Completed first cut at analysis of change in estimate revision impact on intrinsic value. The purpose of the module is to examine if there is a large disconnect between change in market price and price implied by a change in fundamentals.

Screen of DCF is below. Our analysis takes a “theoretical” estimate revision on our base case values. We do this by reducing the size of base case revenue on a proportional basis to revision implied by Wall Street consensus. Then we extract change in growth rate implied, as well as change in margin, and impact our base case accordingly.

Planned analysis to build on this is follows:

• Analysis of business model fundamentals and compare potential estimates vs. current estimates (ideally, we’re examining companies with broken models, selecting companies that are fundamentally dislodged in price)
• On our larger data set, analyze change in implied perpetuity growth and resultant implied terminal profitability
• Need to build in dynamic Perpetuity Growth
• Build infrastructure to feed tech names; start analyzing fundamental tech dislocations

Productive morning. Analysis of historical trends is complete. Built the following today:

• Examined change in margins and NWC over 5, 3, 2 and 1 year periods
• Calculated trends using least-squared regressions
• Simulated forecast values based on regressions to arrive at “theoretical” margin today
• The value closest to the actual value is the trend with the greatest confidence interval
• Resultant output examines trends for the aforementioned (see screen below for output)

Next, planned build includes our analysis of wall street changes in estimates. We’ll focus on the following for revenue, gross profit, EBIT, EBITDA, Net Income (new variable) and UFCF (also new variable)

• Look at aggregate change in forecasts
• Look at % change in forecast
• Relate % change in forecast vs. change in price vs. the associated time period
• Examine if and when change in price reaches “parity” with change in estimate; (eg, if EPS declines by 5%, price declines by 10%, observe if and when price “rebounds” to only a 5% implied decline since date of revised estimate

Once we have the aforementioned values, we can continue on to the planned analysis of implied perpetuity operating figures based on current P/E

• Growth rate implied (in order to “sanity” check at what price growth makes sense)
• Profitability implied (in order to “sanity” check whether the company can grow in to that profitability profile)

Build notes: consider analyzing the following:

• Multivariate regression for price signals
• Drive probability model utilizing statistics derived from historical and projection analysis

Infrastructure for operating metrics bulk download from CapIq complete. Need to build model for statistical analysis on metrics.

The following is currently planned for whole period, last 5, 3 and twelve months analysis:

– Trend of operating metrics
– Average

– Max (75th percentile)

– Min (25th percentile)

Consider building multivariate regression for signal model

Completed building DCF sensitization to observe impact of change in revenue CAGR and FCF margin in FY+1 through FY+4 on share price.

Next module(s) to build include:

• Benchmarking historical (both target company and peer) margin and NWC levels to compare “field” of potential margin or NWC improvement
• Build probability calculator to assess likelihood of various scenarios to arrive at weighted average price

Made progress on model today. DCF built which automatically drops in figures based on Wall Street Consensus. First cut at build for UHS is within $5 of current share price.

Upcoming build functionality:

• Dynamic calculations for implied perpetuity growth rate
• Dynamic calculations for weighted average cost of capital
• Time series analysis of company and competitor comps to determine appropriate terminal operating profile (note: possible to use standard deviation on quarterly results for this)
• Finalize tool builder on easily modifiable opex assumptions vs. Wall Street Consensus

Note to self: recall that final goal is to “back-fill” operating assumptions to arrive at implied share price on illustrative sensitization

Productive day testing new “signal” tools. Currently, we’re screening companies that have missed earnings, and observing the frequency by which they’re missing earnings. We triangulate this back to potential drivers of earnings misses in order to observe if the market is properly assessing the impact of macro factors on individual company performance. The same analysis is run for guidance estimates, but we have not yet dug deeper.

On the earnings surprise front, 3 sub-industries stand out.

– Health care facilities have been battered due to exposure to hurricane battered Florida

– Retail REITs (particularly GGP) has not fared well recently, impacted by street distaste for malls. Important to observe exposure to Class A properties vs. others for these names

– Leisure products an interesting play, particularly Hasbro. Recent Toys R’ Us bankruptcy may hurt distribution, but only for a year or so. We can dig in to strength of Hasbro gaming franchise and sector dynamics with respect to products coming from movie tie-ins