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Course video lectures, content slides and Excel file constantly updated (latest: November 2016, audio reediting)
Learn stock fundamental analysis through a practical course with Excel and real world examples. It explores main concepts from basic to expert level which will help you achieve better grades, review concepts and polish skills for your new finance career or make decisions as DIY investor. All of this while referencing best practitioners and Nobel Prize winners.
Become a Stock Fundamental Analysis Expert in this Practical Course with Excel
Become a Stock Fundamental Analysis Expert and Put Your Knowledge in Practice
Learning stock fundamental analysis is essential for finance careers in areas such as equity research, investment banking, private equity or venture capital. It is also indispensable for academic careers in finance or business research. And it is one of the two most common analysis techniques for DIY investors.
But as learning process can become difficult as complexity grows, this course helps by leading you through step by step real world practical examples for greater effectiveness.
Content and Overview
This practical course contains 50 lectures and 10 hours of content. It's designed for all stock financial analysis knowledge levels and a basic understanding of Excel is recommended.
At first, you'll understand main financial statements which are the balance sheet, income statement and cash flow statement. With these you'll calculate financial ratios to analyze a company's operations, investments, liquidity, solvency and profitability. Then, you'll estimate investment ratios with stock market data such as P/E and dividends yield for valuation purposes.
After that, you'll calculate a company's cost of equity, debt and capital with CAPM, FamaFrench factors, APT and WACC models. With these you´ll estimate a company's stock theoretical value through EVA, DCF models and do a valuation by comparing it to its market price.
Finally, you'll calculate a stock's financial option prices with Black & Scholes, Binomial models and Monte Carlo Simulation method.
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Section 1: Course Overview  

Lecture 1  Article  
Before starting course please download .XLSX Microsoft Excel file as additional resources. 

Lecture 2  7 pages  
This lecture includes slides for course disclaimer, course overview and fundamental analysis lectures. 

Lecture 3  04:33  
This lecture includes course objectives, benefits, requirements, disclaimer, target audience and instructor bio. 

Lecture 4  03:42  
This lecture includes overview of seven sections in the course. 

Lecture 5  06:20  
This lecture includes fundamental analysis definition, data sources and calculations guide. 

Section 2: Financial Statements  
Lecture 6  13 pages  
This lecture includes slides for financial statement overview, balance sheet, income statement and cash flow statement lectures. 

Lecture 7  06:01  
This lecture includes financial statements definition and overview of balance sheet, income statement and cash flow statement. 

Lecture 8  16:49  
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Lecture 9  17:29  
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Lecture 10  13:17  
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Section 3: Financial Ratios  
Lecture 11  21 pages  
This lecture includes slides for financial ratios overview, operating activity ratios, investment activity ratios, liquidity ratios, solvency ratios, profitability ratios, DuPont analysis, Altman ZScore and Piotroski FScore lectures. 

Lecture 12  07:28  
This lecture includes definition of financial ratios and overview of:


Lecture 13  15:50  
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Lecture 14  07:43  
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Lecture 15  09:15  
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Lecture 16  11:55  
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Lecture 17  16:26  
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Lecture 18  08:16  
This lecture includes definition of DuPont analysis and calculation of three component decomposition of return on equity (ROE) ratio. 

Lecture 19  09:41  
This lecture includes definition and calculation of Altman ZScore as a measure of financial distress. 

Lecture 20  11:01  
This lecture includes definition and calculation of Piotroski FScore to identify value companies expected to outperform their benchmarks. 

Lecture 21  16:07  
This lecture includes calculation summary of financial ratios and their analysis models. 

Section 4: Investment Valuation Ratios  
Lecture 22  17 pages  
This lecture includes slides for investment valuation ratios overview, price ratios, price ratios (Lynch, Graham, Shiller), enterprise value ratios, and yield ratios lectures. 

Lecture 23  07:07  
This lecture includes definition of investment valuation ratios and overview of price, enterprise value and yield ratios. 

Lecture 24  18:24  
This lecture includes:


Lecture 25  18:41  
This lecture includes definition and calculation of Peter Lynch's price to earnings growth (PEG), price to Peter Lynch's fair value, price to Benjamin Graham criteria and Robert Shiller's price to earnings (CAPE10) ratios. 

Lecture 26  12:28  
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Lecture 27  16:14  
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Lecture 28  13:10  
This lecture includes calculation summary of investment valuation ratios. 

Section 5: Cost of Equity, Debt and Capital  
Lecture 29  10 pages  
This lecture includes slides for cost of equity, debt and capital overview, historical asset returns, capital asset pricing model (CAPM), FamaFrench three factor model, arbitrage pricing theory model (APT), weighted average cost of capital model (WACC) lectures. 

Lecture 30  06:58  
This lecture includes definition of cost of equity, debt and capital and overview of historical asset returns, capital asset pricing model (CAPM), FamaFrench three factor model, arbitrage pricing theory model (APT) and weighted average cost of capital model (WACC). 

Lecture 31  15:31  
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Lecture 32  12:56  
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Lecture 33  17:23  
This lecture includes definition and calculation of expected asset return through FamaFrench three factor model. 

Lecture 34  19:10  
This lecture includes definition and calculation of expected asset return through arbitrage pricing theory model (APT). 

Lecture 35  12:23  
This lecture includes:


Section 6: Stock Valuation Models  
Lecture 36  19 pages  
This lecture includes slides for stock valuation models overview, economic value added (EVA), discounted dividends model (DDM), discounted free cash flow to equity model (DFCFE), discounted free cash flow to firm model (DFCFF) lectures. 

Lecture 37  05:49  
This lecture includes definition of stock valuation models and overview of economic value added (EVA), discounted dividends model (DDM), discounted free cash flow to equity model (DFCFE) and discounted free cash flow to firm model (DFCFF). 

Lecture 38  13:12  
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Lecture 39  19:05  
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Lecture 40  17:05  
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Lecture 41  14:28  
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Lecture 42  10:22  
This lecture includes calculation summary of economic value added (EVA) and discounted cash flow (DCF) stock valuation models. 

Section 7: Financial Options  
Lecture 43  12 pages  
This lecture includes slides for financial options overview, Black and Scholes option pricing model, one step Binomial Tree option pricing model, two step Binomial Tree option pricing model and Monte Carlo Simulation option pricing method (MCS) lectures. 

Lecture 44  05:30  
This lecture includes definition of financial options, call and put options, American and European options; and an overview of Black and Scholes, Binomial Tree models and Monte Carlo Simulation method for option pricing. 

Lecture 45  16:28  
This lecture includes definition and calculation of call and put option prices' straddle through Black and Scholes model. 

Lecture 46  12:33  
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Lecture 47  16:17  
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Lecture 48  17:02  
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Lecture 49  09:18  
This lecture includes calculation summary of Black and Scholes, Binomial Tree models and Monte Carlo Simulation method (MCS). 

Section 8: Course Bibliography  
Lecture 50  3 pages  
This lecture includes course bibliography in alphabetical order with concept references. 
Diego Fernandez is author of highquality online courses and ebooks at Exfinsis for anyone who wants to become an expert in financial data analysis.
His main areas of expertise are finance and data analysis. Within finance he has focused on stock fundamental, technical and investment portfolio analysis. Within data analysis he has concentrated on applied statistics, probability, optimization methods, forecasting models and machine learning. For all of this he has become proficient in Microsoft Excel®, R statistical software and Python programming language analysis tools.
He has important online business development experience at fastgrowing startups and bluechip companies in several European countries. He has always exceeded expected professional objectives by starting with a comprehensive analysis of business environment and then efficiently executing formulated strategy.
He also achieved outstanding performance in his undergraduate and postgraduate degrees at worldclass academic institutions. This outperformance allowed him to become teacher assistant for specialized subjects and constant student leader within study groups.
His motivation is a lifelong passion for financial data analysis which he intends to transmit in all of the courses.