When reputation value is at a premium, companies leverage this to sell more, faster, and at a higher price; obtain labor, vendor and supplier services, and capital at a lower cost, and hold both regulators and activists at bay.
The value measure of an organization’s reputation is the accumulated revenue and cost-savings arising from stakeholders’ expectations of experiential or remunerative benefits from an association, product, or service. Healthy corporate reputations create a value premium.
Using big data quantitative indicators of stakeholder expectations, Steel City Re has pioneered synthetic quantitative measures of reputational value. The reputation premium-finding ability of our metrics can help companies insure, manage, and arbitrage reputation risk with quantitative rigor.
Steel City Re has been calculating these measures since December 31, 2001 from indications of expected stakeholder behaviors captured by a diversity of publicly accessible prediction markets. Steel City Re acquires these data from Factset (NYSE: FDS), a commercial data aggregator, and transforms them into synthetic measures of reputational value through computer-driven algorithms that involve no human subjective influence.
The major categories of inputs into our “reputation premium finder” reflect the expected economic impact of the behavior of customers, equity investors, creditors, suppliers, and market analysts. The components are joined arithmetically with final values within the range of 0-1. The unit of measure is the Gerken% (GU%).
As of 30 December 2018, Steel City Re’s database comprised 890 continuous weeks of calculations totaling 6.15 million unique values from a median of 7356 public companies per week.
These data and their mathematical progenitors are used by some underwriters at Lloyd’s, and are also used for public equity indices such as the RepuStars Variety Index (Ticker: REPUVAR) and the Conscious Companies ETF (Ticker: KRMA). As of 30 December 2018, a serial equity portfolio named RepuSPX begun 31 December 2001 of reputationally healthy but undervalued companies identified algorithmically from the constituents of the S&P500 index is outperforming the parent S&P500® index by nearly 400%.
The actuarial models derived from this large pool of metrics support the pricing and underwriting of risk transfer solutions. Such models typically comprise overlapping data pairs distributed over more than 125,000 simulated reputation value loss indemnification years. The simulation data sets contain more than 10,000,000 pairs of RVM% and loss measures including more than 25,000 loss events.