The Future Is Now: Benjamin Richard Truitt of BYTZ Fund On How Their Technological Innovation Will Shake Up The Tech Scene

An Interview With Fotis Georgiadis

If even the smallest amount of data contradicts your hypothesis, act as if it is incorrect until proven otherwise. In one phase of development, my back-testing results were based on an assumption that I made about a variable. As I began testing this assumption it took quite some time to accumulate enough data for an accurate measurement. However, the very earliest measurements, though not statistically significant, were correct. I should have revised my back-testing inline with this small set of more conservative results until more data supported a more aggressive strategy.

As a part of our series about cutting edge technological breakthroughs, I had the pleasure of interviewing Benjamin Richard Truitt.

Benjamin Richard Truitt, throughout his career, has endeavored to bring the technical fields of engineering and computer science to the real estate investment and banking industries through the development of innovative applications designed to streamline complex valuation models and better forecast outcomes using artificial intelligence (AI) and Machine Learning. Currently, he is the VP of Products for Lending Standard, where he has been for almost five years. In addition, he is the President & Chief Investment Officer of BYTZ Fund, LP, an algorithmic-based hedge fund he founded in 2019. Ben holds a Bachelor of Science in Mechanical Engineering from the University of Colorado — Boulder as well as an MBA with emphases in Finance and Investments from the University of Colorado — Boulder, Leeds School of Business. He has also completed a Data Science Fellowship at Galvanize — Platte Denver Campus and is a CFA Charter holder.

Thank you so much for doing this with us! Can you tell us a story about what brought you to this specific career path?

Regarding running a hedge fund, it has been a long road. The first pivotal moment that shifted my career happened during the financial crisis that led to The Great Recession. Up until that point, my career had been centered around real estate, commercial lending, and developing software tools for automation in these areas. I was working on turning high-end spec homes that were in the $2–4 million range. During that time, I worked with a great deal of investors who were funding these projects. In early 2007, a very plugged-in mortgage broker that I knew was working in the subprime borrowers’ space. The morning the first set of news came out about a fund within Bear Stearns, he called me, speaking in a very animated tone, forewarning me about what this meant for the industry.

Bear Stearns was one of the first dominos to fall and it raised many eyebrows, including mine. This fund was the one that was investing in the purchase of mortgage securities, and it was indicative of what would happen from this time on. The mortgage broker that told me about this cited, “Ben, you have no idea what this means.” It was so early in the collapse of the financial markets and with his on-the-ground knowledge, he was able to make that call. I realized then it was not only because of what he did daily; he was in tune with the capital markets.

Following that first event, the real estate development industry was just beginning to unwind, until everything came to a screeching halt. With those series of events, the next few years were spent cleaning up. From that point I spent almost every waking moment understanding capital markets and how they drive investment through other industries. Taking the first step in that direction, I completed an MBA and shifted gears to investments, derivatives, and bond pricing. From that point, I went on to complete my CFA charter. It was then I realized that my career had been completely altered in a new direction, and I found myself lucky to have escaped what could have been a crippling situation.

The more I learned, the less I was comfortable investing. It was due to the immense knowledge I was being fed and I wanted to absorb it all and make the right decisions. The one thing that I began to see was that there was a missing piece — automation. That is when I took my studies and my interests to the next level, embarking on a path to leverage Machine Learning, and hence, the fund. It has been and continues to be a marathon and not a sprint and I never forget the lessons I was first exposed to in 2007.

Can you share the most interesting story that happened to you since you began your career?

There are so many interesting things that have taken place during my career. Focusing on the automation, I think that the first time the test for the stock trading Machine Learning algorithm showed promise and potential, was pivotal. Often, when you create something new, spurring from a fresh idea, and you really test the feasibility, it doesn’t work. It took me quite some time to get to that moment of awe when the code worked. I refined and tested it over and again and it continued to show promise. It was a long road to get to that point and it is one I keep in mind on the most frustrating of days!

Can you tell us about the cutting-edge technological breakthroughs that you are working on? How do you think that will help people?

I think that right now, all of us working in this space, are figuring out how to take Machine Learning from an academic abstract to real world use cases. To me, the cutting-edge piece is not creating some new algorithm that does something different; rather it is finding ways to apply existing Machine Learning technology that gives you useful information. For instance, in identifying investments, I look at which ones are likely to be outlier performers. In that perspective, I am working with a snapshot in time. I then start asking myself questions, such as, ‘What is the best company in the moment to invest in?’ ‘Is this investment going to continue to be a good one?’ ‘What might be a better investment?’

Getting technical, in Machine Learning two types of algorithms are classifiers and recurrent neural networks, and we are merging the two together. Recurring neural networks and forecasting stock prices uses a time series of data to make predictions. With that, you can look at metrics that changes over time, and you can use that data to predict something else. Some other value that is important to you, perhaps the probability of the stock being a good investment, is also important. It is vital to review the performance and back test the algorithm. This helps measure performance. If the algorithm shows a 60% chance of working, you must remember that you are still dealing with the flip of a coin as there is still a 40% chance it won’t work. It is not a simple point in time analysis. You are looking at historical data and predicting future performance. I spend a great deal on refinement.

How do you think this might change the world?

As I focus on the applications in capital markets, and as Machine Learning becomes more complex and better simulates processes, we could really see a world where the trading is all conducted by computer algorithms across all markets. When I stop and think about that, if all is done by computers, where is the edge there for an investor or a fund? What is the purpose of trade? I do question the role that capital markets plays if all is done by computer, it brings me back to the fundamental reasons for why we have capital markets. They have morph, enabling them to serve in ways beyond what they do currently. Driven by the urge of investors to do trading activity, Machine Learning can contribute to the efficient use of capital in the economy. If we are all doing the same thing, at some point it could change the purposes of capital markets and how they function.

Keeping “Black Mirror” in mind can you see any potential drawbacks about this technology that people should think more deeply about?

A bit of what I just talked about above is what I think deeply about. We are working so hard to get an edge using technology, and this is not new. High frequency trading has been used to get an edge over other investors in the space. As you do that, those investors invest more. It becomes a competition, and you whittle away the profit potential.

I actually think of the movie, “The Book of Eli,” when I think of near future technology drawbacks. In that movie, all knowledge is stored in electronic form, and people scramble to find books. At some point, society burned the books, which were seen as a political divide, and they ended up destroying that history. With that example, and while it is a movie, I don’t think that we should be reliant on any one medium. We may refine technology to a point of efficiency, but we need to be cognizant and recognize other ways to collect data. I am still using my own collective knowledge to refine the algorithm for my fund, and that is due to amassing 20 years of knowledge from a variety of sources.

Was there a “tipping point” that led you to this breakthrough? Can you tell us that story?

One of the first machines learning algorithms that I wrote did something like the algorithm used for my fund. Using my knowledge of the mortgage industry and going back to my roots, that algorithm forecasted the probability of defaults in Freddie Mac mortgages. Essentially, I used financial ratios to make predictions about a loan. There are a lot of similarities that come from financial ratios and real estate property. I first did this back in late 2016, and I started to work on the code for that in early 2017.

One day, I attended a CFA event on risk first investing. They were looking at ratios and applied a threshold to them, showing us if price to earnings is above a certain amount, that was seen as risky. They then looked at debt to assets and set thresholds. I had an “a-ha” moment following that event. I researched ratios and looked at what was meaningful. I then looked at what other funds have done to find success and it all began to come together. I was now working with about 25–30 different ratios. It was my tipping point.

What do you need to lead this technology to widespread adoption?

Besides raising more capital, I personally think that people are learning about technology uses and it is why I share my knowledge regarding what we can do with technology. As more people enter college and graduate school to work on technology applications, there will be much more widespread use.

However, with that, I am not looking to get widespread adoption right away. I think that widespread adoption will happen in its due time. Right now, the investor community needs to understand this a bit more. Sophisticated investors want to understand things inside and out — and they should! They don’t feel comfortable investing in something they do not understand. It brings me back to being more apprehensive regarding investing after acquiring more knowledge. One of the biggest roadblocks is that the more you share with investors about how things work and how these algorithms are developed, the more it becomes confusing unless you have a deep academic background. There are the early adopters in the AI funds, and eventually the rest of the investment community is going to see the performance and become less apprehensive.

I think one way that investors can become more comfortable is understanding the similarities between Machine Learning algorithms and their own brains. If you’ve ever found yourself glance at something and think it was one thing but then focus your attention and realized it is something else, you have experienced your brain going through a similar analysis to that done by a Machine Learning algorithm. With limited information your brain can determine what the object most likely is to some level of confidence, say 60% certain. As you focus and your brain absorbs more of the surroundings and maybe the orientation of the object, it gradually realized that you are maybe only 40% confident of that original assessment, but you’re actually 80% certain that this object is something else. Your brain then recognizes the object as what it thinks it is with 80% certainty. This is exactly how Machine Learning algorithms identify potential outcomes. They assign probabilities to the potential outcomes and settle on the one with the greatest likelihood of being accurate. I think if investors begin to recognize more that they are walking through life really being 100% certain of very few things, they’ll become more comfortable with the idea that a Machine Learning algorithm may only be correct 60% of the time and that is in fact very good and can generate great profit.

What have you been doing to publicize this idea? Have you been using any innovative marketing strategies?

Not really, and that is intentional. When I first created the algorithm, the back testing is all I had to talk about. However, the live trading is what you are measured on. I made sure my performance was available through different publishers so that investors could see the fund’s performance. Drawing attention through performance is all that really matters.

None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful towards who helped get you to where you are? Can you share a story about that?

A big part of this project, for me, was the fact that I genuinely enjoyed coding and working on the algorithm. I found it fascinating, and it gave me joy. I had been doing work on various coding projects, until I met my first investor, Yan Zheng. This fund was just another project for me at that time. When it began to show promise, that is when I began to think about doing something longer term with it. Yan pushed me from this being an academic project to a live fund. She heard about what I was working on through her son and real estate investing and she became very interested. She gave me the push I needed to get the fund off the ground, and I saw the incredible opportunity to make this happen sooner than I thought possible.

How have you used your success to bring goodness to the world?

The fund is small and young and, at this time I don’t have excess capacity. However, I can offer a few things here. My fund has investors and is not an open fund. I have created relationships with great people. As those people make money and generate wealth, they are doing some amazing things with it, including setting up foundations and charitable organizations. That makes me feel good.

There is also potential to utilize the technology that I have at my disposal in many areas. The computer hardware is very advanced and runs for only about one hour a day, therefore having a huge amount of capacity available. One thing I envision over time is doing pro bono work for other projects given the fact that there are many uses for this kind of technology. One example would be to partner with law enforcement to use technology to help with unsolved cases. Another area that has immense potential is healthcare databases and research. I endeavor making this a priority as the fund continues to mature.

What are your “5 Things I Wish Someone Told Me Before I Started” and why. (Please share a story or example for each.)

  1. If you’re instinctually hesitating, focus on understanding why.
  • I have experienced this multiple times since starting the fund. One benefit of having more experience is that you better understand your instincts, including when to fight through them or allow them to put you at pause. When I find myself not as excited about raising new capital or worried about the market open on a daily basis, something is wrong and its time for me to really focus on figuring it out.

2. If even the smallest amount of data contradicts your hypothesis, act as if it is incorrect until proven otherwise.

  • In one phase of development, my back-testing results were based on an assumption that I made about a variable. As I began testing this assumption it took quite some time to accumulate enough data for an accurate measurement. However, the very earliest measurements, though not statistically significant, were correct. I should have revised my back-testing inline with this small set of more conservative results until more data supported a more aggressive strategy.

3. Your greatest asset is not the capital in your fund, but the investment partners that have contributed. Choose them wisely.

  • I have learned this lesson on several occasions, and it is always worth hearing again. Investors can be your best system of support or your worst fuel for self-doubt. In any startup there are enough hurdles to overcome without your investors being additional obstacles.

4. Nobody wants to hear you whining about how much you pay in taxes.

  • In any successful startup, you may soon begin paying more in taxes than 90% of people make in salary. Nobody will feel sorry for you

5. Whiskey will always taste better in celebration.

  • One of the biggest pieces of being in any type of speculative investment is staying even keel. A good algorithm might only be up 55%-60% of trading days. There are going to be down days and many of them. Find healthy ways of dealing with stress and do your best to leave your fund performance at your desk.

You are a person of great influence. If you could inspire a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. 🙂

As I mentioned earlier, there are ways to help the medical and law enforcement communities and that is something I plan to do on my own. I also think that there is another piece of the puzzle, which is more political in nature.

If there were tax benefits to incentivize corporations to share their resources, we might be able, as a society, to combat obscure medical conditions. That is one example. Companies would be able to provide access to much needed technological tools and resources. There are many ways that we can collectively make a difference, and technology can be used to do good work as non-profits alone cannot accumulate the types of sophisticated technology needed in fields such as, forensics, and medicine.

Can you please give us your favorite “Life Lesson Quote”? Can you share how that was relevant to you in your life?

I would say that the Lao Tzu quote: “Watch your thoughts, they become your words; watch your words, they become your actions; watch your actions, they become your habits; watch your habits, they become your character; watch your character, it becomes your destiny” is something that I think about often.

Those words have always stuck with me. There are little things that develop in my life on a consistent basis that I try to control. A big part of emotional health is being able to start with my thoughts and words as they really do become habits. Nothing happens overnight and success is a journey.

Some very well-known VCs read this column. If you had 60 seconds to make a pitch to a VC, what would you say? He or she might just see this if we tag them 🙂

Machine learning is only as powerful as the system in which it is applied. Just like the human brain is useless without the body, artificial intelligence is useless without being provided the means to both absorb and act upon data. What separates Spire Fund Advisory from other investment firms is that we develop full systems around the use of the latest cutting-edge Machine Learning technology and these systems are scalable and transferable to other markets and sub-markets. BYTZ Fund LP is only in its incubation stage and just starting to show the true potential of this technology. Once fully developed, our technologies can be applied to specific sectors, index composites and other asset classes to create a full spectrum of investment opportunities.

How can our readers follow you on social media?

LinkedIn is the best place to get in touch with me.

Thank you so much for joining us. This was very inspirational.

The Future Is Now: Benjamin Richard Truitt of BYTZ Fund On How Their Technological Innovation Will… was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

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