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• Case | HBS Case Collection | August 2019

  Paradigm Capital Value Fund

  Luis M. Viceira and Elena Corsi

  Karl Jan Erick Hummel had founded Paradigm Capital Value Fund in 2007 together with Columbia Business School Professor Bruce Greenwald, an expert in value investing and now chairman of the fund. The fund followed the principles of value investing to their target universe: publicly traded companies with market capitalization between €100 million and €4.5 billion based in Germany, the Nordic countries, the U.K., and Ireland. Hummel and Greenwald believed they had developed an investing edge in that universe, ignored by large U.S. and U.K. based funds, through deep research and by locating their headquarters close to companies and their management. Hummel and his team of six analysts were based in a small suburb of Munich, Germany. Paradigm Capital Value Fund was highly concentrated, with 60% of its assets invested in four stocks, and held its positions for long periods of time. Hummel had to decide if it was time to divest from one of their main investments, a German manufacturer of roll-over car washing systems, and instead invest in a German truck equipment supplier. At the same time, Hummel was concerned by how to grow the fund.

  Keywords: Investment Activism; Investment Portfolio; Financial Management; Growth and Development Strategy;

  Citation:

  Viceira, Luis M., and Elena Corsi. "Paradigm Capital Value Fund." Harvard Business School Case 220-014, August 2019.  View Details

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• Case | HBS Case Collection | August 2019

  Sustainable Investing at J.P. Morgan Private Bank

  Sara Fleiss and Luis Viceira

  Citation:

  Fleiss, Sara, and Luis Viceira. "Sustainable Investing at J.P. Morgan Private Bank." Harvard Business School Case 220-016, August 2019.  View Details

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• Working Paper | 2019

  Using Models to Persuade

  Joshua Schwartzstein and Adi Sunderam

  We present a framework for analyzing “model persuasion,” where persuaders influence people’s beliefs by proposing models (likelihood functions) that suggest how to organize past data (e.g., on investment performance) to make predictions (e.g., about future returns). Receivers are assumed to find models more compelling when they better explain the past. A key tradeoff persuaders face is that models that better fit the data induce less movement in receivers’ beliefs. Model persuasion sometimes makes the receiver worse off than if he interprets data through the lens of a default model. The receiver is most misled by persuasion when there is a lot of publicly available data that is open to interpretation and exhibits randomness, as this gives the persuader “wiggle room” to highlight false patterns. Even when the receiver is exposed to the true model, the wrong model often wins because it better fits the past. Competition more broadly pushes persuaders towards overfitting available data and as a result tends to neutralize the data by leading receivers to view it as unsurprising. With multiple receivers, a persuader is more effective when receivers share similar priors and default interpretations. We illustrate with examples from finance, politics, and law.

  Keywords: model persuasion; Data and Data Sets; Forecasting and Prediction; Mathematical Methods; Framework;

  Citation:

  Schwartzstein, Joshua, and Adi Sunderam. "Using Models to Persuade." NBER Working Paper Series, No. 26109, July 2019.  View Details

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