How Models are Used in Practice (Fundamentals of Quantitative Modeling)

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Из курса от партнера University of Pennsylvania

Wharton Business and Financial Modeling Capstone

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University of Pennsylvania

Wharton Business and Financial Modeling Capstone

272 оценки

Курс 5 из 5 — Specialization Business and Financial Modeling

In this Capstone you will recommend a business strategy based on a data model you’ve constructed. Using a data set designed by Wharton Research Data Services (WRDS), you will implement quantitative models in spreadsheets to identify the best opportunities for success and minimizing risk. Using your newly acquired decision-making skills, you will structure a decision and present this course of action in a professional quality PowerPoint presentation which includes both data and data analysis from your quantitative models. Wharton Research Data Services (WRDS) is the leading data research platform and business intelligence tool for over 30,000 corporate, academic, government and nonprofit clients in 33 countries. WRDS provides the user with one location to access over 200 terabytes of data across multiple disciplines including Accounting, Banking, Economics, ESG, Finance, Insurance, Marketing, and Statistics.

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Steps 1 and 2: Yahoo Finance

In this module, which correlates to Steps 1 and 2 in the Project Prompt, you'll be working with a historical data set to calculate performance data and to provide summary statistics on that data. These calculations will allow you to practice using Spreadsheets for financial calculations, and provides the foundational skills and numbers for the next steps of the project. First, you'll use the set to calculate daily returns on a set of securities. You'll then use your Spreadsheet skills to calculate summary statistics. You'll be given the opportunity to test your knowledge with a sample return to see if your calculations are correct. And you may want to refresh your recollection of the content from the Specialization with the lectures included here. The work you complete this week allows you to form the basis for comparing stock performance, which you will use in creating the investment portfolio for your final project as well as the comparison to the performance of a single stock.

Definition and Uses of Models, Common Functions (Fundamentals of Quantitative Modeling)14:58

How Models are Used in Practice (Fundamentals of Quantitative Modeling)10:42

Mathematical Functions (Fundamentals of Quantitative Modeling)20:27

Navigating a Spreadsheet and Crafting Formulas (Introduction to Spreadsheets)19:40

How To Build an Optimization Model: Hudson Readers Ad Campaign (Modeling Risk and Realities)13:19

Data and Visualization: Graphical Representation (Modeling Risk and Realities)22:09

Познакомьтесь с преподавателями

Richard Lambert
Professor of Accounting
Accounting- Wharton School
Robert W. Holthausen
Professor
Accounting
Don Huesman
Managing Director, Wharton Online
Innovation Group- Wharton School
Richard Waterman
Professor of Statistics
Statistics-Wharton School

So now that we've had a look and

we understand what a quantitative model is about, let me tell you some of

the specific activities where these models are used.

So, one thing that we can do once we've got a quantitative model is prediction.

And prediction is basically taking the model,

putting in an input, and calculating the output.

And so going back to the diamond ring example, what I mean by prediction.

What's the expected price of a diamond ring that weighs 0.2 of a carat?

If I've got a model I can create that prediction.

And certainly one of the most often used places for

these quantitative models is what we call predictive analytics.

And so if you want to do predictive analytics,

you need some underlying model for the process typically.

Another place that we use these models is in forecasting and

when I talk about forecasting I'm really thinking about a time series.

And I'm thinking about trying to make a comment about what's happening

in the future.

And so forecasting is an activity that most businesses and

entities go through at some stage, often to do with resource planning.

And going back to the example of the epidemic if one was involved in a public

health situation a key question that you would be asking for example would be,

how many people are expected to be infected in six weeks time?

Because that, the answer to that sort of question is going to help me in terms of

resource planning.

Here's another forecasting type of problem where quantitative models can be very,

very useful.

So imagine that you're running a hospital and

you are trying to schedule patients for appointments.

One of the truths out of here is that not everybody shows up for their appointment.

And that lead to inefficiencies in the system.

The system could be improved if we had a sense of who is likely to not show up and

perhaps we could tweet the schedule as a result of that.

And so that would be another example where we'd like to do some forecasting,

forecasting whether or not someone's likely to show up for appointment.

Another activity that we use these quantitative models for

is optimization and so optimization takes a lot of

business thinking and the example that I've

got here is the demand and price.

And so it's a very legitimate question to ask I wonder what price is going to

maximize the profit.

And the keyword there is maximize.

That's to optimize and output the profit.

And optimization activities are going to typically require

an underlying quantitative model.

And so that's one of the places where quantitative models go to work as inputs

for optimization, trying to make your business as good as it can be.

Optimizing price, optimizing the supply chain, etc.

So we use models for optimization.

Another activity that we go through and models help us are ranking and targeting.

And so what I mean there is that we're often looking at a list.

It might be customers, or it might be diamonds for example, and

we'd like to have a look at these diamonds perhaps and figure out which ones

we'd be interested in purchasing, for a diamond merchant for example.

And so I can't look at all the diamonds that are out there in the world because

I simply don't have the resources and

so that's the idea of giving limited resources.

It would be really nice if I could identify potential targets of opportunity

and that essentially is a ranking and targeting exercise.

And if we have a model we can create a set of predictions.

We can sort those predictions and that creates a ranking.

And then, we can work our way down that list of predictions in order,

that ranked list, in order to optimize our own time.

And so another example would be that I'm interested in real estate and

I'm considering potential properties to buy.

There are millions of properties potentially for

sale in the country at any one point in time.

I don't have an opportunity to look at all of them.

It would be nice if I could create a model that would help me identify

those properties that are of the most interest to me and

that's something that a model can help you do.

Here are some more things that can use our quantitative models for.

What if scenarios, scenario planning.

We will often like to understand

what might happen to the world if certain things changed.

And so going back to the epidemic, we might want to ask ourself the question,

well what if the growth rate changed, had increased to 20% per week.

Then how many infections are we going to expect in the next ten weeks?

So if we have a model where we are able

to examine the consequences of the change of some of our assumptions and

that's the idea of scenario planning and what if analysis.

So that's something else that a model can help you do.

Certain models lend themselves to interpretation.

And in terms of the price and quantity demanded model, there was a coefficient

in the equation if you look back and that coefficient was the power, which was -2.5.

So, it's a number, but sometimes these coefficients have interpretations.

And that interpretation can be helpful, and it turns

out that the interpretation of that -2.5 is what's known as an elasticity.

And it tell us that as the price goes up by 1%,

we can anticipate the demand to fall by 2.5%.

And so that's what I mean by interpreting a coefficient in a model and

that can give us additional insight, and help us explain the model to other people.

So our models involve mathematical equations.

The mathematical equations often have coefficients in them.

The coefficients can have real meanings and interpretations.

Another task that models are used for is to conduct the sensitivity analysis.

And so, I pointed out earlier on that pretty much every model you

create is going to rely on some assumptions and

a sensitivity analysis is a process where we look to see how sensitive

the outputs of the model are to some of those assumptions.

And if we find the model is particularly sensitive to an assumption then that tells

us that we need to think a little bit more carefully about that assumption.

Maybe we'll try and confirm that the assumption is realistic or

collect more information to try and tie that assumption down more precisely.

So there's a sensitivity analysis typically with a model

that helps us figure out which of the assumptions are really important, and

which aren't so important.

And therefore how we might want to use our time,

in confirming that the underlying assumptions are reasonable.

After having gone through this whole modeling business hopefully there's some

benefits, and here I've listed out a set of benefits.

There are undoubtedly more than up here on this slide, but

these are benefits that I think, and

have experienced myself after having gone through this modelling activity.

So one of the things that can be the outcome of a model is

the identification that there's some gaps in the current understanding.

You've tried to lay out your understanding of the business process, And

there's just some big gaps sitting there in the middle.

And that's only become apparent because you've taken the time to

lay out your current understanding.

So identifying gaps is certainly a benefit.

It's often the case that people

are using models without having made underlying assumptions explicit.

And so one of the benefits of creating a useful model

is that you will have explicated what those assumptions are.

So they're on the table, everybody can see what you're assuming

as you come up with your recommendations from that model.

And sometimes those assumptions have been implicit,

therefore not everyone is aware of them.

And so explicating the assumptions can be very useful.

You will also have, at the end of the modeling process,

a well-defined description of the business process, how the pieces fit together.

And that can be a benefit its own right.

Fourth one I've got here isn't entirely obvious but

one of the things a model can do is create what I call an institutional memory.

So many businesses will have some smart person who is relied on for

doing certain things, certain forecasting activities.

What do you think we're going to sell next month?

Someone who's worked at the company 25 years.

You go to them, they have a good sense of what's going on.

But what happens if that person leaves?

Knowledge goes with them.

And so you can think of a model as creating an institutional memory

because that model is going to be a set of equations, a set of inputs and outputs,

and that's going to stick around beyond any individual.

And so I think, models can be useful from that point of view.

Ultimately, the model is going to be used as a decision support tool and

I've bolded the word support here because it's a little naive

to think that the model is going to reveal truth, there are always approximations.

And the model is typically going to be used as one of a suite of

tools to help support the decision making within a company.

And so it's very much not an end in its own right but

a support to other activities.

My final comment here is that sometimes,

I think that models are serendipitous insight generators.

By going through the modeling process you learn something that you hadn't thought

about at all, something very unexpected, and that happens quite often, and

so on that's another benefit of modeling.

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