John Alberg's guest appearances

John Alberg

Flirting with Models

Excess Returns 

Tobias Carlisle

Eric Crittenden

Adam Butler

Jack Vogel

Kai Wu

Antti Ilmanen

Corey Hoffstein

Jim Masturzo

Meb Faber

Michael Batnick

Rob Arnott

Gary Antonacci

Kevin Carter

Ehren Stanhope

Wes Gray

OwlTail

Value investing has produced some of the greatest investors of all time. Whether it be Benjamin Graham's deep value approach or Warren Buffett's quality at a reasonable price strategy, value has a long track record of success. But what if the great value investors of the future won't be human at all? What if machines can be trained to identify the best value stocks? In this episode, we talk to John Alberg of Euclidean Technologies about applying machine learning to the process of selecting value stocks. We talk about the different types of machine learning and how these techniques can be utilized to select fundamentally sound stocks, We also discuss using machine learning techniques to avoid value traps, whether intuitive factors are more successful, areas where humans can still perform better than machines and much more. We hope you enjoy the discussion. ABOUT THE PODCAST Excess Returns is an investing podcast hosted by Jack Forehand (@practicalquant) and Justin Carbonneau (@jjcarbonneau), partners at Validea. Justin and Jack discuss a wide range of investing topics including factor investing, value investing, momentum investing, multi-factor investing, trend following, market valuation and more with the goal of helping those who watch and listen become better long term investors. SEE LATEST EPISODES https://www.validea.com/excess-returns-podcast FIND OUT MORE ABOUT VALIDEA https://www.validea.com FOLLOW OUR BLOG https://blog.validea.com FIND OUT MORE ABOUT VALIDEA CAPITAL https://www.valideacapital.com FOLLOW JACK Twitter: https://twitter.com/practicalquant LinkedIn: https://www.linkedin.com/in/jack-forehand-8015094 FOLLOW JUSTIN Twitter: https://twitter.com/jjcarbonneau LinkedIn: https://www.linkedin.com/in/jcarbonneau

Value investing has produced some of the greatest investors of all time. Whether it be Benjamin Graham's deep value approach or Warren Buffett's quality at a reasonable price strategy, value has a long track record of success. But what if the great value investors of the future won't be human at all? What if machines can be trained to identify the best value stocks? In this episode, we talk to John Alberg of Euclidean Technologies about applying machine learning to the process of selecting value stocks. We talk about the different types of machine learning and how these techniques can be utilized to select fundamentally sound stocks, We also discuss using machine learning techniques to avoid value traps, whether intuitive factors are more successful, areas where humans can still perform better than machines and much more. We hope you enjoy the discussion. ABOUT THE PODCAST Excess Returns is an investing podcast hosted by Jack Forehand (@practicalquant) and Justin Carbonneau (@jjcarbonneau), partners at Validea. Justin and Jack discuss a wide range of investing topics including factor investing, value investing, momentum investing, multi-factor investing, trend following, market valuation and more with the goal of helping those who watch and listen become better long term investors. SEE LATEST EPISODES <a href="https://www.validea.com/excess-returns-podcast" rel="nofollow" target="_blank">https://www.validea.com/excess-returns-podcast</a> FIND OUT MORE ABOUT VALIDEA <a href="https://www.validea.com/" rel="nofollow" target="_blank">https://www.validea.com</a> FOLLOW OUR BLOG <a href="https://blog.validea.com/" rel="nofollow" target="_blank">https://blog.validea.com</a> FIND OUT MORE ABOUT VALIDEA CAPITAL <a href="https://www.valideacapital.com/" rel="nofollow" target="_blank">https://www.valideacapital.com</a> FOLLOW JACK Twitter: <a href="https://twitter.com/practicalquant" rel="nofollow" target="_blank">https://twitter.com/practicalquant</a> LinkedIn: <a href="https://www.linkedin.com/in/jack-forehand-8015094" rel="nofollow" target="_blank">https://www.linkedin.com/in/jack-forehand-8015094</a> FOLLOW JUSTIN Twitter: <a href="https://twitter.com/jjcarbonneau" rel="nofollow" target="_blank">https://twitter.com/jjcarbonneau</a> LinkedIn: <a href="https://www.linkedin.com/in/jcarbonneau" rel="nofollow" target="_blank">https://www.linkedin.com/in/jcarbonneau</a>

Applying Machine Learning to Value Investing with Euclidean’s John Alberg

"How do you come to a rational conclusion as to what a company is worth?"  A seemingly simple question with little-to-no clear answer. For John Alberg, a background in computer science and a passion for machine learning led him to view the problem through the lens of data.  "If it is true that you can use publicly available information to buy companies for less than their economic worth," he thought, "then you should be able to see it in the data." And thus was born Euclidean, an investment firm that marries machine learning with a deep value mentality. Our conversation spanned more than 2.5 hours and covered everything from the basics of machine learning, to the evolution of Euclidean's approach over the last decade, to the implications of adversarial examples in neural networks. This podcast, an abridged version of our conversation, picks up the thread mid-way through, where I have asked John to expand upon his experience with his startup, Employease, and how it influenced his value-based thinking at Euclidean. I hope you enjoy.

"How do you come to a rational conclusion as to what a company is worth?"  A seemingly simple question with little-to-no clear answer. For John Alberg, a background in computer science and a passion for machine learning led him to view the problem through the lens of data.  "If it is true that you can use publicly available information to buy companies for less than their economic worth," he thought, "then you should be able to see it in the data." And thus was born Euclidean, an investment firm that marries machine learning with a deep value mentality. Our conversation spanned more than 2.5 hours and covered everything from the basics of machine learning, to the evolution of Euclidean's approach over the last decade, to the implications of adversarial examples in neural networks. This podcast, an abridged version of our conversation, picks up the thread mid-way through, where I have asked John to expand upon his experience with his startup, Employease, and how it influenced his value-based thinking at Euclidean. I hope you enjoy.

John Alberg - The Man in the Machine (Learning) (S1E7)