Popular in Professional

Architecture

Connecting Cities and Nature

University of Virginia Timothy Beatley

Human Rights Law

Human Rights and Corruption: Problems and Potential of Individualising a Systemic Problem

Max Planck Institute Anne Peters

Finance

Chinese Exchange Rate Policy: Lessons for Global Investors

University of California San Diego Michael Melvin

Law

A statistical approach to law school citation rankings

University of Virginia Joshua Fischman

Education

Firm Performance Pay as Insurance against Promotion Risk

Stockholm School of Economics Alvin Chen

Constitutional Law

What is constitutional ideology?

University of Oxford Nick Barber

The Dodd-Frank Act and Hedge Fund Operational Risk

View more insights in Professional - Finance

View more figures from Yale University

View the page of William N. Goetzmann

Read the Study

Next in Finance

Relationship Lending: That Ship Has Not Sailed for Community Banks

Dmytro Holod | Stony Brook University

FinTech Credit and Entrepreneurial Growth

Harald Hau | University of Geneva

How did the 2011 post-Dodd-Frank changes to Form ADV impact hedge fund disclosures, operational risk prediction, and investor decision-making?

Image courtesy of interviewee. December 4, 2024

Log-in or Sign-up to Faculti
Currently viewing this subject insight as a guest. You have insight(s) remaining for this month. Login to view 8000+ figures on the platform.

Generating Workspace...

Export as PDF

This video discusses the regulatory environment surrounding hedge funds following major financial scandals, particularly the Bernard Madoff case, which highlighted the risks of operational fraud. It emphasizes the need for enhanced information collection from hedge funds, a response shaped by the Dodd-Frank Act aimed at preventing similar disasters. A research team examined data collected post-Dodd-Frank to assess its efficacy in predicting adverse outcomes in hedge funds.

Key findings show that certain variables, such as relationships with external investors and connections to companies in futures and swap markets, can help predict the likelihood of regulatory violations or fund failures. The study utilized machine learning techniques to filter through extensive data, concluding that the SEC's updated information requirements were valuable in enhancing predictive capabilities. The results indicate that there are significant implications for systemic risk in the hedge fund sector, justifying continued regulatory oversight.

Additionally, the text touches on the potential of using non-numeric data, like text analysis and personal interviews, in enhancing fraud detection and understanding behaviors that could lead to exploitation of investors. Overall, there's a strong recommendation for utilizing both quantitative and qualitative data to protect investors and improve trust in financial systems.

Language:

Export as PDF

Export as PDF
Here are five key points from the video:

1. **Bernard Madoff and Operational Risk**: The video highlights the Madoff scandal as a significant case that brought attention to the potential for operational risk in large funds, emphasizing the importance of identifying and avoiding fraud in investment opportunities.

2. **Dodd-Frank Act and Information Gathering**: The Dodd-Frank Act was introduced after the financial crisis to enhance transparency in the hedge fund industry. It required more extensive data collection from hedge funds to assist in predicting negative outcomes, raising questions about the balance between the burden of data reporting and the benefits of the information collected.

3. **Predictive Analysis using Machine Learning**: The authors used machine learning techniques, specifically the Lasso method, to analyze extensive variables collected post-Dodd-Frank. This approach aimed to identify key predictors of potential adverse outcomes for investment funds.

4. **Importance of Ownership and External Relationships**: The analysis revealed that certain ownership structures and external relationships with related firms could increase the risk of adverse outcomes, such as regulatory violations and fund failures, hinting at the need for careful scrutiny of hedge fund partnerships and affiliations.

5. **Trust and Predictability in Hedge Funds**: The study found that historical information, regulatory sanctions, and new reporting variables introduced by the SEC significantly aid in predicting fund outcomes. The data suggested that understanding the convideo of hedge funds and their regulatory history could improve investor trust and decision-making in the financial markets.

Export as PDF

X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:90000

1
00:00:00.185 --> 00:00:05.045
One easy thing to point to is Bernard Madoff, eh,

2
00:00:05.385 --> 00:00:09.845
one of the biggest scandals in financial history, maybe

3
00:00:09.845 --> 00:00:11.205
of the last 30 or 40 years.

4
00:00:11.745 --> 00:00:14.725
It alerted everybody to the possibility that,

5
00:00:14.795 --> 00:00:19.645
even really large funds, might be subject to,

6
00:00:20.085 --> 00:00:21.965
euphemistically called Operational Risk.

7
00:00:22.025 --> 00:00:24.405
But in that case, it was a massive fraud.

8
00:00:24.945 --> 00:00:28.445
And, that's something that the everybody would like

9
00:00:28.445 --> 00:00:30.485
to avoid when they're investing.

10
00:00:30.945 --> 00:00:35.725
And, we, we focused on how to

11
00:00:36.385 --> 00:00:40.845
use information to, to help investors,

12
00:00:41.095 --> 00:00:42.885
avoid the next Bernie Madoff scandal.

13
00:00:43.025 --> 00:00:46.685
The Securities Exchange Commission has to balance between

14
00:00:47.715 --> 00:00:50.525
gathering a lot of information, which is expensive,

15
00:00:50.585 --> 00:00:53.565
and I guess you'd say it's, it's onerous for the,

16
00:00:53.565 --> 00:00:55.205
investment companies to provide.

17
00:00:56.225 --> 00:00:59.725
Uh, and, and then also thinking about what's the benefit

18
00:01:00.195 --> 00:01:03.565
that all of this new information's gonna be to investors.

19
00:01:04.465 --> 00:01:08.925
And so, that, that trade off is what, the,

20
00:01:09.265 --> 00:01:13.365
the Dodd-Frank Act, which is an act that, was,

21
00:01:14.465 --> 00:01:15.885
you know, something that

22
00:01:16.055 --> 00:01:19.965
after the financial crisis was just, was put in place to try

23
00:01:19.965 --> 00:01:22.005
and avoid some of the worst problems.

24
00:01:22.705 --> 00:01:24.965
Um, that act said,

25
00:01:24.975 --> 00:01:28.525
let's collect a lot more information from hedge funds.

26
00:01:29.465 --> 00:01:31.565
And, and why,

27
00:01:31.565 --> 00:01:34.685
because we would like to avoid, some

28
00:01:34.685 --> 00:01:36.445
of the disasters that we had seen.

29
00:01:37.425 --> 00:01:41.245
And, so, the question arises,

30
00:01:41.465 --> 00:01:43.885
are they just collecting too much information?

31
00:01:43.985 --> 00:01:47.005
Is it onerous? Uh, is it redundant,

32
00:01:47.065 --> 00:01:48.325
or is it useful for

33
00:01:48.845 --> 00:01:52.925
forecasting bad outcomes like funds crashing and burning,

34
00:01:53.265 --> 00:01:57.965
or funds, committing, offenses in the market,

35
00:01:58.585 --> 00:01:59.965
those kind of things.

36
00:02:00.065 --> 00:02:02.085
So that, that's the context.

37
00:02:02.945 --> 00:02:07.725
And so, we have, together, as,

38
00:02:07.745 --> 00:02:12.125
as a research team, we have spent years studying,

39
00:02:12.545 --> 00:02:15.165
the, the information that's available

40
00:02:15.305 --> 00:02:18.285
to investors about, about, hedge funds.

41
00:02:19.175 --> 00:02:23.605
And, what we did was we said,

42
00:02:24.055 --> 00:02:28.085
let's look before and after this Dodd-Frank Act

43
00:02:28.225 --> 00:02:30.845
and see whether the new information,

44
00:02:31.265 --> 00:02:35.315
see if we can do some tests to see about, do some tests

45
00:02:35.335 --> 00:02:37.835
to see whether these new variables

46
00:02:38.185 --> 00:02:40.515
that the SEC has required,

47
00:02:40.905 --> 00:02:42.555
predict different kinds of outcomes.

48
00:02:42.725 --> 00:02:46.775
We're putting in a lot of variables,

49
00:02:47.595 --> 00:02:50.975
and we're looking for some outcomes in the model.

50
00:02:51.385 --> 00:02:55.255
After the Dodd-Frank Act, we had a massive number

51
00:02:55.275 --> 00:02:58.815
of variables that the SEC had required, 44

52
00:02:58.845 --> 00:03:01.085
Different quantitative variables.

53
00:03:01.345 --> 00:03:05.125
And then within some of those variables that there were,

54
00:03:05.125 --> 00:03:07.605
there was text information and so forth.

55
00:03:07.785 --> 00:03:10.365
So, you know, we call that kind

56
00:03:10.365 --> 00:03:13.885
of model a kitchen sink model because you have everything

57
00:03:14.065 --> 00:03:16.605
but the kitchen sink in the predictor.

58
00:03:17.345 --> 00:03:22.285
Um, and so we used a machine learning tool

59
00:03:22.825 --> 00:03:24.965
called Lasso, which says,

60
00:03:25.355 --> 00:03:27.605
give me just the really important variables,

61
00:03:27.935 --> 00:03:29.245
let's just focus on those.

62
00:03:30.025 --> 00:03:33.725
And, when we lassoed those important variables up,

63
00:03:34.065 --> 00:03:36.445
we found a few that were really important.

64
00:03:37.425 --> 00:03:42.365
Um, one of, well, the top three, one of them has to do

65
00:03:42.955 --> 00:03:47.845
with whether or not a hedge fund, has a, an external

66
00:03:48.525 --> 00:03:50.245
investor that might be another company.

67
00:03:50.985 --> 00:03:53.725
Um, and, that was a bit of a puzzle,

68
00:03:53.865 --> 00:03:57.085
but it turns out that if you do have,

69
00:03:57.585 --> 00:04:02.565
an ownership structure with another interested party, in

70
00:04:02.565 --> 00:04:07.325
that case, that party seems to be more, I guess a,

71
00:04:07.345 --> 00:04:09.365
a constraint on whether

72
00:04:09.365 --> 00:04:11.165
or not something bad could happen,

73
00:04:11.315 --> 00:04:13.045
like a regulatory violation.

74
00:04:13.265 --> 00:04:17.045
Now, there other two variables have to do with,

75
00:04:17.385 --> 00:04:21.525
how you're related to, firms, whether

76
00:04:21.525 --> 00:04:24.445
or not your, your company is connected to a firm

77
00:04:24.445 --> 00:04:27.645
that deals in the futures markets in the swap markets

78
00:04:28.235 --> 00:04:30.365
that seems to heighten the probability

79
00:04:30.545 --> 00:04:34.125
of an adverse outcome, like, future regulatory sanctions

80
00:04:34.185 --> 00:04:38.125
or even, even the crash

81
00:04:38.145 --> 00:04:39.245
and burn outcome.

82
00:04:39.945 --> 00:04:42.325
And, so why would that be?

83
00:04:42.395 --> 00:04:45.965
Well, that seems to be, well,

84
00:04:45.965 --> 00:04:49.965
could possibly be related to the fact that, the more

85
00:04:50.545 --> 00:04:54.085
of these ex more of these external relationships

86
00:04:54.115 --> 00:04:55.845
that you have with companies

87
00:04:55.845 --> 00:04:59.205
that are dealing in other capital markets,

88
00:04:59.585 --> 00:05:02.245
the more opportunity there might be

89
00:05:02.545 --> 00:05:03.925
for conflicts of interest.

90
00:05:03.985 --> 00:05:05.765
In other words, you're maybe buying

91
00:05:05.765 --> 00:05:10.725
and selling securities, that, the hedge fund itself,

92
00:05:11.385 --> 00:05:14.685
owns on behalf of clients.

93
00:05:15.505 --> 00:05:19.445
Um, and, so that might be a hint

94
00:05:19.445 --> 00:05:21.245
that something could go wrong in the

95
00:05:21.245 --> 00:05:22.405
whole operational process.

96
00:05:22.735 --> 00:05:26.645
We're not saying that those are signs of,

97
00:05:27.065 --> 00:05:28.205
of wrongdoing,

98
00:05:28.625 --> 00:05:30.125
but they have an association

99
00:05:30.155 --> 00:05:32.325
that might reflect these opportunities

100
00:05:32.395 --> 00:05:35.325
that could arise in a complex trading environment where,

101
00:05:35.505 --> 00:05:38.685
and sometimes, sometimes you're, you're, you're acting

102
00:05:38.745 --> 00:05:43.325
as a broker, on with one hand as an investor,

103
00:05:43.325 --> 00:05:48.135
with the other hand, task regulatory, sanctions,

104
00:05:48.915 --> 00:05:51.415
are, are not a good sign.

105
00:05:52.075 --> 00:05:54.615
Um, and, that's a simple notion,

106
00:05:54.635 --> 00:05:58.125
but when you're looking at a fund, you might wanna look

107
00:05:58.145 --> 00:06:00.685
and see whether they have been sanctioned in the past.

108
00:06:00.905 --> 00:06:05.245
Uh, so our big picture history matters, for, for those,

109
00:06:05.465 --> 00:06:06.965
for bad outcomes.

110
00:06:07.425 --> 00:06:11.605
For some outcomes it does very well, particularly if the,

111
00:06:11.745 --> 00:06:14.085
you know, the crash and burnout outcome of disappearance.

112
00:06:14.745 --> 00:06:16.605
Um, other ones, it's a bit marginal.

113
00:06:16.745 --> 00:06:18.085
Um, any,

114
00:06:18.105 --> 00:06:21.885
but anything you can find that might help you predict,

115
00:06:22.305 --> 00:06:25.415
future risk adjusted return,

116
00:06:26.395 --> 00:06:27.575
or, whether

117
00:06:27.595 --> 00:06:32.335
or not there is trust in the system for these, funds,

118
00:06:32.825 --> 00:06:35.015
which we measure by whether somebody lends them money

119
00:06:35.555 --> 00:06:38.855
or not, anything you can find

120
00:06:38.855 --> 00:06:42.015
that will help you predict those, outcomes is helpful.

121
00:06:42.915 --> 00:06:47.015
So we use measures called R Square, which is the

122
00:06:47.555 --> 00:06:50.055
amount of variation that is explained

123
00:06:50.055 --> 00:06:51.655
by these, indicators.

124
00:06:52.515 --> 00:06:56.375
And, they range anywhere from about

125
00:06:57.235 --> 00:07:00.375
5% to 80%, depending on which one of those outcomes

126
00:07:00.445 --> 00:07:02.615
that you, are interested in.

127
00:07:03.275 --> 00:07:07.775
The data that's supplied now with the updated form A DV

128
00:07:08.435 --> 00:07:10.175
is significant.

129
00:07:10.395 --> 00:07:14.175
And, and, and in that matter, in that sense, very helpful,

130
00:07:14.795 --> 00:07:16.455
to predicting outcomes.

131
00:07:16.755 --> 00:07:21.735
The SEC, when they redesigned their form,

132
00:07:21.925 --> 00:07:25.095
figured out in questions that added value.

133
00:07:25.355 --> 00:07:28.415
In other words, you know,

134
00:07:28.795 --> 00:07:30.975
the SEC must have had suspicions

135
00:07:31.115 --> 00:07:33.215
before the revision of the form about

136
00:07:33.215 --> 00:07:35.375
what variables might be useful for investors.

137
00:07:36.235 --> 00:07:38.735
It appears to us, and we did that formal test,

138
00:07:38.765 --> 00:07:43.375
that these new variables were, extremely useful compared

139
00:07:43.375 --> 00:07:47.375
to just using the old things that, that funds had to,

140
00:07:48.195 --> 00:07:50.615
the, funds had to report.

141
00:07:51.035 --> 00:07:55.335
So my takeaway is that, the re the regulators,

142
00:07:55.605 --> 00:07:58.015
were doing their job and,

143
00:07:58.475 --> 00:08:01.775
and so we should, trust,

144
00:08:02.105 --> 00:08:04.055
their, insights.

145
00:08:05.035 --> 00:08:09.115
Um, and, then the other,

146
00:08:09.395 --> 00:08:14.235
takeaway is that, well, you know, we asked, is this

147
00:08:15.075 --> 00:08:17.795
phenomenon, is this predictability relevant?

148
00:08:17.935 --> 00:08:19.835
Um, how, how big a deal is it?

149
00:08:20.455 --> 00:08:23.275
And so when we made some calculations about

150
00:08:23.785 --> 00:08:27.355
what we call the value at risk in terms of

151
00:08:27.965 --> 00:08:32.195
total dollars that are exposed to the probability of,

152
00:08:32.225 --> 00:08:35.795
fund failure, for example, it's a large number.

153
00:08:36.015 --> 00:08:37.355
In other words, we're not talking

154
00:08:37.355 --> 00:08:38.555
about things at the margin.

155
00:08:38.765 --> 00:08:42.075
We're talking about issues that could affect,

156
00:08:42.075 --> 00:08:45.115
concerns about systemic risk in, in terms

157
00:08:45.115 --> 00:08:46.355
of the next to big crash.

158
00:08:46.975 --> 00:08:50.755
And also just in general, the amount of institutional

159
00:08:50.755 --> 00:08:54.995
and individual money in hedge funds, would require

160
00:08:55.065 --> 00:08:58.805
The SEC, to take a, a hard look at it and,

161
00:08:59.145 --> 00:09:02.405
and to, continue this regulatory oversight with, with,

162
00:09:02.435 --> 00:09:04.605
with this kind of data collection process.

163
00:09:05.305 --> 00:09:09.685
The new dimension of analysis in academia,

164
00:09:10.145 --> 00:09:12.045
is, text information

165
00:09:12.305 --> 00:09:15.365
and non quanti, non-numeric information.

166
00:09:16.265 --> 00:09:19.565
And, so, that's something that,

167
00:09:19.995 --> 00:09:21.685
that I've been working on my, my,

168
00:09:21.745 --> 00:09:23.485
my co-authors have been working on.

169
00:09:23.945 --> 00:09:27.605
You know, how much can we learn from what people say,

170
00:09:27.915 --> 00:09:32.605
what else they do, the broader social context, you know,

171
00:09:33.275 --> 00:09:38.085
detection of fraud, in certain circumstances is

172
00:09:39.015 --> 00:09:43.605
DeVol has devolved, to, to

173
00:09:44.245 --> 00:09:48.885
personal interviews and, even to, nonverbal cues,

174
00:09:49.145 --> 00:09:50.685
that might be relevant.

175
00:09:51.185 --> 00:09:55.325
So, how much, that kind

176
00:09:55.325 --> 00:09:59.925
of information could be adapted to, to,

177
00:10:00.195 --> 00:10:02.925
helping investors avoid, people

178
00:10:02.945 --> 00:10:04.245
who are gonna take advantage of them.

179
00:10:04.505 --> 00:10:06.125
That's a really exciting thing.

180
00:10:06.745 --> 00:10:08.725
And I know that in our field

181
00:10:08.825 --> 00:10:12.085
and, the field of, academic finance,

182
00:10:13.115 --> 00:10:16.085
this has just been, there has just been an explosion

183
00:10:16.085 --> 00:10:17.965
of research to use this kind of data.

Faculti AI can make mistakes

Research

Policy

Analysis