Microsoft Corp (NASDAQ:MSFT) Premium Earnings Model
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This Excel spreadsheet model applies growth rates to the four reportable segments: Productivity & Business Processes, Intelligent Cloud, More Personal Computing, and Corporate, to project revenue. Ratio analysis is used to complete the Income Statement.The model is calibrated to meet management's guidance and consensus estimates as a "base-case" scenario. You can enter your own assumptions into the blue cells to form your own earnings forecast.
If you are new to modeling, be sure to read the notes and instructions (included within the spreadsheet), as well as the comments embedded within the cells which will help guide you to creating your own forecast.
Model Developer: John Moschella, CFA, CPA
Date of Last Model Update: 6/1/2019
Note: This model is for demonstration purposes only, and does not represent investment advice. By downloading you are deemed to have read and agreed to our Terms of Use.
About the Model Developer - John Moschella
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I have spent nearly 15 years analyzing companies in various capacities. After earning a BSBA in Finance, MS in Accounting and MBA from Northeastern University, I began my professional career at PricewaterhouseCoopers (PwC) in New York as an Assurance Associate in the Financial Services practice, where I earned a CPA license. During my time at PwC I participated in a rotational assignment within the Financial Service Research Institute at PwC where I studied bank mergers throughout the financial crisis, drawing on my experience as an intern bank examiner with the Federal Deposit Insurance Corporation (FDIC) during my undergraduate career.
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After PwC, I spent five years at UBS Investment Bank where I worked first as a Capital Specialist, and then as an Equity Research Associate. During this time I became a CFA Charterholder. In my research role I built and maintained earnings models, contributed to research reports, and participated in client conferences, covering the Semiconductor and Semiconductor Capital Equipment Industries. I then moved to General Electric Capital Corp in 2014 as a Risk Analyst where I built regression models to predict asset losses based on various macroeconomic scenarios. After the sale of the majority of GE Capital’s assets, I started a consulting firm which provides capital planning support to banks, in addition to running Gutenberg Research.
What is Gutenberg Research?
Notes From Our Founder: John Moschella
Each day we are directly influenced by the information we allow into our world: the news channels we watch, the communities we live in, the websites we visit. To explain what Gutenberg Research is, and to help you decide whether or not to use our website in your universe of information, I have summarized my thoughts on earnings modeling, equity valuation, and the equity research industry. I have also included my professional background, which highlights how I have shaped the beliefs that form the core approach taken in all of our Gutenberg Research programs.
Thoughts on Modeling
I believe modeling is about educating ourselves on the potential prospects of a particular company. This education is derived through the consideration of past events, as well as potential upside and downside possibilities which could change the company’s financial path. Our modeling efforts represent an attempt to organize the chaos of variables and forces which drive our theories of the future. At the center of modeling and forecasting, is the concept that as research market participants, we seek to have a reasonable basis for our ideas, whether we use them to make investment decisions, publish articles, communicate in an interview, or tweet a comment.
I am confident that anyone can create a model. In fact, we all model everyday. Whether we formalize it in a spreadsheet is a separate point. When someone asks your opinion about a product, another person, or a company, in your mind you will consider the different aspects which go into forming your opinion, before giving your response. This is what modeling means to me. The spreadsheet simply acts as a vessel for the various factors we have used to form our opinions.
My modeling approach does not encompass advanced Excel features or automation. Nor do I employ complex quantitative theories. I believe these tools drive people away from modeling, which contradicts the natural progression of knowledge. We all benefit from inclusion. Mankind’s greatest achievements have come from many people collectively looking at the same problem (in this case the future prospects for a company). The collective thought and effective challenge is what enables us to reach a more accurate answer as a community of researchers.
I believe in simplicity over complexity. Sometimes we fall trap to the fallacy that complexity equals certainty. It does not. After reading through all the steps in my modeling approach, you may begin to believe that I have violated this basic concept; however, the detail used in my models is in fact relatively simple compared to the models of sell-side analysts at investment banks. After our model has been created, it will empower us to cut through the noise, and draw very simple conclusions about whether we expect a company’s earnings and cash flows to grow or shrink in the future, and how that will compare to competitors or the broader market. These are the simple answers we seek. How we decide to achieve them is up to us individually.
I am confident that anyone can create a model. In fact, we all model everyday. Whether we formalize it in a spreadsheet is a separate point. When someone asks your opinion about a product, another person, or a company, in your mind you will consider the different aspects which go into forming your opinion, before giving your response. This is what modeling means to me. The spreadsheet simply acts as a vessel for the various factors we have used to form our opinions.
My modeling approach does not encompass advanced Excel features or automation. Nor do I employ complex quantitative theories. I believe these tools drive people away from modeling, which contradicts the natural progression of knowledge. We all benefit from inclusion. Mankind’s greatest achievements have come from many people collectively looking at the same problem (in this case the future prospects for a company). The collective thought and effective challenge is what enables us to reach a more accurate answer as a community of researchers.
I believe in simplicity over complexity. Sometimes we fall trap to the fallacy that complexity equals certainty. It does not. After reading through all the steps in my modeling approach, you may begin to believe that I have violated this basic concept; however, the detail used in my models is in fact relatively simple compared to the models of sell-side analysts at investment banks. After our model has been created, it will empower us to cut through the noise, and draw very simple conclusions about whether we expect a company’s earnings and cash flows to grow or shrink in the future, and how that will compare to competitors or the broader market. These are the simple answers we seek. How we decide to achieve them is up to us individually.
Thoughts on Equity Valuation
Your belief on valuation may differ from mine. I believe that most equity markets are highly efficient, meaning all current information is incorporated into equity prices during market hours, at times of sufficient liquidity(1). As a result, I believe that stocks are never overvalued or undervalued, in the true definition of the terms. They are always fairly valued based on the information available at the time. The evidence of this point can be demonstrated by making a transaction. The transaction price proves that the true value of any asset (a security, commodity, real estate, art, anything) is what someone is willing to pay for it now.
If the market is efficient, why would anyone waste time modeling earnings for a company? The answer is that the market incorporates all information into its price discovery efforts: macroeconomic, political, competitive, and of course company specific upside and downside forecasts. The latter point is where I see the added value proposition.
I believe that the market’s development of a future forecast includes a broad range of potential upside and downside cases. We can approximate the market’s view using the consensus analyst estimates, although this represents a very narrow sample of market participants, typically with a broad range of outcomes. You could talk to 20 different analysts about the same stock, with 20 different earnings estimates, 3 different recommendations 15 different target prices, and 10 different approaches to how they reached their conclusions.
By entering your opinions about the earnings capacity of a company into an earnings model, you are forming your own view which may lie below or above the average. Naturally, this will produce a future valuation which is different from the current fair value. This is the reason we forecast, besides the pure joy of investigation and analysis which may be enough for many of you (as it is for me).
Valuation has a short shelf life. It is a very delicate concept which can disappear in the wind, the minute a geopolitical risk, economic downturn, emerging technology, or any other number of developments capture the market’s attention. My view is we should not fight the market by implying things are over or undervalued. Instead we should continually challenge our own views and biases, stay true to our analysis, but incorporate new information as it is released.
This is why I emphasize the modeling of earnings and cash flow, and deemphasize exact valuations. As you build your earnings models I encourage you to challenge my approach in your mind, seek out other methodologies from other analysts and writers, and develop your own ideas of how efficient the market is, and how you should think about valuation.
If the market is efficient, why would anyone waste time modeling earnings for a company? The answer is that the market incorporates all information into its price discovery efforts: macroeconomic, political, competitive, and of course company specific upside and downside forecasts. The latter point is where I see the added value proposition.
I believe that the market’s development of a future forecast includes a broad range of potential upside and downside cases. We can approximate the market’s view using the consensus analyst estimates, although this represents a very narrow sample of market participants, typically with a broad range of outcomes. You could talk to 20 different analysts about the same stock, with 20 different earnings estimates, 3 different recommendations 15 different target prices, and 10 different approaches to how they reached their conclusions.
By entering your opinions about the earnings capacity of a company into an earnings model, you are forming your own view which may lie below or above the average. Naturally, this will produce a future valuation which is different from the current fair value. This is the reason we forecast, besides the pure joy of investigation and analysis which may be enough for many of you (as it is for me).
Valuation has a short shelf life. It is a very delicate concept which can disappear in the wind, the minute a geopolitical risk, economic downturn, emerging technology, or any other number of developments capture the market’s attention. My view is we should not fight the market by implying things are over or undervalued. Instead we should continually challenge our own views and biases, stay true to our analysis, but incorporate new information as it is released.
This is why I emphasize the modeling of earnings and cash flow, and deemphasize exact valuations. As you build your earnings models I encourage you to challenge my approach in your mind, seek out other methodologies from other analysts and writers, and develop your own ideas of how efficient the market is, and how you should think about valuation.
Thoughts on the Equity Research Industry
Over time the Equity Research Industry has adapted to changing circumstances. In the early 2000s information equality took a leap forward with the passing of Regulation Fair Disclosure (Reg FD), which prohibited the dissemination of material information to select analysts or investors. This changed the nature of the relationship between analysts and the companies they cover. At the same time technological advances continued to progress, making it easier for companies to communicate directly to investors, analysts, and news outlets. In general, the availability of data has also come a long way in the last 10-20 years, which has lead to the advent of contributor-based stock analysis websites, investment blogs, and even crowdsourced earnings estimate services.
The latest development in sell-side research at investment banks has comes in the form of a required change in pricing. Banks typically package their research products with equity trading services. Now regulators in certain markets are pushing to break the services apart, in an effort to improve cost transparency.
With the pricing change comes new pressure to cut research costs at banks. As market forces act on the industry, the quality of research, which has already declined relative to what is currently available free of cost, will continue its descent. Equity research reports are beginning to blend together, and it is getting more and more difficult to recognize the value proposition of multiple identical reports that track the earnings preview/review cycle. Analysts are to some extent incentivized to follow the pack in order to protect a stable, yet unremarkable, career. The pricing change will amplify this effect as many cling to a bygone era of growth and prosperity in research.
These forces have led the industry to become reactive instead of proactive. There is nothing more frustrating then watching all analysts cut their earnings and price targets after company management has decreased their guidance. What is the value in this research, when clients had no chance to execute on the recommendation prior to the announcement.
The improvement in information flow, rise of the independent blog-style stock analyst, and changing price environment for sell-side research, has brought the Equity Research Industry to a pivotal point in its history. I believe that the market for equity research will shift to favor those who are best suited to provide it. In many cases this will remain top sell-side analysts whose research is valued at or above the equilibrium point of its cost. However, sell-side research must shift to providing only primary high-value research, and leave the lower-end earnings preview/review style reporting to firms that can provide it at the lowest cost possible.
I believe these developments will drive the age of the truly independent analyst. While individuals have already proved successful in gaining recognition through investment articles and blogs, the next logical step is to move up the traditional equity research value chain, and add the sell-side’s most valuable tool to the mix: earnings models.
The latest development in sell-side research at investment banks has comes in the form of a required change in pricing. Banks typically package their research products with equity trading services. Now regulators in certain markets are pushing to break the services apart, in an effort to improve cost transparency.
With the pricing change comes new pressure to cut research costs at banks. As market forces act on the industry, the quality of research, which has already declined relative to what is currently available free of cost, will continue its descent. Equity research reports are beginning to blend together, and it is getting more and more difficult to recognize the value proposition of multiple identical reports that track the earnings preview/review cycle. Analysts are to some extent incentivized to follow the pack in order to protect a stable, yet unremarkable, career. The pricing change will amplify this effect as many cling to a bygone era of growth and prosperity in research.
These forces have led the industry to become reactive instead of proactive. There is nothing more frustrating then watching all analysts cut their earnings and price targets after company management has decreased their guidance. What is the value in this research, when clients had no chance to execute on the recommendation prior to the announcement.
The improvement in information flow, rise of the independent blog-style stock analyst, and changing price environment for sell-side research, has brought the Equity Research Industry to a pivotal point in its history. I believe that the market for equity research will shift to favor those who are best suited to provide it. In many cases this will remain top sell-side analysts whose research is valued at or above the equilibrium point of its cost. However, sell-side research must shift to providing only primary high-value research, and leave the lower-end earnings preview/review style reporting to firms that can provide it at the lowest cost possible.
I believe these developments will drive the age of the truly independent analyst. While individuals have already proved successful in gaining recognition through investment articles and blogs, the next logical step is to move up the traditional equity research value chain, and add the sell-side’s most valuable tool to the mix: earnings models.
About Gutenberg Research
Gutenberg Research will facilitate the next leg of the equity research transformation. Our consensus-based models will provide the basis for discussion, while our contributors will provide the voices, and the best analysts will rise to the top. At Gutenberg we are creating the platform for analysts, regardless of where they come from, to be recognized in all aspects of their work: Written reports/articles, backed by detailed analysis within their models, and explained in commentary.
We believe that our community’s collective knowledge will provide the best forecasting insight; however, the complexity of hundreds of forecasting possibilities, coupled with thousands of different investing theories must be tamed to facilitate the discussion among analysts. To maintain order and cut through the convoluted web of potential forecasting approaches, we provide spreadsheet templates for analysis: our consensus-based earnings models which represent a “base-case” scenario. Our community members can then download these base-case scenario spreadsheets, and input their own assumptions to add their opinion related to a particular stock’s earnings and valuation prospects.
Our ultimate goal is to have an inventory of models for all publicly traded companies with many variations of bull- and bear-cases for each. At the heart of our goal, is the commitment to financial modeling education. The Certificate in Financial Modeling Program, and the self-study style guide of our textbook Financial Modeling for Equity Research drives the execution of this educational effort. We are growing our network of contributors, first seeking out those with prior modeling experience to assist in the build of our consensus-based model inventory, and then expanding the network to capture the opinion of a wide group of market participants. We have a long road ahead of us, but we are off to a great start…stay tuned!
Gutenberg Research Structure: Our parent company, Gutenberg Research LLC, operates under three segments:
- Our Purpose: is to drive the evolution of equity research through financial modeling.
- Our Goal: is to make earnings models for all publicly traded companies available to all research participants.
- Our Vision: is an ultra-efficient research market where all participants are able to contribute their opinions in a challengeable environment backed by both qualitative and quantitative support.
- Our Mission: is to grow the Gutenberg community, seeking out like minded analysts who share our vision for the future of research, and educate and empower those who wish to join our efforts.
We believe that our community’s collective knowledge will provide the best forecasting insight; however, the complexity of hundreds of forecasting possibilities, coupled with thousands of different investing theories must be tamed to facilitate the discussion among analysts. To maintain order and cut through the convoluted web of potential forecasting approaches, we provide spreadsheet templates for analysis: our consensus-based earnings models which represent a “base-case” scenario. Our community members can then download these base-case scenario spreadsheets, and input their own assumptions to add their opinion related to a particular stock’s earnings and valuation prospects.
Our ultimate goal is to have an inventory of models for all publicly traded companies with many variations of bull- and bear-cases for each. At the heart of our goal, is the commitment to financial modeling education. The Certificate in Financial Modeling Program, and the self-study style guide of our textbook Financial Modeling for Equity Research drives the execution of this educational effort. We are growing our network of contributors, first seeking out those with prior modeling experience to assist in the build of our consensus-based model inventory, and then expanding the network to capture the opinion of a wide group of market participants. We have a long road ahead of us, but we are off to a great start…stay tuned!
Gutenberg Research Structure: Our parent company, Gutenberg Research LLC, operates under three segments:
- GutenbergResearch.com: Internet-based community of financial modeling contributors. Members can access our consensus-based models, and share their model forecasts on our site. The Gutenberg Financial Modeling Virtual Intern Program also runs on GutenbergResearch.com, providing a modeling introduction to students interested in fundamental analysis, and creating a platform for modelers to showcase their skills to prospective employers.
- The Equity Research Institute at Gutenberg Research: Provides live classroom and virtual modeling training including the Certificate in Financial Modeling Program.
- Gutenberg Publishing Company: Publishes and distributes course content and the Financial Modeling for Equity Research textbook.