Machine Learning

AgPa #66: Machine-Learned Manager Selection (2/4)

Machine Learning and Fund Characteristics Help to Select Mutual Funds with Positive Alpha (2023)
Victor DeMiguel, Javier Gil-Bazo, Francisco J. Nogales, Andre A. P. Santos
SSRN Working Paper, URL

The second AGNOSTIC Paper on the application of machine learning in manager selection. This week’s paper follows essentially the same idea as Kaniel et al. (2022) in AgPa #65. The authors also examine a comprehensive sample of US mutual funds and although they use slightly different methodology, arrive at generally similar conclusions. This, of course, increases the evidence that machine learning is indeed helpful for manager selection…

  • Machine learning helps to identify outperforming funds
  • Past performance and measures of activeness are the most relevant variables
  • Given their alpha, machine-selected funds remain too small

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AgPa #65: Machine-Learned Manager Selection (1/4)

Machine-Learning the Skill of Mutual Fund Managers (2022)
Ron Kaniel, Zihan Lin, Markus Pelger, Stijn Van Nieuwerburgh
NBER Working Paper 29723, URL

To conclude the posts on manager selection, at least for the moment, I will dive into one of the most recent research frontiers in this area. Since the application of machine learning in investment management has been intensively studied among equities for more than three years now, it is not surprising that researchers also start to apply such algorithms to other asset classes. A natural candidate for this are equity mutual funds and this is exactly where this and the next four week’s AGNOSTIC Papers come in.

  • Machine learning helps to identify outperforming funds
  • Less is more – not all information is necessary
  • Alpha is easier to predict than total returns

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AgPa #55: Backtests in the Age of Machine Learning

A Backtesting Protocol in the Era of Machine Learning (2019)
Rob Arnott, Campbell R. Harvey, Harry Markowitz
The Journal of Financial Data Science Winter 2019, URL/SSRN/PDF

I have already written about the pitfalls of research in asset management and the importance of good research practices for the application of machine learning. This week’s AGNOSTIC Paper takes this idea even further and provides a seven-point protocol for empirical research in finance.

Exhibit 2 of Arnott et al. (2019).

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AgPa #49: Machine Learning in Quant Asset Management

How Can Machine Learning Advance Quantitative Asset Management? (2023)
David Blitz, Tobias Hoogteijling, Harald Lohre, Philip Messow
The Journal of Portfolio Management Quantitative Tools 2023, URL/SSRN

This week’s AGNOSTIC Paper is a broad overview about machine learning in investment management. The authors outline the benefits and pitfalls of machine learning compared to “traditional” econometrics and present several use cases in the world of (quantitative) asset management. They also provide ideas for research governance to keep those powerful methods under control.

  • Benefits and pitfalls of machine learning in finance
  • Use cases of machine learning in asset management
  • Keeping it under control: research governance and protocol

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AgPa #48: Investable Machine Learning for Equities

Investable and Interpretable Machine Learning for Equities (2022)
Yimou Li, Zachary Simon, David Turkington
The Journal of Financial Data Science Winter 2022, 4(1), URL

Regular readers of this blog know that machine learning in asset management is one of my favorite topics and I recently found new interesting material. This week’s AGNOSTIC Paper is the first of two studies and examines an important issue with machine learning models in great detail: interpretability…

  • Machine learning models outperform simpler methods
  • Different models learn different investment approaches

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AgPa #32: Agnostic Fundamental Analysis (3/3)

Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets (2022)
Matthias X.Hanauer, Marina Kononova, Marc Steffen Rapp
Finance Research Letters 48, URL/SSRN

The third and final post about agnostic fundamental analysis. This week’s AGNOSTIC Paper challenges the simple linear methodology and introduces vastly improved valuation models…

  • More sophisticated valuation models yielded better performance
  • Different models emphasize different fundamental variables

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AgPa #26: Trading on Price Charts

(Re-)Imag(in)ing Price Trends (2022)
Jingwen Jiang, Bryan T. Kelly, Dacheng Xiu
The Journal of Finance, Forthcoming, URL

This week’s AGNOSTIC Paper is about technical analysis. Full disclosure: I never believed in technical analysis in the sense of drawing lines on charts or imagining somewhat arbitrary patterns.

But the approach of this week’s authors is quite different. They borrow methodology from image recognition and train a machine learning model to detect predictive patterns in price charts (Yes, the machine receives the price chart as picture, not the underlying numbers!)…

  • The model identifies very profitable short-term signals
  • The signals are also profitable over longer horizons
  • Some of the machine-learning-signals are explainable
  • The model disagrees with conventional technical analysis

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AgPa #21: AI-Powered vs. Human Funds

Do AI-Powered Mutual Funds Perform Better? (2022)
Rui Chen, Jinjuan Ren
Finance Research Letters, Volume 47, Part A, URL/SSRN

This week’s AGNOSTIC Paper compares the performance of AI-powered- and human mutual funds between 2017 and 2019 in the US. Although AI-powered funds are not the holy grail some investors may have hoped for, they still added value compared to their human peers…

  • AI-powered mutual funds did not outperform the US market
  • But AI-powered funds outperformed their human peers
  • And AI-powered funds avoided the disposition- and rank effect

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#3: Big Data & Machine Learning in Asset Management

This week I gave a talk on “Big Data and Machine Learning in Asset Management” at Goethe-University in Frankfurt. Thanks again to my thesis-supervisor Sasan Mansouri for the invitation. In this post I will summarize a few points of the talk and share the slides. The key result is the following framework to evaluate investment strategies that claim to use big data and machine learning. I also apply this to several real world funds.

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