Defined Contribution Pension Plans: Sticky or Discerning Money?
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- Opal Murphy
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1 Defined Contribution Pension Plans: Sticky or Discerning Money? Clemens Sialm University of Texas at Austin Laura Starks University of Texas at Austin Hanjiang Zhang Nanyang Technological University, Singapore June, 2014 Sialm, Starks, and Zhang Defined Contribution Pension Plans 1 / 42
2 Motivation Introduction Over the last decades there have been significant changes in the structure of retirement savings in the United States: The relative importance of government-provided social security has declined. Firms have switched from Defined Benefit (DB) to Defined Contribution (DC) plans. DC pension plans (e.g., 401(k) and 403(b)) have become an important source of retirement funding for many households. Sialm, Starks, and Zhang Defined Contribution Pension Plans 2 / 42
3 Introduction Mutual Funds and DC Plans Mutual funds are the main investment vehicle in tax-qualified DC plans. However, the same mutual funds can also be held directly in traditional taxable accounts. These mixed clienteles have different investment horizons, different tax statuses, and different distribution channels. Our paper analyzes the properties of money flows into mutual funds from DC investors and other investors. Sialm, Starks, and Zhang Defined Contribution Pension Plans 3 / 42
4 Mutual Fund Choice Introduction Directly Held Accounts Investors generally have the flexibility to choose among the universe of mutual funds. Defined Contribution Plan Accounts Plan sponsors (i.e., employers) offer a limited number of mutual fund investment options and adjust these menus by removing or adding options. Plan participants (i.e., employees) allocate DC balances among the available investment options. Sialm, Starks, and Zhang Defined Contribution Pension Plans 4 / 42
5 Mutual Fund Choice Introduction Directly Held Accounts Investors generally have the flexibility to choose among the universe of mutual funds. Defined Contribution Plan Accounts Plan sponsors (i.e., employers) offer a limited number of mutual fund investment options and adjust these menus by removing or adding options. Plan participants (i.e., employees) allocate DC balances among the available investment options. Sialm, Starks, and Zhang Defined Contribution Pension Plans 4 / 42
6 Introduction DC and Non-DC Fund Flows Conventional wisdom suggests that Defined Contribution (DC) plan assets are sticky and not discerning. Many participants in DC pension plans are inert and reluctant to adjust portfolio allocations. Benartzi and Thaler (2001); Madrian and Shea (2001); Choi, Laibson, Madrian, and Metrick (2002, 2004); Huberman and Jiang (2006) Retail mutual fund investors have been shown to chase prior fund performance. Brown, Harlow, and Starks (1996); Chevalier and Ellison (1997); Sirri and Tufano (1998) Sponsors in pension plans may actively monitor investment options. DelGuercio and Tkac (2002); Goyal and Wahal (2008) Sialm, Starks, and Zhang Defined Contribution Pension Plans 5 / 42
7 Introduction DC and Non-DC Fund Flows Conventional wisdom suggests that Defined Contribution (DC) plan assets are sticky and not discerning. Many participants in DC pension plans are inert and reluctant to adjust portfolio allocations. Benartzi and Thaler (2001); Madrian and Shea (2001); Choi, Laibson, Madrian, and Metrick (2002, 2004); Huberman and Jiang (2006) Retail mutual fund investors have been shown to chase prior fund performance. Brown, Harlow, and Starks (1996); Chevalier and Ellison (1997); Sirri and Tufano (1998) Sponsors in pension plans may actively monitor investment options. DelGuercio and Tkac (2002); Goyal and Wahal (2008) Sialm, Starks, and Zhang Defined Contribution Pension Plans 5 / 42
8 Introduction DC and Non-DC Fund Flows Conventional wisdom suggests that Defined Contribution (DC) plan assets are sticky and not discerning. Many participants in DC pension plans are inert and reluctant to adjust portfolio allocations. Benartzi and Thaler (2001); Madrian and Shea (2001); Choi, Laibson, Madrian, and Metrick (2002, 2004); Huberman and Jiang (2006) Retail mutual fund investors have been shown to chase prior fund performance. Brown, Harlow, and Starks (1996); Chevalier and Ellison (1997); Sirri and Tufano (1998) Sponsors in pension plans may actively monitor investment options. DelGuercio and Tkac (2002); Goyal and Wahal (2008) Sialm, Starks, and Zhang Defined Contribution Pension Plans 5 / 42
9 Introduction DC and Non-DC Fund Flows Conventional wisdom suggests that Defined Contribution (DC) plan assets are sticky and not discerning. Many participants in DC pension plans are inert and reluctant to adjust portfolio allocations. Benartzi and Thaler (2001); Madrian and Shea (2001); Choi, Laibson, Madrian, and Metrick (2002, 2004); Huberman and Jiang (2006) Retail mutual fund investors have been shown to chase prior fund performance. Brown, Harlow, and Starks (1996); Chevalier and Ellison (1997); Sirri and Tufano (1998) Sponsors in pension plans may actively monitor investment options. DelGuercio and Tkac (2002); Goyal and Wahal (2008) Sialm, Starks, and Zhang Defined Contribution Pension Plans 5 / 42
10 Main Results Introduction Is DC money sticky? DC fund flows have a more sensitive flow-performance relation than non-dc flows. Most of the sensitivity of DC money is driven by plan sponsors and not by plan participants. Is DC pension plan money discerning? DC fund flows do not have significant predictability for future performance, whereas non-dc flows predict future performance negatively. Sialm, Starks, and Zhang Defined Contribution Pension Plans 6 / 42
11 Main Results Introduction Is DC money sticky? DC fund flows have a more sensitive flow-performance relation than non-dc flows. Most of the sensitivity of DC money is driven by plan sponsors and not by plan participants. Is DC pension plan money discerning? DC fund flows do not have significant predictability for future performance, whereas non-dc flows predict future performance negatively. Sialm, Starks, and Zhang Defined Contribution Pension Plans 6 / 42
12 Data Data Assets held in DC plans: Annual surveys of Pensions & Investments of large mutual fund families between Mutual fund size, characteristics, and performance: CRSP survivor-bias free mutual fund database. Plan flows into mutual funds: Hand-collected data from Form 11-K filed with the SEC on the allocation of plan assets from Pool, Sialm, and Stefanescu (2013). Sialm, Starks, and Zhang Defined Contribution Pension Plans 7 / 42
13 Sample Description Data We focus our sample on domestic equity funds from fund families that participate in the surveys. Families in the sample control about 77% of total mutual fund assets. Our sample covers 1,078 distinct equity funds and 5,808 fund-year observations over the period between 1997 and Sialm, Starks, and Zhang Defined Contribution Pension Plans 8 / 42
14 Fund Flow Definitions Fund Flows DC Flows: DCFlow f,t = DCAssets f,t DCAssets f,t 1 (1 + R f,t ) DCAssets f,t 1 (1 + R f,t ) Non-DC Flows: NonDCFlow f,t = NonDCAssets f,t NonDCAssets f,t 1 (1 + R f,t ) NonDCAssets f,t 1 (1 + R f,t ) Sialm, Starks, and Zhang Defined Contribution Pension Plans 9 / 42
15 Fund Flow Definitions Fund Flows DC Flows: DCFlow f,t = DCAssets f,t DCAssets f,t 1 (1 + R f,t ) DCAssets f,t 1 (1 + R f,t ) Non-DC Flows: NonDCFlow f,t = NonDCAssets f,t NonDCAssets f,t 1 (1 + R f,t ) NonDCAssets f,t 1 (1 + R f,t ) Sialm, Starks, and Zhang Defined Contribution Pension Plans 9 / 42
16 Fund Flows Flow Performance Sensitivity We estimate the following model: Flow f,t = β 1 LowPerf f,t 1 + β 2 MidPerf f,t 1 + β 3 HighPerf f,t 1 + β 4 DCSize f,t 1 + β 5 NonDCSize f,t 1 + β 6 FamSize f,t 1 + β 7 Age f,t 1 + β 8 Exp f,t 1 + β 9 Turn f,t 1 + β 10 Vol f,t 1 + β 11 StyleFlow f,t + β t + ɛ f,t Performance percentiles Perf f,t are calculated based on raw returns of domestic equity funds in the CRSP database over the prior year. To adjust for non-linearities we use a piecewise linear performance specification following Sirri and Tufano (1997): LowPerf f,t = min(perf p,f,t, 0.2), MidPerf f,t = min(perf p,f,t LowPerf f,t, 0.6), HighPerf f,t = Perf p,f,t LowPerf f,t MidPerf f,t. The regressions include time-fixed effects and the standard errors are adjusted for clustering at the fund level. Sialm, Starks, and Zhang Defined Contribution Pension Plans 10 / 42
17 Fund Flows Flow Performance Sensitivity We estimate the following model: Flow f,t = β 1 LowPerf f,t 1 + β 2 MidPerf f,t 1 + β 3 HighPerf f,t 1 + β 4 DCSize f,t 1 + β 5 NonDCSize f,t 1 + β 6 FamSize f,t 1 + β 7 Age f,t 1 + β 8 Exp f,t 1 + β 9 Turn f,t 1 + β 10 Vol f,t 1 + β 11 StyleFlow f,t + β t + ɛ f,t Performance percentiles Perf f,t are calculated based on raw returns of domestic equity funds in the CRSP database over the prior year. To adjust for non-linearities we use a piecewise linear performance specification following Sirri and Tufano (1997): LowPerf f,t = min(perf p,f,t, 0.2), MidPerf f,t = min(perf p,f,t LowPerf f,t, 0.6), HighPerf f,t = Perf p,f,t LowPerf f,t MidPerf f,t. The regressions include time-fixed effects and the standard errors are adjusted for clustering at the fund level. Sialm, Starks, and Zhang Defined Contribution Pension Plans 10 / 42
18 Fund Flows Flow Performance Sensitivity We estimate the following model: Flow f,t = β 1 LowPerf f,t 1 + β 2 MidPerf f,t 1 + β 3 HighPerf f,t 1 + β 4 DCSize f,t 1 + β 5 NonDCSize f,t 1 + β 6 FamSize f,t 1 + β 7 Age f,t 1 + β 8 Exp f,t 1 + β 9 Turn f,t 1 + β 10 Vol f,t 1 + β 11 StyleFlow f,t + β t + ɛ f,t Performance percentiles Perf f,t are calculated based on raw returns of domestic equity funds in the CRSP database over the prior year. To adjust for non-linearities we use a piecewise linear performance specification following Sirri and Tufano (1997): LowPerf f,t = min(perf p,f,t, 0.2), MidPerf f,t = min(perf p,f,t LowPerf f,t, 0.6), HighPerf f,t = Perf p,f,t LowPerf f,t MidPerf f,t. The regressions include time-fixed effects and the standard errors are adjusted for clustering at the fund level. Sialm, Starks, and Zhang Defined Contribution Pension Plans 10 / 42
19 Fund Flows Flow Performance Sensitivity We estimate the following model: Flow f,t = β 1 LowPerf f,t 1 + β 2 MidPerf f,t 1 + β 3 HighPerf f,t 1 + β 4 DCSize f,t 1 + β 5 NonDCSize f,t 1 + β 6 FamSize f,t 1 + β 7 Age f,t 1 + β 8 Exp f,t 1 + β 9 Turn f,t 1 + β 10 Vol f,t 1 + β 11 StyleFlow f,t + β t + ɛ f,t Performance percentiles Perf f,t are calculated based on raw returns of domestic equity funds in the CRSP database over the prior year. To adjust for non-linearities we use a piecewise linear performance specification following Sirri and Tufano (1997): LowPerf f,t = min(perf p,f,t, 0.2), MidPerf f,t = min(perf p,f,t LowPerf f,t, 0.6), HighPerf f,t = Perf p,f,t LowPerf f,t MidPerf f,t. The regressions include time-fixed effects and the standard errors are adjusted for clustering at the fund level. Sialm, Starks, and Zhang Defined Contribution Pension Plans 10 / 42
20 Fund Flows Flow-Performance Relation Sialm, Starks, and Zhang Defined Contribution Pension Plans 11 / 42
21 Fund Flows Flow-Performance Relation Sialm, Starks, and Zhang Defined Contribution Pension Plans 12 / 42
22 Fund Flows Flow-Performance Sensitivity (Raw Perf; 1-Year) DC Flow Non-DC Flow Difference Low Perf (0.377) (0.142) (0.374) Mid Perf (0.086) (0.037) (0.090) High Perf (0.497) (0.180) (0.476) Log DC Size (0.017) (0.006) (0.016) Log Non-DC Size (0.016) (0.009) (0.018) Log Family Size (0.014) (0.007) (0.013) Log Age (0.024) (0.010) (0.022) Expense Ratio (0.551) (0.219) (0.511) Turnover (0.019) (0.008) (0.016) Volatility (0.870) (0.317) (0.857) Style Flow (0.324) (0.132) (0.295) Observations 3,851 3,851 3,851 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 13 / 42
23 Fund Flows Flow-Performance Sensitivity (Raw Perf; 1-Year) DC Flow Non-DC Flow Difference Low Perf (0.377) (0.142) (0.374) Mid Perf (0.086) (0.037) (0.090) High Perf (0.497) (0.180) (0.476) Log DC Size (0.017) (0.006) (0.016) Log Non-DC Size (0.016) (0.009) (0.018) Log Family Size (0.014) (0.007) (0.013) Log Age (0.024) (0.010) (0.022) Expense Ratio (0.551) (0.219) (0.511) Turnover (0.019) (0.008) (0.016) Volatility (0.870) (0.317) (0.857) Style Flow (0.324) (0.132) (0.295) Observations 3,851 3,851 3,851 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 14 / 42
24 Fund Flows Flow-Performance Sensitivity (Raw Perf; 1-Year) DC Flow Non-DC Flow Difference Low Perf (0.377) (0.142) (0.374) Mid Perf (0.086) (0.037) (0.090) High Perf (0.497) (0.180) (0.476) Log DC Size (0.017) (0.006) (0.016) Log Non-DC Size (0.016) (0.009) (0.018) Log Family Size (0.014) (0.007) (0.013) Log Age (0.024) (0.010) (0.022) Expense Ratio (0.551) (0.219) (0.511) Turnover (0.019) (0.008) (0.016) Volatility (0.870) (0.317) (0.857) Style Flow (0.324) (0.132) (0.295) Observations 3,851 3,851 3,851 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 15 / 42
25 Fund Flows Flow-Performance Sensitivity (Raw Perf; 1-Year) DC Flow Non-DC Flow Difference Low Perf (0.377) (0.142) (0.374) Mid Perf (0.086) (0.037) (0.090) High Perf (0.497) (0.180) (0.476) Log DC Size (0.017) (0.006) (0.016) Log Non-DC Size (0.016) (0.009) (0.018) Log Family Size (0.014) (0.007) (0.013) Log Age (0.024) (0.010) (0.022) Expense Ratio (0.551) (0.219) (0.511) Turnover (0.019) (0.008) (0.016) Volatility (0.870) (0.317) (0.857) Style Flow (0.324) (0.132) (0.295) Observations 3,851 3,851 3,851 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 16 / 42
26 Fund Flows Flow-Performance Sensitivity Sialm, Starks, and Zhang Defined Contribution Pension Plans 17 / 42
27 Robustness Tests Fund Flows The results remain robust using alternative samples or specifications: Different Performance Horizons 5-Year Perf Different Performance Measures Obj-Adj Style-Adj Carhart Different Performance Functional Forms Linear Cubic Inclusion of Size and Age Interactions Size Age Analysis of Flow Volatilities and Correlations Sample Selection: Entry and Exit Decisions Moments Selection Different Subsample Periods Subsamples Sialm, Starks, and Zhang Defined Contribution Pension Plans 18 / 42
28 Flow Decomposition Decomposition into Sponsor and Participant Flows Are the flow-performance results driven by plan sponsors or participants? Sponsors of 401(k) plans that have employer stock as an investment option need to annually file Form 11-K with the SEC (Pool, Sialm, and Stefanescu, 2013). We decompose the DC fund flows into: Sponsor Flows: Flows driven by the addition and the deletion decisions taken by the plan sponsors (i.e., employers). Participant Flows: Flows driven by the portfolio allocation decisions taken by the plan participants (i.e., employees). Sialm, Starks, and Zhang Defined Contribution Pension Plans 19 / 42
29 Flow Decomposition Decomposition into Sponsor and Participant Flows Are the flow-performance results driven by plan sponsors or participants? Sponsors of 401(k) plans that have employer stock as an investment option need to annually file Form 11-K with the SEC (Pool, Sialm, and Stefanescu, 2013). We decompose the DC fund flows into: Sponsor Flows: Flows driven by the addition and the deletion decisions taken by the plan sponsors (i.e., employers). Participant Flows: Flows driven by the portfolio allocation decisions taken by the plan participants (i.e., employees). Sialm, Starks, and Zhang Defined Contribution Pension Plans 19 / 42
30 Flow Decomposition DC Flow Decomposition Sialm, Starks, and Zhang Defined Contribution Pension Plans 20 / 42
31 Flow Decomposition DC Flow Decomposition Sialm, Starks, and Zhang Defined Contribution Pension Plans 21 / 42
32 Flow Decomposition Flow-Performance Sensitivity (Pensions & Investments) Total Flow Low Perf (0.399) Mid Perf (0.091) High Perf (0.482) Log Plan Size (0.010) Log Fund Size (0.021) Log Family Size (0.017) Log Age (0.033) Expense Ratio (0.528) Turnover (0.025) Volatility (1.056) Style Flow (0.341) Observations 2,815 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 22 / 42
33 Flow Decomposition Flow-Performance Sensitivity (Pensions & Investments) Total Flow Sponsor Flow Low Perf (0.399) (0.376) Mid Perf (0.091) (0.083) High Perf (0.482) (0.427) Log Plan Size (0.010) (0.009) Log Fund Size (0.021) (0.019) Log Family Size (0.017) (0.015) Log Age (0.033) (0.029) Expense Ratio (0.528) (0.478) Turnover (0.025) (0.025) Volatility (1.056) (0.919) Style Flow (0.341) (0.303) Observations 2,815 2,815 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 23 / 42
34 Flow Decomposition Flow-Performance Sensitivity (Pensions & Investments) Total Flow Sponsor Flow Participant Flow Low Perf (0.399) (0.376) (0.111) Mid Perf (0.091) (0.083) (0.024) High Perf (0.482) (0.427) (0.136) Log Plan Size (0.010) (0.009) (0.003) Log Fund Size (0.021) (0.019) (0.006) Log Family Size (0.017) (0.015) (0.005) Log Age (0.033) (0.029) (0.009) Expense Ratio (0.528) (0.478) (0.165) Turnover (0.025) (0.025) (0.007) Volatility (1.056) (0.919) (0.354) Style Flow (0.341) (0.303) (0.087) Observations 2,815 2,815 2,815 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 24 / 42
35 Performance Performance Predictability Do fund flows predict fund performance? Berk and Green (2004) derive in a rational model that flows should not predict future abnormal performance. The empirical evidence suggests that flows are smart in the short term (Gruber (1996) and Zheng (1999)) but dumb at longer horizons (Frazzini and Lamont (2008)). Sialm, Starks, and Zhang Defined Contribution Pension Plans 25 / 42
36 Performance Performance Predictability Do fund flows predict fund performance? Berk and Green (2004) derive in a rational model that flows should not predict future abnormal performance. The empirical evidence suggests that flows are smart in the short term (Gruber (1996) and Zheng (1999)) but dumb at longer horizons (Frazzini and Lamont (2008)). Sialm, Starks, and Zhang Defined Contribution Pension Plans 25 / 42
37 Performance Performance Predictability Do fund flows predict fund performance? Berk and Green (2004) derive in a rational model that flows should not predict future abnormal performance. The empirical evidence suggests that flows are smart in the short term (Gruber (1996) and Zheng (1999)) but dumb at longer horizons (Frazzini and Lamont (2008)). Sialm, Starks, and Zhang Defined Contribution Pension Plans 25 / 42
38 Performance Performance Predictability To investigate whether DC and Non-DC flows have differential predictability of fund returns, we run the following regression: Perf f,t = β 1 DCFlow f,t 1 + β 2 NonDCFlow f,t 1 + β 3 Perf f,t 1 + β 4 Size f,t 1 + β 5 FamSize f,t 1 + β 6 Age f,t 1 + β 7 Exp f,t 1 + β 8 Turn f,t 1 + β 9 DCRatio f,t 1 + β t + ɛ f,t We use various performance measures (raw returns, objective-code adjusted performance, style-adjusted performance, CAPM alpha, Fama-French alpha, Carhart alpha). The regressions include time-fixed effects and the standard errors are adjusted for clustering at the fund level. Sialm, Starks, and Zhang Defined Contribution Pension Plans 26 / 42
39 Performance Performance Predictability Performance Measures Raw Return Obj-Adj Ret Style-Adj Ret CAPM Alpha FF Alpha Carhart Alpha DC Flow (0.163) (0.160) (0.133) (0.144) (0.128) (0.121) Non-DC Flow (0.455) (0.436) (0.351) (0.405) (0.286) (0.276) Past Year Return (0.021) (0.022) (0.023) (0.019) (0.019) (0.018) Log Size (0.183) (0.179) (0.145) (0.169) (0.118) (0.115) Log Family Size (0.168) (0.162) (0.134) (0.153) (0.106) (0.103) Log Age (0.295) (0.292) (0.228) (0.261) (0.196) (0.184) Expense Ratio (0.408) (0.405) (0.327) (0.388) (0.253) (0.247) Turnover (0.231) (0.231) (0.205) (0.205) (0.162) (0.145) DC Ratio (0.818) (0.786) (0.633) (0.777) (0.516) (0.517) Observations 4,116 4,075 3,999 4,009 4,009 4,009 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 27 / 42
40 Performance Performance Predictability Performance Measures Raw Return Obj-Adj Ret Style-Adj Ret CAPM Alpha FF Alpha Carhart Alpha DC Flow (0.163) (0.160) (0.133) (0.144) (0.128) (0.121) Non-DC Flow (0.455) (0.436) (0.351) (0.405) (0.286) (0.276) Past Year Return (0.021) (0.022) (0.023) (0.019) (0.019) (0.018) Log Size (0.183) (0.179) (0.145) (0.169) (0.118) (0.115) Log Family Size (0.168) (0.162) (0.134) (0.153) (0.106) (0.103) Log Age (0.295) (0.292) (0.228) (0.261) (0.196) (0.184) Expense Ratio (0.408) (0.405) (0.327) (0.388) (0.253) (0.247) Turnover (0.231) (0.231) (0.205) (0.205) (0.162) (0.145) DC Ratio (0.818) (0.786) (0.633) (0.777) (0.516) (0.517) Observations 4,116 4,075 3,999 4,009 4,009 4,009 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 28 / 42
41 Performance Performance Predictability Performance Measures Raw Return Obj-Adj Ret Style-Adj Ret CAPM Alpha FF Alpha Carhart Alpha DC Flow (0.163) (0.160) (0.133) (0.144) (0.128) (0.121) Non-DC Flow (0.455) (0.436) (0.351) (0.405) (0.286) (0.276) Past Year Return (0.021) (0.022) (0.023) (0.019) (0.019) (0.018) Log Size (0.183) (0.179) (0.145) (0.169) (0.118) (0.115) Log Family Size (0.168) (0.162) (0.134) (0.153) (0.106) (0.103) Log Age (0.295) (0.292) (0.228) (0.261) (0.196) (0.184) Expense Ratio (0.408) (0.405) (0.327) (0.388) (0.253) (0.247) Turnover (0.231) (0.231) (0.205) (0.205) (0.162) (0.145) DC Ratio (0.818) (0.786) (0.633) (0.777) (0.516) (0.517) Observations 4,116 4,075 3,999 4,009 4,009 4,009 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 29 / 42
42 Conclusions Conclusions Our paper documents important differences across DC and non-dc flows: Is DC money sticky? DC fund flows have a more sensitive flow-performance relation than non-dc flows. Most of the sensitivity of DC money is driven by plan sponsors and not by plan participants. Is DC pension plan money discerning? DC fund flows do not have significant predictability for future performance, whereas non-dc flows predict future performance negatively. Sialm, Starks, and Zhang Defined Contribution Pension Plans 30 / 42
43 Conclusions Conclusions Our paper documents important differences across DC and non-dc flows: Is DC money sticky? DC fund flows have a more sensitive flow-performance relation than non-dc flows. Most of the sensitivity of DC money is driven by plan sponsors and not by plan participants. Is DC pension plan money discerning? DC fund flows do not have significant predictability for future performance, whereas non-dc flows predict future performance negatively. Sialm, Starks, and Zhang Defined Contribution Pension Plans 30 / 42
44 Appendix Flow-Performance Relation (Raw Perf; 5-Years) Back DC Flow Non-DC Flow Difference Low Perf (0.334) (0.166) (0.330) Mid Perf (0.082) (0.036) (0.083) High Perf (0.329) (0.154) (0.334) Log DC Size (0.018) (0.006) (0.016) Log Non-DC Size (0.014) (0.010) (0.016) Log Family Size (0.014) (0.007) (0.013) Log Age (0.024) (0.011) (0.024) Expense Ratio (0.509) (0.227) (0.481) Turnover (0.018) (0.011) (0.018) Volatility (0.963) (0.477) (0.951) Style Flow (0.319) (0.138) (0.300) Observations 3,249 3,249 3,249 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 31 / 42
45 Appendix Flow-Performance Relation (Obj-Adj Perf; 1-Year) Back DC Flow Non-DC Flow Difference Low Perf (0.389) (0.150) (0.394) Mid Perf (0.090) (0.036) (0.095) High Perf (0.473) (0.181) (0.455) Log DC Size (0.017) (0.006) (0.016) Log Non-DC Size (0.017) (0.009) (0.018) Log Family Size (0.014) (0.007) (0.013) Log Age (0.024) (0.010) (0.023) Expense Ratio (0.547) (0.218) (0.506) Turnover (0.019) (0.008) (0.016) Volatility (1.304) (0.468) (1.284) Style Flow (0.322) (0.132) (0.293) Observations 3,851 3,851 3,851 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 32 / 42
46 Appendix Flow-Performance Relation (Style-Adj Perf; 1-Year) Back DC Flow Non-DC Flow Difference Low Perf (0.420) (0.161) (0.448) Mid Perf (0.097) (0.035) (0.100) High Perf (0.470) (0.180) (0.475) Log DC Size (0.018) (0.006) (0.017) Log Non-DC Size (0.018) (0.009) (0.019) Log Family Size (0.015) (0.007) (0.013) Log Age (0.024) (0.010) (0.022) Expense Ratio (0.556) (0.221) (0.513) Turnover (0.019) (0.008) (0.017) Volatility (1.914) (0.506) (1.881) Style Flow (0.229) (0.089) (0.214) Observations 3,780 3,780 3,780 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 33 / 42
47 Appendix Flow-Performance Relation (Carhart-Adj Perf; 1-Year) Back DC Flow Non-DC Flow Difference Low Perf (0.406) (0.168) (0.426) Mid Perf (0.100) (0.037) (0.106) High Perf (0.504) (0.188) (0.474) Log DC Size (0.018) (0.006) (0.017) Log Non-DC Size (0.017) (0.009) (0.019) Log Family Size (0.015) (0.007) (0.014) Log Age (0.027) (0.010) (0.026) Expense Ratio (0.579) (0.226) (0.536) Turnover (0.020) (0.008) (0.018) Volatility (0.008) (0.003) (0.008) Style Flow (0.331) (0.131) (0.301) Observations 3,408 3,408 3,408 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 34 / 42
48 Appendix Linear Flow-Performance Relation (Raw Perf; 1-Year) DC Flow Non-DC Flow Difference Perf (0.059) (0.023) (0.058) Log DC Size (0.017) (0.006) (0.016) Log Non-DC Size (0.017) (0.009) (0.018) Log Family Size (0.014) (0.007) (0.013) Log Age (0.024) (0.010) (0.022) Expense Ratio (0.543) (0.216) (0.499) Turnover (0.019) (0.008) (0.016) Volatility (0.815) (0.314) (0.813) Style Flow (0.326) (0.132) (0.297) Constant (0.130) (0.058) (0.122) Observations 3,851 3,851 3,851 R-squared Back Sialm, Starks, and Zhang Defined Contribution Pension Plans 35 / 42
49 Appendix Cubic Flow-Performance Relation (Raw Perf; 1-Year) Back DC Flow Non-DC Flow Difference (Perf 0.5) (0.126) (0.053) (0.129) (Perf 0.5) (0.243) (0.084) (0.235) (Perf 0.5) (0.855) (0.331) (0.849) Log DC Size (0.017) (0.006) (0.016) Log Non-DC Size (0.016) (0.009) (0.018) Log Family Size (0.014) (0.007) (0.013) Log Age (0.024) (0.010) (0.022) Expense Ratio (0.556) (0.220) (0.515) Turnover (0.019) (0.008) (0.016) Volatility (0.871) (0.317) (0.863) Style Flow (0.324) (0.132) (0.295) Observations 3,851 3,851 3,851 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 36 / 42
50 Appendix Flow-Performance Relation (Raw Perf; 1-Year) DC Flow Non-DC Flow Difference DC Flow Non-DC Flow Difference Low Perf (0.630) (0.223) (0.649) (0.473) (0.196) (0.462) Mid Perf (0.141) (0.051) (0.148) (0.111) (0.053) (0.113) High Perf (0.733) (0.297) (0.717) (0.650) (0.208) (0.625) Log DC Size (0.028) (0.008) (0.028) (0.017) (0.008) (0.016) Log Non-DC Size (0.029) (0.013) (0.032) (0.015) (0.011) (0.017) Log Family Size (0.023) (0.010) (0.022) (0.015) (0.008) (0.014) Log Age (0.034) (0.013) (0.034) (0.032) (0.015) (0.031) Expense Ratio (0.815) (0.331) (0.772) (0.673) (0.284) (0.619) Turnover (0.027) (0.009) (0.024) (0.023) (0.013) (0.025) Volatility (1.104) (0.354) (1.110) (1.803) (0.726) (1.756) Style Flow (0.596) (0.186) (0.612) (0.375) (0.171) (0.345) Observations 1,759 1,759 1,759 2,092 2,092 2,092 R-squared Back Sialm, Starks, and Zhang Defined Contribution Pension Plans 37 / 42
51 Appendix Flow-Performance Relation with Size Interactions (Raw Perf; 1-Year) Back DC Flow Non-DC Flow Difference Low Perf (0.370) (0.151) (0.372) Mid Perf (0.089) (0.038) (0.092) High Perf (0.418) (0.159) (0.414) Low Perf x Log DC Size (0.218) (0.091) (0.223) Mid Perf x Log DC Size (0.083) (0.034) (0.081) High Perf x Log DC Size (0.389) (0.138) (0.379) Low Perf x Log Non-DC Size (0.307) (0.165) (0.313) Mid Perf x Log Non-DC Size (0.074) (0.047) (0.085) High Perf x Log Non-DC Size (0.451) (0.221) (0.481) (...) Observations 3,851 3,851 3,851 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 38 / 42
52 Appendix Flow-Performance Relation with Age Interactions (Raw Perf; 1-Year) DC Flow Non-DC Flow Difference Low Perf (0.381) (0.141) (0.379) Mid Perf (0.092) (0.039) (0.095) High Perf (0.489) (0.171) (0.476) Low Perf x Log Age (0.445) (0.146) (0.458) Mid Perf x Log Age (0.135) (0.046) (0.141) High Perf x Log Age (0.686) (0.279) (0.641) (...) Observations 3,851 3,851 3,851 R-squared Back Sialm, Starks, and Zhang Defined Contribution Pension Plans 39 / 42
53 Appendix Fund Flow Variability and Autocorrelation Standard Deviation of Flows Autocorrelation of Flows Constant (0.012) (0.023) (0.023) (0.029) DC Indicator (0.033) (0.031) (0.026) (0.034) Log Size (0.014) (0.011) Log Family Size (0.012) (0.014) Log Age (0.026) (0.022) Expense Ratio (0.460) (0.501) Turnover (0.014) (0.011) Observations 1, , R-Squared Back Sialm, Starks, and Zhang Defined Contribution Pension Plans 40 / 42
54 Appendix Multinomial Logit for Sample Entry and Exit Decisions Exit Entry Perf (0.221) (0.203) Log Size (0.060) (0.063) Log Family Size (0.058) (0.057) Log Age (0.107) (0.099) Expenses (1.828) (1.607) Turnover (0.058) (0.055) Volatility (2.734) (2.906) Style Flow (1.289) (1.169) Observations 5,006 Back Sialm, Starks, and Zhang Defined Contribution Pension Plans 41 / 42
55 Appendix Flow-Performance Sensitivity (11-K Sample) Total Flow Sponsor Flow Participant Flow Low Perf (0.299) (0.274) (0.100) Mid Perf (0.068) (0.062) (0.021) High Perf (0.324) (0.291) (0.101) Log Plan Size (0.006) (0.005) (0.002) Log Fund Size (0.012) (0.011) (0.004) Log Family Size (0.007) (0.006) (0.003) Log Age (0.023) (0.021) (0.007) Expense Ratio (0.420) (0.353) (0.142) Turnover (0.018) (0.018) (0.005) Volatility (0.746) (0.647) (0.254) Style Flow (0.280) (0.254) (0.082) Observations 8,268 8,268 8,268 R-squared Sialm, Starks, and Zhang Defined Contribution Pension Plans 42 / 42
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