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The Investment Banking Model Is Flawed

Question: Is the investment banking model fundamentally flawed?  

Robert Engle:
My way of understanding this financial crisis is in terms of two different observations.  One is that risk managers and investment bankers and actually, all kinds of investors took on more risk than they expected.  So there was a failure of risk management.  There was a failure to recognize how much risk there was in some of these securities that people bought. 

But a second and perhaps more important point is that many of these same people were paid very well to ignore the risks, and so there are incentives which are – which distort our ability to measure risk.  That is, many times we’re not risking our own money, we’re risking somebody else's money, or maybe that someone is going to back stop or downside, but we still get the upside.  There are a lot of ways that investment banking models work, but these risks are not internalized by the people that are taking them.  And so, I think that’s something that investment banks have worried about for a long time and are continuing to worry about, but it’s not an easy solution when you have lots of people betting the company’s money, how do you really allocate those risks?  How do you make sure that the people that take the risks are feeling the risks in an appropriate kind of fashion?

Question: Do you agree with author Nassim Taleb that we put too much faith in financial experts?

Robert Engle: Oh well I would agree with that.  I don’t agree – I agree with a lot of the points in Taleb’s book, but I don’t agree with many of his conclusions.  It seems to me that he rightly points out that risk managers miss a lot of the risks, but the conclusion is that he draws, is that we should abandon risk management, whereas my conclusion is we should improve it.  I don’t see what the alternative to risk management is.  If it’s just getting rid of the models and instead using the smart people who can figure it out?  How do you train them?  What do you teach them?  Do you just put them in a cockpit and let them stumble for 10 years of their life and then after that they’re good at it?  I think that **** have gotten so complicated that we need risk management, but we just need to make it better.  We need to be able to understand better where these risks are coming from. 

And I think actually one of the – coming back to one of your previous points about investment strategies.  One of the things we might come out of this crisis and I think that Nassim is sort of an example of this as well is that we might invest a part of our assets in portfolios that we don’t expect to do well not, but we would expect them to do well if we have another crisis.  And this is a different way of thinking about asset allocation.  It’s an old idea in economics, but it hasn’t’ really been at the center of much investment analysis, but I think now there’s a lot of interest in these so called hedge portfolios which will out perform in a crisis.  I mean, we’ve always had gold bugs, but now we sort of realize that Treasure Bills might be in the same category.  And we have derivatives like credit default swaps which are in this category, and we have derivatives like volatilities that are actually an asset class that we can invest in which are now – would out perform if we have another financial crisis.

And so thinking about these different assets and I should say, this extends to lots of other kinds of long term risks, for example, if we think about the long term risks of global warming.  You know, some of the portfolios we might consider buying are portfolios which would do especially well if we have an economy-wide, or I mean, a global climate change that impacts us very negatively there are some companies that will do well, and so it might make sense to hold some of those in your portfolio. 

Recorded May 25, 2010
Interviewed by Andrew Dermont

Investment banks encourage faulty risk management because the risks are "not internalized by the people that are taking them."

Does conscious AI deserve rights?

If machines develop consciousness, or if we manage to give it to them, the human-robot dynamic will forever be different.

Videos
  • Does AI—and, more specifically, conscious AI—deserve moral rights? In this thought exploration, evolutionary biologist Richard Dawkins, ethics and tech professor Joanna Bryson, philosopher and cognitive scientist Susan Schneider, physicist Max Tegmark, philosopher Peter Singer, and bioethicist Glenn Cohen all weigh in on the question of AI rights.
  • Given the grave tragedy of slavery throughout human history, philosophers and technologists must answer this question ahead of technological development to avoid humanity creating a slave class of conscious beings.
  • One potential safeguard against that? Regulation. Once we define the context in which AI requires rights, the simplest solution may be to not build that thing.

A new hydrogel might be strong enough for knee replacements

Duke University researchers might have solved a half-century old problem.

Photo by Alexander Hassenstein/Getty Images
Technology & Innovation
  • Duke University researchers created a hydrogel that appears to be as strong and flexible as human cartilage.
  • The blend of three polymers provides enough flexibility and durability to mimic the knee.
  • The next step is to test this hydrogel in sheep; human use can take at least three years.
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Hints of the 4th dimension have been detected by physicists

What would it be like to experience the 4th dimension?

Two different experiments show hints of a 4th spatial dimension. Credit: Zilberberg Group / ETH Zürich
Technology & Innovation

Physicists have understood at least theoretically, that there may be higher dimensions, besides our normal three. The first clue came in 1905 when Einstein developed his theory of special relativity. Of course, by dimensions we’re talking about length, width, and height. Generally speaking, when we talk about a fourth dimension, it’s considered space-time. But here, physicists mean a spatial dimension beyond the normal three, not a parallel universe, as such dimensions are mistaken for in popular sci-fi shows.

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Predicting PTSD symptoms becomes possible with a new test

An algorithm may allow doctors to assess PTSD candidates for early intervention after traumatic ER visits.

Image source: camillo jimenez/Unsplash
Technology & Innovation
  • 10-15% of people visiting emergency rooms eventually develop symptoms of long-lasting PTSD.
  • Early treatment is available but there's been no way to tell who needs it.
  • Using clinical data already being collected, machine learning can identify who's at risk.

The psychological scars a traumatic experience can leave behind may have a more profound effect on a person than the original traumatic experience. Long after an acute emergency is resolved, victims of post-traumatic stress disorder (PTSD) continue to suffer its consequences.

In the U.S. some 30 million patients are annually treated in emergency departments (EDs) for a range of traumatic injuries. Add to that urgent admissions to the ED with the onset of COVID-19 symptoms. Health experts predict that some 10 percent to 15 percent of these people will develop long-lasting PTSD within a year of the initial incident. While there are interventions that can help individuals avoid PTSD, there's been no reliable way to identify those most likely to need it.

That may now have changed. A multi-disciplinary team of researchers has developed a method for predicting who is most likely to develop PTSD after a traumatic emergency-room experience. Their study is published in the journal Nature Medicine.

70 data points and machine learning

nurse wrapping patient's arm

Image source: Creators Collective/Unsplash

Study lead author Katharina Schultebraucks of Columbia University's Department Vagelos College of Physicians and Surgeons says:

"For many trauma patients, the ED visit is often their sole contact with the health care system. The time immediately after a traumatic injury is a critical window for identifying people at risk for PTSD and arranging appropriate follow-up treatment. The earlier we can treat those at risk, the better the likely outcomes."

The new PTSD test uses machine learning and 70 clinical data points plus a clinical stress-level assessment to develop a PTSD score for an individual that identifies their risk of acquiring the condition.

Among the 70 data points are stress hormone levels, inflammatory signals, high blood pressure, and an anxiety-level assessment. Says Schultebraucks, "We selected measures that are routinely collected in the ED and logged in the electronic medical record, plus answers to a few short questions about the psychological stress response. The idea was to create a tool that would be universally available and would add little burden to ED personnel."

Researchers used data from adult trauma survivors in Atlanta, Georgia (377 individuals) and New York City (221 individuals) to test their system.

Of this cohort, 90 percent of those predicted to be at high risk developed long-lasting PTSD symptoms within a year of the initial traumatic event — just 5 percent of people who never developed PTSD symptoms had been erroneously identified as being at risk.

On the other side of the coin, 29 percent of individuals were 'false negatives," tagged by the algorithm as not being at risk of PTSD, but then developing symptoms.

Going forward

person leaning their head on another's shoulder

Image source: Külli Kittus/Unsplash

Schultebraucks looks forward to more testing as the researchers continue to refine their algorithm and to instill confidence in the approach among ED clinicians: "Because previous models for predicting PTSD risk have not been validated in independent samples like our model, they haven't been adopted in clinical practice." She expects that, "Testing and validation of our model in larger samples will be necessary for the algorithm to be ready-to-use in the general population."

"Currently only 7% of level-1 trauma centers routinely screen for PTSD," notes Schultebraucks. "We hope that the algorithm will provide ED clinicians with a rapid, automatic readout that they could use for discharge planning and the prevention of PTSD." She envisions the algorithm being implemented in the future as a feature of electronic medical records.

The researchers also plan to test their algorithm at predicting PTSD in people whose traumatic experiences come in the form of health events such as heart attacks and strokes, as opposed to visits to the emergency department.

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