Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.
Speaker 1: This weekend. On the podcast, I have an extra special
Speaker 1: guest and it is filled. Our conversation is filled with
Speaker 1: geeky goodness. Uh Andrew Ang, head of Factor Investing at
Speaker 1: black Rock name run I don't know six points something
Speaker 1: trillion dollars um. Ang is really a born and bread
Speaker 1: uh factor investor. Not only does he have a background
Speaker 1: in stats and finance from from UM Stanford, but he
Speaker 1: taught finance at Columbia and the opportunity to put his
Speaker 1: theories into actual practice at black Rock just proved to
Speaker 1: be too tempting. He had to leave UM the theoretical
Speaker 1: practice of teaching and working at Columbia that he really
Speaker 1: enjoyed to give it a shot at black Rock, and
Speaker 1: it's worked out extremely well. If you are at all
Speaker 1: interested in quantitative investing, modeling, factor investing, anything remotely involved
Speaker 1: in the wonky goodness of mathematical theory and investing, you're
Speaker 1: gonna love this conversation. So, with no further ado, my
Speaker 1: conversation with Black Rocks Andrew Ang. My extra special guest
Speaker 1: this week is Andrew Ang. He is the head of
Speaker 1: Factor investing at black Rock, the firm manages over six
Speaker 1: trillion dollars. He comes to us with a PhD in
Speaker 1: finance and a master's degree in statistics from Stanford and
Speaker 1: he is the author of a book called Asset Management,
Speaker 1: A Systemic Approach to Factor Investing. Andrew Ang, Welcome to Bloomberg.
Speaker 1: Thank you. Barrett's a real pleasure to be here. Thanks
Speaker 1: for coming in. You have You have a really fascinating background.
Speaker 1: You spend the first half of your career in academia,
Speaker 1: made you decide to transition from theory to practice. Indeed,
Speaker 1: I was a professor for fifteen years at Columbia University
Speaker 1: and ended as chair of the Finance and Economics Division
Speaker 1: and was the Anne F. Kaplan Professor of Business. What's
Speaker 1: interesting is that my wife, she was born in China.
Speaker 1: Her parents have this long, very scholarly Confucian style tradition
Speaker 1: and the highest life form for them is a tenured
Speaker 1: professor at an Ivy League institution. And I thought I
Speaker 1: was absolutely crazy and leaving that and coming to industry.
Speaker 1: My parents, on the other hand, they didn't go to university.
Speaker 1: They actually I really don't know still today what a
Speaker 1: professor does well what do you mean you're not teaching.
Speaker 1: You mean you're on vacation. And they were really proud
Speaker 1: of me for getting a real job. So the disparity
Speaker 1: in the attitudes, well, one thing that was very interesting
Speaker 1: is my wife said to me, Andrew, you're a hypocrite,
Speaker 1: because I used to feel that a lot of academics,
Speaker 1: and I was myself one. They're very theoretical, but they
Speaker 1: believe that the world should operate in a particular way,
Speaker 1: of course, the way that they study the world, but
Speaker 1: they he she called me a hypocrite because actually I
Speaker 1: believed so much that I accepted a job offer to
Speaker 1: come to Black Crop because I wanted to change the
Speaker 1: way that finance was practiced in accordance to the research
Speaker 1: and factors that I was doing. So let's talk about
Speaker 1: that research a little bit. You have a background and statistics,
Speaker 1: You get a PhD in finance that really lends itself
Speaker 1: to factor investing. That sort of quantitative approach is practically
Speaker 1: made for this. But I have to ask from your research,
Speaker 1: how did you find your way to a factor investing.
Speaker 1: I was a professor, and I did a lot of
Speaker 1: consulting while I was a professor, and I had the
Speaker 1: privilege of working for some very large institutions, including the
Speaker 1: Norwegian Sovereign Wealth Fund, and this is a very special fund.
Speaker 1: It's a trillion dollars today, it's multiple multiples times the
Speaker 1: country's GDP, and they went through some tough times in
Speaker 1: two thousand and eight, like many institutions, and I was
Speaker 1: tasked by the Ministry of Finance representing Parliament, together with
Speaker 1: two other academics to take a deep look at the fund,
Speaker 1: to analyze it, to see where the losses were coming from,
Speaker 1: and to make recommendations. And what we found was that
Speaker 1: despite this fund owning tens of thousands of securities dozens
Speaker 1: of active managers, what mattered at the end of the
Speaker 1: day were these factors broad and persistent sources of returns.
Speaker 1: Macro factors like economic growth, real rates and inflation which
Speaker 1: comes through market cap indices, and then relative to those
Speaker 1: market cap benchmarks, style factors like value and momentum quality,
Speaker 1: minimum volatility explain two thirds of the variation of these
Speaker 1: active returns, and so factors really mattered, and it was
Speaker 1: entirely appropriate for Norway to have these exposures to these
Speaker 1: factors that resulted in long term superior returns. So, so
Speaker 1: let me jump in right here and ask this. You're
Speaker 1: you're viewing a trillion dollar portfolio. You're really separating the
Speaker 1: wheat from the chaff. You're identifying what the source of
Speaker 1: returns within that trillion dollar portfolio is. Do do the
Speaker 1: managers of the Norwegian Sovereign Wealth Fund then turn around
Speaker 1: and say, we're going to move away from the parts
Speaker 1: of our portfolio that aren't performing and towards where our
Speaker 1: out performances coming from. Or did they consider that diversification
Speaker 1: and they leave it as it was. Norway has always
Speaker 1: used different sources of return, including fundamental analysis, and they
Speaker 1: continue to do that. But what we recommended and they
Speaker 1: did adopt, was to make a very top down, deliberate
Speaker 1: decision on these factors. And as a result, Norway started
Speaker 1: directly allocating to these factors to manage, better understand their risks,
Speaker 1: to enhance their returns, and as you say, Barry too,
Speaker 1: improve the diversification. So so they seem to be pretty
Speaker 1: well versed in understanding the academic literature. But out in
Speaker 1: the real world, which is a fair statement to say,
Speaker 1: out in the rest of the real world, how does
Speaker 1: the way most people invest differ from from what's in
Speaker 1: the textbook. What what are the big disparities between people
Speaker 1: who aren't the Norway sovereign wealth funds and and the
Speaker 1: average investor? What what's the difference in their process and
Speaker 1: in their results? Great question bear because I really think
Speaker 1: they are more similarities than differences. But if you have
Speaker 1: to think about you know, I used to be a
Speaker 1: professor and now you're a practitioner. What what are these differences?
Speaker 1: A lot of people talk about implementation short for and
Speaker 1: a lot of a jargon, the difference with transaction costs
Speaker 1: and stale prices or stale data. But actually the real
Speaker 1: difference between academic and practice is you've got to work
Speaker 1: with a lot of people to get these things to fruition.
Speaker 1: And as a professor, you're sitting there in your office,
Speaker 1: you're sitting there by yourself often and you just do
Speaker 1: your own thing. But to make a difference, you have
Speaker 1: dozens of people and teams that you've got to partner
Speaker 1: with in order to make a product come until an
Speaker 1: advisor shelf. Let's talk a little bit about the best
Speaker 1: way to use factors. Are they designed to manage risk
Speaker 1: or are they designed to deliver market out performance. Well, yes, actually,
Speaker 1: I think factors should be used in all facets of
Speaker 1: the investment process. We definitely need factors to look at
Speaker 1: really what drives our returned So there is an angle
Speaker 1: for risk management, and I think your firm has exemplified
Speaker 1: that very But we also would like to use factors
Speaker 1: to enhance our returns as well, and we can do
Speaker 1: that with value, quality, momentum size, and combinations of these
Speaker 1: return enhancing factors. We can also use factors to target
Speaker 1: specific outcomes like minimizing our downside risk exposure through minimum
Speaker 1: volatility strategies. Factors can be used for all of these,
Speaker 1: And what's really exciting is that we can ask, what's
Speaker 1: the outcome you want to achieve. Perhaps it has created
Speaker 1: a versification or put full of resilience, and we will
Speaker 1: have some combination of factors that's right for you. So
Speaker 1: here's the question that always comes up when I discuss
Speaker 1: factors with other people. It seems once everybody discovers a
Speaker 1: new idea, it's power has a tendency to sort of
Speaker 1: fade away. Now that we know so much about factors,
Speaker 1: we know about value, we know about small cap, we
Speaker 1: know about quality, why hasn't that been arbitraged away. How
Speaker 1: is it that long term factors still deliver some degree
Speaker 1: about formants. Ultimately, that's a question of who's on the
Speaker 1: other side, because not everyone can buy cheap. For every
Speaker 1: value stock that's cheap, there's got to be a stock
Speaker 1: that's relatively expensive. The economic rationales behind all these factors
Speaker 1: are the same reasons why we think that these sources
Speaker 1: of returns are going to persevere for a long time.
Speaker 1: And there are three. There's a reward for bearing risk,
Speaker 1: a structural impediment, and investors behavioral biases. So wait before
Speaker 1: we get to the first and the third one, which
Speaker 1: I have a feeling, I know what you're gonna say,
Speaker 1: tell me about the second one. What is the structural
Speaker 1: impediment to buying cheap or buying quality? Well, for value,
Speaker 1: there is no structural impediment because you can buy cheap.
Speaker 1: But for minimum volatility though, this is where structural impediments
Speaker 1: come in. And if we look at the United States,
Speaker 1: there are a large number of very large funds, a
Speaker 1: lot of public pension plans, but also other large institutions
Speaker 1: that have high to to return targets but a lot
Speaker 1: of restrictions on what they can do with their investment policies.
Speaker 1: Some of those institutions will gravitate to higher risk stocks
Speaker 1: in an attempt to meet those high told to return targets,
Speaker 1: and then underweight the low volatility or low risk stocks,
Speaker 1: and that gives rise to minimum volatility strategies. Now, if
Speaker 1: that structural impediment disappeared and suddenly all of those institutions
Speaker 1: had much more flexible investment policies than perhaps we might
Speaker 1: see minimum volatility go away. But let's go to the
Speaker 1: first and third of those right, Well, well, what about
Speaker 1: the reward for bearing risk? And here I'll give value
Speaker 1: as an example, since you raised this before, or small cap,
Speaker 1: because I keep having discussions with people who insist that
Speaker 1: the small cap premium is all risk, and I'm not
Speaker 1: sure how true that is. And if it is largely
Speaker 1: risk that we're shown, but that's a risk that you
Speaker 1: should be comfortable bearing and will result in the long
Speaker 1: term with compensated higher returns. Now, Value a lot of
Speaker 1: value companies a little bit old fashioned. They often manufacture
Speaker 1: things or produce services. They're very good at that, often
Speaker 1: with a lot of fixed or physical capital. And when
Speaker 1: you get into a lite economic cycle or an economic recession.
Speaker 1: It's very hard to change what your factory is currently manufactured,
Speaker 1: and so not surprisingly, those value firms tend to underperform
Speaker 1: those fixed costs. All of that physical capital give those
Speaker 1: value companies economies of scale, so they tend to perform
Speaker 1: the best coming out from the recessions in the recovery. Now,
Speaker 1: you can't stomach these cyclical losses, and value absolutely no doubt,
Speaker 1: has had a pretty rough ride of it over the
Speaker 1: past couple of quarters, consistent where where we are in
Speaker 1: the late economic cycle. If you can't stomach that under performance,
Speaker 1: then well values not for you, but for those who
Speaker 1: can bear those risks of short term to performance, you
Speaker 1: will be compensated with a long term value premium. That's
Speaker 1: the reward for barons. So how long is long term?
Speaker 1: Because since the OH nine crisis ended, value is under
Speaker 1: underperformed growth. That's a solid decade. We've been having an
Speaker 1: argument in my office. Is it one decade, two decades?
Speaker 1: It goes quite a while. Since value has consistently outperformed growth,
Speaker 1: What is the long term for for a factor manifesting
Speaker 1: itself as alpha. Yeah, these cycles can be three to
Speaker 1: five years, but over the last ten years value has outperformed.
Speaker 1: But we give a little bit of a story coming
Speaker 1: into twenty six and that was a really great year
Speaker 1: for value. In fact, particularly in the last part of
Speaker 1: the year. Started before Trump's election in November, value was
Speaker 1: pretty much flat, and then there was tremendous under performance.
Speaker 1: It was until the fourth quarter of the fourth quarter
Speaker 1: Uarter four, and then through twenty eighteen we saw those losses.
Speaker 1: Ex sorry, we find that twenty eighteen to now where
Speaker 1: in May is the fourth worst value draw down, fourth
Speaker 1: worst value draw down in almost a hundred years of
Speaker 1: data using the data set that starts in five constructed
Speaker 1: by Nobel Prize winning Gene Farmer and his longtime co
Speaker 1: author Kenneth French. Isn't that amazing fourth worth through century?
Speaker 1: That that's quite fascinating. Well again, fourth quarter of eighteen,
Speaker 1: when the market SMP fell about we saw value indexes
Speaker 1: do much better. They fell a little bit, but not
Speaker 1: nearly as much as the growth in this disease. The
Speaker 1: fang stocks got she lacked in the fourth quarter, But
Speaker 1: here we are pretty close to near all time highs
Speaker 1: and it looks like growth has kind of caught up again.
Speaker 1: So is the expectation we're going to see some serious
Speaker 1: mean reversion and at some point in the not to
Speaker 1: distant future. Well, I believe in value, I'm a value investor.
Speaker 1: M H. We do need to stay the course. Let's
Speaker 1: put that fourth worst draw down in context, so it's
Speaker 1: not unprecedented. There are some worst times, and if we
Speaker 1: look at the top six to eight episodes of really
Speaker 1: bad value performance, they're characterized by an environment like what
Speaker 1: we've had late economic cycle, except our cycle today has
Speaker 1: perhaps been very prolonged to be in this late stage,
Speaker 1: and I mean I think that's a separate macro post
Speaker 1: financial credit crisis, FED intervention, etcetera, monetary policy and all
Speaker 1: the rest of that. We also see some very severe
Speaker 1: accessions that There are two episodes in the nineteen thirties
Speaker 1: as well that are but the worst one is the
Speaker 1: late nineteen nineties in fact, and we're about half as
Speaker 1: bad as So let's talk a little bit about fixed
Speaker 1: income investors. Are they actually beginning to use factors now?
Speaker 1: They are, and we've just introduced a few fixed income
Speaker 1: factor e t f s. That's part of the next
Speaker 1: evolutionists pushing these time tested concepts of like buying cheap
Speaker 1: and finding higher quality names, finding trends, but pushing that
Speaker 1: from where it's being mostly equities to fixed income and
Speaker 1: then to other multi assets applications like currencies and commodities,
Speaker 1: and then also to go invest in a long short
Speaker 1: manner as well, and fixed income. It's right there at
Speaker 1: the frontier. So when I think of factor investing, I
Speaker 1: think of capsize, and I think of quality, and I
Speaker 1: think of price, namely value. Are you creating parallel versions
Speaker 1: of this for UM bonds? I mean our bonds cheaper
Speaker 1: expensive as a function relative to UM the combination of
Speaker 1: their credit quality and their forward expected cash flow based
Speaker 1: on will this default or not? How do you determine
Speaker 1: value or is it yield relative to to what the
Speaker 1: ten your treasury is doing. All of these factors are
Speaker 1: broad and persistent, so they are seen in many different areas,
Speaker 1: but you do need some research to apply it in
Speaker 1: these different asset classes. So value it's all about buying
Speaker 1: cheap relative to intrinsic right, what's intrinsic or fundamental value
Speaker 1: for a bond? And so we can measure or apply
Speaker 1: value by looking at the yield of a bond. Well,
Speaker 1: that's equivalent to price. But we might now have intrinsic
Speaker 1: value versus a forward rate curve or versus an option
Speaker 1: adjusted spread, for example. But we can apply the same
Speaker 1: concepts price or yield relative to a measure of intrinsic value.
Speaker 1: How does how does the risk free um treasury fit
Speaker 1: into figuring out what value is for a bond? The
Speaker 1: risk free rate operates across these different asset classes, and
Speaker 1: it's a base rate. It's sort of like the opportunity cost.
Speaker 1: Instead of parking your money in the bank, well, we're
Speaker 1: now going to take risk and were going to be
Speaker 1: rewarded for it. So it's coming across asset classes now.
Speaker 1: The risk free rate, though in fixed income, gives you
Speaker 1: a term structure, and that term structure will translate into
Speaker 1: different factor strategies. So some people will talk about curve
Speaker 1: or row down. That's certainly a part of income investing
Speaker 1: or carry. We can also talk about risk free rates
Speaker 1: affecting different countries, and so now we think of an
Speaker 1: uh an international version of fixed income factor investing as well.
Speaker 1: Since we're talking about fixed income investing, there's now about
Speaker 1: twelve trillion dollars of bonds that carry in negative yield. Here,
Speaker 1: I'm gonna lend you money, and I'm gonna pay you
Speaker 1: to hold my cash for me. How does that figure
Speaker 1: into factor investing for for bonds? And what does this
Speaker 1: say about the world of the cost of capital? I
Speaker 1: and almost guilty, really, because a lot of academics spent
Speaker 1: an enormous amount of time writing down very complicated models
Speaker 1: to ensure that interest rates remain positive. And what are
Speaker 1: they now? They're they're not positive. It's almost like all
Speaker 1: that literature could have been thrown out more seriously, More seriously,
Speaker 1: what's really relevant here is the real rate rather than
Speaker 1: the nominal relative to inflation, relative to inflation, and there
Speaker 1: we've seen many episodes of negative real rates, and some
Speaker 1: of them I've I've worked with some co authors in
Speaker 1: papers about. The second is that if we think about
Speaker 1: the negative yielders, that you invest in bonds and you're
Speaker 1: going to lose money. But we've had debt consolidations, we've
Speaker 1: had demonetizations, confiscations in the past. In the United States,
Speaker 1: we've actually seen some wealth destruction. In the nine thirties,
Speaker 1: we had a ban on individuals holding gold right, and
Speaker 1: that's a near money substitute, or at least it was
Speaker 1: at the time, because we were on the gold standard,
Speaker 1: So these negative yielding in a much order context. It's
Speaker 1: actually we've had these episodes before. Factors will continue to
Speaker 1: have a place in this period. I think you still
Speaker 1: want to buy cheap, you still want to find these trends,
Speaker 1: you want higher quality names probably that's more important than ever.
Speaker 1: And you want to have portfolio resilience. And we can
Speaker 1: hold a combination of factors to help these investors. So
Speaker 1: you mentioned the evolution of factors and the evolution of
Speaker 1: new products. What what do you see coming down the pike?
Speaker 1: What sort of stuff we've been hearing for a long
Speaker 1: time about these one thirty thirty portfolios, these long short
Speaker 1: et f s. What are the next things that factor
Speaker 1: investing is going to drive From the product side, we
Speaker 1: talked about some of these already pushing out these concepts
Speaker 1: from equities to fix income and other multi asset classes.
Speaker 1: But I think the real gains will come from applying
Speaker 1: data and technology. Two. The mom and dad sitting across
Speaker 1: the table from a financial advisor. And my vision is
Speaker 1: that if you're having that conversation with an individual and
Speaker 1: that individual says I'm really worried about losing my job,
Speaker 1: they're making a statement about economic growth, and we can
Speaker 1: have some factors to help hedge that bad outcome. What's
Speaker 1: a day in the life of Andrew Ang, Like, yeah,
Speaker 1: what does that mean? Leading factor investing at black Rock
Speaker 1: means like talk with a lot of people. I have
Speaker 1: the privilege of working with some really talented people, and
Speaker 1: I feel like a little kid in a candy store
Speaker 1: because there's all this great data and technology at Black
Speaker 1: Crock and we can put things to work and introduce
Speaker 1: new products and make it happen and have some factor analytics,
Speaker 1: data and technology all based on factors as well. So
Speaker 1: let's talk a little bit about your book. This is
Speaker 1: a serious um quantitative work. Tell us how the book
Speaker 1: came about, and uh, who's it for. I wrote the
Speaker 1: book after working for several large sovereign institutions, sovereign wealth fronts,
Speaker 1: sovereign pension plans, and I talked all about factors, and
Speaker 1: I wanted to bring all of that knowledge that just
Speaker 1: even a decade two decades ago, were only available to
Speaker 1: really large sophisticated institutions, and I wanted to democratize access.
Speaker 1: In fact, that's our mission statement. It's to democratize the
Speaker 1: broad and persistent democratize access to factors. And this book
Speaker 1: really put into context with case studies based on some
Speaker 1: of those, Uh, some of those institutions how to use
Speaker 1: factors in portfolios. So you said factors plural um and
Speaker 1: you mentioned Gene Farmer before, So the original Farmer French
Speaker 1: model was right. Then we got the five factor model,
Speaker 1: then the seven factor model. And some people have made
Speaker 1: the claim that and I'm a little skeptical that most
Speaker 1: of these are of of really significant value, that there
Speaker 1: are four hun drew to a five factors. Some people
Speaker 1: have said a thousand factors. How many factors are there
Speaker 1: and how many really can be implemented? There are half
Speaker 1: a dozen macro factors and half a dozen style factors.
Speaker 1: Macro factors drive returns across asset classes. The big three
Speaker 1: are economic growth, real rates, and inflation, and they explain
Speaker 1: about eight of the variation of returns across these different
Speaker 1: asset classes. Give us private markets. Give us those three again,
Speaker 1: economic growth, real rates, and inflation. Those are the big three,
Speaker 1: and within each different asset class, within equities, we can
Speaker 1: find pockets of securities that over the long run have
Speaker 1: resulted in higher risk adjusted returns. Those securities that are
Speaker 1: cheap or high quality that we talked about earlier, and
Speaker 1: we can find those same patents in bonds and the commodities.
Speaker 1: We can even find them in private markets like private
Speaker 1: equity and real estate. Those are style factors and they
Speaker 1: operate within an asset class, and in equities we think
Speaker 1: of value, quality, momentum, size, a minimum volatility. Now the
Speaker 1: criteria for these and why there's only like half a
Speaker 1: dozen macro half a dozen style, there's four criteria that
Speaker 1: whittles down the potential hundreds or thousands to just these narrows,
Speaker 1: just this narrow set. The first is that economic rationale
Speaker 1: that we talked about earlier, reward for bearing risks, structural
Speaker 1: and pediment or behavioral bias. We want very long histories
Speaker 1: and that removes basically most of that. A lot of these,
Speaker 1: a lot of these don't don't have decades worth of history,
Speaker 1: and we would like that so that it informs how
Speaker 1: we can build those strategies and offer them. We want
Speaker 1: differentiated returns, particularly with respect to market cap benchmarks, right,
Speaker 1: what is it giving us that that that is different
Speaker 1: And then finally we want and this is a choice
Speaker 1: for black Rock, we want to be able to offer
Speaker 1: these in scale, so that means we can pass on
Speaker 1: low costs to our clients. After him imposing all those
Speaker 1: full criteria, we're only left with that half a dozen
Speaker 1: macro half a dozen stuff quite interesting. So so what
Speaker 1: is next in factor land? Are there any yet undiscovered
Speaker 1: factors out there that might fall into either of these
Speaker 1: um two half dozen groups or have we pretty much
Speaker 1: squeezed all the juice out of the orange at this point.
Speaker 1: There's always continued development, but I think it's a little
Speaker 1: bit like Shakespeare. You know, he wrote some great plays
Speaker 1: and sonnets back in Elizabeth E. Times, did that with
Speaker 1: quill and inc. Right, we still have well, he will
Speaker 1: be writing screenplays today. Right, Perhaps we have some streaming
Speaker 1: TV and other things like that, but there's still character
Speaker 1: and plot. But it's done in different forms. And we
Speaker 1: want to evolve buying cheap finding train, so the implementations
Speaker 1: of cost will change. We can do this better with
Speaker 1: more efficient data and technology to lower transaction costs. We
Speaker 1: would also like to see how we can use them
Speaker 1: in portfolios. Factor analytics, factor allocation that I talked about earlier, Right,
Speaker 1: that's really what's new. But we're always going to have
Speaker 1: these half dozen macro and half dozen style. So you
Speaker 1: wrote a white paper that UM, I want to want
Speaker 1: out about a bit. The title was what does the
Speaker 1: yield curve tell us about GDP growth? And there's a
Speaker 1: professor at UM the University at Duke University who has
Speaker 1: a recession forecasting model which has a perfect track record,
Speaker 1: at least in the limited time it's existed, it's been perfect.
Speaker 1: The fourth factor in his model is the inverted yield
Speaker 1: curve uses the five year in the three months UM
Speaker 1: and only when it's inverted for a substantial period of time,
Speaker 1: which in his measurement is ninety days a full quarter.
Speaker 1: Last week we passed that we've already been inverted for
Speaker 1: that period of time. So I'm curious about what you found.
Speaker 1: What the yield curve means for future GDP growth? He
Speaker 1: suggests it's an indicator of recession twelve to eighteen months later.
Speaker 1: What what did you find? The YU curve has a
Speaker 1: lot of information about future economic activity, and there's always
Speaker 1: been a slowdown after a negative UM. There's always been
Speaker 1: a slowdown following a negative yelk of six quarters afterwards,
Speaker 1: meaning an inversion. There's been actually one false positive, and
Speaker 1: that's in the late nineteen sixties, but there was still
Speaker 1: a slowdown in that period. Now that's an ode paper
Speaker 1: baron that you brought up, and we actually showed that
Speaker 1: in addition to the term spread negative term spread forecasting
Speaker 1: poor economic activity, the level of the interest rate was
Speaker 1: also pretty important too. And interest rates are fairly low
Speaker 1: now and they've actually decreased over the last couple of
Speaker 1: months around the world. The level of that you curve
Speaker 1: also forecasts slowdowns. So it's not just the inversion, but
Speaker 1: inversion from a relatively low level also has a negative
Speaker 1: conta both the level. Low levels predict slowdowns and spreads.
Speaker 1: Negative spreads also predicts So why would low levels predict
Speaker 1: the slowdown? Is it a function of demand for capital
Speaker 1: that's used by an expanding economy or something else? This
Speaker 1: several explanations. See I'll just give one by John Taylor.
Speaker 1: All Right, the Taylor rule? When is that? Is that
Speaker 1: still in effect? I thought we sort of didn't. Didn't
Speaker 1: we repeal the tailor rule. We've used it as the
Speaker 1: basis for many different policy and macro models, just perhaps
Speaker 1: not in its purest form to John Taylor, But it's
Speaker 1: gone through very iterations, and I think the intuition is
Speaker 1: still sound. Policymakers generally will raise interest rates when we're
Speaker 1: in very good times, right Inflation tends to pick up
Speaker 1: there and we want to take the punch bowl away.
Speaker 1: During bad times, policymakers tend to lower interest rates to
Speaker 1: simulate economic activity. And all these types of policy interactions
Speaker 1: will give rise to when bad times come, interest rates
Speaker 1: tend to be low. So that sounds a little bit
Speaker 1: like policymakers are engaging a little bit of market timing themselves.
Speaker 1: Let's talk about another paper of yours where you look
Speaker 1: at factor timing and time series. Can an investor use
Speaker 1: factors as part of a market timing approach? Are there
Speaker 1: better or worse times for some factors? Or should it
Speaker 1: just be full factor diversification across the board. That's a
Speaker 1: paper we just published in the General Portfolio Management not
Speaker 1: so long ago. Investors should start with a long term
Speaker 1: strategic combination too. Lots of factors, don't hold just one.
Speaker 1: If you hold just value, well, I felt it, you
Speaker 1: felt it over the last couple of quarters, it's being painful.
Speaker 1: We want lots of factors for diversification, but around that
Speaker 1: long term strategic multi factor combination. We might think about tilting,
Speaker 1: and I like the word tilting rather than the word timing,
Speaker 1: because sometimes timing has these connotations are really short term
Speaker 1: global macro that's not what we're about. But around those
Speaker 1: strategic benchmarks you might tilt, and the paper gives a
Speaker 1: framework to think about how to do that. So first,
Speaker 1: factors become rich or cheap, just like every asset. So wait,
Speaker 1: so we then within let's say the value factor, which
Speaker 1: is looking at stocks that might be expensive or cheap,
Speaker 1: there are times when that factor itself is expensive or chieved.
Speaker 1: That's correct, So it's a second derivative removed once for um,
Speaker 1: the underlying cheapness of it's. Now I'm gonna blow your
Speaker 1: mind because that's truthful. Momentum momentum also helps. The pilsum
Speaker 1: too has momentum. They are valuam momentum of valid momentum,
Speaker 1: in fact, value momentum of each factor. But that's, by
Speaker 1: the way, that's the most interesting thing I've heard today.
Speaker 1: I just have to share that with you. Now that
Speaker 1: that each of these have a derivative that is reflective,
Speaker 1: it's almost like a mental brought reflexivity um or higher level,
Speaker 1: meta factor, meta factor. Okay, yeah, but so there are
Speaker 1: value and momentum effects. Will call that second one relative strength,
Speaker 1: because we want to measure this trends of these factors
Speaker 1: to each other. I know that stocks can be cheaper
Speaker 1: or more expensive at different times, but I always assumed, Hey,
Speaker 1: the bottom least expensive let's call a third of stocks,
Speaker 1: is always going to be cheaper than everything else. I
Speaker 1: never stopped to think that, sure they're they're relatively inexpensive,
Speaker 1: but on an absolute basis, cheap stocks can be expensive.
Speaker 1: That's that meta value is really quite fascinating. How do
Speaker 1: you incorporate that into what you do? Very that's such
Speaker 1: a really deep comment that you've just made. Because value
Speaker 1: is always cheap, So what do you mean about using
Speaker 1: value for value? So what we really mean here is
Speaker 1: if we take the value factor, how cheap is value
Speaker 1: currently relative to how cheap it's been in the past,
Speaker 1: its own its own history, right, And then we can
Speaker 1: also compare how cheap value is to other factors. And
Speaker 1: if you're a quort, you would call this a time
Speaker 1: series and cross sectional score. And that also applies to
Speaker 1: relative strength or momentum, because momentum, by definition, the momentum
Speaker 1: factor always has the most momentum. So what you really
Speaker 1: mean here is what's the current trend of momentum relative
Speaker 1: to the past trends that momentum has had. And then
Speaker 1: once the relative strength of my even factor relative to
Speaker 1: the trends of other factors. Again, it's this time series
Speaker 1: and cross sectional. So in other words, it's which factor
Speaker 1: is doing the best relative to other factors. That's right,
Speaker 1: And so we actually put all these and one of
Speaker 1: the you talked about a bit before about the frontiers
Speaker 1: of factor investing is factor investing is really about taking
Speaker 1: active insights things like value and momentum, but we can
Speaker 1: also apply them in other active ways. Factor tilting is
Speaker 1: one of those ways. So so let's talk about that,
Speaker 1: because years ago there were a number of models that
Speaker 1: came out and they didn't do factor tilting. They tried
Speaker 1: to do sector tilting. They would rotate within the SMP five,
Speaker 1: within the different groups that would go from technology to healthcare,
Speaker 1: um to to finance, and they always sounded great on paper,
Speaker 1: and then in the real world they didn't do so well.
Speaker 1: So on a on on this sort of factor tilting model,
Speaker 1: how can you capture in real time those benefits. Aren't
Speaker 1: you always going to be lagging? What do you use
Speaker 1: as a signal to say, all right, now is the
Speaker 1: time to over emphasize cap as opposed to quality or
Speaker 1: is there too much of a lag to capture that?
Speaker 1: Or do you get enough of the heads up? Hey,
Speaker 1: here's the direction this is shifting. You can move some
Speaker 1: of the portfolio quickly enough to take advantage of it. Well,
Speaker 1: I believe all types of tilting it they're hot and
Speaker 1: factor tilting. It's hard to but done in a discipline way.
Speaker 1: There's a couple of differences to country or sector rotation,
Speaker 1: so they're nice compliments. So often we like to apply
Speaker 1: factors within a particular sector or within a particular region,
Speaker 1: and so that gives room for factor rotation to sit
Speaker 1: side by side with these others. Second, is that exposure
Speaker 1: to sectors over the long run. In fact, actually the
Speaker 1: capa and works fine. There are some academic papers on
Speaker 1: that too. If we take a strategic portfolio that bias
Speaker 1: cheap finds, trends finds, high quality names, right, all those
Speaker 1: factors those are long term determinants or performance. Whereas static
Speaker 1: sector exposure, well, actually the market has sectors might as well,
Speaker 1: do that, but these factors, the strategic tilt gives you
Speaker 1: an uplift over the long run innovat of itself, and
Speaker 1: then around that you might incrementally add returns with the
Speaker 1: factor rotation. And the third difference I think is that
Speaker 1: with these factors we can employ them in different ways.
Speaker 1: So we want to do this transparently. We have this paper,
Speaker 1: we've introduced some some products. We want to be active
Speaker 1: with factors. Let's not just use one signal. Let's look
Speaker 1: at definitely and how cheap something is. We talked about
Speaker 1: relative strength as well. We'll use the economic regime measures
Speaker 1: of the opportunity set or dispersion. But we want to
Speaker 1: use all of those insights to other m HM. So
Speaker 1: so let's talk about something UM not market timing, but
Speaker 1: factor tilts. If I had could have my way, I
Speaker 1: would at the end of a recession lean as heavily
Speaker 1: towards growth as I could. Not always easy to do.
Speaker 1: Everybody is miserable. No one wants to hear you. In
Speaker 1: March O nine say okay, now is the time to
Speaker 1: buy the growth stocks UM that have done nothing but
Speaker 1: get killed for the past two years and towards the
Speaker 1: end of the cycle, and that assumes you know in
Speaker 1: the end of the cycle is in advance. Typically we
Speaker 1: don't know till after the fact. Gradually move that tilt
Speaker 1: away from growth towards value, because if your charges, you
Speaker 1: must be fully invested at all times. On the equity side,
Speaker 1: the assumption is that in any sort of recession, be
Speaker 1: it a mild recession or something like oh, eight oh
Speaker 1: nine or two thousand oh two, you're gonna see growth
Speaker 1: gets relaxed and value is going to hold up much better.
Speaker 1: And I can't help but recall hearing this Warren Buffett
Speaker 1: guy is washed up. That sort of value crap is
Speaker 1: never gonna work again. And as people said, that was
Speaker 1: really when he began another period of huge out performance. So, first,
Speaker 1: is that something that you can accomplish with tilts? And second,
Speaker 1: how do you get the timing right at the end
Speaker 1: of a market crash. It's pretty clear, Um, when you're
Speaker 1: closer let's let's say, closer to the end than the beginning. Um,
Speaker 1: So whether it was January O nine or juneo nine,
Speaker 1: anywhere in that range, you're you're pretty close. You're you're
Speaker 1: much closer to the end of that than the beginning.
Speaker 1: How how does one make that determination that we want
Speaker 1: to tilt towards growth here and here's how to do it.
Speaker 1: And then at the other end of the cycle, Hey,
Speaker 1: we want to tilt more towards value here and here
Speaker 1: are the signals that send us. How would one do that.
Speaker 1: Let's remember first, diversification, diversification, diversification, that's the key, so
Speaker 1: you have it's really hard too, I think, to to
Speaker 1: to call anything with precision or make decisions about individual
Speaker 1: factors or any type of investment. Diversification is that key,
Speaker 1: and that provides that long term strategic benchmark. But around that,
Speaker 1: if you have the rest tolerance and the capability and
Speaker 1: be active with factors, then would like to use information
Speaker 1: about how cheap a given factor is. We'll see if
Speaker 1: the factor is trending up right versus trending down. In fact,
Speaker 1: value has been trending down over the past couple of
Speaker 1: of quarters, but value is cheap today. Would also like
Speaker 1: to see where we are in the economic cycle. The
Speaker 1: fact that we're in that late stage where we said
Speaker 1: that value firms tend to underperform. That's not very favorable
Speaker 1: to value. We also look at dispersion. Dispersion for value,
Speaker 1: it's okay, but it doesn't scream like it's a it's
Speaker 1: a big I. We use all of those together and
Speaker 1: then we'll have an aggregate view on these different factors.
Speaker 1: Quite quite interesting. There were a few other questions I
Speaker 1: want to get to before we get to our standard
Speaker 1: um question. We we mentioned value stocks underperforming. I saw
Speaker 1: something recently that said they've underperformed for thirty five years?
Speaker 1: Is that remote? Well, that seems wrong, doesn't it? Even?
Speaker 1: In fact, if I'll performed in the last ten years,
Speaker 1: but they've been difficult periods under the past past decade,
Speaker 1: growth is outperformed in the past. I had actually values
Speaker 1: done quite well. Value since oh not? But value, yes,
Speaker 1: value over the past uh two years has suffered. Okay,
Speaker 1: that's interesting though. I I have looked at value as
Speaker 1: let me rephrase that. I've looked at growth as doing
Speaker 1: exceedingly well since the end of the financial crisis. Think
Speaker 1: about Amazon at dollars and Apple at twelve dollars or
Speaker 1: whatever the prices were, and they've all exploded, and I
Speaker 1: guess they're categorized as growth. Although with those prices you
Speaker 1: can really call those value stacks. You know, that's a
Speaker 1: great a great point. That comes one of the topics
Speaker 1: that I wrote about recently in my blog Andrew's Angle,
Speaker 1: and it's growth. That's not the opposite of value, all right,
Speaker 1: And we kind of used the word well, it's certainly
Speaker 1: value for cheap. We've used the word growth to denote expensive.
Speaker 1: But actually there's two other connotations of growth which are
Speaker 1: quite distinct from the opposite of value. I mean, the
Speaker 1: first one is that a lot of growth managers will
Speaker 1: search for trends, and you'd actually like to a trend
Speaker 1: to be sustainable, and that's an aspect of momentum investing,
Speaker 1: and that's rewarded over the long run. Another aspect of
Speaker 1: growth is something that you alluded to, is like, what's
Speaker 1: the quality actually behind that? And indeed, if you look
Speaker 1: at many growth funds, certainly they will load many of
Speaker 1: them on momentum and quality factors. Growth itself is not
Speaker 1: the opposite of value. But I think you don't want
Speaker 1: to buy expensive. If a stock does tend to be
Speaker 1: more expensive, that is not value. It might be justifiable
Speaker 1: because it might have aspects of quality or momentum in there.
Speaker 1: So when you are defining something as a growth stock
Speaker 1: or a value stock, you know my frame of references.
Speaker 1: There's the SMP five growth group and the SMP five
Speaker 1: hundred value group, and never the twins shall meet. But
Speaker 1: I suspect you might take issue with some of the
Speaker 1: stocks they call growth in something, and I think it's
Speaker 1: a little bit more nuanced. I would call the first
Speaker 1: generation exactly just splitting the thing into two, and today
Speaker 1: we would think a little bit harder, and many stocks
Speaker 1: will have aspects of multiple factors within that same stock.
Speaker 1: So let's talk a little bit about back testing. We're
Speaker 1: really gonna go deep into the weeds here. Um. It
Speaker 1: seems that a lot of back tests show these great
Speaker 1: returns for different combinations of factors, and then implementing them
Speaker 1: in the real world becomes challenging. You mentioned the problems
Speaker 1: with organizations and getting everybody pulling in the same direction. Um,
Speaker 1: But there have been instances of small um hedge funds
Speaker 1: quantitative hedge funds that try to implement these and momentum
Speaker 1: is a perfect example. Momentum has some real application in
Speaker 1: real portfolios, but it seems the back tests are always
Speaker 1: much better than the actual implementation. What is it about
Speaker 1: momentum and some of these other factors that makes it
Speaker 1: so challenging to capture what theory says in practice, momentum
Speaker 1: has pretty high turnover, So a momentum funds run at
Speaker 1: turnover above significantly about Because of that, transaction costs are crucial.
Speaker 1: So you see some research in the literature that says, actually,
Speaker 1: we can't really do momentum and practice, and some others
Speaker 1: that will say, well, if you're very good at transaction
Speaker 1: costs optimization and you have access to transaction cost minimization
Speaker 1: in your execution, then momentum will be a favorable and
Speaker 1: profitable factor. So it's really key that you have to
Speaker 1: really look at the details once you implement a factor.
Speaker 1: The devil is always in the details. Let's let's let's
Speaker 1: look at another one. Theoretically, high beta stocks should do
Speaker 1: really well, but your research in as implemented in black
Speaker 1: Rock has found low volatility stocks have done well. Why
Speaker 1: is it that the high beta stocks aren't capturing those
Speaker 1: gains once you have a portfolio implement implementation, it's the
Speaker 1: low vall stocks that seem to be doing better. Yeah,
Speaker 1: and this is a paper that I wrote in the
Speaker 1: two thousands, and this paper, I'm lucky and very fortunate,
Speaker 1: has played a really important role in building out the
Speaker 1: minimum volatility and factor industry more broadly, and you've hit
Speaker 1: on the key note here that in theory we should
Speaker 1: have higher risk stocks should have higher returns, but actually
Speaker 1: we found the opposite. And in the paper my co
Speaker 1: authors and I said, the higher risk stocks have quote
Speaker 1: abysmally low returns unquote abysmally low. And if we rank
Speaker 1: stocks based on risk, and we did this by volatility
Speaker 1: idios and critic and total volaty of the paper, subsequent
Speaker 1: papers did this by beta or downside risk measures. The
Speaker 1: general pattern is that stocks have the same expected return
Speaker 1: and then as the volatility increases, there's a very steep
Speaker 1: drop off in returns for the very high risk stocks.
Speaker 1: And that's actually this low volatility effect. If you construct
Speaker 1: a portfolio of minimum volatility, and you can do that
Speaker 1: by holding low data stocks or stocks with low idiosyncratic risk,
Speaker 1: or both, you form a portfolio of low volatility that
Speaker 1: gives you the same return over the long run as
Speaker 1: the market, but it does so with reduced risk. The
Speaker 1: shop ratio is high not because of the numerator of
Speaker 1: the higher expected return, but it's because of the decrease
Speaker 1: in the denominator the reduced risk. So if someone were
Speaker 1: to come to me and say, listen, I could give
Speaker 1: you market returns, but much lower draw downs, much lower volatility.
Speaker 1: Of course I want to say, I want some of that.
Speaker 1: If you're not going to get out performance for the
Speaker 1: same um volatility, well the same performance for less volatility.
Speaker 1: That seems like it's much more livable for the average investor.
Speaker 1: And I think that's one of the great benefits for
Speaker 1: minimum volatility strategies is it just helps an investor stay
Speaker 1: the course. So you're an subject to those tremendous swings,
Speaker 1: particularly on the downside, and we can mitigate some of
Speaker 1: that downside risk with these minimum volatility strategies. Upside downside
Speaker 1: capture ratios for minimum volatility alright, you well, it's all
Speaker 1: about trying to uh participate in as few as possible
Speaker 1: of these drawdowns. It's around fifty downside and upside for
Speaker 1: these downside upside risk capture issues. That that's really quite interesting.
Speaker 1: Um So one of the questions I mentioned to somebody
Speaker 1: I was speaking to you, and they asked a really
Speaker 1: interesting question. Do you consider factor indexes to be closer
Speaker 1: to the active spectrum or closer to the passive end
Speaker 1: of the spectrum. Where where do you put factor investing
Speaker 1: on that continuum from active to passive? Oh, this is
Speaker 1: another one of these uh yes questions. That's right. You
Speaker 1: know that this is a bugbear of mine. I have
Speaker 1: to say barriers that everything is active and it's just
Speaker 1: a question of greater or lescent degree. I totally agree.
Speaker 1: I I've written and discussed that even the basic SMP
Speaker 1: five hundreds somebody made. That's right. That's a bunch of
Speaker 1: active decisions about one market cap rated and where do
Speaker 1: you draw the what's the free float and what gets
Speaker 1: in there? Right? And then while do you use the
Speaker 1: SMP five founderversus some other index? Right? And then you
Speaker 1: know when we go to other asset classes, you know
Speaker 1: it's almost all active implementation. Right. So I think I
Speaker 1: would like to rephrase that question, if I may, on
Speaker 1: the difference between index or average and then taking um
Speaker 1: deviations from there. And in this context, factors are absolutely active.
Speaker 1: We're tilting towards broad and persistent sources of returns. We
Speaker 1: don't want to hold a market cup. Folil would favor
Speaker 1: overweighting stocks that have low prices relative to intrinsic value.
Speaker 1: Those stocks that are trending up right, those stocks of
Speaker 1: high quality earnings, and those are active decisions. What we're
Speaker 1: doing it in a transparent way. It's low cost, we
Speaker 1: can put it into an easy to access fund, right,
Speaker 1: and we can put these insights into multiple asset classes too.
Speaker 1: So you keep referencing this is being done in a
Speaker 1: transparent way. Why is that important? Because I look at
Speaker 1: places like the Shore or renaissance technologies that have generated
Speaker 1: out performance for decades, they're not transparent. Those are their
Speaker 1: secret sauce that goes into their alpha generation. Why does
Speaker 1: black Rock feel we're creating something and we want to
Speaker 1: be completely transparent in this product. I believe in active,
Speaker 1: I believe in alpha, and I define these factors as
Speaker 1: broad you see them in many places and persistently rewarded.
Speaker 1: We've got decades of academic research, so is this a
Speaker 1: peer reviewed approach to invest in now? Alpha is actually
Speaker 1: not broad and persistent, right requires specialized skills like the
Speaker 1: firms that you've talked about. Alpham two will use sophisticated
Speaker 1: techniques with big data and machine learning. You could have
Speaker 1: a fundamental approach that news a lot about just a
Speaker 1: few stocks. The complete opposite of broad and those when
Speaker 1: you find those skills, you should reward them. Sometimes you
Speaker 1: might be able to generate alpha insights by taking advantage
Speaker 1: of very short term high frequency market dislocations. When we
Speaker 1: find that school, we should pay up for it. But
Speaker 1: those things that have been in the literature for decades,
Speaker 1: that have been well studied, that the game is all
Speaker 1: about implementation and efficiency, well, I don't think we should
Speaker 1: be paying very expensive fees for that. We should be
Speaker 1: giving control to the client. We should be paying less
Speaker 1: and getting more. And that's where factors come in. So
Speaker 1: so where does the transparency on some of these new
Speaker 1: models come in? Why share your findings as opposed to
Speaker 1: keeping it secret. We believe in sharing, and we know
Speaker 1: that these factors are going to endure because of that
Speaker 1: economic rationale. Right, there's always going to be the reward
Speaker 1: for bearing risk. Unless these structural impediments get removed, they're
Speaker 1: going to be there. And investors, well, they're going to
Speaker 1: be investors. There's going to be these behavioral biases as
Speaker 1: long as these economic rationales endure, these factors are going
Speaker 1: to be with us. They're going to be cyclical. Absolutely,
Speaker 1: so sometimes there might be room for a factor tilting.
Speaker 1: But these factors are going to be with us for
Speaker 1: decades to come, and let's share this and democratize access
Speaker 1: to all of this. That's our purpose, and we can
Speaker 1: do that so that you understand what's inside, how we
Speaker 1: exactly buy cheap and we want to make sure that
Speaker 1: you see it. So sometimes you might want to have
Speaker 1: position level information available. It helps you fit that with
Speaker 1: the rest of your portfolio, or integrate it with data
Speaker 1: and technology, and you might have better risk management. I
Speaker 1: think that approach is unusual. Not a lot of firms
Speaker 1: the size of black Rock are comfortable sharing their research.
Speaker 1: Although I get us. Black Rock could say, hey, we're
Speaker 1: so big, we're so efficient. Here here's the secret source.
Speaker 1: You can never do this as cheaply as we could. Anyway,
Speaker 1: I'm that's my words. I'm not under means, but word.
Speaker 1: We always put the client first. So but you got
Speaker 1: I mean, not to the clients, to competitors, to other
Speaker 1: people who might say, oh, here's a new paper from
Speaker 1: black Rock, let's see if we can find something to
Speaker 1: implement from this. I find it to be a typical,
Speaker 1: although I guess that's not Lots of firms published white
Speaker 1: papers lots of firms do that, so maybe I'm over
Speaker 1: emphasizing the transparency aspect of it. I just find that
Speaker 1: intriguing that the secret source from a particular group of funds.
Speaker 1: You guys are that open with and I guess I think,
Speaker 1: I think maybe you would agree with my wife once
Speaker 1: you called me a hypocrite because I'm I'm the ultimate
Speaker 1: true believer. There you go. So so that makes a
Speaker 1: lot of sense to me. So let me jump to
Speaker 1: my favorite questions. This is our speed round, and we
Speaker 1: asked this of all our guests. UM, let's start with
Speaker 1: a simple question. What was the first car you've ever owned?
Speaker 1: Year making model Toyota Corolla three one point six Leader,
Speaker 1: kind of maroon color, which was really fortunate because the
Speaker 1: amount of rust and there you kind of you couldn't see.
Speaker 1: They always made good cars, but in the early days,
Speaker 1: that was a very thin metal and it was a rustbucket.
Speaker 1: It happens a lot. I remember that drove that car
Speaker 1: across Australia. Really, Um, where you originally from? I was
Speaker 1: born in Malaysia, and uh during the late nineteen sixties
Speaker 1: and early nineteen seventies, Malaysia went through a series of
Speaker 1: race rights and my parents wanted somewhere safe to raise
Speaker 1: their family. And then White Australia Policy ended and that
Speaker 1: was actually the official name of the policy Australia was
Speaker 1: ended by Gulf Whitlam Australian Prime Minister in nineteen seventy three.
Speaker 1: And we were one of the first Asian families after
Speaker 1: the wide Australian policy to move to Perth. And I
Speaker 1: remember growing up, I was the only non white kid
Speaker 1: in class and I was really different, kind of marked
Speaker 1: my whole worldview. UM factors really are all about walking
Speaker 1: through and being different too. I did well in school,
Speaker 1: was so thankful for UM, for the opportunities that were
Speaker 1: given to me. And and then you know, I ended
Speaker 1: up in the US for for graduate school. I got
Speaker 1: to work on it Scotinamian who was a professor. And
Speaker 1: you know, now I'm I'm like every other person who
Speaker 1: lives in New York City. That's so, that's so interesting.
Speaker 1: So I was going to ask you a question, what's
Speaker 1: the most important thing we don't know about you? But
Speaker 1: I suspect you maybe I don't know. I'm I'm a musician.
Speaker 1: Oh really, so with the piano, I am a classical pianist.
Speaker 1: I used to play the violin, but I've always loved
Speaker 1: the piano more. I've played in a few black rock
Speaker 1: corporate bands, so I'm trying to expand my musical genres
Speaker 1: away from thelassical towards So have you ever done like
Speaker 1: full classical m I've concertos. Have you played for audiences?
Speaker 1: How far did your music career take? Yes, I can
Speaker 1: play those. Really, that doesn't mean I'm very good at it,
Speaker 1: but I love I love playing. So so tell us
Speaker 1: about some of your mentors who helped develop the way
Speaker 1: you think about markets. I would like to answer that
Speaker 1: in two ways. So the first one is like, who
Speaker 1: do I model myself on in black rock running a
Speaker 1: business and trying to change the world with factors? And
Speaker 1: that person is Walt Disney. Really it's not an investor.
Speaker 1: But if we look at Walt Disney, he didn't invent
Speaker 1: animated films. He didn't invent amusement parks right, or people
Speaker 1: dressed up in uh in different characters. But what he
Speaker 1: did he brought all of those together and he just
Speaker 1: by integrating all that created something new. And that's actually
Speaker 1: what factors are doing too. We didn't invent buying cheap right.
Speaker 1: We didn't invent momentum, but bring of those together with
Speaker 1: darn too technology, Yes, we can remake the world and
Speaker 1: give people a better experience. Interesting. Just a footnote, I
Speaker 1: was at Disneyland two weeks ago. Is the first time
Speaker 1: I've ever gone to any Disney property, and it's quite
Speaker 1: the experience too. In your fifties experience a Disney park
Speaker 1: for for the first time all ages, it is the
Speaker 1: happiest and I basically any ride, I don't care, fast,
Speaker 1: upside down, doesn't matter. I'm I'm right there, and we
Speaker 1: had a blast. It was absolutely you could see why.
Speaker 1: Oh no, I kind of get Disney. This makes a
Speaker 1: lot of sense. But but the other mentors, Yeah, you
Speaker 1: know I I was pretty nerdy, as you can tell. Uh.
Speaker 1: And one of the weight nerdy I have not noticed
Speaker 1: in this book on quantitative factor investing, I did not
Speaker 1: notice anything at all. Uh. And when I was in
Speaker 1: school high school, I got to go to National Mathematics
Speaker 1: summer school and that was just an eye open up
Speaker 1: for that. There were people kind of like me that
Speaker 1: liked math and it really changed my life. So let's
Speaker 1: talk a little bit about investors who influenced your approach
Speaker 1: to investing. Who were the folks that really shaped your
Speaker 1: investing worldview? If any reader or listener out there hasn't
Speaker 1: read Graham and Odd security analysis, but those were two
Speaker 1: professors that the institution I taught up for many years
Speaker 1: Columbia University. You've got to read that book. It's the
Speaker 1: basis for value. Quality is in there because they teach
Speaker 1: us that in order to estimate intrinsic value, you got
Speaker 1: to use the more permanent components of anage, things that
Speaker 1: we use in quality today. I have to mention Bogel.
Speaker 1: When I met him for the first time, he he
Speaker 1: actually was citing some things out of my book, particularly
Speaker 1: that chapter on governance or agency theory. And one other
Speaker 1: person is Joe Grin, but just to look at look
Speaker 1: at us thematic approach to out to some capital. Yeah,
Speaker 1: he's a very interesting guy. So let's talk about books.
Speaker 1: What are some of your favorite books, be they market related,
Speaker 1: not fiction, nonfiction. What have you enjoyed reading. I like
Speaker 1: reading popular science books. My most recent one is by
Speaker 1: Simontgomery and called The Soul of an Octopus. That amazing creatures, right,
Speaker 1: and they just look so alien, but their emotional vally
Speaker 1: in Teargent like them all like Custin we think. I'm
Speaker 1: gonna tell you something. I read that book and I
Speaker 1: stopped eating octopus afterwards. It basically and I eat pretty
Speaker 1: much everything except cauliflower and brussels sprouts. That book is
Speaker 1: the first thing I've ever read trying not to eat
Speaker 1: brussel sprouts. I can't. I can't eat octopus anymore. They
Speaker 1: They're just too intelligent and too soulful. One of the
Speaker 1: things about popular science books that I like is even
Speaker 1: for the areas that I'm familiar with and in some
Speaker 1: cases you would say beat in the weeds with in
Speaker 1: the research, you always learned something from them, because the
Speaker 1: best ones just present information in a new way, or
Speaker 1: they just open up your frontier, like like the Simon
Speaker 1: Comery book. Give us another I think, UM like some
Speaker 1: popular books on number theory and just physics and sciences
Speaker 1: in general. Le let's hear some titles. Well. One of
Speaker 1: them is Moonshot. It's about the American um policy. Yeah,
Speaker 1: it's amazing book as well, moon Shot. That's um who
Speaker 1: wrote that. I can't remember the full title right now.
Speaker 1: Let's let's have Google rescue us while and then you
Speaker 1: can mention it shot and we'll edit this out, uh,
Speaker 1: and I have to. It's a pretty long title. In fact,
Speaker 1: that Soul of an Octopus is a pretty long title too. So,
Speaker 1: by the way, while I'm while I'm looking for this,
Speaker 1: I recommended that book to my friends David Nodded, who
Speaker 1: send set me that book and said thanks for the recommendation.
Speaker 1: That book made me cry. There's another one by what
Speaker 1: a Landing on the Moon Teaches? That's why I don't
Speaker 1: Richard Wiseman. There's another one very very similar to Symontgomery's
Speaker 1: book called by france To Rudin called uh, I Think
Speaker 1: Mama's Hug, Mama's Last Hug. It's about Mama's last It's
Speaker 1: about the great apes and their their intelligence in the capacity.
Speaker 1: I'd enjoyed that one too. If if you I'm gonna
Speaker 1: put that one, I put all these on my list.
Speaker 1: But if if you like that, have you ever read
Speaker 1: Last Ape Standing? So it's basically about the thirty or
Speaker 1: so proto human species that had come out. You know,
Speaker 1: you know chro Magnum, you know Neanderthal, but you don't
Speaker 1: know there's thirty others and how close they all came
Speaker 1: to being wiped out in the ice age, and how
Speaker 1: this particular last ape standing UM, the Homo sapiens ended
Speaker 1: up being the ones who who survived and eventually took over.
Speaker 1: But if you're at all interested in nonfiction, I always
Speaker 1: recommend that book. I've found that delightful. All right, so
Speaker 1: we have three books. Let's jump to UM failure. Tell
Speaker 1: us about a time you failed and what you learned
Speaker 1: from the experience. I turn up at grad school, went
Speaker 1: to Stanford, and I did pretty well. My undergrad one
Speaker 1: a university medal, rote a dissertation, you know, Dunning Kruger
Speaker 1: kind of effect, and you get to grad school n Kruger,
Speaker 1: such a humbling experience. I did so badly. I thought
Speaker 1: about withdrawing. I had to take all these classes in
Speaker 1: the statistics department. That's actually why I have this Masters
Speaker 1: of Science and Statistics was just because I was in
Speaker 1: UM the remedial program. To take all these extra things
Speaker 1: that I should have known before I entered my degree.
Speaker 1: That was a really humbling experience. So that's very high
Speaker 1: level Dunning Krueger meta cognition. I experienced that in college.
Speaker 1: It's like high school was easy. You get to college
Speaker 1: and suddenly it's like, Oh, these people are really smart
Speaker 1: and they work really hard. I can't just you know,
Speaker 1: phone it in. I don't know what your experience was
Speaker 1: like in grad school, but it was in hindsight, pure
Speaker 1: Dunning Krueger. What did I learn is, um, you can't
Speaker 1: do it on your own. So I think everyone off
Speaker 1: my class, June and June, Mark, Maria, Eric. Without you,
Speaker 1: I could not have got through my homeworks and got
Speaker 1: that gotten through. Wow, that's that's quite interesting. So what
Speaker 1: do you do for fun? What do you do when
Speaker 1: you're not crunching numbers? That's that's your that's your UM,
Speaker 1: that's your stress release. That's my stress relase. Sitting at
Speaker 1: a keyboard and just working your way through a Grand
Speaker 1: Masters composition. Well, right now I'm also trying to do pilates.
Speaker 1: I'm very very stiff. So my goal is to try
Speaker 1: to touch my Okay, uh, tell us what your most
Speaker 1: optimistic and pessimistic about today. It could be markets, investing, economy.
Speaker 1: What what do you most optimistic and most pessimistic about? Oh?
Speaker 1: I love all of the opportunities that are here today,
Speaker 1: particularly for the fact is what we've been talking talking
Speaker 1: about and the great advances that we will make to
Speaker 1: put all those benefits in the hands of consumers and clients.
Speaker 1: One of my pessimistic about well, my parents were migrants.
Speaker 1: Really glad my parents migrated and gave me opportunities in
Speaker 1: Australian then you know, living here in the US, and
Speaker 1: there's this expression I got a fairgo and people a
Speaker 1: fair go, And I'm a little pessimistic that there's increasing inequality,
Speaker 1: lack of mobility, and bottom line is we should be
Speaker 1: trying to give as many people a fair go, very
Speaker 1: very reasonable. Let's uh, let me get to my two
Speaker 1: favorite questions. Um, a millennial or someone just beginning their
Speaker 1: career in finance comes up to you and ask for
Speaker 1: some advice. What sort of advice would you give them.
Speaker 1: It's actually advice I give myself. It was given to
Speaker 1: me by Bob Hodrick, a colleague and co author and friend,
Speaker 1: and he said, it's not your life. Don't presume to
Speaker 1: to suggest that it's it's your life either, but explain,
Speaker 1: give me, give me a little more. Do you make
Speaker 1: your choices. My preferences aren't yours, And you go and
Speaker 1: do what you think is best, and I'll go and
Speaker 1: support you the best that you can the best I can.
Speaker 1: That's quite intriguing. Um tell us for our final question,
Speaker 1: what do you know about the world of investing today?
Speaker 1: You wish you knew thirty years ago when you were
Speaker 1: first getting started. I think very often the most important
Speaker 1: problems in investments are actually not about investing really. For institutions,
Speaker 1: they're about management, structure, governance and incentives. And for individuals, well,
Speaker 1: you've had many guests on your show to all about
Speaker 1: tackling investors behavioral bodies, all right, and those sometimes are
Speaker 1: even more important than the actual investment problems. Sometimes the
Speaker 1: investment problem is the easy pot, all right, and then
Speaker 1: sitting the context of the investment problem in the wider
Speaker 1: portfolio or the wider structure in someone's family or an institution,
Speaker 1: that's actually the harder problem. Quite fascinating. We have been
Speaker 1: speaking with Andrew Ang. He is the head of factor
Speaker 1: investing at black Rock and the author of Asset Management,
Speaker 1: A Systematic Approach to Factor Investing. If you enjoy this conversation, well,
Speaker 1: look up an Inch or down an Inch on Apple
Speaker 1: iTunes and you could see any of the other two
Speaker 1: hundred and fifties such conversations we've had over the past
Speaker 1: five years and check that out. We love your comments,
Speaker 1: feedback and suggestions right to us at m IB podcast
Speaker 1: at Bloomberg dot net. I would be remiss if I
Speaker 1: did not thank the crack staff that helps put these
Speaker 1: conversations together. Attikavl Bron is our project manager. Michael Boyle
Speaker 1: is our head of booking slash producing. Michael Batnick is
Speaker 1: my head of research. I'm Barry Results. You've been listening
Speaker 1: to Master's Business on Bloomberg Radio