Evaluating Financial Statements– Few Important Metrics– Part 2

 

I wrote  my first blog on this subject on Sept 27. Spreadsheet shown in that blog lets me compare three metrics (Altman-Z, Beneish M and Piotroski F) across different firms for last financial year. I have created another version of this spreadsheet that lets me evaluate these metrics for a single company over last 10 years. This spreadsheet also provides a summary of financial statements for that company for last 10 years.

I will be more than happy to share this sheet with you and solicit your feedback. Please send me your request for download location via email.

Thanks

 

Use caution when using forecasting and predictive models

 

Forecasting and predictive analysis is used to model or predict future behavior of a system using previous observations or facts about the system. Such analysis is used extensively by scientists, economists and financial analysts in scientific as well as business applications.

Approach used for such analysis can be summarized as

1. Identify facts or variables
2. Categorize these variables in independent and dependent variables. Independent variables are inputs to the system. They are controlled by factors external to the system. Dependent variables are outcome or outputs of the system. Goal of predictive analysis is to predict dependent variables on the basis of a given combination of independent (input) variables.
3. Through statistics or some other kind of analysis, find a set of mathematical equations that can mimic system, and replicate prediction of output variables from a given set of input variables.

Such analysis is frequently used by financial analysts to predict future stock prices, financial viability of a project, growth rates etc.  Businesses use such models to predict supply, demand, prices and various other factors.

I have been developing my own models and using models generated by others for several years. Very often, as soon as people see a computer, spreadsheet or set of mathematical equations, they assume their results to be absolute truth. I would like to caution and share following comments.

1. Model or a set of equations is only as good as data used to generate the model. If you use incorrect data as input to analysis, you will get a wrong model.
2. Even if you have a good model, your predicted results will depend on accuracy of new set of input data. If you feed wrong data, you will get wrong results.

Recently I was looking at Altman-Z financial analysis presented at a web site.  It was a very impressive analysis and gave very detailed in-depth recommendation on growth rate & future price targets. However, when I tried to verify the results, I found that this analysis was using incorrect historical stock prices. Thirty pages of recommendations were generated using incorrect set of input stock prices!

To conclude, always remember that predictive analysis depends on accuracy of

1. Historical data used for initial model building

2. Assumptions about future input variables.

you will get incorrect prediction if either of these are not inaccurate.

Understanding DVA (Debt Value Adjustment)

 

Recently there has been a consistent stream of news reports on debt value adjustment (DVA) and possibility of FASB getting rid of this concept.

What is DVA?

In order to explain concept of DVA in layman language, assume that company A has floated long term bonds with face value of $100. Further consider a drop in credit rating of company A and a resultant fair market value of $70 for this bond. Concept of DVA allows company A to recognize difference ($100 – $70 = $30) as revenue.

Concept of DVA was  introduced by FASB in 2007 after intense lobbying by financial companies, such as Merrill, Morgan Stanley, Goldman Sachs and Citigroup, which wrote letters to FASB arguing that it wasn’t fair to make them mark their assets to market value if they couldn’t also mark their liabilities. Their argument was that firm’s liability on day of reporting is lower because it can buy back its debt instruments from market at reduced price. (ref:  Wall Street Says -2 + -2 = 4 as Liabilities Get New Bond Math )

Detractors of  DVA find it against principle of conservatism, which states that accountant must choose reporting alternative that will result in less net income and/or less asset account. Argument against DVA is that if firm attempts to buy back its instruments,

1.  It will need to borrow money at a higher rate in line with its reduced credit rating, and

2. Bond holders may decide not to sell their instruments and to wait for duration of bond and wait for full payment.

How to identify DVA in annual report of a company

Unfortunately DVA is not shown on balance sheet as a line item. You will need to read full annual report and search for word like “DVA” or “Fair market value of liabilities”. For example, following is an extract from page 55 of Bank of America Corporation’s (Ticker: BAC) annual report for 2011

[“Net income decreased $3.3 billion to $3.0 billion in 2011 primarily driven by a decline of $4.2 billion in sales and trading revenue. The decrease in sales and trading revenue was due to a challenging market environment, partially offset by DVA gains, net of hedges. In 2011, DVA gains, net of hedges, were $1.0 billion compared to $262 million in 2010 due to the widening of our credit spreads.”]

BAC’s  net income, inclusive of DVA for 2011 and 2010 was $2.967B and $6.297B respectively. If you get rid of DVA fluctuations, actual income numbers for two years are really $1.967B and $6.035B!

Today most commentators agree that DVA rule must go. I fully agree.

 

I look forward to your feedback, comments and suggestions on this post.

Detecting Fraudulent Financial Reporting

 

Recently I came across a very interesting paper on detection of fraud in financial reporting. This paper “Major Financial Reporting Frauds of the 21st Century: Corporate Governance and Risk Lessons Learned”, authored by Hugh Grove and Elisabetta Basilico was published in Journal of Forensic & Investigative Accounting (Vol. 3, Issue 2, Special Issue, 2011 of).

Authors of this paper start by explaining 10 red flags in corporate governance. Their premise is that if one was looking at these flags or factors, it should have been possible to detect several large corporate accounting scandals. Authors discuss nine accounting frauds (Citigroup, Worldcom, Enron, Qwest, Tyco, Global Crossing, Lehman Brothers, Satyam and Paramlat) to illustrate their idea.

I liked very user friendly tone of this article which doesn’t assume prior knowledge of complex accounting ideas. However, best part of this article is its appendix. Authors have provided a very readable and concise summary of important accounting metrics and ratios.

When I read it, this paper was available at following link.

http://www.bus.lsu.edu/accounting/faculty/lcrumbley/jfia/Articles/FullText/2011_v3n2a7.pdf

I look forward to your feedback, suggestions and comments.

Evaluating financial statements–few important metrics

I have burnt my fingers few times by blind reliance on analyst reports or my own intuition. Today I will like to discuss few financial metrics that I have found of immense help in navigating through maze of financial numbers and analyst reports. These metrics allow me to view financial numbers in perspective and identify areas that need further exploration.

Before I delve further into what I know so far, I must caution that these metrics are empirical & should be used only for guidance. They should not be treated as an absolute statement on a stock’s worthiness. 

First metric that I find very useful is Beneish M-Score. This number, devised by Professor Messod Beneish, highlights probability of earning manipulation. This number, in its larger form, is based on a weighted sum of eight factors (called indexes) that are derived by comparing past and current financial statements. Another version of this number uses five of those indexes. Professor Beneish’s analysis showed that there is a very high probability of manipulation in financial statements if Beneish number is greater than -2.22. One should analyze financial statements with much caution and further dig into each of 8 factors if Beneish analysis raises an alert. It is also important to do this analysis over several years to identify a pattern and to check any possibility of financial manipulation in past. Further details of 8 factors are available from multiple sources on internet.

Second metric that I like is Altman Z score. This score measures financial health of a company and indicates probability that a firm will go into bankruptcy within two years. This score depends on four or five business ratios. There are few variants of Altman-Z formula for different industry segments. In general, a score above 3 indicates that a company is unlikely to enter bankruptcy. A score below 1.8 indicates a highly likelihood of financial distress within next two years.

Third metric that I will like to discuss is Piotroski F-score. This score measures relative financial health of firms. It is based on 9 factors. A score of 0 or 1 is assigned to each of these factors and total of all 9 factors if F-score for the firm. For example, Net income is one of the Piotroski factors. It will get a score of 1 if net income for that year is positive. A score of 7 or more indicates a financially strong company.

Three metrics written above should be used during preliminary analysis to identify areas that need deeper investigation. One should look at those factors of these scores that indicate any kind of distress. Ideally, these scores should be calculated over several years. These scores, coupled with other financial ratios are helpful in weeding out risky companies.

In near future I will share my spreadsheet tool that I created for my own use. Till then,  Arivederchi!

PS: Following is result on an analysis I did on September 27 2012

My favorite web sites on investing and finance

 

I routinely struggle with my savings and investments. Few months ago I found few excellent sources of information that have helped me in navigating maze of confusing financial statements. I would like to share these sites with whoever is reading this blog. I hope that you will also benefit. If you know of other sites, please feel free to share that information.

1. Grumpy old accountants - http://blogs.smeal.psu.edu/grumpyoldaccountants/ - I can’t stop praising this site. Professor Anthony H. Catanach, and Professor J. Edward Ketz do an excellent job of explaining nuances of financial statements. Check for series of blogs on groupon. Also look for a recent blog on intangible assets.

2. Seeking Alpha –http://seekingalpha.com - This site is actually a complete portal on financial information. You should subscribe to their newsletters. Check an excellent article on “Dividend paying companies” at http://seekingalpha.com/article/836971-not-all-dividends-are-worth-it?source=yahoo.

3. Old School Value - http://www.oldschoolvalue.com – This site, owned by Jae Jun, is another excellent source of education for newbie investors. His newsletters contain excellent implementable information. Jae Jun also provides several free stock evaluation spreadsheets. For more serious and professional investors it has an option of upgraded priced version of tools. Best section of this site is blog at http://www.oldschoolvalue.com/blog/. While you are at this site, look for blog on financial ratios.

This is all for today. I will share more on what I learnt through next few blogs. Till then, ciao!