Dynamic Regulation Via Investment Data as a Remedy for Law’s Diminishing Capacity to React to Innovation

First published at: https://www.law.ox.ac.uk/business-law-blog/blog/2016/09/dynamic-regulation-investment-data-remedy-law’s-diminishing-capacity

Key words: Disruptive Innovation, Venture Capital, Venture Investments, Dynamic Regulation, Feedback Effects, Optimized Information for Regulation, Anticipatory Regulation, Big Data

In a series of recent papers – ‘Dynamic Regulation for Innovation’, ‘Regulation Tomorrow: What Happens When Technology Is Faster Than the Law?’, and ‘How to Regulate Disruptive Innovation – From Facts to Data’ – I evaluate the diminishing relationship between regulation and innovation. The so-called ‘pacing problem’ between innovation and regulation suggests that innovation driven by science and technology is accelerating, yet, simultaneously, federal and state agencies’ regulatory processes have slowed down.

Several factors contribute to this pacing problem. Some consensus exists that the legal and evidentiary burdens placed on regulatory authorities have increased substantially over time, precipitating the remarkable slowdown in rulemaking by regulatory agencies. The growing divergence between the time cycles of technological innovation and the time cycles of governments around the world contributes significantly to the pacing problem. Systemic factors such as the political and ideological structures in the rulemaking process, the political gridlock in a two-party system that impedes the passing of legislation, legislators’ disagreement on how outdated statutes should be updated, and the need for crises to precipitate legislative action also help to explain the pacing problem.


The existing regulatory infrastructure contributes significantly to this problem. In the existing regulatory framework, the regulatory challenges presented by disruptive innovation are largely associated with facts-based, ex-post, trial-and-error rule-making, with stable and presumptively optimal rules. In essence, some evidence exists that governments mostly regulate to react ex-post, after problems arose in the existing regulatory framework but rules are always assumed to be optimal even though optimality is unattainable in a rapidly innovating and increasingly complex world. Other regulatory challenges are presented by the slow speed of regulation, and ever-increasing unknown future contingencies in rulemaking. Because facts-based, ex-post, trial-and-error rule-making cannot anticipate regulatory issues created by innovation, rule-makers may not – or may much too late – realize what new regulatory demands apply to a given innovation. Rule-makers’ near exclusive reliance on stable and presumptively optimal rules created to attain permanent solutions for perceived regulatory issues ignores the ever-changing environment for rules driven by the exponential growth of technology, and the associated exponential growth of innovation.  The timing of regulation in an environment of exponential innovation is a primary problem for regulators. Formal rulemaking in the existing regulatory infrastructure is almost always too time-consuming because the speed of product innovation often makes regulations pertaining to an innovative product obsolete before such regulations are finalized. Finally, the existing regulatory infrastructure, with stable and presumptively optimal rules, is largely incapable of addressing the ever-increasing unknown future contingencies associated with disruptive innovation. Given the pace of innovation, future contingencies in rulemaking are likely to increase substantially, making the dynamic anticipation of future contingencies more important for rulemaking.

Lawmaking and regulatory design need to become more dynamic and anticipatory in their efforts to address this pacing problem. Increased reliance on different sources of data surrounding new technologies can provide some signals or clues about what, when and, to a certain extent, how to regulate.

Here, I show that data relating to investment in new technology and innovation is of particular importance in this context. A plethora of investment data is readily available to make accurate predictions regarding what the next ‘big thing’ is likely to be.  Such data can be used as an index or proxy for the necessity of regulation. Because start-up companies are the ones that usually challenge existing rules, laws and regulations, private data sources are widely available. The proliferation of the better hand-collected global databases on the market, such as CB Insights, PitchBook and Mattermark, can make an important contribution to a ‘data-driven’ regulatory approach.

Investment data can help to develop a list of technologies and issues that need to be the focus of regulatory attention. From such data, rule-makers can get a better – and earlier – sense of which technologies are developing and which technologies need regulatory attention. This might then allow regulators to be more pro-active and avoid wasting resources on technologies that are unlikely to make it to market. It would also allow regulators to more accurately define the scope of a technology by focusing on the type of firm that is attracting attention.

Wulf A. Kaal is an Associate Professor at the University of Saint Thomas School of Law (Minneapolis).

Empirical Evidence on proposed Investment Advisers Modernization Act

The NYT today summarized the political background on the proposed Investment Advisers Modernization Act here:

My Colleague, Jennifer Taub (Vermont) testified in the House Subcommittee on Financial Services in this context and was kind enough to cite some of my work:

Investment Advisers Modernization Act of 2016

Here the two perhaps most relevant empirical findings on the Investment Advisers Modernization Act of 2016:




Private Fund Investor Due Diligence – Evidence from 1995 to 2015

Private Fund Investor Due Diligence – Evidence from 1995 to 2015


Wulf A. Kaal

University of St. Thomas, Minnesota – School of Law; European Corporate Governance Institute (ECGI)

July 19, 2016

Review of Banking and Financial Law, 2016
U of St. Thomas (Minnesota) Legal Studies Research Paper No. 16-15 

The importance of private fund investor due diligence in the investment allocation process, in capital formation, and in private fund litigation has reached unprecedented levels and is further increasing. To provide the industry with data, data trend analyses, and guidance on applicable legal standards, the author examines two datasets: (1) private investment fund advisers’ SEC Form ADV II filings from 2007 to 2014 (N=100392), and (2) the publicly available litigation record pertaining to private fund investor due diligence from 1995 to 2015 (N=572). After highlighting important changes in the quality and quantity of private fund investor due diligence in SEC Form ADV Part II, the author evaluates the corresponding litigation record and analyzes expert guidance on applicable best practices.

Number of Pages in PDF File: 37

Keywords: Private Investment Fund, Investor, Investor Due Diligence, SEC Form ADV Part II, Disclosures, Case Law, Legal Standards, Expert Testimony, Time Series

JEL Classification: G23, G24, G28, K22

Regulation Tomorrow: What Happens When Technology is Faster than the Law?

Regulation Tomorrow:
What Happens When Technology is Faster than the Law?


Mark Fenwick – Faculty of Law, Kyushu University

Wulf A. Kaal  – University of St. Thomas, Minnesota – School of Law; European Corporate Governance Institute (ECGI)

Erik P. M. Vermeulen  – Tilburg University – Department of Business Law; Philips Lighting – Legal Department; Tilburg Law and Economics Center (TILEC); Kyushu University – Graduate School of Law

September 4, 2016

Lex Research Topics in Corporate Law & Economics Working Paper No. 2016-8 

In an age of constant, complex and disruptive technological innovation, knowing what, when, and how to structure regulatory interventions has become much more difficult. Regulators can find themselves in a situation where they believe they must opt for either reckless action (regulation without sufficient facts) or paralysis (doing nothing). Inevitably in such a case, caution tends to trump risk. But such caution merely functions to reinforce the status quo and the result is that new technologies struggle to reach the market in a timely or efficient manner.

The solution: lawmaking and regulatory design needs to become more proactive, dynamic and responsive. So how can regulators actually achieve these goals? What can they do to promote innovation and offer better opportunities to people wanting to build a new business around a disruptive technology or simply enjoy the benefits of a disruptive new technology as a consumer?

Number of Pages in PDF File: 16

Keywords: Airbnb, Artificial Intelligence, Big Data, Drones, FinTech, Principles, Regulation, Regulatory Sandbox, Robotics, Rules, Uber

JEL Classification: E22, G2, K2, K22, K32, K40, L26, L43, L51, O3, O30, O31, O33

Dynamic Regulation for Innovation

Dynamic Regulation for Innovation


Wulf A. Kaal

University of St. Thomas, Minnesota – School of Law; European Corporate Governance Institute (ECGI)

August 27, 2016

Perspectives in Law, Business & Innovation (Mark Fenwick, Wulf A. Kaal, Toshiyuki Kono & Erik P.M. Vermeulen eds.), New York Springer (2016) 

A large consensus in the literature suggests that law has a diminishing capacity to react to innovation. After summarizing the commonalities between the law and technology literature and the literature on dynamic regulation in the analysis of the so-called pacing problem between regulation and innovation, the chapter evaluates proposed remedies for the pacing problem and distinguishes dynamic regulatory remedies.

Number of Pages in PDF File: 30

Keywords: Growth of Technology, Innovation, Regulation of Innovation, Pacing Problem, Dynamic Regulation, Feedback Effects, Optimized Information for Regulation, Anticipatory Regulation

JEL Classification: K20, K23, K32, L43, L5, O31, O32





The proliferation of unconstrained mutual funds calls into question the effectiveness of retail investor protections under the Investment Company Act of 1940. Analyzing trading data and prospectuses of a hand-selected sample of all unconstrained mutual funds launched from 2010 through 2015 (N=449), the authors provide an overview of the evolution of unconstrained mutual funds, contrasting core characteristics with publicly available data pertaining to benchmarked mutual fund investment indices. The article demonstrates that unconstrained mutual funds share multiple investment strategy and risk attributes with fixed income hedge funds. The authors evaluate associated investor protection concerns.

Keywords: Unconstrained Mutual Funds, Private Fund, Mutual Fund, Hedge Funds, Hybrid Funds, Liquid Alternative Assets, Retail Alternative Funds, Asset Classes, Proliferation, Confluence, Investment Styles, Securities Regulation, Retail Investors, Investor Protection, Risk Attributes, Investment Strategy

JEL Classification: F33, G15, G23, G28, K22

How to Regulate Disruptive Innovation

How to Regulate Disruptive Innovation – from Facts to Data by Wulf A. Kaal , Erik P. M. Vermeulen :: SSRN

Wulf A. Kaal  
University of St. Thomas, Minnesota – School of Law; European Corporate Governance Institute (ECGI)

Erik P. M. Vermeulen 
Tilburg University – Department of Business Law; Philips Lighting – Legal Department; Tilburg Law and Economics Center (TILEC); Kyushu University – Graduate School of Law


Disruptive innovation creates increasing regulatory challenges. The reason for this is simple: Regulation is usually reactive, responding to facts. However, we currently live in a world of data, not facts. Regulation must therefore be proactive and dynamically responsive to data and trends. This paper discusses the following questions: (1) Why should regulators be proactive?, (2) When should regulators respond?, (3) What should regulators respond to? and (4) How should regulators respond? Using a dataset comprising over 77,000 venture capital deals with over 35,000 companies in the United States from 2005 to 2015, the paper shows how investment data can provide important feedback on innovation trends and associated risks for regulators, optimize the timing of regulation, and support anticipatory rulemaking.

Number of Pages in PDF File: 42

Keywords: Disruptive Innovation, Venture Capital, Venture Investments, Dynamic Regulation, Feedback Effects, Optimized Information for Regulation, Anticipatory Regulation, Big Data

JEL Classification: K20, K23, K32, L43, L5, O31, O32

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