In this particular case, the infrastructure problem was resolved without government intervention. The credit-rating debacle is thus a good example of how adopting a model not fit for your purpose—in this case, using a model for predicting the likelihood of default rather than one for valuing bonds to manage a portfolio—can result in disastrous decisions. Most recently, intermediaries have begun to use equity return swaps to create custom contracts that specify the stock index, the investment time horizon, and even the currency mix for payments. That’s because probability of default is not the only factor determining the value of a bond and its risk. How to measure innovation including early stage, late stage and overall program metrics. Attempts to gauge the riskiness of an innovation must take into account the limitations of the models—formal and informal—on which people base their decisions about how to use the innovation, warns Robert C. Merton, MIT professor and Nobel laureate in economics. If the riskiness of an innovation depends on the choices people make, it follows that the more informed and conscious their choices are, the lower the risk will be. The result was that many of them faced the same exposure to the risk of a decline in house prices at the same time—creating a systemic risk. (For more on asymmetry in risk adjustment, see “Systemic Risk and the Refinancing Ratchet Effect,” by Amir Khandani, Andrew W. Lo, and Robert C. Merton, forthcoming, Journal of Financial Economics.) Copyright © 2020 Harvard Business School Publishing. Innovation is an approach to change that seeks revolution over improvement. Even today, when driving a car we reflexively draw on imprecise but robust mental models where relationships between factors are guessed at based on experience. That might lead you to conclude that the innovation hasn’t made driving in the snow any safer. The 2007–2009 financial crisis provides a good example of such unintended consequences. No human being can possibly foresee all the consequences of an innovation, no matter how obvious they may seem in hindsight. Digital innovation could breed new risks that only become apparent over time and may not fit into established taxonomies such as credit and compliance risk. A temporary solution was achieved through cooperative action by the major stock exchanges. It is difficult to imagine that any regulatory agency would raise a red flag about any one of these conditions. On the other side, time risk is the least hidden. Models are also constrained by their users’ proficiency, and they can easily be misapplied. This is nothing new for the financial system. Both surely contributed materially to the crisis. Suppose a bank or broker introduces a customized product into the financial markets. The intended (and good) consequence of the mortgage-lending innovations was to increase the availability of this low-cost choice. Thus, the institutional means of stock diversification for households was initially markets for individual company shares. All of business, it has been argued, boils down to weighing the likely rewards of a decision against the likely risk. A definition of user innovation with examples. If we are to make progress, however, that’s a fact we need to accept and to manage. By the same token, the models used for high-speed trading are useless in the corporate reporting of executive stock options’ expense value in accordance with generally accepted accounting principles. But innovations also carry risks. Financial firms use models like Black-Scholes to allow computers to conduct trades. The result was that over time the leverage of homeowners of all vintages began to creep up, often to levels as high as those of new purchasers, instead of declining, as it normally would when house prices are on the rise. The trend was self-reinforcing—rising house prices increased homeowner equity, which could then be extracted and used for consumption—and mortgage holders began to repeat the process over and over. But if you and everyone else were to drive a lot faster, you’d face the same amount of risk you’ve always had in a snowstorm. It also follows that you do not need to impute dubious motives and behavior on the part of financial professionals to explain the crisis. For example, in response to the bursting of the tech bubble in 2000, the shock of 9/11, and the threat of recession, the U.S. Federal Reserve systematically lowered its bellwether interest rate—the Fed funds rate—from 6.5% in May 2000 to 1% in June 2003, which stimulated mortgage refinancing and the channels for doing so. Cookies help us deliver our site. Similarly, two bonds could get the same rating even though one was likely to give more back in the event of default than the other. There’s nothing inherently wrong in doing this, of course; it’s a matter of personal choice. And many of these models do a pretty good job. A model of 3.14159 is less incomplete. This was expensive and infeasible for all but a handful of large investors—transaction costs were often very high, and the desired stocks were frequently not available in small enough lot sizes to accommodate full diversification. But innovations also carry risks. As the trend continued, homeowners came to view these extractions as a regular source of financing for ongoing consumption, rather than as an occasional means of financing a particular purchase or investment. In particular, index futures made feasible the creation of exchange-traded options on diversified portfolios. Under these conditions, the mortgage market can be particularly vulnerable to even a modest dip in house prices and rise in interest rates. Innovations always involve particular risks for the organization. Innovations inherently have a wide array of risks that depend on attempting to predict the unknown. The common types of innovation objectives with examples. To put it simply, you wouldn’t choose a Ferrari for off-road travel any more than you would use a Land Rover to cut a dash on an Italian autostrada. A case in point is the recent U.S. credit-rating crisis.