1 00:00:02,480 --> 00:00:26,960 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:28,280 --> 00:00:33,080 Speaker 2: Have you taken full advantage of automating your investments, You 3 00:00:33,120 --> 00:00:38,680 Speaker 2: can improve your returns, reduce emotional decision making, and generally 4 00:00:38,960 --> 00:00:42,920 Speaker 2: end up with better results simply by putting your investing 5 00:00:43,400 --> 00:00:47,040 Speaker 2: on autopilot. To help us figure out how, let's bring 6 00:00:47,080 --> 00:00:51,120 Speaker 2: in Jeffrey Pattak. He's managing director at morning Star. Previously 7 00:00:51,159 --> 00:00:53,720 Speaker 2: he was the chief rating officer there. He's been with 8 00:00:53,880 --> 00:00:57,400 Speaker 2: morning Star since two thousand and two, and his research 9 00:00:57,480 --> 00:01:03,400 Speaker 2: has shown features like auto enrollment or contribution increases, default investments, 10 00:01:03,680 --> 00:01:09,200 Speaker 2: and target date funds enable investors to bypass common pitfalls 11 00:01:09,319 --> 00:01:14,440 Speaker 2: of market timing and emotional trading. So, Jeffrey, let's define 12 00:01:14,440 --> 00:01:18,560 Speaker 2: the automation features you're discussing in your research, things like 13 00:01:18,600 --> 00:01:22,920 Speaker 2: steady paycheck deductions and regularly balancing. How can an investor 14 00:01:23,000 --> 00:01:23,640 Speaker 2: set that up? 15 00:01:25,200 --> 00:01:29,240 Speaker 3: Sure, so you know, it's relatively straightforward. If you're working 16 00:01:29,280 --> 00:01:33,600 Speaker 3: with a brokerage platform to enable those types of features. 17 00:01:33,640 --> 00:01:36,760 Speaker 3: In some other context, like a retirement plan, it might 18 00:01:36,840 --> 00:01:39,600 Speaker 3: be standard plan features. In fact, you might be defaulted 19 00:01:39,680 --> 00:01:43,119 Speaker 3: into them, and so away you go. And so it's 20 00:01:43,200 --> 00:01:47,120 Speaker 3: well within our reaches investors either to switch these features 21 00:01:47,160 --> 00:01:50,040 Speaker 3: on at our own election or to be opted into 22 00:01:50,080 --> 00:01:52,720 Speaker 3: them as we would be in a retirement plan. 23 00:01:53,560 --> 00:01:57,640 Speaker 2: So explain to me the difference between auto enrollment and 24 00:01:57,840 --> 00:02:00,600 Speaker 2: auto escalation for sure. 25 00:02:00,720 --> 00:02:05,040 Speaker 3: Yeah, So auto enrollment the notion is your auto enrolled 26 00:02:05,640 --> 00:02:10,000 Speaker 3: you become a participant in the retirement plan. Auto escalation 27 00:02:10,240 --> 00:02:13,760 Speaker 3: is you're in the plan and then your contribution rate 28 00:02:13,960 --> 00:02:17,840 Speaker 3: is steadily increased at a predetermined level. And so you know, 29 00:02:17,880 --> 00:02:21,760 Speaker 3: one is about being in participating, the second is about 30 00:02:21,840 --> 00:02:24,679 Speaker 3: the extent to which you are participating. Both valuable. 31 00:02:25,400 --> 00:02:30,760 Speaker 2: So your research has found automatic investing reduces bad investor outcomes, 32 00:02:30,880 --> 00:02:36,119 Speaker 2: reduces behavioral errors, promotes consistency. Sounds a little too good 33 00:02:36,120 --> 00:02:38,880 Speaker 2: to be true. What sort of data have you found 34 00:02:38,919 --> 00:02:43,720 Speaker 2: that supports automation leading to improved investor returns? 35 00:02:44,280 --> 00:02:47,280 Speaker 3: Yeah, real good question. So we it's a bit inferential 36 00:02:47,360 --> 00:02:50,320 Speaker 3: because we're not a brokerage platform and so we don't 37 00:02:50,360 --> 00:02:53,480 Speaker 3: have sort of the tick data. Nevertheless, we can look 38 00:02:53,480 --> 00:02:55,600 Speaker 3: at the types of funds and where they tend to 39 00:02:55,639 --> 00:02:58,880 Speaker 3: be used and whether automation is common in those settings, 40 00:02:59,240 --> 00:03:01,160 Speaker 3: you know, and draw some conclusions. And one of the 41 00:03:01,200 --> 00:03:03,720 Speaker 3: more striking findings from our research this is the mind. 42 00:03:03,760 --> 00:03:06,840 Speaker 3: The gap study that we conduct is that investors in 43 00:03:06,880 --> 00:03:10,040 Speaker 3: allocation funds, the most popular version of which are target 44 00:03:10,120 --> 00:03:13,040 Speaker 3: date funds, they do the best job of capturing their 45 00:03:13,040 --> 00:03:16,559 Speaker 3: funds total returns. That is, they experience the fewest frictions 46 00:03:17,040 --> 00:03:20,519 Speaker 3: related to the timing and magnitude of their transactions over time. 47 00:03:20,560 --> 00:03:22,640 Speaker 3: And what do we know about target date funds. We 48 00:03:22,720 --> 00:03:25,120 Speaker 3: know that people are commonly defaulted into them, that they 49 00:03:25,320 --> 00:03:28,520 Speaker 3: regularly invest in them just as part of their regular 50 00:03:29,080 --> 00:03:33,080 Speaker 3: payroll deduction that takes place, and so they're kind of 51 00:03:33,120 --> 00:03:37,080 Speaker 3: the signal example of automation. Then take some other examples 52 00:03:37,200 --> 00:03:40,200 Speaker 3: of fun types that you wouldn't find in a retirement plan, 53 00:03:40,320 --> 00:03:43,000 Speaker 3: Like maybe the quintessential example would be something like a 54 00:03:43,080 --> 00:03:46,480 Speaker 3: sector fund or a thematic fund. You're typically not going 55 00:03:46,480 --> 00:03:48,840 Speaker 3: to find those in a plan lineup. We found those 56 00:03:48,880 --> 00:03:51,040 Speaker 3: that have some of the widest gaps, and why is 57 00:03:51,080 --> 00:03:53,680 Speaker 3: that they're not used within that gilded cage of a 58 00:03:53,720 --> 00:03:58,800 Speaker 3: retirement plan. Furthermore, they might be more subject to discretionary 59 00:03:58,960 --> 00:04:01,960 Speaker 3: ad hoc off site go trading decisions, where there might 60 00:04:01,960 --> 00:04:04,800 Speaker 3: be a greater propensity to trade an emotion than would 61 00:04:04,800 --> 00:04:07,400 Speaker 3: be the case with something like a target date fund. 62 00:04:08,040 --> 00:04:12,080 Speaker 2: And it sounds like the key advantage of automation is 63 00:04:12,520 --> 00:04:17,640 Speaker 2: it tends to reduce unnecessary trading and it also reduces 64 00:04:17,760 --> 00:04:21,640 Speaker 2: the emotional responses to just ordinary market volatility. 65 00:04:21,680 --> 00:04:24,360 Speaker 3: It does, yeah, it, you know it. Basically, it's the 66 00:04:24,400 --> 00:04:29,080 Speaker 3: best kind of inertia. I would say, we know that, 67 00:04:29,320 --> 00:04:33,440 Speaker 3: you know, market bobbles can be unnerving to investors, and 68 00:04:33,600 --> 00:04:35,960 Speaker 3: left to their own devices, they might make a change 69 00:04:36,000 --> 00:04:39,400 Speaker 3: of their allocation. They could elect to remove capital from 70 00:04:39,480 --> 00:04:42,080 Speaker 3: the markets, and we know how harmful that can be 71 00:04:42,160 --> 00:04:45,760 Speaker 3: to their long term compounding power. Whereas in these settings, 72 00:04:45,839 --> 00:04:50,279 Speaker 3: because they just continue to mechanically add to their investments, 73 00:04:50,680 --> 00:04:53,320 Speaker 3: and those investments in turn, you know, take care of 74 00:04:53,360 --> 00:04:56,120 Speaker 3: some of the mundane tasks like rebalancing and adjusting the 75 00:04:56,160 --> 00:04:59,400 Speaker 3: asset mix, they just get on with it. And I 76 00:04:59,400 --> 00:05:02,159 Speaker 3: think that worked to their benefit over the long term, 77 00:05:02,160 --> 00:05:04,359 Speaker 3: and certainly our research seems to bear that out. 78 00:05:04,600 --> 00:05:09,560 Speaker 2: So we talked about the investor gap between their actual 79 00:05:09,600 --> 00:05:13,760 Speaker 2: performance and their funds performance. When we're looking at automated 80 00:05:13,839 --> 00:05:18,120 Speaker 2: target date funds or automated allocation funds, how measurable is 81 00:05:18,160 --> 00:05:22,080 Speaker 2: the gap between those and people who kind of self 82 00:05:22,120 --> 00:05:23,240 Speaker 2: manage that allocation. 83 00:05:24,080 --> 00:05:27,240 Speaker 3: Yeah, So with allocation funds, the largest subset of which 84 00:05:27,240 --> 00:05:29,680 Speaker 3: are target date funds, we found almost no gap. It 85 00:05:29,720 --> 00:05:33,839 Speaker 3: was basically point one percentage points per year. Then when 86 00:05:33,839 --> 00:05:36,720 Speaker 3: you focus on every other type of fund, we found 87 00:05:36,800 --> 00:05:39,919 Speaker 3: that the gap was around one point two percentage points 88 00:05:39,960 --> 00:05:42,760 Speaker 3: per year. Now, yes, among those other types of funds, 89 00:05:42,800 --> 00:05:45,000 Speaker 3: it is quite possible that some are using them in 90 00:05:45,040 --> 00:05:48,440 Speaker 3: an automated fashion. Maybe they have some sort of investment 91 00:05:48,440 --> 00:05:51,480 Speaker 3: plan that they've set up, or they've otherwise mechanized the process. 92 00:05:51,640 --> 00:05:53,960 Speaker 3: But I think it stands the reason that for a 93 00:05:54,000 --> 00:05:57,880 Speaker 3: fairly large subset of that capital, it's being invested in 94 00:05:57,920 --> 00:06:01,720 Speaker 3: a more discretionary fashion. You can see the difference between 95 00:06:01,720 --> 00:06:03,880 Speaker 3: the two of those. It amounts to around one point 96 00:06:03,880 --> 00:06:07,800 Speaker 3: one percentage points annually of return that's being foregone effectively. 97 00:06:08,320 --> 00:06:12,800 Speaker 2: So what are the automation features that have consistently good 98 00:06:12,839 --> 00:06:14,760 Speaker 2: benefits for investors? 99 00:06:15,600 --> 00:06:19,600 Speaker 3: So I would say that probably the biggie is auto enrollment. 100 00:06:20,040 --> 00:06:23,440 Speaker 3: We don't have as much data that we collect, but 101 00:06:23,560 --> 00:06:26,760 Speaker 3: there are others like Vanger puts out a terrific annual 102 00:06:26,800 --> 00:06:30,279 Speaker 3: study called How America Saves. In the most recent edition, 103 00:06:30,360 --> 00:06:33,080 Speaker 3: they reported a sixty one percent of the plans they 104 00:06:33,160 --> 00:06:36,680 Speaker 3: service as clients at auto enrollment, and two thirds of 105 00:06:36,720 --> 00:06:41,479 Speaker 3: those plans that offered auto enrollment also offered auto escalation. 106 00:06:42,240 --> 00:06:44,760 Speaker 3: And then of those that auto and roll, ninety eight 107 00:06:44,800 --> 00:06:47,160 Speaker 3: percent of them are defaulted into a target date and 108 00:06:47,520 --> 00:06:50,960 Speaker 3: striking me, the average participant holds only two funds, So 109 00:06:51,000 --> 00:06:53,960 Speaker 3: it gives a sense of the reach of automation in 110 00:06:54,000 --> 00:06:56,719 Speaker 3: our retirement system. If I had to choose between the 111 00:06:56,720 --> 00:07:00,160 Speaker 3: two of those, auto enrollment versus auto escalation, it's a 112 00:07:00,160 --> 00:07:02,360 Speaker 3: bit of a false binary, but all the same, I 113 00:07:02,360 --> 00:07:06,000 Speaker 3: would say auto enrollment is far far more important. Why 114 00:07:06,120 --> 00:07:09,159 Speaker 3: is that. It's because we want people participating so that 115 00:07:09,200 --> 00:07:11,840 Speaker 3: they can compound their wealth. Even if they were to 116 00:07:11,920 --> 00:07:15,360 Speaker 3: experience a return gap, we would rather that they get some, 117 00:07:15,640 --> 00:07:18,400 Speaker 3: if not all, of their fund's returns and auto enrollment 118 00:07:18,400 --> 00:07:19,240 Speaker 3: and cease to that. 119 00:07:19,880 --> 00:07:23,200 Speaker 2: Yeah, before the default settings, there were stories were rife 120 00:07:23,240 --> 00:07:26,240 Speaker 2: about people working at places for years and the money 121 00:07:26,280 --> 00:07:28,520 Speaker 2: just piled up in cash and did nothing. It's kind 122 00:07:28,520 --> 00:07:31,559 Speaker 2: of it's kind of crazy. So, but that that leads 123 00:07:31,560 --> 00:07:35,320 Speaker 2: to an obvious question. How widespread has the adoption of 124 00:07:35,360 --> 00:07:39,880 Speaker 2: animation been in the various retirement ecosystems that are out there. 125 00:07:40,440 --> 00:07:43,240 Speaker 3: It's become very widespread, as you know, as I might 126 00:07:43,280 --> 00:07:47,320 Speaker 3: have mentioned before, you're talking about two thirds of plans 127 00:07:47,320 --> 00:07:51,960 Speaker 3: that offer auto enrollment and and then also a very 128 00:07:52,000 --> 00:07:57,120 Speaker 3: significant number auto escalation as well. And you know, I 129 00:07:57,200 --> 00:07:59,760 Speaker 3: think that one other thing from the Vanguard study that 130 00:07:59,760 --> 00:08:02,280 Speaker 3: I've mentioned before that I found quite telling. They found 131 00:08:02,280 --> 00:08:05,880 Speaker 3: that one percent of target date fund investors transacted last 132 00:08:05,960 --> 00:08:08,960 Speaker 3: year that'd be twenty twenty four, compared to eleven percent 133 00:08:09,000 --> 00:08:11,960 Speaker 3: of investors and other types of funds. And so it 134 00:08:12,120 --> 00:08:15,120 Speaker 3: just gives a sense not only the breadth of automation 135 00:08:15,200 --> 00:08:17,920 Speaker 3: that's taking place here, but also some of the benefits 136 00:08:17,920 --> 00:08:20,720 Speaker 3: it confers and tamping down transacting that we see within 137 00:08:20,760 --> 00:08:21,400 Speaker 3: these plans. 138 00:08:21,960 --> 00:08:26,200 Speaker 2: Any particular demographic groups stand to benefit more or less 139 00:08:26,200 --> 00:08:28,200 Speaker 2: from automating these strategies. 140 00:08:28,800 --> 00:08:31,160 Speaker 3: That is a great question. Was it was one of 141 00:08:31,160 --> 00:08:35,120 Speaker 3: the most eye opening findings from that study. They found 142 00:08:35,360 --> 00:08:42,120 Speaker 3: that auto enrollment disproportionately benefited younger and lower earning participants. 143 00:08:42,640 --> 00:08:46,640 Speaker 3: So you were really talking about a quantum among those cohorts, 144 00:08:47,040 --> 00:08:49,000 Speaker 3: and I think that's critical because we want to get 145 00:08:49,000 --> 00:08:53,040 Speaker 3: those folks into plans. You know, in some senses, you 146 00:08:53,080 --> 00:08:57,359 Speaker 3: were talking about socioeconomic demographics that may be more vulnerable 147 00:08:58,080 --> 00:09:01,719 Speaker 3: that otherwise wouldn't have the opportunity to compound wealth, and 148 00:09:01,760 --> 00:09:04,560 Speaker 3: the way we'd like to see, auto enrollment has helped 149 00:09:04,600 --> 00:09:07,719 Speaker 3: to ensure that those gaps get closed. And so I 150 00:09:07,800 --> 00:09:10,520 Speaker 3: think that that's a really really telling and encouraging finding 151 00:09:10,559 --> 00:09:11,280 Speaker 3: from their study. 152 00:09:11,760 --> 00:09:16,679 Speaker 2: What about non qualified plans, portfolios outside of for one 153 00:09:16,760 --> 00:09:20,400 Speaker 2: ks or eras, what can we do to automate those 154 00:09:20,440 --> 00:09:21,559 Speaker 2: sort of holdings? 155 00:09:22,640 --> 00:09:24,800 Speaker 3: Yeah, so I think to the extent, so one, I 156 00:09:24,840 --> 00:09:26,319 Speaker 3: think that one thing that you can do is you 157 00:09:26,360 --> 00:09:29,920 Speaker 3: can set up sort of an auto investment plan, very 158 00:09:29,920 --> 00:09:32,120 Speaker 3: similar to the kind of setup that you would find 159 00:09:32,120 --> 00:09:35,560 Speaker 3: in a retirement plan. Put that on autopilot, and then 160 00:09:35,600 --> 00:09:38,120 Speaker 3: I would say to the extent that you can automate 161 00:09:38,160 --> 00:09:42,120 Speaker 3: your investments. I may have mentioned in other settings that 162 00:09:42,160 --> 00:09:44,400 Speaker 3: it's important to have a plan first of all, But 163 00:09:44,440 --> 00:09:46,760 Speaker 3: then once you've got that plan, you know, maybe it's 164 00:09:46,760 --> 00:09:49,600 Speaker 3: an allocation fund, a target day fund, or a target 165 00:09:49,679 --> 00:09:53,520 Speaker 3: risk fund where you're fixing the percentage of equity, fixed income, 166 00:09:53,559 --> 00:09:56,320 Speaker 3: and other asset classes, and that obviates the need for 167 00:09:56,400 --> 00:09:59,960 Speaker 3: you to go in and make adjustments on your own. Automate, automate, automate. 168 00:10:00,480 --> 00:10:02,440 Speaker 3: I think those are the key things to ensure that 169 00:10:02,440 --> 00:10:04,520 Speaker 3: we capture as much of our funds, total returns, and 170 00:10:04,559 --> 00:10:05,559 Speaker 3: compound as we can. 171 00:10:06,080 --> 00:10:08,840 Speaker 2: So there are a lot of new digital investing tools 172 00:10:08,960 --> 00:10:13,400 Speaker 2: and AI is starting to have an impact on various strategies. 173 00:10:13,880 --> 00:10:16,160 Speaker 2: What do you think is going to have a powerful 174 00:10:16,160 --> 00:10:20,040 Speaker 2: impact on both automation and future investor outcomes? 175 00:10:21,000 --> 00:10:23,880 Speaker 3: Yep, And so I think, you know, I'm an avid 176 00:10:23,960 --> 00:10:27,520 Speaker 3: user of AI. I know how beneficial it's been in 177 00:10:27,600 --> 00:10:31,240 Speaker 3: my own work, making me more productive. I think that 178 00:10:31,320 --> 00:10:36,200 Speaker 3: it confer the same sorts of benefits to investors, you know, 179 00:10:36,240 --> 00:10:39,080 Speaker 3: maybe helping them to formulate a plan, maybe figuring out 180 00:10:39,120 --> 00:10:42,600 Speaker 3: the optimal way for them to allocate their assets, you know, 181 00:10:42,679 --> 00:10:46,800 Speaker 3: and otherwise sort of keeping them to you know, sort 182 00:10:46,800 --> 00:10:50,120 Speaker 3: of the goals that they've set consistent with their risk parameters. 183 00:10:50,600 --> 00:10:52,040 Speaker 3: You know. The other side of it is it can 184 00:10:52,080 --> 00:10:55,120 Speaker 3: engender over confidence. You know, maybe we feel like we've 185 00:10:55,160 --> 00:10:59,400 Speaker 3: got the capacity to make trading decisions that maybe really 186 00:10:59,480 --> 00:11:01,520 Speaker 3: are outside of our circle of competence, and so we 187 00:11:01,600 --> 00:11:03,240 Speaker 3: just want to make sure, like so many of these 188 00:11:03,240 --> 00:11:06,200 Speaker 3: other tools and resources we have available to us, we 189 00:11:06,360 --> 00:11:08,640 Speaker 3: use it in a way that advances our goals and 190 00:11:08,679 --> 00:11:12,120 Speaker 3: we don't get carried away in an overconfident way, you know, 191 00:11:12,160 --> 00:11:15,000 Speaker 3: sort of an impulse that we're you know, maybe all 192 00:11:15,920 --> 00:11:18,320 Speaker 3: you know, likely to succumb to from time to time. 193 00:11:19,160 --> 00:11:23,640 Speaker 2: And for either an individual investor or perhaps a financial advisor, 194 00:11:24,280 --> 00:11:27,800 Speaker 2: if they're seeking to automate investments, what are the most 195 00:11:27,840 --> 00:11:31,200 Speaker 2: important factors they should be thinking about when they're either 196 00:11:31,240 --> 00:11:35,199 Speaker 2: selecting a platform or a tool to use to help automate. 197 00:11:35,920 --> 00:11:38,960 Speaker 3: That's a great question. So, you know, one of the 198 00:11:38,960 --> 00:11:42,520 Speaker 3: corollaries to automating, at least in a retirement plan context, 199 00:11:42,559 --> 00:11:43,920 Speaker 3: is it is a little bit of an all in 200 00:11:44,000 --> 00:11:47,360 Speaker 3: one decision. So typically if the target DAED fund is 201 00:11:47,360 --> 00:11:50,160 Speaker 3: going to be offered by a single provider, and so 202 00:11:50,520 --> 00:11:53,760 Speaker 3: what that means is that we want to make sure that, 203 00:11:54,200 --> 00:11:58,920 Speaker 3: you know, we're feeling very confident about that organization's culture, 204 00:11:59,320 --> 00:12:03,240 Speaker 3: about its staying power, about its overall investor centricity. Those 205 00:12:03,240 --> 00:12:05,840 Speaker 3: aren't necessarily easy things to tease out, but I think 206 00:12:05,880 --> 00:12:07,959 Speaker 3: a little bit of research can tell you whether or 207 00:12:08,000 --> 00:12:09,600 Speaker 3: not this is a firm that is a certain kind 208 00:12:09,640 --> 00:12:12,360 Speaker 3: of pedigree, a certain kind of reputation. Look at the 209 00:12:12,400 --> 00:12:17,640 Speaker 3: fees that at levees. Fees speak volumes about organizational fibers. 210 00:12:17,160 --> 00:12:17,880 Speaker 2: So to speak. 211 00:12:18,240 --> 00:12:20,240 Speaker 3: And I think if you can go through and satisfy 212 00:12:20,280 --> 00:12:22,839 Speaker 3: yourself that this is an organization that has my best 213 00:12:22,840 --> 00:12:25,800 Speaker 3: interest at heart, that is levying a fair fee, and 214 00:12:25,920 --> 00:12:27,719 Speaker 3: is likely to be around for the years to come 215 00:12:27,760 --> 00:12:30,959 Speaker 3: over which I'm looking to compound. Those are all good 216 00:12:31,040 --> 00:12:33,520 Speaker 3: facts and I think that they portend well for you 217 00:12:33,840 --> 00:12:36,600 Speaker 3: to succeed in capturing your fund's return and compound some 218 00:12:36,679 --> 00:12:37,720 Speaker 3: real wealth over time. 219 00:12:38,360 --> 00:12:41,840 Speaker 2: So to wrap up, there are lots of automated tools 220 00:12:41,880 --> 00:12:46,800 Speaker 2: that you could use, platforms, specific allocation funds, other things 221 00:12:46,800 --> 00:12:52,000 Speaker 2: you can do to improve your returns, reduce emotional decision making, 222 00:12:52,440 --> 00:12:56,840 Speaker 2: and generally end up with better performance simply by putting 223 00:12:56,840 --> 00:13:02,839 Speaker 2: your investments on autopilot. Barry whittlets, you're listening to Bloombergs 224 00:13:02,840 --> 00:13:04,640 Speaker 2: at the money. 225 00:13:05,600 --> 00:13:07,760 Speaker 1: This is a desist. 226 00:13:09,040 --> 00:13:14,800 Speaker 2: She said, Oh baby, do it man,