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Invest Like the Best

Exploring the ideas, methods, and stories of people that will help you better invest your time and money. Learn more and stay-up-to-date at InvestorFieldGuide.com
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Now displaying: September, 2018
Sep 28, 2018

“This is an unusual early episode release thanks to the timing of the recent news on Tesla. In this short episode, Danny and I discuss cannabis stocks, Tesla, and his “wild bill” story about quant investing.”

For more episodes go to InvestorFieldGuide.com/podcast.

Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub.

Follow Patrick on Twitter at @patrick_oshag

Sep 25, 2018

My guest this week is one of my best and oldest friends, Jeremiah Lowin. Jeremiah has had a fascinating career, starting with advanced work in statistics before moving into the risk management field in the hedge fund world. Through his career he has studied data, risk, statistics, and machine learning—the last of which is the topic of our conversation today. 

He has now left the world of finance to found a company called Prefect, which is a framework for building data infrastructure. Prefect was inspired by observing frictions between data scientists and data engineers, and solves these problems with a functional API for defining and executing data workflows. These problems, while wonky, are ones I can relate to working in quantitative investing—and others that suffer from them out there will be nodding their heads. In full and fair disclosure, both me and my family are investors in Jeremiah’s business.

You won’t have to worry about that potential conflict of interest in today’s conversation, though, because our focus is on the deployment of machine learning technologies in the realm of investing. What I love about talking to Jeremiah is that he is an optimist and a skeptic. He loves working with new statistical learning technologies, but often thinks they are overhyped or entirely unsuited to the tasks they are being used for. We get into some deep detail on how tests are set up, the importance of data, and how the minimization of error is a guiding light in machine learning and perhaps all of human learning, too. Let’s dive in.

For more episodes go to InvestorFieldGuide.com/podcast.

Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub.

Follow Patrick on Twitter at @patrick_oshag

Show Notes

2:06 - (First Question) – What do people need to think about when considering using machine learning tools

3:19 – Types of problems that AI is perfect for

6:09 – Walking through an actual test and understanding the terminology

11:52 – Data in training: training set, test set, validation set

13:55 – The difference between machine learning and classical academic finance modelling

16:09 – What will the future of investing look like using these technologies

19:53 – The concept of stationarity

21:31 – Why you shouldn’t take for granted label formation in tests

24:12 – Ability for a model to shrug

26:13 – Hyper parameter tuning

28:16 – Categories of types of models

30:49 – Idea of a nearest neighbor or K-Means Algorithm

34:48 – Trees as the ultimate utility player in this landscape

38:00 – Features and data sets as the driver of edge in Machine Learning

40:12 – Key considerations when working through time series

42:05 – Pitfalls he has seen when folks try to build predictive market investing models

44:36 – Getting started

46:29 – Looking back at his career, what are some of the frontier vs settled applications of machine learning he has implemented

49:49 – Does intereptability matter in all of this

52:31 – How gradient decent fits into this whole picture  

 

Learn More

For more episodes go to InvestorFieldGuide.com/podcast

Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub

Follow Patrick on twitter at @patrick_oshag

Sep 18, 2018

(0:49) This week, to mark the two-year anniversary of the podcast, I offer a quick summary looking back and forward.

(0:55) Yesterday I heard about an Appalachian Trail thru hiker named Croatoan, or Crow for short. Crow was his trail name, which all A.T. thru hikers carry. Importantly, you can’t give yourself a trail name. Someone else has to name you along the way. Crow’s girlfriend was named Porridge. Another hiker he encountered along the way was named Bear Wrestler…more on him in a few minutes.

Crow was a Sobo, a south bound hiker heading from Maine to Georgia. This is a far more unique route, as most thru hikers are Nobos, hiking north. These hikers maintain a rich culture. Each wears their own trail flare, and has their own trail style. They are obsessed with their gear and food. They develop their own improved walking method to cover ground efficiently. Hikers typically won’t veer far off course, no more than a tenth of a mile, for almost any reason. Crow once left a meaningful gift he had received by a river bed, realized it two tenths of a mile later, and just kept moving. Two exception to this rule are to visit a brewery or find some homemade ice cream.

(1:50) There are different types of thru hikers. White blazers are hikers who follow the main trail, lit by the famous white blazes marking the way. Blue blazers often go a step further, exploring side trails in addition to the main trail. Green blazers smoke weed the whole time. There are other colorful ones I’ll stay away from here as they aren’t safe for work.

Apparently you can spot an imposter in a number of ways. My favorite was that anyone wearing big, sturdy hiking books should be questioned, because most thru hikers realize quickly that they are way too heavy and opt instead for lightweight shoes. Crow had a nice pair of Altras.

(2:22) This brings us back to Bear Wrestler. Around a campfire, Bear Wrestler was telling Crow and his girlfriend all about his long trail adventures and feats, but Crow noticed that Bear Wrestler was still chubby, carrying 40 pounds of fat. This is a second way to spot a potential imposter. When hiking intensely for months on end, it is impossible to keep any weight on, so Bear Wrestler was clearly a yellow blazer, a type of hiker who drives between trail heads instead of hiking the entire way like the purists.

As I heard about Crow and his adventure, I was thinking about what to say in this short episode about what I’ve learned across two years running this podcast. What I quickly realized is how many yellow blazers there are in the world, and that at many times in my life, I too have been a yellow blazer—opting for easier but less authentic, and less interesting, routes. The podcast is part of a portfolio of things that I’ve put in place in my life to try to avoid being a yellow blazer. To instead push myself to be more like a blue blazer, exploring anywhere I can.

(3:16) Looking back on the incredible guests I’ve had, I realize now the common mindset that unites them, and I’d like to highlight that mindset here. Even though my guests have come from just about every conceivable background, investing and otherwise, they are all in persistent and consistent pursuit of original experience. Now, that might sound obvious, but its rare to meet people whose default is to chase original experience. These people stand out quickly now to me, because I can recognize freshness in them, patterns I haven’t already seen 10 other times elsewhere. I now think often: am I doing this because its conventional, and/or because I’m watching what other people do? I think if you do the same exercise, you’ll be alarmed by how often the answer is “yes.”

Diving a bit deeper into these people and what unites so many of my past guests, there are four elements that I see over and over again.

(4:01) The first is common trait is deep curiosity. My take on curiosity after meeting all these people is that it works best in two ways: through building units of exploration, and through embracing strange intersections.

When people ask me what I do, I’ll sometimes just list the actual things I do, instead of a job title. So I say, I read books, papers, and articles. I run tests on data, using many of the same scripts and tools. I have tons of individual conversations with people in nooks and crannies of the investing world. I talk to clients and prospects. I write letters and white papers. These are my units of exploration, and I expect that I’ll keep repeating each of them forever. I have no clue where that might lead, but I’m confident that through curiosity fueled repetition, I’ll find good things. My close friend and most frequent podcast guest Brent Beshore has looked through 12,000 business deals. Talk about repetitions. I think curiosity, and the interesting investing opportunities it creates, is just a set of habits. Finding the right habits, the right units, is a great start.

I also often see what I call strange intersections. Picture a Venn diagram with tiny, but interesting, overlap. Some of the most intriguing things I’ve learned about live in these strange intersections. Ali Hamed and Savneet Singh, who are partners at a firm called CoVenture, have found interesting overlap between the worlds of lending, technology, and old world business. Whether it be shoe returns online or watermelons, they’ve found unique ways to lend at high rates on unique platforms enabled by technology. I often see people using seemingly unrelated interested, ideas, or strategies together to produce something different. I encourage everyone to think about strange ways of combining their areas of expertise and interest.

(5:40) The second common trait is persistence through randomness. Sometimes when I talk with people about the importance of curiosity, they say it sounds too easy and fun. The good news for the skeptics is that more often than not, its not fun, it is a total slog. When I looked back recently, I found that I only finish about 1 in 7 books that I start. Even most that I finish aren’t great. Put differently, I read an incredible amount of mediocre books to find just one book that makes a difference. This happens everywhere. The vast majority of data and ideas that we investigate at O’Shaughnessy Asset Management go nowhere at all.

I think most people will agree that the journey of discovery is often tedious, filled with dead ends, and above all random. My favorite example of this persistence through randomness was my conversation with Josh Wolfe, which I recommend in its entirety.

One of my favorite phrases picked up in the past two years is the Shangaan phrase Hi Ta Xi Uma, which I learned from Reinius Mflongo, one of the top trackers in Africa. It means “we will find it,” and Reinius will keep muttering it when he loses a track and struggles to find the next one. Everything is hard, and usually much harder than we can fathom. All the best people I’ve met through the podcast just don’t let that stop them. They also seem to develop an awareness of this constant difficulty and just become used to it.

(6:55) This second trait, persistence through randomness, is perhaps my favorite way to test for yellow blazers. There are many people in the world of business and investing who can talk extremely well. But if you keep peeling back the onion, asking more and more specific questions of a yellow blazer, you’ll find nothing original. But when you do hit on something, several layers down, that you’ve never heard before, that to me is a mark of persistent inquiry. That’s the kind of people I’m after.

(7:21) The third common trait is risk management. It is tempting to view uncertainty as a sort of risk, but I think that is a large mistake. All the good stuff is found in places that haven’t been mapped already. In fact, to take the idea of original experience a step further, what is common across the best people I’ve met is not just having the experiences, but then bringing some sort of order to the chaos they found in uncertainty. This isn’t risk, in my opinion. If anything, not seeking out chaos is what’s risky.

But then there are the conceivable risks: things that could go wrong that we can list ahead of time. On this front, guests were often very thoughtful: developing plans to be deployed when specific risk scenarios play out. I loved Mike Zapata’s story about the darkest night. He and his SEAL team would prepare and practice every tiny detail of a mission, creating plans for all risks, then wait to attack on the darkest night they could, because even though the conditions were hardest in the dark, their preparation and risk mitigation would shine in that difficult environment.

More specific to investing, many of my guests have a clear focus on downside risk protection. Several people have told me that there are common ways that things go wrong, but many more unknowable reasons things go right. So instead of trying to predict what will work, focus on avoiding the common pitfalls. My favorite example again came in Africa, being told 100 times to not run when lions charged us. It is a common and known risk factor (each of our guides had been charged more than 50 times), but one that was easily mitigated. If you don’t run, the lion will stop short and maul and eat you. You just have to have that lesson beat into your brain a hundred times ahead of time because the basic instinct, as is so often the case with investing, is to run.

(8:57) For the fourth common trait, we return to our thru hiker Crow one last time. I heard Crow’s story from my friend Bill, who picked up Crow hitchhiking to give him a quick ride into town. Bill offered to buy Crow dinner. He accepted with a huge smile, telling Bill “wow, that is some real trail magic right there.” Trail magic is my favorite piece of lingo in the thru hiking culture. Hikers tell endless stories about trail magic, which is what they call the acts of kindness and goodwill bestowed upon them by strangers along their journey. Food, shelter, a quick lift, a homemade cookie. Consider how incredibly positive sum trail magic is. The givers and the receivers of the magic both come out ahead. Despite all I’ve learned about business and investing over these two years, my favorite question to ask is still my final one in each episode, about acts of kindness. Getting to hear more than 100 stories of kindness from these people has been the highlight for me, and the best lesson.

(9:49) Summed up, what I’ve learned from these people is to follow your own way, always. Figure out the right units of exploration, embrace strange intersections, and carefully consider what could go wrong. Rest when you need it, be dogged and aggressive when the situation calls for it, but just keep going. Do it all with respect for others and as much trail magic as you can muster.

Thanks to all the great people I’ve had on the show, and thanks to you for listening for these two years, I promise to keep this discovery process going in some way, shape, or form forever.

Learn More

For more episodes go to InvestorFieldGuide.com/podcast

Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub

Follow Patrick on twitter at @patrick_oshag

Sep 11, 2018

My guest this week is Kathryn Minshew, the co-founder and CEO of the Muse, and the co-author of The New Rules for Work: the Modern Playbook for Navigating Your Career. I’ve learned in business is that the quality of people and the culture they create dictate outcomes. Having made plenty of mistakes hiring, and having had many enormous successes, I am always interested in best practices for finding and successfully recruiting the right people.

Given that Kathryn runs a jobs marketplace and has written a book on the topic, she is the perfect person to explore some the core concepts around pairing people with the right positions. We discuss how companies should market to prospective employees, how employees should represent themselves to employers, and the most common mistakes she sees across the hiring landscape.

Please enjoy our conversation.

For more episodes go to InvestorFieldGuide.com/podcast.

Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub.

Follow Patrick on Twitter at @patrick_oshag

 

Show Notes

1:31 - (First Question) Largest changes in the nature of work and how people approach finding the right job for them

3:27 – Can this work be jammed into a formula

5:18 – What strategies is she sharing with employers when it comes to hiring

8:31 – How long should the process take

9:33 – Biggest mistakes employers make in this process

10:39 – Besides the usual stuff, what can perspective employees do to bolster their chances

12:50 – How much more efficient will matching technology get in the years to come

16:00 – What will be the largest changes to work itself

19:09 – Will we move away from full time work into parsels of work units

20:50 – Most successful piece of content or content strategy the Muse has employed

22:34 – Advice for early stage entrepreneurs

26:24 – Kindest thing anyone has done for Kathryn

 

Learn More

For more episodes go to InvestorFieldGuide.com/podcast

Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub

Follow Patrick on twitter at @patrick_oshag

Sep 4, 2018

I intentionally avoid the world of quantitative investing on this podcast. The whole point of this format is to learn about many different fields, and the vast majority of my time is already spent in quant world.

Occasionally I’ve broken this rule because of something unique, including this week’s conversation with Richard Craib, the founder and CEO of Numerai. If you listen to the podcast often you’ll have heard me reference Numerai, a hedge fund which blends quant investing, cryptocurrencies, crowdsourcing, and machine learning — talk about a PR company’s dream.

One important note: Numerai is both incredibly open and very secretive. You may sense a bit of frustration on my part, but that is only because, as a fellow quant who loves details about data and modeling, we couldn’t go deeper into the details on the record.

We discuss how Numerai has created an incentive structure to work with data scientists around the world in an attempt to build better investing models. The idea of having data scientists stake cryptocurrency in support of the quality of their models is fascinating. Like many hedge funds, Numerai doesn’t share its track record, so we don’t know if this works—but I hope you, like me, use this conversation as inspiration for how different technologies can intersect.

Hash Power is presented by Fidelity Investments

Please enjoy my conversation with Richard Craib.

 

For more episodes go to InvestorFieldGuide.com/podcast.

Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub.

Follow Patrick on Twitter at @patrick_oshag

 

Show Notes

2:32 - (First Question) – How he came up with Numerai and how its related to his background

4:08 – How he works with and models the data for his system

5:24 – Describing machine learning as it relates to his work, and specifically linear regression

7:11 – The important stages in his sequence

8:46 – How the scale in the number of data scientists they use is different from other areas

11:30 – Which is the most important aspect of creating alpha; their data, algorithm work, proprietary ensembling of those algorithms.

14:30 – The idea of staking in blockchain

17:30 – Does the magnitude of the stake matter in blockchain

19:10 – Understanding the full incentive structure for both staked and unstaked work

21:07 – How is the prize pool determined

22:29 – Philosophy on how to source interesting data

26:11 – His thoughts on the crowd model and the wisdom of crowds

27:12 – The size of stakers for Numerai

27:51 – Interpreting the models and knowing when something is broken

30:03 – How they think about people not submitting their models

31:48 – Their model building

32:39 – Most interesting set of things they are working on to improve the overall process

            35:38 – The Market for "Lemons": Quality Uncertainty and the Market Mechanism

37:11 – How people can come along with their own data

39:00 – His thoughts on the quantitative investment community

40:44 – What else is interesting him in the hedge fund world

44:03 – Building a marketplace and staving off competition

46:16 – Kindest thing anyone has done for him

 

Learn More

For more episodes go to InvestorFieldGuide.com/podcast

Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub

Follow Patrick on twitter at @patrick_oshag

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