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Speaker 1: finance and investment on Bloomberg Radio. Good morning everyone, Michael
Speaker 1: McKee and Tom Kane. The drag press conference will see
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Speaker 1: This is gonna be interesting, Michael McKee on the Bloomberg
Speaker 1: There is t y L go, that Taylor rule go,
Speaker 1: and you can plug in and hug the Newtonian mechanics
Speaker 1: of the algebraic function, which approximates John B. Taylor's great
Speaker 1: work out at Stanford. But there's other ways to do
Speaker 1: monetary policy, aren't there approximates? Of course John always said
Speaker 1: it wasn't a way to forecast where rates should be,
Speaker 1: just a way to double check rate where rates should
Speaker 1: have been in the past. But the question is can
Speaker 1: you come up with rules? Can you come up with
Speaker 1: some sort of reliable way of setting rates based on
Speaker 1: data that takes human emotion sort of out of it
Speaker 1: um And that's been a focus of the work of
Speaker 1: Michael Currn's. He's a professor and a National Center Chair
Speaker 1: at the University of Pennsylvania down in Philadelphia, where, of
Speaker 1: course Janet yellen Um, the real Janet you know, not
Speaker 1: the computerized version, is speaking on Monday, and that highly
Speaker 1: anticipated speech are Christopher Connon talked with you, professor, not
Speaker 1: long ago, about the possibility of using artificial intelligence to
Speaker 1: improve economic forecasting. And I guess that's the key, uh,
Speaker 1: to to improve forecasting, and then you can use a
Speaker 1: rule based on the forecast to figure out where your
Speaker 1: rate should be. Yeah, that's right. Chris and I had
Speaker 1: a long conversation about the possible application of machine learning
Speaker 1: to sort of macro economic forecasting in general and kind
Speaker 1: of policy setting more specifically, and UM, you know what
Speaker 1: I told Chris was that, UM, I'm sort of a
Speaker 1: machine learning advocate in the sense that I think that
Speaker 1: such a thing as possible in principle, but probably pretty
Speaker 1: far from being practical at this point. Yeah. I mean,
Speaker 1: it's not like the FED and other commercial enterprises don't
Speaker 1: have models, but they find that there are so many
Speaker 1: millions of influences that it's almost impossible to find tune
Speaker 1: them enough. Yeah, that's right. And I think that, you know,
Speaker 1: with all of the kind of media frenzy about machine
Speaker 1: learning these days, we forget that, um many people in
Speaker 1: many fields have been doing what has now been rebranded
Speaker 1: machine learning for a very long time. I think in
Speaker 1: terms of kind of macroeconomic policy prediction, the thing that's
Speaker 1: particularly difficult at this point for machines is just incorporating
Speaker 1: knowledge about you know, how markets work, how policy interacts
Speaker 1: with markets, how international events shape you know, the future. UM,
Speaker 1: and it's just very difficult to get clean data that
Speaker 1: you know kind of UM relates all of those working
Speaker 1: parts over a long enough history that you would have
Speaker 1: seen cycles, you would have seen market crashes, you would
Speaker 1: have had a lot of examples of how economy has
Speaker 1: changed and responses to policy. That goes to the heart
Speaker 1: of the matter. And course this goes to artificial intelligence
Speaker 1: and the rest. Where what have we accomplished in the
Speaker 1: last thirty years? If Governor Tarula who was just on
Speaker 1: with us, Cherry Yellen, Mario Doroggi, who's gonna speak in
Speaker 1: twenty five minutes. If people like that are working with
Speaker 1: basic algebra, Newtonian mechanics, and maybe something is fungible, is dynamics,
Speaker 1: stochastic general equilibrium theory, How can AI help them? I mean,
Speaker 1: it's very to me. It's your world is very nonlinear,
Speaker 1: it's it's got huge degrees of freedom issues. How can
Speaker 1: AI assist economists to do a better job? Well, I
Speaker 1: mean I think A can help, and in machine learning
Speaker 1: more specifically, can help in any domain in which you
Speaker 1: have massive amounts of historical data, including very high dimensional
Speaker 1: data um that's sort of relatively clean and and sort
Speaker 1: of drawn under you know, kind of similar conditions over
Speaker 1: a long period of time. The more sort of you know,
Speaker 1: heterogeneous your data is, the more diversity is, the harder
Speaker 1: it is to kind of apply machine learning. And this
Speaker 1: is why you see machine learning especially succeeding in either
Speaker 1: cases where there's a massive amount of clean data, as
Speaker 1: in areas like speech recognition or exactly, or in sort
Speaker 1: of very closed environments like game playing like chests or go,
Speaker 1: which is difficult as those problems are. You know you're
Speaker 1: in a closed world, right, you know that the rules
Speaker 1: are very Yeah, well there's I get the beautifully explained.
Speaker 1: There's sixty four squares and chess, I get it. Can
Speaker 1: you take your world and help Janet Yellen with the
Speaker 1: data that goes back to Lawrence client at pen In
Speaker 1: or whatever. I mean, was that data then clean or
Speaker 1: is that data now clean? I think in the case
Speaker 1: I mean, and and mind you, I'm not an economist
Speaker 1: per se, you know, and even less we take that
Speaker 1: as an advantage. Certain I appreciate that, but I think
Speaker 1: my short answer to that is no, and it's not
Speaker 1: so much whether the data is clean or not, it's
Speaker 1: just sort of the length of history. I mean to
Speaker 1: give an analogy, right, Um, you know, if you go
Speaker 1: back thirty years before sort of the automation of Wall
Speaker 1: Street more generally, there were a lot of you know,
Speaker 1: trading problems that at that time really required human expertise, intuition,
Speaker 1: knowledge of how markets work, how they might react to
Speaker 1: certain exogenous events, and so on and so forth. And
Speaker 1: now a lot of that stuff has been automated, right,
Speaker 1: I mean, sort of the brokerage business has become a
Speaker 1: very difficult business, um, at least being a sort of
Speaker 1: the human sort of specialist brokerage. But this has become
Speaker 1: very difficult as algorithmic trading has risen. And the real
Speaker 1: reason now that the trading has taken over is partly
Speaker 1: the automation. But the automation has you know, generated years
Speaker 1: and years a very clean data. Um. That makes machine
Speaker 1: learning a much more practical, you know, sort of practical approach.
Speaker 1: And by the way, you tend to see the you know,
Speaker 1: the greatest uses of machine learning in trading or economic
Speaker 1: settings where the most data is generated, so high frequency
Speaker 1: trading for instance, right, just because of the speed at
Speaker 1: which thing is things are happening, you know, in the
Speaker 1: same minute. Um, I get way more data about high
Speaker 1: frequency trading that I do about macro economous policy predictions.
Speaker 1: So can your model tell me if there's going to
Speaker 1: be a junior July rate increase? I couldn't tell you
Speaker 1: if I knew good. No one else can either, So
Speaker 1: can like, like one more question? Please? Well, I just
Speaker 1: car is because Bill Dudley and others are de fitive
Speaker 1: noted that one failure of their models is that doesn't
Speaker 1: take financial markets into account. Financial markets, much of it
Speaker 1: is high frequency trading, but much of it is also
Speaker 1: market psychology. How does machine learning account for psychological factors? Yeah,
Speaker 1: so that's a great question. And um, one of you
Speaker 1: mentioned equilibrium a little while ago, and you know, the
Speaker 1: term equilibrium kind of refers to taking the strategic considerations
Speaker 1: of the parties into account, you know, or or you know,
Speaker 1: kind of counter factuals, if you like, sort of not
Speaker 1: just how did the data look historically, but how might
Speaker 1: it have looked different if we had done something differently
Speaker 1: and something different had happened. And one thing I like to,
Speaker 1: you know, one way I like to put it when
Speaker 1: I talk to people about machine learning in finance or
Speaker 1: economics versus in other domains, which is, you know, as
Speaker 1: hard as the problem of let's say, recognizing whether there's
Speaker 1: a cat in a video on YouTube or not might be,
Speaker 1: and you know, you might think I'm joking, but actually
Speaker 1: that's not an easy problem. As hard as that problem
Speaker 1: might be, it one one advantage you have an applying
Speaker 1: machine learning to such a problem is that you can
Speaker 1: be pretty sure that you know the world is not
Speaker 1: you know that your very effort to decide whether there's
Speaker 1: a cat in the video or not is not you know,
Speaker 1: changing the nature and casts um you know themselves right.
Speaker 1: Where this is not true in trading, you can be
Speaker 1: quite sure that you know your very effort to predict
Speaker 1: something in financial markets and then act on that prediction
Speaker 1: link fact change the markets in a way that makes
Speaker 1: what you're doing less effective. Right, So it's that kind
Speaker 1: of adaptive dynamic, the market reacting to what you're doing
Speaker 1: because of the strategic considerations the other parties involved, and
Speaker 1: there there are branches of machine learning that are trying
Speaker 1: to seriously take those kind of strategic considerations and counter
Speaker 1: factuals in to account. But it's a much much more
Speaker 1: difficult problem. And I think that you know, we're very
Speaker 1: very far from kind of understanding how to deal with
Speaker 1: such problems at the at the kind of scale that
Speaker 1: you guys are talking about. Michael Currents, thank you so much.
Speaker 1: At the University of Pennsylvania and machine learning and a
Speaker 1: touch there and AI as well, our machine learning, I mean,
Speaker 1: we are robots, Mike Is. We're twenty minutes away from
Speaker 1: the drug press comments. I would don't, Mike. Not much
Speaker 1: movement in the market. I guess that's not a surprise, Mike.
Speaker 1: I would know two days in a row, strong and
Speaker 1: strong Yen one O eight ninety two and a yen
Speaker 1: as well, indeed, but but no real reaction to Torulo
Speaker 1: or to the e c B. And I guess, uh
Speaker 1: Jim vot will put it, well, it's all down to
Speaker 1: Janet Yellen on Monday, the futures and negative four down
Speaker 1: futures at negative yield one point eight three. Time now
Speaker 1: to check in with Michael Barr and get caught up
Speaker 1: on world in the national headlines. Mike, Tom, thank you
Speaker 1: very much. Hillary Clinton has sat to unleash a four
Speaker 1: in policy attack on Donald Trump. The former Secretary of
Speaker 1: State will use a speech in San Diego today to
Speaker 1: cast the Republican as unqualified and dangerous. Trump accused Clinton
Speaker 1: of lying about his foreign policy plans at a rally
Speaker 1: in Sacramento, California, last night. Classes will resume next week.
Speaker 1: At u c l A yesterday, a professor was shot
Speaker 1: and killed before police say the gunman took his own life.
Speaker 1: The head of the Global Airline Industry Association says terrorist
Speaker 1: attacks will not stop surging travel demand. International air Transport
Speaker 1: Association CEO Tony Tyler told Bloomberg people don't get frightened
Speaker 1: off by these thugs. Global News twenty four hours a day,
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Speaker 1: Michael Barr, Thanks so much, che life. If you've got
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