1.
It fetches your data for you.
Just point and click for the data points you want.
2.
You build the model in the software using
familiar operators and not in an obscure programming language.
Suppose you want to calculate the forward dividend yield and compare it to the five year average and score/rank the quotient. You can create this in INVRS using the same operators
and shortcuts you use in excel, optionally validating your equations as you go.
3.
Each model is a template that you can use repeatedly.
No need to establish another data source, no risk of it
breaking and no need to copy and paste each time you have a new stock to
analyze. Once you’ve built your model
you can run it repeatedly on different stocks.
4.
You can experiment.
Change any of the elements in your models,
whether data or formula.
5.
Perform peer-based analysis.
Running a model against one stock hardly seems worth it because there’s no context for the result. With peer-based analysis, you can answer the
question “how does my target company compare to similar companies?”
6.
It archives every analysis automatically.
In excel, refreshing data can lead to the loss
of an old analysis. Your ideal software would never let that happen.
7.
Find every archived analysis easily.
You can search chronologically, by portfolio and by template. An important feature when you are doing hundreds or
thousands of analyses.
8.
No flipping between data source and formula.
Best practice in excel separates data and formula. Sometimes you can keep everything on one
sheet, often you can’t. Flipping between
sheets is a pain when you are checking a
complicated formula and if you combine your formulas and data,
reproducing it or changing variables becomes difficult.
INVRS separates data and formula while keeping it all visible on screen.
9.
Fast graphing.
You can create graphs of your analyses. You can sort the results, add a benchmark, annotate
and export. It's easier than in excel and supports other options
specific to investment analysis, such as price graphing.
10.
Monitor.
Did your analysis uncover the best stock of the group? Use performance graphs and monitor your work.
11.
Back-test.
Build a back-test by selecting earlier dates and run your models using historical data. Note the results and create a back-dated performance graph to see how predictive your model was.
12.
Communicate.
Create notes and articles using INVRS and include graphs, share them using a link.