferred sectors were construction, Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 75 automobile, and chemicals. The descriptive statistics of the performance measures for the sample diversified equity funds is shown in Table 13: Table 13. Descriptive statistics of performance measures of equity funds Descriptive Statistics 3.4580 .73472 -1.094 7.769 5.7580 1.10827 -2.950 14.705 .9396 .58738 9.548 97.341 .7598 .16722 -.860 1.659 mean returns std dev of ret beta R2 Statistic Statistic Statistic Statistic Mean Std. Skewnes Kurtosis The allocation in each type of equity fund in the sample is shown in Table 14: Table 14. Allocation in different sectors for different equity funds Report Mean 8.7543% 3.8033% 10.0953% 8.2821% 2.2921% .0000% 1.8665% 1.9103% 8.3540% 19.3540% 8.1688% 9.8389% .6219% .0000% .3882% .4999% 2.1240% .0000% 3.1347% 1.9894% 6.1670% 2.4740% 3.2994% 5.2116% 2.4527% .0000% 2.6994% 2.1537% 16.7930% 24.7440% 16.1447% 17.7861% 8.9595% 19.7433% 7.5541% 10.2243% 1.9922% 3.0187% 2.5724% 2.2239% 3.9439% 2.5767% 4.8576% 3.8983% 4.9114% .0673% 2.7553% 3.9085% 32.6339% 24.2187% 36.4635% 32.0731% Engineering Automobile Energy Chemicals Construction Diversified Health Care Technology Fin Services Consu Non Dur Metals services Others Equity: diversified Equity: index Equity: tax-planning Total Category Amongst the Equity: diversified funds, others had the highest allocation (32.63%), followed by technology (16.79%), financial services (8.95%), and engineering (8.75%). In the case of Equity: index funds, technology had the highest allocation (24.74%), followed by others (24.21%), financial services (19.74%), and energy (19.35%). Finally, in the case of Equity: tax planning funds, others had the highest allocation (36.46%), followed by technology (16.14%) and engineering (10.09%). The descriptive statistics of the performance measures for each type of equity fund are shown in Table 15, and the ANOVA tests for differences in performance between different types of debt/ income funds are shown in Table 16: Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 76 Table 15. Descriptive statistics of performance measures for each type of equity fund Report 3.4764 5.6803 .9508 .7400 .80126 1.22193 .69799 .15960 77 77 77 77 3.1700 5.6573 .9400 .9560 .32894 .88905 .06908 .07908 15 15 15 15 3.6288 6.1988 .8888 .6765 .62378 .51169 .08455 .13005 17 17 17 17 Mean Std. Deviation N Mean Std. Deviation N Mean Std. Deviation N Category Equity: diversified Equity: index Equity: tax-planning mean returns std dev of ret beta R2 Table 16. ANOVA tests for differences in performance between different types of equity funds ANOVA Table 1.766 2 .883 1.656 .196 56.534 106 .533 58.300 108 3.920 2 1.960 1.614 .204 128.731 106 1.214 132.652 108 .053 2 .027 .076 .927 37.208 106 .351 37.262 108 .726 2 .363 16.764 .000 2.294 106 .022 3.020 108 (Combined)Between Groups Within Groups Total (Combined)Between Groups Within Groups Total (Combined)Between Groups Within Groups Total (Combined)Between Groups Within Groups Total mean returns * Category std dev of ret * Category beta * Category R2 * Category Sum of Squares df Mean Square F Sig. It was found that there is no statistically significant difference in mean returns, standard deviation of returns, and beta between different types of equity funds. Among the sample funds, the Equity: tax planning funds had the highest mean returns, but with highest variation and lowest beta. On the other hand, there were statistically significant differences in R2 between different types of equity funds. Among the sample funds, the Equity: index funds had the highest mean R2, followed by the Equity: diversified funds, while the Equity: tax planning funds had the lowest mean R2. The correlation of the allocations of the equity funds in the different sectors is shown in Table 17: Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 77 Table 17. Correlation of the allocations of the equity funds in different sectors Correlations 1 .328** -.340** -.121 .217* -.060 .087 -.194* -.236** .053 -.096 -.205* -.049 .000 .000 .105 .012 .269 .183 .022 .007 .291 .160 .016 .308 109 109 109 109 109 109 109 109 109 109 109 109 109 .328** 1 -.341** -.039 .078 .144 .031 -.317** -.192* .006 -.163* -.120 .142 .000 .000 .344 .210 .068 .373 .000 .023 .474 .046 .107 .070 109 109 109 109 109 109 109 109 109 109 109 109 109 -.340** -.341** 1 -.205* -.348** -.169* -.255** .454** .198* -.093 -.004 -.326** -.349** .000 .000 .016 .000 .039 .004 .000 .019 .168 .483 .000 .000 109 109 109 109 109 109 109 109 109 109 109 109 109 -.121 -.039 -.205* 1 -.101 -.105 .206* -.159* -.079 -.033 .211* .112 .114 .105 .344 .016 .149 .139 .016 .050 .206 .365 .014 .124 .119 109 109 109 109 109 109 109 109 109 109 109 109 109 .217* .078 -.348** -.101 1 .017 -.047 -.262** -.260** -.194* .126 .166* .108 .012 .210 .000 .149 .429 .314 .003 .003 .022 .096 .042 .132 109 109 109 109 109 109 109 109 109 109 109 109 109 -.060 .144 -.169* -.105 .017 1 -.080 -.160* -.076 -.153 -.141 -.009 -.004 .269 .068 .039 .139 .429 .203 .049 .217 .056 .071 .464 .485 109 109 109 109 109 109 109 109 109 109 109 109 109 .087 .031 -.255** .206* -.047 -.080 1 .033 -.299** .289** -.157 -.067 -.045 .183 .373 .004 .016 .314 .203 .367 .001 .001 .051 .245 .321 109 109 109 109 109 109 109 109 109 109 109 109 109 -.194* -.317** .454** -.159* -.262** -.160* .033 1 .095 .039 -.311** -.189* -.545** .022 .000 .000 .050 .003 .049 .367 .162 .343 .001 .025 .000 109 109 109 109 109 109 109 109 109 109 109 109 109 -.236** -.192* .198* -.079 -.260** -.076 -.299** .095 1 -.149 -.106 -.066 -.465** .007 .023 .019 .206 .003 .217 .001 .162 .061 .135 .246 .000 109 109 109 109 109 109 109 109 109 109 109 109 109 .053 .006 -.093 -.033 -.194* -.153 .289** .039 -.149 1 -.192* -.061 -.100 .291 .474 .168 .365 .022 .056 .001 .343 .061 .023 .265 .150 109 109 109 109 109 109 109 109 109 109 109 109 109 -.096 -.163* -.004 .211* .126 -.141 -.157 -.311** -.106 -.192* 1 .184* .027 .160 .046 .483 .014 .096 .071 .051 .001 .135 .023 .028 .389 109 109 109 109 109 109 109 109 109 109 109 109 109 -.205* -.120 -.326** .112 .166* -.009 -.067 -.189* -.066 -.061 .184* 1 .017 .016 .107 .000 .124 .042 .464 .245 .025 .246 .265 .028 .432 109 109 109 109 109 109 109 109 109 109 109 109 109 -.049 .142 -.349** .114 .108 -.004 -.045 -.545** -.465** -.100 .027 .017 1 .308 .070 .000 .119 .132 .485 .321 .000 .000 .150 .389 .432 109 109 109 109 109 109 109 109 109 109 109 109 109 Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N Engineering Automobile Energy Chemicals Construction Diversified Health Care Technology Fin Services Consu Non Dur Metals services Others Engineering Automobile Energy Chemicals Construction Diversified Health Care Technology Fin Services Consu Non Dur Metals services Others Correlation is significant at the 0.01 level (1-tailed).**. Correlation is significant at the 0.05 level (1-tailed).*. Correlation analysis of the allocations to the different sectors has yielded the following results:  Allocation in the engineering sector was positively correlated with allocation in the automobiles and construction sectors, and negatively correlated with allocation in the energy, technology, financial services and services sectors.  Allocation in the automobile sector was positively correlated with allocation in the engineering sector, and negatively correlated with allocation in the energy, technology, and financial services sectors.  Allocation in the energy sector was positively correlated with allocation in the technology and financial services sectors, and negatively correlated with allocation in the engineering, automobile, chemical, construction, diversified, health-care, and services sectors.  Allocation in the health-care sector was positively correlated with allocation in the metals sector, and negatively correlated with allocation in the energy and technology sectors.  Allocation in the construction sector was positively correlated with allocation in the engineering and services sectors, and negatively correlated with allocation in the energy, technology, financial services, and consumer non-durables sectors. Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 78  Allocation in the diversified sector was negatively correlated with allocation in the energy and technology sectors.  Allocation in the health-care sector was positively correlated with allocation in the chemical and consumer non-durables sectors, and negatively correlated with allocation in the energy and financial services sectors.  Allocation in the technology sector was positively correlated with allocation in the energy sector, and negatively correlated with allocation in the automobile, chemical, construction, metals, services, and diversified sectors.  Allocation in the financial services sector was positively correlated with allocation in the energy sector, and negatively correlated with allocation in the engineering, automobile, construction, and health-care sectors.  Allocation in the consumer non-durables sector was positively correlated with allocation in the health-care sector, and negatively correlated with allocation in the construction and metals sectors.  Allocation in the metals sector was positively correlated with allocation in the chemicals and services sectors, and negatively correlated with allocation in the automobiles, technology, and consumer non-durables sectors.  Allocation in the services sector was positively correlated with allocation in the construction and metals sectors, and negatively correlated with allocation in the engineering, energy, and technology sectors. Regression analysis was performed to analyze the effect of allocation in different instruments on the performance of equity funds. The results of stepwise multiple regression of mean returns of equity funds on the portfolio allocation in different sectors is shown in Table 18: Table 18. Stepwise multiple regression of mean returns on portfolio allocation in different sectors Coefficientsa,b .021 .007 .298 2.764 .013 .094 .031 .313 2.985 .008 .193 .050 .414 3.864 .001 .184 .062 .283 2.963 .009 Others Engineering Energy services Model B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: mean returnsa. Linear Regression through the Originb. It was found that variation in mean returns of equity funds was explained by variation in allocation in four sectors, viz. others, engineering, energy and services sectors. Together, Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 79 allocation in these four sectors explained 88% of the variation in mean returns of the equity funds, and this effect was statistically significant. Of the allocations in the three sectors, allocation in financial services had the greatest impact on mean returns. The results of stepwise multiple regression of standard deviation of returns of equity funds on the portfolio allocation in different sectors is shown in Table 19: Table 19. Stepwise multiple regression of mean returns on portfolio allocation in different sectors Coefficientsa,b .030 .007 .276 4.211 .001 .135 .032 .288 4.179 .001 .308 .056 .304 5.539 .000 .242 .046 .332 5.316 .000 .166 .058 .171 2.878 .011 .378 .135 .145 2.801 .013 Others Engineering services Energy Automobile Chemicals Model B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: std dev of reta. Linear Regression through the Originb. It was found that variation in standard deviation of returns of equity funds was explained by variation in allocation in six sectors, viz. others, engineering, services, energy, automobiles and chemicals. Together, allocation in these six sectors explained 96.6% of the variation in standard deviation of returns of the equity funds, and this effect was statistically significant. Of the allocations in the six sectors, allocation in energy and services had the greatest impact on standard deviation of returns. The results of stepwise multiple regression of beta of equity funds on the portfolio allocation in different sectors is shown in Table 20: Table 20. Stepwise multiple regression of beta on portfolio allocation in different sectors Coefficientsa,b .009 .001 .495 8.994 .000 .027 .007 .185 3.964 .001 .021 .005 .200 3.841 .002 .052 .008 .303 6.443 .000 .059 .020 .139 2.901 .011 .029 .008 .182 3.707 .002 Others Consu Non Dur Fin Services Construction Chemicals Automobile Model B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: betaa. Linear Regression through the Originb. It was found that variation in beta of equity funds was explained by variation in allocation in six sectors, viz. others, consumer non-durables, financial services, construction, chemicals and automobiles. Together, allocation in these six sectors explained 97.3% of the variation in Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 80 beta of the equity mutual funds, and this effect was statistically significant. Of the allocations in the six sectors, allocation in others had the greatest impact on beta. The results of stepwise multiple regression of R2 of equity funds on the portfolio allocation in different sectors is shown in Table 21: Table 21. Stepwise multiple regression of R2 on portfolio allocation in different sectors Coefficientsa,b .009 .001 .612 8.424 .000 .008 .004 .195 2.162 .045 .012 .005 .199 2.401 .028 .015 .007 .178 2.160 .045 Others Technology Engineering Fin Services Model B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: R2a. Linear Regression through the Originb. It was found that variation in R2 of equity funds was explained by variation in allocation in four sectors, viz. financial services, technology, others and engineering. Together, allocation in these four sectors explained 93.7% of the variation in R2 of the equity funds, and this effect was statistically significant. Of the allocations in the four sectors, allocation in others had the greatest impact on R2. The results of stepwise multiple regression of the Sharpe ratio of equity funds on the portfolio allocation in different sectors is shown in Table 22: Table 22. Stepwise multiple regression of the Sharpe ratio on portfolio allocation in different sectors Coefficientsa,b .004 .001 .414 5.528 .000 .014 .002 .427 6.973 .000 .026 .005 .348 5.569 .000 -.052 .015 -.199 -3.414 .004 .017 .006 .182 2.925 .010 Others Technology Metals Chemicals Diversified Model B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: Sharpea. Linear Regression through the Originb. It was found that variation in Sharpe ratio of equity funds was explained by variation in allocation in five sectors, viz. others, technology, metals, chemicals, diversified and health-care. Together, allocation in these five sectors explained 95.3% of the variation in Sharpe ratio of the equity funds, and this effect was statistically significant. Of the allocations in the five sectors, allocation in technology had the greatest impact on the Sharpe ratio. Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 81 The results of stepwise multiple regression of the Treynor ratio of debt/ income funds on the portfolio allocation in different sectors is shown in Table 23: Table 23. Stepwise multiple regression of the Treynor ratio on portfolio allocation in different sectors Coefficientsa,b .000 .000 .234 2.096 .051 .001 .000 .456 4.908 .000 .002 .000 .399 4.201 .001 .001 .001 .213 2.268 .037 Others Technology Metals Diversified Model B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: Treynora. Linear Regression through the Originb. It was found that variation in Treynor ratio of equity funds was explained by variation in allocation in four sectors, viz. others, technology, metals and diversified. Together, allocation in these four sectors explained 8.84% of the variation in Treynor ratio of the equity funds, and this effect was statistically significant. Of the allocations in the four sectors, allocation in technology had the greatest impact on Treynor ratio. Discussion The findings from the study indicate that, for diversified debt/ income funds, allocation in bonds and government securities impact the performance of the fund, while for diversified equity funds, allocation in engineering, energy, and service sector stocks tend to impact the performance of the fund. The study suffers from a few mild limitations. Firstly, the study considers a sample of one hundred and fifty-nine mutual funds only. Though this is a reasonably-sized sample, a larger sample would have yielded more statistically significant results. Further, the high level of variation observed in the sample indicates that the sampling method used may be inadequate – i.e. stratified sampling may have been more appropriate in this situation. It may be possible that, along with the classification of funds used in the study, other moderating factors would be required to stratify the funds. Though the results of the study are statistically significant, there is scope for further research in order firstly to identify such factors, and secondly to take these factors into consideration in examining the effect of portfolio allocation on performance of mutual funds. A further limitation of the study is the limited period (one month) which it encompasses. In order to generalize the results, a similar methodology would have to be applied to monthly data for different months. This would have to be undertaken in subsequent studies. It would perhaps be expected that the results of the analysis for diversified debt/ income funds would be relatively unchanged, while the results of the analysis for diversified equity funds would vary, depending on the performance of the sectors; of course, sectors which have been Asian Journal of Finance & Accounting ISSN 1946-052X 2023, Vol. 15, No. 1 ajfa.macrothink.org/ 82 performing consistently well would be expected to have a significant effect throughout. References Ibbotson, R.G. and Kaplan, P.D. (1998). Does Asset Allocation Policy Explain 40%, 90%, or 100% of Performance?. Yale Working Paper Series Kadiyala, P. (2004). Asset Allocation Decision of Mutual Fund Investors. Financial Services Review, Academy of Financial Services Markowitz, H.M. (1987). Mean-Variance Analyses in Portfolio Choice and Capital Markets. Oxford: Basil Blackwell, Inc. Sharpe, W.F. (1988). Determining a Fund's Effective Asset Mix. Investment Management Review Sharpe, W.F. (1994). Asset Allocation: Management Style and Performance Measurement. Journal of Portfolio Management, Institutional Investor, Inc.