By: Simon Burns
Quant Development Intern24.06.2013
Interview with Mebane Faber of Cambria Investment Management
Mebane Faber had a chat with our Growth Hacker Simon Burns on the learning curve in becoming an algorithmic/quant trader, correlating non-market data and the move towards democratization of algorithmic model creation among. Mebane runs a long form qualitative and quantitative analysis blog at Mebane Faber, is the author of Shareholder Yield: A Better Approach to Dividend Investing and is the portfolio manager at Cambria Investment Management. He is a frequent speaker and writer on investment strategies and has been featured in Barron’s, The New York Times, and The New Yorker. We enjoyed the opportunity to listen to Mebane’s insightul, empirically tested and eloquently stated views and analysis on the state of algorithmic trading, HFT and markets. Enjoy our interview with Mebane Faber:
Simon Burns: Thank you so much for joining us Mr. Faber. One of the things that caught our attention recently from your blog is when your post titled, “Sell in May or November Timber.” And your line here, which I think is really pertinent in the world of big of data is “if you cannot explain why an inefficiency exist or the fundamentals behind a technical strategy, then you’re likely just data mining.” Now the majority of trading on markets is algorithmic, could you please elaborate for us how much of the trading is really in true correlations and how much is data mining?
Mebane Faber: It goes back to any approach, not just technical but also fundamental, behavioral, or the example we used, calendar based. And the challenge from those investors is being able to come up with a fundamental reason why something works and why it should work in the format of capitalism or in markets. One of the difficulties about being human is that we so often learn by telling stories. In many cases, stories make a lot of sense and they sound great in the investing world, but don’t necessarily fit either history or common sense. So, we try to spend as much time with the historical data possible but also take a step back and say, does this make sense from either statistical foundation or fundamental backdrop and in many cases likely if you can’t come up with a pretty common sense explanation, you can probably describe to your 12-year-old niece or nephew then good chances are, you’re simply data mining, coming up with something that won’t exist in the future.
Simon Burns: Right the idea of curve setting or fitting. So, what we do in QuantConnect is work closely in the algorithmic trading space, we allow coders to use our platform to engineer solutions much faster than ever before in terms of building, backtesting and iterating on algorithmic models. Do you have any advice to potential individuals looking to go into the algorithmic space in terms of how to avoid those historical biases based on the historical data. How can they avoid curve fitting?
…if you can’t come up with a pretty common sense explanation, you can probably describe to your 12-year-old niece or nephew then good chances are, you’re simply data mining, coming up with something that won’t exist in the future. – Mebane Faber
Mebane Faber: It’s very subjective, most older traders or clients can certainly sympathize with the early days of experience where you build a system. It looks unbelievable or amazing! You think you’ve found the Holy Grail only to find out that either one of your inputs is wrong or you’re looking into the future or it fell apart in real time. Most clients that are older have a lot of battle scars either from real money or paper trading portfolios that they’ve learned a lot over the years. The biggest difficulty is being able to come up with a system, or multiple systems (which I think is more important) that fits your personality but is also robust over time. With a lot of the fine tuning of parameters and fine tuning of systems, its really easy to come up with systems that probably aren’t robust or ’more fragile’ in the words of other author.
Simon Burns: Well said. So you’ve been talking mostly about the small scale trading space. Let’s move to talking about the HFT space and how it’s been regulated in Germany just last week, which has hampered the space quite a bit. Where do you see HFT going forward and its effects on markets?
Mebane Faber: It’s always challenging to try and guess what politicians are up to or what regulators may be doing. There are many cases in the past in the US where we think they make boneheaded moves and eventually tend to get something right. The recent dividend tax legislation was a step in the right direction in the US but still sub-optimal. I don’t spend a significant amount of time in HFT because most of my timeframe is on the other end. We’re often re-balancing in much much longer time frames. But in general, HFT is one of the areas that you’d love to have a lot of the rules developed by the market practitioners that makes it fair for everyone. There are plenty of test cases around the world where some countries have a transaction tax, some don’t, some have different varying tax features for different levels of holding periods as well as tax rates for various individual levels. It is nice in the sense that you have a wide variety of small samples to test and see how it affects different markets, and see how structural changes strongly effect markets. So, it will be an amazing scene in the next handful of years. As an ironic aside, I was just in Long Island last weekend, hiking in the woods and walked passed one of the most famous high frequency guys in the world, probably the most successful Hedge Funds there has ever been, Renaissance Technologies, Jim Simons, on just a random trail deep in the woods.
Simon Burns: [chuckles]
Mebane Faber: I got a lot of excitement from that. All of the other writers and philosophers that I happened to be walking with didn’t seem to share my enthusiasm.
Simon Burns: Do you mind sharing what were the highlights of your conversation with Jim?
Mebane Faber: I didn’t pester him. I let him go in his way. It’s kind of like running across Michael Jordan and just saying “Hey man. I think you’re great” I didn’t want to bother him. It’s was a gorgeous day in Stony Brooke in the woods. I let him have his peace.
Simon Burns: That’s a great story. Obviously, on the very short term, we saw the AP market crash in large part due to high frequency trading. There’s a been a lot of speculation from market commentators that HFT is pushing small time traders, out of the markets. We’ve seen a large move in the data out of Equities and into Forex. So in terms of pushing small time traders out, is that something that you’re seeing within your readership or any sort of commentary that you’re reading?
Mebane Faber: I haven’t noticed it but I don’t know if I would notice it. I’m not operating on the high frequency levels other than through our trading relationships and we utilize over 12 different layers and brokers to execute for us. So I imagine they have opinions on it but, our time-frame is on such a different scale that we’re not necessarily as concerned about a lot of the high frequency world. I mean, we’re often trading; updating our portfolios monthly, quarterly or even yearly. So a lot of intraday gyrations often don’t even show up on our radar.
Simon Burns: Right. HFT doesn’t really affect people like yourself investing in long term portfolios of diversified asset classes?
Mebane Faber: No, I mean, it doesn’t affect the majority of the investing world if you start from the standpoint of the majority of asset holders are longer term in nature, they are investors rather than speculators. Although, by definition you can say an investor is anyone that buys and holds these assets rather than actively trading them. Other than an emotional reaction to the news, whether it would be Flash Crashes or other news flows, it really has no practical effect on them whatsoever.
Simon Burns: Great point. There’s another post of yours that I want to bring up, the post is titled “Combining Value Trend and Sentiment“, and it’s a really interesting piece, pulling together all sorts of different market data and making a long term model. One of the things that we’ve been working on at QuantConnect is bringing in as much data as possible, so right now we have Forex historical tick data and we have US equities historical tick data. We’re working on different sources of data as well. One of the main things we’re looking at is to move into non-market data, so we also have Estimize which is crowdsourced estimates data. Based on your post here, obviously you’ve been working to integrate in different sorts of data and especially non-market data into what you do. With the advancement of non-market data in the future, where do you see algorithmic trading now when signals from non-market data is in play?
Mebane Faber: There’s always room to add on correlated factors to impact on market course and it is a challenge of course. That’s what a lot of big institutions focus their time on — adding uncorrelated asset classes. I mean there’s really only four asset classes, stock bonds, commodities and currencies and everything else is kind of an amalgamation of those. And so a lot of institutions are trying to find new asset classes. The funds try and find new signals for these asset classes. In general, that challenge is a lot of, at least on my timeframe, a lot of the indicators that end up lining on the same side. So when you have an extreme value in the market, like Greece in the past year, all the value indicators line up on the same side. Regarding sentiment or other ways of measuring interest in stocks – I think they certainly is value to it, I don’t know how much history you need or how valid they are, but it’s certainly an area that’s ripe for study to be sure.
Simon Burns: What’s your take on hedge funds moving into of exclusively non-market data quant space? And more directly for yourself, how do you incorporate non-correlated asset classes into your investments at Cambria?
Mebane Faber: If it works, that’s great. The biggest challenge for any non-market factor is believed value – for a fundamental metric, sentiment, whatever it may be. To quote Ned Davis – “[Non-market data] is a derivative, whereas price is unique and it’s the only indicator that can’t diverge from itself”.
Simon Burns: [chuckles]
Mebane Faber: So, if you’re using a non-market factor, let’s call it Twitter sponsored sentiment or even an arbitrary value. The challenge is that, the derivative is not what you’re trading. What you’re trading is the price of the instrument! A good example was starting in Greece where at one point, it was trading at a P/E ratios…a long term ratio of about 10, and it went to 5 and then it went to 2. You can make the argument based on valuation alone that you should have been buying it at 10, at 5 or even 4. In which case, you still would have lost 50% to 90% of your money. The biggest challenge in what we do at Cambria is to integrate multiple factors, often value and momentum types of factors, or technical and fundamental factors to come up with a better picture, but the Holy Grail really is coming up with a number of uncorrelated factors and a system to diversify asset classes. So you have a more robust portfolio really for any market environment.
Simon Burns: Right. Great point, it’s going to go down to history books as attributed to Mr. Faber, what was the quote?
Mebane Faber: Well. That quote should be attributed to, I believe, Ned Davis out of Florida. He runs Davis Research, he’s one of the all-time great quants. I believe he said “Price is only indicator that can’t diverge from itself”.
Simon Burns: That’s a great one. I love it. So, I was going to ask you about, you do some really great quantitative as well as qualitative blog posts. The link is that they are all quality long-form blog posts. In the last few years, you would have seen the rise of Twitter with lots of market chatter. The market chatter has been shown to be affect markets in lots of cases. Where do you see the future of online investment advice now that we have this very short term focused and very prolific source for investment advice?
Mebane Faber: Well, if you’re like an old guy like me, there have been lots of different sources for this kind of online inflammation. Previous to Twitter you had Yahoo Message Boards. Previous to Yahoo Message Boards you had Raging Bull. Previous to that, who knows? It’s all very dependent on the quality of information and quality of the network. I, for the most part, see Twitter as somewhat of a pleasant distraction. I would probably even argue the longer I’ve been using it, it’s become more of an unpleasant distraction. Now having said that, there’s plenty of research blogs, and institutional access online that is as good as you’ll find anywhere. It’s less about the medium I think and more about the quality of the content and where you find it. There are plenty of great investors that don’t even have e-mail or sit online, but their networks have developed, and/or their own processes, where they’re just incredible at investing. So, I think it’s less about the medium and more about what you’re finding, and the quality of your network, wherever that may be.
Simon Burns: Right, great point. One of the phenomena that some people in the space are saying is happening, is that Twitter is taking the market share in terms of short form investment advice pieces and it’s pushing other blogs, take yourself, to go for longer form, more technical, more analytical pieces. Do you feel that push from StockTwits, Twitter to go to a longer form?
Mebane Faber: We’ve always been somewhat a reluctant publisher. We started writing by publishing white papers. Then starting writing the blog mainly as a way to try to interact and find information on two subjects. One was for foreign-invested hedge funds, the other was 13-F Hedge Funds. So it was for data mining and really interacting with the community for the purposes of writing about them. Again, it’s incredibly default to me to publish more of the couple pieces a week, most of what we do ends up being a lot longer form. The main benefit I have in Twitter is as a news aggregation for being able to find reading material. I don’t want to rain on the Twitter parade. I love it but it’s mainly kind of a newsstand for me, of interesting things to read in an airplane or to take offline. I’m honestly surprised in how the long form analysis space, I know SumZero and Value Investor’s Club do it a little bit here with equities but I am surprised I haven’t seen more really thoughtful longer form pieces exist online. Maybe partly because of the effort it takes but it actually surprises me.
Simon Burns: Yeah, great point. For the last question here, this is an open-ended question. Take us through the inspiration for your book. I know you’ve written quite a bit about why you chose the eBook format and what you did there. I would really like to give our audience sort an overview of the inspiration for the book.
Mebane Faber: We’ve been writing about yield strategies for a long time approach and one of the challenges for investors and this goes back to what we were talking about earlier. Is that, there are often structural changes in markets and the challenge is always to find out. Is this a real structural change, or you can say this time is different. Or even, is this nothing more than a bubble or a euphoria for investors’ perception. One example is the dividend space where investors have a long history of understanding and loving dividends, as they should, dividends are great. Dividends are one of the largest components of stock markets return over time. The problem, especially in the US, starting in the early 80s is that there was a very clear structural change when the government allowed US companies to give them safe provision from buying back their stocks. So we started seeing a huge boom in stock buybacks because from the corporate finance perspective, buybacks and dividends are basically the same thing. Buybacks are just more tax efficient.
And in the case where buybacks are done for a stock that is trading below intrinsic value, buybacks are much more efficient. Buffett said way back in ‘84 that when companies find their shares below intrinsic value, there’s no alternative that can benefit shareholders more than repurchases. Because you’re getting basically a value arbitrage in that if you’re buying something for 80 cents that’s worth a dollar. It acts as a direct transfer of wealth from the sellers to the buyers. So, once you think about it that way, then you realize that it makes no sense to look at just the dividend yield. If you’re looking for a cash distribution, you should be looking at all the ways the company distributes their cash. Once you do that, you’d come up with a much more holistic view of cash flows on the company’s yield. If you sort companies on what we call shareholder yield rather than just dividend yield then it outperforms all of the dividend strategies over time.
We wanted to kind of create a document that was a summary of the literature, as well as adding our own research. Having been a publisher that’s written a blog, written white papers and a previous book. We wanted to try a new medium and just sell it publicly in Amazon as an eBook because it’s rather short. It’s only around 60 pages. It’s weird for me because I’m a traditionalist. I like reading physical books but most people seem to have converted to the electronic space. We posted it up on Amazon, and there has been a great response. Our new ETF has resonated with a lot of people in the last 2 weeks. Its great, it has raised over 60 million dollars. The ticker is SYOB. It’s something that people thankfully understand and get the challenge of being able to go find the buyback data as individuals. I think it is a concept that will become more and more accepted over time in the coming years.
Simon Burns: The fact that the response has been so material, raising 60 million dollars with a hugely positive investor response. It’s really incredibly impressive. So with that, I would like to thank you for your time today, Mr. Faber. Mr. Faber, can be found at http://www.MebaneFaber.com and his books can be bought down on Amazon (Shareholder Yield: A Better Approach to Dividend Investing).
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