HiddenLevers uses statistical analysis and other approaches to model the relationships between different industries and levers (economic factors). Scenarios are then created as a set of up or down changes in levers based on the event being modeled. The scenarios thus move the levers, which in turn move industries, which move individual stocks. HiddenLevers' Scenario Analysis makes use of the entire process detailed below, while the stock screener depends only on the industry and lever mappings.

Here's a Visualization of HiddenLevers' Patent-Pending Approach:

The Details:

1. Mapping Stocks To Industries: HiddenLevers starts by categorizing companies (stocks) into industries. We have created a proprietary industry list with more detail and accuracy than traditional classifications. For instance, we classify companies in the solar panel business under Solar Energy, not semiconductors. And we separate dry-bulk shippers from oil tanker companies. HiddenLevers is constantly improving its industry classifications, since this is critical to correctly assessing the macroeconomic risks facing each company.
2. Mapping Industries To Levers: HiddenLevers then analyzes the relationships between each industry and each lever, starting with simple hypotheses like "airlines go up when oil goes down." The relationship between industries and levers is measured through statistical analysis like measurement of correlation and multiple regression analysis. In performing statistical analyses, we control for fluctuations of the market as a whole, as the movement of the broader market is responsible for as much as 70% of any given stock's movements on a single day. HiddenLevers also aggregates data from multiple stocks in an industry to derive the industry's relationship with a lever, since looking at a single company may obscure trends in the data.
3. Mapping Levers To Scenarios: The final step in HiddenLevers' scenario modeling involves the creation of the macroeconomic scenarios. The scenarios are modeled as a group of simultaneous changes in different levers. For instance, the scenario "Oil Spike" has the Oil lever rise 100%, has other commodities rise by smaller margins, and has US auto sales and retail sales fall. Where possible, an analysis of past events (previous oil spikes in this instance) is used to help determine how the different levers should move. For certain scenarios however, no good historical analog exists (e.g. Healthcare Reform), and the scenario setup is qualitative in nature. HiddenLevers will soon allow users to customize and create their own scenarios, so that they can change the assumptions freely.
4. Detailed Analysis Of Major Stocks: HiddenLevers takes the lever mapping process a step further by performing a detailed analysis of individual companies to ensure that their lever relationships are correct. Apple (AAPL) for instance is as much a mobile phone maker as it is a computer maker, and this should be reflected in its lever relationships. HiddenLevers is analyzing individual companies on an ongoing basis, starting from the largest companies traded on US markets and moving down the list.

In some cases, the relationship between a lever and industry is quite obvious, as for instance with oil prices and oil exploration companies. The oil industry's total market capitalization has grown in almost perfect lockstep with the price of its key product, and this is neither unexpected nor unknown to most investors. In other cases, relationships can be less obvious, as with lenders and interest rates. Do lenders benefit from rising interest rates if their lending margins are able to expand, or are lenders impacted as rates rise because they are borrowing on short term rates but lending at fixed long term rates?

Since answering these questions is difficult, and the nature of measurement imprecise, HiddenLevers' ultimate goal is not to present a hard-and-fast numerical relationship between companies and levers. These relationships can fluctuate over time, and the key is to understand the direction and magnitude of the relationship, so that potential risks can be visualized. Remember, all the fancy Wall Street VaR (value-at-risk) models didn't help Wall Street avoid the meltdown - the key is to use tools to be aware of risks so that we can act on them, and to understand that models help visualize different possibilities, and do not predict the future.