Q&A

Answers to student questions about Financial Modeling with Python and Excel

finstmt Q&A 12/14/2020


Questions

  • Should we be worried about the data that did not get extracted in finstmt?

  • Could you show finstmt being used with xlwings?

  • Is the copy function only in finstmt?

  • Could you give an example of how to take specific values out of finstmt? E.g. the revenue for the 3rd forecasted period.

  • How can I see the effects of adjusting the financial statements manually?

Transcript

  • 00:04: hey everyone
  • 00:04: this is nick dear burtis teaching you
  • 00:06: financial modeling today i'm going to be
  • 00:09: answering some student questions
  • 00:11: regarding finn's statement
  • 00:13: so the first question that we have here
  • 00:17: is should we should we be worried about
  • 00:20: data
  • 00:20: that did not get extracted in statement
  • 00:24: so let's uh set up a simple example with
  • 00:28: fin statement so that we can take a look
  • 00:30: at that
  • 00:32: so first thing that we'll need to do is
  • 00:35: um let's load the
  • 00:38: statements with pandas so import pandas
  • 00:41: as
  • 00:42: pd and then from then statement
  • 00:46: i'm going to import the
  • 00:49: financial statements income statement
  • 00:52: and balance sheet
  • 00:58: statement
  • 01:02: a little bit of a lag there uh financial
  • 01:04: statements
  • 01:06: income statements and balance sheets
  • 01:11: and then we can load uh
  • 01:14: the income statement
  • 01:19: um walmart income statement i have in
  • 01:22: the same directory
  • 01:26: um and we'll need to set the first
  • 01:31: column as the index
  • 01:39: okay um and now same thing
  • 01:42: for the balance sheet we'll load that
  • 01:45: into a data frame
  • 01:47: um and now this is just balance sheet
  • 01:54: okay that looks good as well so now we
  • 01:57: can
  • 01:58: create the statement classes
  • 02:02: objects um so we'll have
  • 02:06: the income statement is equal to income
  • 02:09: statements
  • 02:10: from the data frame of the
  • 02:14: income data frame and
  • 02:17: the balance sheet um
  • 02:22: is balance sheet from data frame
  • 02:26: um the balance sheet data frame and then
  • 02:29: finally
  • 02:31: the statements are going to be the
  • 02:33: financial statements of the
  • 02:39: income statement and a balance sheet
  • 02:45: so this now that we have our setup here
  • 02:48: this first question again is asking
  • 02:50: about
  • 02:51: basically these messages that come up
  • 02:54: here
  • 02:55: about was not able to extract data from
  • 02:57: the following names
  • 02:59: and should we be worried about that
  • 03:02: so it is worth it to take a look
  • 03:05: over what those items are the first time
  • 03:08: that you load your statements in
  • 03:09: just to make sure that it's not missing
  • 03:12: anything important
  • 03:13: but hopefully most of the data items you
  • 03:17: would need to use are already covered
  • 03:19: in the package um so when we look at
  • 03:21: some of these items
  • 03:24: we're seeing a lot of basically
  • 03:25: calculated
  • 03:27: items which are not
  • 03:30: actually part of the financial statement
  • 03:32: so that's why they're not getting
  • 03:34: picked up lots of different margin
  • 03:36: calculations
  • 03:38: the number of shares outstanding
  • 03:41: so these are just various items which
  • 03:46: are not really part of the traditional
  • 03:48: financial statements and that's why
  • 03:50: they're being excluded
  • 03:54: and in some case items
  • 03:57: have other names which are getting used
  • 03:59: like you can see
  • 04:01: ebitda was not um loaded here but that's
  • 04:04: because
  • 04:05: operating income should already be there
  • 04:08: so you can also just look at statements
  • 04:12: and see what you have
  • 04:16: and
  • 04:19: we can see
  • 04:23: that we
  • 04:26: have the data that we need loaded in
  • 04:28: here so i think
  • 04:30: you know just just check over what names
  • 04:32: are not getting loaded and check
  • 04:33: over the data which is being loaded and
  • 04:36: make sure that there's not
  • 04:38: anything that's missing here that you do
  • 04:40: actually have in your
  • 04:42: financial statements
  • 04:46: so on the next question um could you
  • 04:49: show
  • 04:50: finn statement being used with excel
  • 04:52: wings
  • 04:54: yeah certainly so um that
  • 04:57: is very similar here i mean ultimately
  • 04:59: we're just passing data frames
  • 05:02: into uh fin statement so any way that we
  • 05:05: can make a data frame
  • 05:07: we can then upload that in the fin
  • 05:09: statement
  • 05:11: and so as long as we can
  • 05:12: [Music]
  • 05:14: get back to this same kind of structure
  • 05:16: where we have the statement items
  • 05:17: on the index and the dates as the column
  • 05:21: headers
  • 05:22: it doesn't matter how that was
  • 05:24: originally loaded in
  • 05:26: um so
  • 05:28: [Music]
  • 05:29: we can import excel wings
  • 05:33: um and then we can get a reference to
  • 05:38: the income statement book um so that's
  • 05:42: going to be xw dot book
  • 05:44: of the walmart income statement
  • 05:50: um and then uh
  • 05:55: you can just take a look at what's
  • 05:57: actually in there for
  • 05:59: um targeting the sheets
  • 06:09: so we can see uh so walmart
  • 06:12: uh she wmt is the sheet name
  • 06:16: so i'm going to
  • 06:20: um the income statement sheet
  • 06:23: is the income statement book
  • 06:26: sheets walmart
  • 06:30: um and then
  • 06:35: we want to basically get everything here
  • 06:38: um
  • 06:43: but the my initial instinct would be to
  • 06:46: start from here and expand
  • 06:48: but the top left cell is blank so i'm
  • 06:51: not
  • 06:52: certain whether that's going to work but
  • 06:55: we can give it a try
  • 06:58: um so range a1
  • 07:02: dot expand dot options
  • 07:06: pdi data frame
  • 07:14: this is going to be income df and we
  • 07:17: need to pick the value
  • 07:18: of that let's go what do we get there
  • 07:23: that did work okay that's great um
  • 07:26: yeah now basically this looks the same
  • 07:28: as the one we had loaded in before
  • 07:30: let's do the same thing with the balance
  • 07:32: sheet as well
  • 07:36: here for the balance sheet we're trying
  • 07:39: to match that
  • 07:42: we can do the same kind of thing on
  • 07:45: uh the balance sheet
  • 07:54: um and this would be balance sheets
  • 08:01: and this would be the balance sheet df
  • 08:05: give that a shot um
  • 08:09: and then look at that and looks like we
  • 08:11: got the same thing loaded in there
  • 08:14: so this would go here instead of
  • 08:18: where we have it being loaded in via
  • 08:21: pandas right now
  • 08:25: um
  • 08:28: so then we can run all that and it
  • 08:31: should
  • 08:32: it looks like it's loading everything
  • 08:33: into
  • 08:35: uh in statement properly
  • 08:38: so we'll just look at what the resulting
  • 08:41: statements are to confirm
  • 08:43: that worked correctly um
  • 08:47: but yeah basically any way that you can
  • 08:50: create that data frame
  • 08:51: with the index as the statement items
  • 08:54: and the columns as the dates
  • 08:57: it'll be able to be loaded into if in
  • 09:00: statements so even if you're just
  • 09:01: manually creating that data frame or
  • 09:04: it's
  • 09:04: coming from some other source that will
  • 09:07: work is fine
  • 09:08: that will work fine as well um
  • 09:12: it's as long as you can match that
  • 09:14: structure it'll work so
  • 09:15: yeah now we can see how the statements
  • 09:18: are loaded in we're seeing the same
  • 09:19: thing here
  • 09:21: as when we had used the pandas approach
  • 09:28: next is is the copy function only
  • 09:32: in fin statement
  • 09:35: so this is referring to that um
  • 09:38: in the statement we can do um
  • 09:42: statements.copy um
  • 09:46: and now we have a second statements
  • 09:48: which have the same thing
  • 09:50: um and
  • 09:54: i can go and i can update
  • 09:58: say you know for revenue
  • 10:01: i want to change the forecast method
  • 10:06: sorry forecast config i want to change
  • 10:09: the method
  • 10:10: to
  • 10:14: the average approach
  • 10:17: now when i look at the forecast
  • 10:21: assumptions
  • 10:22: uh we can see that revenue has gone to
  • 10:24: the mean here
  • 10:25: but if we look at the original
  • 10:27: statements
  • 10:30: that was unaffected by the changes that
  • 10:32: we made
  • 10:33: so this is a nice way to be able to do
  • 10:36: multiple different
  • 10:37: forecasts uh with the same financial
  • 10:40: statements
  • 10:41: object you just copy it and make
  • 10:44: whatever
  • 10:44: forecast adjustments run your forecast
  • 10:48: analyze the result as needed and then
  • 10:51: for the next
  • 10:52: configuration it just copies it again
  • 10:56: and makes whatever changes
  • 10:59: so the question is actually asking
  • 11:01: though is this
  • 11:03: just a feature of in statement or can we
  • 11:06: do that elsewhere
  • 11:08: so there are a number of different
  • 11:11: libraries as well as some built-in
  • 11:14: features of python which
  • 11:15: support the dot copy method
  • 11:19: so another good example is uh
  • 11:22: pandas supports it we have this income
  • 11:25: data frame
  • 11:26: we can do income data frame dot copy and
  • 11:29: we get a copy of it and any changes that
  • 11:31: we make
  • 11:32: this copy are not going to affect the
  • 11:34: original
  • 11:36: so same exact kind of thing an example
  • 11:39: with
  • 11:40: um just built in python you can actually
  • 11:43: do that with a list so my list
  • 11:47: is say one two three
  • 11:51: um second list is wireless dot copy
  • 11:57: then you can append to that
  • 12:02: and then we'll see that the original
  • 12:04: list was not affected
  • 12:06: but the second list is affected
  • 12:10: so that's there as well now
  • 12:14: say that you encounter some object in
  • 12:17: python
  • 12:18: that you want to copy and it does not
  • 12:21: have a dot copy
  • 12:22: method what can you do how can you copy
  • 12:25: it
  • 12:26: so there is actually a
  • 12:30: module in python built into every python
  • 12:32: installation that's made
  • 12:34: for copying objects so if you do from
  • 12:37: copy
  • 12:39: import deep copy
  • 12:43: um then this deep copy can be used
  • 12:47: in place of
  • 12:51: the copy method on an object so i'm
  • 12:55: gonna do deep copy
  • 12:56: of the list there instead of dot copy
  • 13:00: and we'll see that we get uh the exact
  • 13:03: same
  • 13:04: result whenever my system wants to
  • 13:06: resolve
  • 13:08: uh that will show up with one two three
  • 13:10: four
  • 13:12: um and you can do that with any object
  • 13:14: in python
  • 13:16: so actually that was what i recommended
  • 13:19: people do there we go
  • 13:20: with in statement before i added the dot
  • 13:23: copy method
  • 13:24: just do a deep copy and you can do that
  • 13:27: with
  • 13:28: anything in python so um
  • 13:32: copy is supported in a number of
  • 13:34: different places but if it's not
  • 13:36: then just use deep copy instead
  • 13:43: the next question is could you give an
  • 13:45: example of how to take specific values
  • 13:48: out of in statement for example
  • 13:51: the revenue for the third forecasted
  • 13:53: period
  • 13:57: so a nice thing about
  • 14:00: statement is well you do have these
  • 14:03: custom classes
  • 14:04: um for the for the financial statements
  • 14:06: the income statement and balance sheet
  • 14:08: it's made to be very compatible with
  • 14:11: other
  • 14:12: workflows in that when you access
  • 14:15: statement items
  • 14:17: you actually get a pandas series
  • 14:21: so when we do statement style revenue
  • 14:25: you can see we get that out of it all
  • 14:27: those different values
  • 14:28: if we check the type of this what we
  • 14:31: have
  • 14:32: is a pandas series and so anything that
  • 14:35: you can do with a series
  • 14:37: you can do with the statement item from
  • 14:40: pen statement um so you know we've
  • 14:44: looked previously in the course how we
  • 14:46: can
  • 14:47: uh index on series and data frames so
  • 14:50: here this would be getting the last
  • 14:52: value out
  • 14:53: just to be getting the first value out
  • 14:56: um
  • 14:58: and then if you have a certain date in
  • 15:01: mind
  • 15:02: you can uh grab that date and
  • 15:08: do dot look and that will allow you to
  • 15:11: pull out a certain date
  • 15:13: um so
  • 15:16: yeah for those methods you can pull
  • 15:18: whichever value you want
  • 15:21: another thing which may be useful for
  • 15:23: you
  • 15:25: is if you don't want to work with series
  • 15:28: all
  • 15:28: series can be converted into lists
  • 15:31: so um just do dot two lists on the end
  • 15:34: and then you have a list of
  • 15:36: values and a lot of people are more
  • 15:38: comfortable working with lists than they
  • 15:40: are with series
  • 15:41: so you have that um you know and then
  • 15:44: you can
  • 15:45: do all the same indexing stuff
  • 15:49: that you can normally do with lists so
  • 15:53: number of different ways to pull out
  • 15:56: individual items
  • 15:58: but the key factor there is that
  • 16:02: it's just a panda series so anything
  • 16:03: that works in panas
  • 16:05: is going to work here as well
  • 16:11: and the last question here is how can i
  • 16:13: see the effects
  • 16:15: of adjusting the financial statements
  • 16:17: manually
  • 16:18: so this is talking about in the
  • 16:22: forecasting approach um
  • 16:26: so after we do a forecast then we can
  • 16:29: adjust the results of that forecast
  • 16:33: so let me look at the forecast
  • 16:35: assumptions
  • 16:37: um so we can go ahead and run the
  • 16:40: forecast
  • 16:41: um statements.forecast
  • 16:45: uh let's just uh
  • 16:49: youtube periods here
  • 16:54: then we can look at the forecasted
  • 16:57: statements
  • 16:59: [Music]
  • 17:02: and we can look at the
  • 17:05: plot of those
  • 17:17: so we see all different forecasts there
  • 17:21: um and what this question is referring
  • 17:24: to is the fact that
  • 17:26: um we can
  • 17:32: adjust one of the existing um
  • 17:36: or any of the existing forecasts to add
  • 17:39: other adjustments
  • 17:41: um so forecast dot
  • 17:45: forecast and then we would grab
  • 17:48: whatever we want to adjust um
  • 17:51: let's say um
  • 17:56: we have what's the key to our retained
  • 17:59: earnings
  • 18:00: um so we can pull out the retained
  • 18:04: earnings forecast
  • 18:06: and do to manual um
  • 18:15: and then in the two manual method we can
  • 18:18: pass the adjustments or replacements
  • 18:23: so um i'm gonna here
  • 18:26: adjust and let's just say
  • 18:29: we want the um first period
  • 18:32: first forecasted period to grow by 40
  • 18:35: percent
  • 18:39: and that didn't work oh we do have to
  • 18:41: put
  • 18:42: um same length as there are a number of
  • 18:45: periods so
  • 18:46: not adjusting the second period
  • 18:51: [Music]
  • 18:54: um
  • 18:58: so now we can take a look and plot
  • 19:01: uh the retained earnings forecast and
  • 19:04: where as you can see before
  • 19:06: it was just kind of you know very slight
  • 19:08: curve a basically straight line
  • 19:11: um now when we look at it
  • 19:14: um we see that we have this big jump
  • 19:17: here and this is our 40
  • 19:18: adjustment that we made um so now that
  • 19:22: has jumped way up here
  • 19:23: and then it goes back to uh no
  • 19:26: adjustment
  • 19:27: so it's going to take the growth that it
  • 19:29: had forecasted before
  • 19:31: and now apply that for the final period
  • 19:35: so you can see it increases just
  • 19:36: ever so slightly here
  • 19:40: and you should be able to also
  • 19:43: see that in the statement
  • 19:47: view as well so now when we look at the
  • 19:51: retained earnings jumps to 78 million
  • 19:54: there
  • 19:57: whereas in the statements you can go
  • 20:00: back
  • 20:03: to statements the last retained earnings
  • 20:06: here
  • 20:08: um was
  • 20:13: okay so it's not showing there
  • 20:17: um reason
  • 20:25: that is insane um
  • 20:38: um
  • 20:43: i think it's because you need to
  • 20:45: actually run the forecast again
  • 20:49: to see it make its way into the
  • 20:52: statements
  • 20:56: yeah there we go now we had the big
  • 20:59: growth
  • 21:01: in the first period and then you know
  • 21:03: smaller more normal growth
  • 21:05: in the second period um
  • 21:09: and we should be able to see that in the
  • 21:11: plot as well
  • 21:13: after you do your adjustment um
  • 21:17: you will be able to see it in the plot
  • 21:19: but you gotta actually
  • 21:21: run a second forecast in order to have
  • 21:24: that
  • 21:24: come into uh the financial statements so
  • 21:28: now we can see that there in the plot
  • 21:31: and also
  • 21:37: sorry forecasts two
  • 21:41: and we can see that in the actual values
  • 21:44: there
  • 21:44: as well so
  • 21:49: that's all the questions that i had for
  • 21:52: today
  • 21:52: on finstateman thank you for the
  • 21:55: students
  • 21:56: who submitted those questions and
  • 22:00: i will see you next time

Project 4 Q&A 12/14/2020


Questions

  • What are the variables I should include in the Monte Carlo simulation?

  • Should I use annual or quarterly financial statements?

  • How should I approach the write-up around the forecasting assumptions?

Transcript

  • 00:03: hey everyone this is nick dear burtis
  • 00:05: teaching you financial modeling
  • 00:07: today i'm going to be answering some
  • 00:09: student questions
  • 00:10: on project four
  • 00:15: so the first question that we have is
  • 00:18: what variables should i include in the
  • 00:21: monte carlo
  • 00:22: simulation so
  • 00:25: [Music]
  • 00:26: you want to try to include the most
  • 00:29: impactful
  • 00:30: variables in your model now depending on
  • 00:33: how you set things up
  • 00:35: and depending on your company that's
  • 00:37: going to change which
  • 00:38: variables those are now i would say that
  • 00:41: generally
  • 00:42: using more variables is better if you
  • 00:45: could
  • 00:46: run the simulation on every single input
  • 00:48: in your model that would be
  • 00:49: ideal but that's also a substantial
  • 00:52: challenge to
  • 00:53: set up all that infrastructure and
  • 00:56: some of those inputs may not have much
  • 00:58: of it an impact
  • 01:00: and so it's not um very high value
  • 01:04: to target all of those um
  • 01:08: so you know if your company
  • 01:12: is mostly equity financed certainly you
  • 01:15: would want to make sure
  • 01:16: that um inputs like beta and the market
  • 01:19: return
  • 01:21: are in the simulation if they're heavily
  • 01:24: debt financed
  • 01:25: then your inputs into the cost of debt
  • 01:28: and market value of debt
  • 01:29: are going to be more important to be in
  • 01:31: there
  • 01:33: one that you should essentially always
  • 01:36: have
  • 01:38: is the terminal growth rate for the
  • 01:40: perpetuity growth
  • 01:42: method for the terminal value that
  • 01:46: assumption tends to change the resulting
  • 01:49: stock price
  • 01:50: stock price by quite a bit and so i
  • 01:53: would always recommend
  • 01:55: including that in simulations as well as
  • 01:58: the sensitivity analysis
  • 02:02: um and you want to try to
  • 02:05: include assumptions which are going into
  • 02:08: your forecasting
  • 02:09: as well maybe you have so
  • 02:12: assumed a certain growth rate or the
  • 02:14: revenue
  • 02:15: uh and a number of things are forecasted
  • 02:18: as a percentage of revenue
  • 02:19: so that is a very impactful
  • 02:23: assumption in your model
  • 02:26: then you would certainly want to include
  • 02:27: that revenue growth rate in the
  • 02:29: simulations
  • 02:30: as well so it's worth it to
  • 02:33: just go through your model and try
  • 02:36: changing around some inputs
  • 02:38: and see how much of an impact it has on
  • 02:40: the stock price
  • 02:41: if it's fairly large then that is
  • 02:44: something you should include in the
  • 02:45: simulation
  • 02:46: if it makes very little impact then
  • 02:48: there's not a lot of value
  • 02:50: to add it won't hurt it's always better
  • 02:52: to include more variables
  • 02:54: but you know considering everyone has
  • 02:57: limited time
  • 02:59: i would prioritize those which have the
  • 03:01: biggest
  • 03:02: impact on the stock price
  • 03:06: so the next question is should i use
  • 03:09: annual or
  • 03:10: quarterly financial statements
  • 03:13: so it certainly is a little bit more
  • 03:15: challenging to use
  • 03:16: quarterly financial statements but you
  • 03:18: can get a little bit more accurate
  • 03:20: result
  • 03:21: as well um so that's something that
  • 03:25: um as far as the grading is concerned
  • 03:28: using quarterly
  • 03:29: financials uh would earn you a bonus
  • 03:32: but it also can add a fair bit of
  • 03:35: complication to your project
  • 03:36: as well i would say that
  • 03:40: most
  • 03:41: [Music]
  • 03:43: ecf models i see are using annual
  • 03:46: that's kind of the standard but you can
  • 03:50: get a little bit more accurate by using
  • 03:52: quarterly
  • 03:53: i mean really getting a deeper
  • 03:56: understanding of the seasonality
  • 03:58: of the company's operations and and
  • 04:00: modeling that appropriately
  • 04:02: um it's more complicated to
  • 04:06: work with the quarterly financials
  • 04:08: mainly because of that seasonality
  • 04:10: effect
  • 04:11: some companies aren't going to have that
  • 04:13: but the majority do
  • 04:15: and so that moves you from a simple time
  • 04:18: series
  • 04:19: world into a complex time series world
  • 04:22: and so you have to use methods which are
  • 04:25: geared towards
  • 04:26: these more complex time series and can
  • 04:29: handle the the seasonality
  • 04:31: inherent in most quarterly financials
  • 04:36: so that's why i say you would earn a
  • 04:39: bonus for
  • 04:41: using the quarterly because you have to
  • 04:42: go to these complex
  • 04:44: uh time series methods
  • 04:48: and then one other thing you'll have to
  • 04:52: consider
  • 04:54: is um once you
  • 04:58: go to uh you'll have to think about
  • 05:02: everything
  • 05:02: in terms of quarters right four per year
  • 05:05: and so if you want a five year forecast
  • 05:07: you need to forecast 20 periods there
  • 05:11: and when you think about
  • 05:14: um if you
  • 05:18: want to get the net income for the year
  • 05:20: well you have to sum all the four
  • 05:21: quarters in the year
  • 05:22: so these things
  • 05:26: little things working with the quarterly
  • 05:29: financials
  • 05:30: are a little bit more complicated
  • 05:34: so if you want to just get the model
  • 05:36: done i would recommend
  • 05:38: just going with annual it's going to be
  • 05:39: substantially simpler
  • 05:41: but if you want to perform at the
  • 05:43: highest level and have a little bit more
  • 05:45: accurate model
  • 05:47: then it makes sense to go for the
  • 05:49: quarterly financials
  • 05:52: so the last question here is how should
  • 05:54: i approach the write-up
  • 05:56: around the forecasting assumptions
  • 06:00: um so
  • 06:03: there's really two different
  • 06:07: stages as you go through the forecasting
  • 06:10: the one first stage is that
  • 06:13: you just want to be able to predict
  • 06:17: the future in a way that makes sense
  • 06:21: based upon the historical values
  • 06:24: so does you know when you look at a plot
  • 06:27: of
  • 06:28: the forecasted versus the historical
  • 06:30: does it look
  • 06:31: like the historical naturally leads to
  • 06:34: the future forecasted is there a pattern
  • 06:38: there which is being continued
  • 06:40: um so that's kind of the first step in
  • 06:43: the forecasting process
  • 06:44: you want to select the methods for
  • 06:47: each statement item
  • 06:51: such that you see that these historical
  • 06:54: patterns are repeated in the future
  • 06:56: and you can consider that kind of the
  • 06:58: original baseline
  • 07:00: forecast so there will be a write up for
  • 07:03: that component
  • 07:04: in terms of um what
  • 07:08: why did you choose uh the forecasting
  • 07:11: methods
  • 07:12: for each of the line items and that's
  • 07:13: where you're talking about
  • 07:15: historical data and how you had to use
  • 07:19: different methods to fit
  • 07:20: the data um you know such as
  • 07:24: say it was you know very flat and then
  • 07:27: there's a jump and then it's flat again
  • 07:29: um
  • 07:31: it might make more sense to use the
  • 07:32: recent value approach to say well
  • 07:35: it's just going to stay at the new level
  • 07:37: um so you can kind of
  • 07:38: talk through that in the write-up
  • 07:41: well it was flat and then it jumped and
  • 07:43: then it was flat again and so i went
  • 07:45: with the recent value approach because
  • 07:46: it seems like
  • 07:48: that value is going to stay at that
  • 07:50: level in the future
  • 07:53: so that's the first stage of the
  • 07:56: forecasting and then the second stage is
  • 08:00: well i have kind of basically this naive
  • 08:02: forecast
  • 08:03: which is only thinking about the
  • 08:05: historical financials
  • 08:07: and not incorporating any kind of
  • 08:09: information that we have
  • 08:11: about the company's operations in the
  • 08:13: future or
  • 08:14: about economic conditions that are
  • 08:16: changing or other world events
  • 08:20: so the second stage is where you take
  • 08:22: all this qualitative
  • 08:23: information about the company that you
  • 08:26: uncover through your research
  • 08:28: and your your thinking and your analysis
  • 08:32: and you want to incorporate that into
  • 08:34: the forecast
  • 08:36: [Music]
  • 08:37: so you know maybe
  • 08:41: for example just say that the company
  • 08:43: you're analyzing
  • 08:44: is pfizer um
  • 08:47: and your historical data is basically
  • 08:50: pre covered 19 time frame
  • 08:54: um well we can't really just assume that
  • 08:58: the historical is going to predict the
  • 09:00: future now that we've had
  • 09:02: uh you know covid come to the situation
  • 09:04: and kind of
  • 09:05: switch things up a lot for the company
  • 09:08: um so you may want to go in
  • 09:11: and read about the the rollout of their
  • 09:13: vaccine
  • 09:14: um and kind of the distribution plans
  • 09:17: and
  • 09:18: how much revenue they they plan to bring
  • 09:20: in from all this distribution
  • 09:23: and work that into your forecast um
  • 09:26: so it would be good to go and um read
  • 09:30: different disclosures that the company
  • 09:32: has put out such as 10k
  • 09:34: 10q and any other
  • 09:37: disclosures that are relevant
  • 09:41: and see the company's plans see their
  • 09:44: estimates
  • 09:45: and you know critically evaluate their
  • 09:47: estimates
  • 09:48: um to see if you know you think it would
  • 09:52: be different
  • 09:53: and then take that knowledge that you've
  • 09:56: learned about the company
  • 09:57: and work that into your forecast well
  • 10:00: you know now they have all this
  • 10:01: additional
  • 10:02: demand with the vaccine so maybe you're
  • 10:05: going to boost
  • 10:06: the revenue growth for the next couple
  • 10:09: of years
  • 10:12: and that's not something you could ever
  • 10:14: get by just
  • 10:15: looking at the historical data and just
  • 10:19: you know using the time series to
  • 10:20: predict the future
  • 10:22: um so this would be
  • 10:26: more of the bulk of the write-up for
  • 10:28: most people
  • 10:30: is what qualitative information have you
  • 10:33: uncovered about your company
  • 10:35: uh which changes how we should think
  • 10:39: about what's going to happen in the
  • 10:40: future
  • 10:42: um there's not going to be any right or
  • 10:45: wrong answers
  • 10:46: on these things it is ultimately your
  • 10:49: opinion
  • 10:49: about what's going to happen in the
  • 10:51: future uh
  • 10:53: but it should be a logical analysis
  • 10:56: um the uh
  • 11:00: you should be kind of trying to break
  • 11:02: down the news
  • 11:04: or expectations for the future of the
  • 11:07: company
  • 11:08: and trying to analyze how that will
  • 11:10: impact the financial statements
  • 11:12: um and then justifying
  • 11:15: the adjustments that you've made to the
  • 11:17: financial statements
  • 11:19: by that way
  • 11:22: you'll have a little bit on well i chose
  • 11:25: the
  • 11:26: the recent value for this item i chose
  • 11:28: the trend for this item
  • 11:31: et cetera um you could say well you know
  • 11:34: i go with the default for cagger and
  • 11:35: then i've changed it
  • 11:38: for these certain items um and then
  • 11:42: you say but beyond the baseline forecast
  • 11:44: then
  • 11:45: uh i adjusted revenue up
  • 11:49: 10 for the next two years because
  • 11:52: of this vaccine roll out and um you know
  • 11:54: give a lot more detail
  • 11:56: there of why you think 10 is an
  • 11:58: appropriate number
  • 11:59: based on the disclosures um
  • 12:02: and make sure you're incorporating what
  • 12:04: other impacts that's going to have
  • 12:06: on the financial statements well now
  • 12:09: you know they need additional logistics
  • 12:11: to ship all these vaccines around the
  • 12:13: world quickly
  • 12:15: um so their uh you know operating costs
  • 12:19: for those logistics are are going to
  • 12:20: increase uh proportionately
  • 12:24: so you want to just think through
  • 12:28: all of the information that you've
  • 12:29: gathered about your company
  • 12:31: and how that can affect all the
  • 12:33: different statement line items
  • 12:35: and that will be the bulk of your
  • 12:37: write-up around the forecast
  • 12:39: assumptions so
  • 12:42: that's all the questions that i had for
  • 12:45: today
  • 12:45: on project four from the students
  • 12:49: and definitely feel free to um
  • 12:52: send me questions on any topics
  • 12:56: throughout the course
  • 12:58: either by email which you can find the
  • 13:01: email on the
  • 13:03: channel page or you can also feel free
  • 13:06: to comment
  • 13:07: on videos and i can reply there as well
  • 13:11: thanks for listening and see you next
  • 13:16: time