Value Creation by Active Investors (and Its Potential for Addressing Social Problems)
Bibliographic record
Abstract
A distinguished Columbia academic discusses the methods and outcomes of “active investing” with the co‐founder of a leading private equity firm and a former senior partner of a well‐known hedge fund. In the case studies used by both panelists to illustrate their investment selection and management processes, the investors provided not only capital, but oversight and expertise that helped bring about significant increases in the productivity and value of their portfolio companies. What's more, in both cases, the changes that contributed to high returns for investors also ended up having major benefits for the companies' non‐investor stakeholders, especially their consumers. In the first of the two cases, Paul Hilal explains the thinking behind Pershing Square's $1.1 billion purchase of 14% of the Canadian Pacific Railway in 2012. With the help of intensive “fundamental” analysis of the company, Hilal recognized that Canadian Pacific was substantially underperforming its rival, the Canadian National Railway, in a number of important ways. And when CP's management and board rejected his plan for changes, Hilal led a proxy battle that ended in a landslide victory for the proposed slate of directors, including Hilal himself. Then, after bringing in a new CEO, the restructured board presided over operating changes that, during Pershing's four‐year ownership, increased the market value of CP from $8 billion to $30 billion. By 2016, when Pershing sold its investment (for $4 billion), Canadian Pacific was “shipping 20% more freight … 40% faster than ever before, with record on‐time performance, 40% fewer locomotives, 35% fewer people, and 14% improved fuel efficiency—all while maintaining an industry‐leading safety record.” In the second case, Russ Carson describes the success of Welsh, Carson, Anderson, and Stowe in turning its purchase in 1998 of a single oncology practice in Denver into a publicly traded company with more than 90 outpatient cancer centers throughout the U.S. When it was sold in 2011, US Oncology was producing $4 billion in revenue while employing 1,000 oncologists, who, as significant equity owners, shared in the success of Welsh Carson. Using modern management techniques to create “enormous efficiencies out of an extraordinarily fragmented system,” Carson and his colleagues created “the single largest—and, by all accounts, most reputable—outpatient provider of cancer services in the country.” One of the keys to this success was “getting the doctors to spend their time seeing patients, not looking for records,” which contributed greatly to Welsh Carson's ability to improve “both the quantity and quality of cancer care in the 90 communities that we were operating in.”
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".