Design of Corporate Restructuring and Mergers

Recently I was going through JDA Software’s (JDAS) Form 8K dated Nov 2 2012. Something interesting caught my eye that I will like to discuss here.

“Item 1.01. Entry into a Material Definitive Agreement.

On November 1, 2012, JDA Software Group, Inc., a Delaware corporation (the "Company"), entered into an Agreement and Plan of Merger (the "Merger Agreement") with RP Crown Parent, LLC, a Delaware limited liability company ("Parent"), and RP Crown Acquisition Sub, LLC, a Delaware limited liability company and a wholly owned subsidiary of Parent ("Merger Sub"). Parent and Merger Sub are affiliates of RedPrairie. The Merger Agreement was unanimously approved by the Company's Board of Directors.

Pursuant to the Merger Agreement, upon the terms and subject to the conditions thereof, Merger Sub will commence a tender offer (the "Offer") no later than November 15, 2012 to acquire all of the outstanding shares of common stock, $0.01 par value per share, of the Company (the "Company Common Stock") at a purchase price of $45.00 per share, net to the seller in cash without interest (the "Offer Price"). As promptly as practicable after the expiration of the Offer, and subject to the satisfaction or waiver of certain conditions set forth in the Merger Agreement, Merger Sub will accept for payment, and pay for, any shares of Company Common Stock validly tendered and not validly withdrawn pursuant to the Offer, at which point Merger Sub will merge with and into the Company (the "Merger") and the Company will become a wholly-owned subsidiary of Parent.”

This section of SEC filing describes how this reorganization is being accomplished. Design of corporate restructuring is a complex task and is accomplished by a specialized team of corporate lawyers and tax experts. Such designs are done to properly address issues related to liabilities, cost basis of assets, asset ownership, contractual obligations, financial obligations, capital gain taxation, licensing, customer support and country specific laws. Even though such design is usually transparent to most of us, it is still worthwhile to know how it gets accomplished.

In this bog I will describe three well known designs for corporate mergers.

Most simple and straight-forward of these structure is a direct sales and transfer of ownership. In such reorganization, called “A” reorganization, acquiring company (acquirer) buys shares of target company (target) from target company’s shareholders.  Payment can be in form of acquirer’s shares or equivalent cash.

Another commonly used approach for corporate merger is a “Forward Triangular Merger”. In this approach, Acquirer  created a fully owned subsidiary (Merger Sub). Target merges with “Sub” with “Sub” surviving as final company. Ownership of Target’s asset is transferred to Sub.

Third and most commonly used approach in USA is “Reverse Triangular Merger”. This kind of merger is also accomplished through a subsidiary of Acquirer. However, in this case, “sub” merges in to “target” and “target” survives as a wholly owned subsidiary of Acquirer. Ownership of target’s assets remain with “target” that is now a subsidiary. JDA-Red Prairie merger is a Reverse Triangular Merger.

Such structures become even more complex when you look at international mergers. For example, Eaton Corporation of Ohio USA recently acquired Cooper Industries of Ireland. This merger was accomplished through a complex set of subsidiaries in the Ireland, the Netherlands and the USA. First, a new company, called “New Eaton”, was created in Ireland. Cooper Industries got acquired by and became a wholly owned subsidiary of “New Eaton”.  New Eaton through a chain of wholly owned subsidiaries (Comdell Ireland: a subsidiary of New Eaton,   Turlock B.V. The Netherlands: a subsidiary of Comdell) established a subsidiary “Turlock Corporation: A subsidiary of Turlock B.V” in USA. Eaton Corporation USA merged with Turlock Corporation and surviving as merged company. In summary, both Eaton Corporation USA and Cooper Industries Ireland become wholly owned subsidiaries of the “New Eaton”. Subsequently “New Eaton” requested SEC to list its share on stock exchange in replacement of Eaton Corporation USA’s shares. Such complex structures are needed to stay in compliance with country specific rules and regulations.

 

I will update this blog and add few more real-life examples. I hope that you will also find design of corporate mergers an interesting subject and share your insights and other examples. I look forward to your feedback.    

RedPrairie JDA Merger

On November 1, 2012, JDA Software and RedPrairie announced that they have entered into a definite merger agreement, wherein, JDA Software Group agreed to be acquired by privately held RedPrairie for a total value of $1.9 billion. According to this agreement, RedPrairie will make an offer to buy all outstanding shares of JDA for $45 per share, representing a 33% premium to JDA stock price on Oct 26.

In this brief note I will share my perspective on JDA-RedPrairie merger and discuss few vital financials that caught my attention.

First item that we must examine is any redundancies & conflicts between solutions provided by JDA and RP. Following table examines overlap between software products of JDA and RedPrairie.

 

 

JDA’s products are more focused on supply chain planning, optimization and forecasting. RedPrairie’s strength is in Supply chain execution. JDA has some products in supply chain execution (JDA logistics) that directly compete with RP’s warehouse and transportation management products. Both companies have their own set of platform tools to handle message integration & business process management. In short term, JDA-RP combine will need to convince customers about future roadmap for such conflicting products. In long term, they will need to drop one of the offering, and provide upgrade path to impacted customers. Payback period on a supply chain implementation is approximately 5 years. Hence JDA-RP combine may need to keep supporting existing products for at least 5 and maybe for many more years. There is a risk that some of the new customer implementations of products in supply chain execution may be put on hold till new combined company provides clear direction to its customers.

Second, we must also pay attention to difference in underlying architecture & technology behind software products from JDA and RP. Any desire to leverage synergies of software development will require that JDA-RP combine must bring all products to a common architecture. Such process can take anything between 2 to 4 years. Till that time customers will see a collection of different looking products that they will need to integrate using complex tools. This scenario can be used as a weapon by competitors.

Third, we should look at balance sheets of JDA for any obvious challenges that will hamper value creation. I am copying below an extract of balance sheet for Q3 2012

First item that caught my eye was $231M goodwill and $108M other intangibles on balance sheet. “Other intangibles” include three components and I will like to quote directly from JDA’s annual report for Q3 2012

“Customer-based intangible assets include customer lists, maintenance relationships and future technological enhancements, service relationships and covenants not-to-compete; technology-based intangible assets include acquired software technology; and marketing-based intangible assets include trademarks and trade names. Customer-based and marketing-based intangible assets are being amortized on a straight-line basis. Technology-based intangible assets are being amortized on a product-by-product basis with the amortization recorded for each product being the greater of the amount computed using (a) the ratio that current gross revenues for a product bear to the total of current and anticipated future revenue for that product, or (b) the straight-line method over the remaining estimated economic life of the product including the period being reported on.”

I will be wary of value placed on Goodwill and other-intangibles as they are prone to impairment. JDA owns lots of patents. However, in my opinion, these patents do not help JDA-RP in getting a virtual monopoly or dominant position in that business domain. Supply chain domain is well researched and a competitor with sufficient will and investment can create competing products that do not conflict with JDA-RP patents. In short, I will heavily discount values of these assets.

Second item on balance sheet that we should look at is $273M of long term debt. JDA-RP combine will need to also pay this debt.

RP’s private equity owners will need to find $1.9B upfront for JDA and $273M for paying debt. JDA’s (current asset – current liabilities) are $273M. It requires $270M annually as cost of revenue.

Hence, my conclusion is that road for JDA-RP combine is full of challenges.

Overall, in long run, reduction in competing software solutions should help customers. Market has now consolidated to 2 or 3 dominant players.

PS: Those aware of M&A activities will recognize this merger as a “reverse triangular merger”. Functionally it will be categorized a co-generic merger as merging companies are complementing each other in market space.

A nice blog on relationship between firm’s market price and its vision, strategy and goals

 

Today I came across a blog that nicely describes how a company’s clarity of vision and strategy influence its market evaluation.

That blog  analyzes Oracle, HP, Apple, Google, Microsoft and SAP to support and explain its arguments. Rather than I summarizing that blog here, I would direct you to original blog.

Evaluating Financial Statements– Few Important Metrics – Part 3– analyzing Beneish

We discussed three metrics, Altman-Z, Beneish M and Piotroski F, in my first blog on this subject. These metrics are very helpful in pointing out something unusual in financial statements of a company.  Companies will usually be able to explain flags raised by these metrics. One must carefully examine each factor contributing to red-flags. That is where an expert adds value.

For example, an analysis on Enron written by by graduate students of Cornel University is frequently quoted whenever Beneish M-Score is mentioned. However,  if you read that paper, students analyzed their results on Beneish M-score and concluded that “However, further examination of these indicators showed no cause for concern.” Now, in hindsight, we all know that there was a cause for concern.

Let me showcase another analysis that I did recently on Apple (AAPL). As you will notice in picture 1, Beneish analysis raises red flags for 2006, 2007, 2009 and 2010. You will need to analyze Apple’s annual reports to find causes of these red flags.

If you dig further into data for 2006 , you will notice a sudden change in AQI (asset quality index) and LVDI (Leverage Index). You will further find that apple paid $1.2 billion for prepayment of NAND memory modules, other current assets increased from 648M to 2270M and vendor non-trade receivables increase by $1B.

Data for 2007 tells us that receivables jumped from $1252M to $4029M. Also worth reading is a note in annual report

“The Company is exposed to credit risk on its accounts receivable and prepayments related to long-term supply agreements. This risk is heightened during periods when economic conditions worsen.

A substantial majority of the Company's outstanding trade receivables are not covered by collateral or credit insurance. The Company also has unsecured non-trade receivables resulting from the sale by the Company of components to vendors who manufacture sub-assemblies or assemble final products for the Company. In addition, the Company has entered into long-term supply agreements to secure supply of NAND flash-memory and has prepaid a total of $1.25 billion under these agreements, of which $208 million had been used as of September 29, 2007. While the Company has procedures to monitor and limit exposure to credit risk on its trade and non-trade receivables as well as long-term prepayments, there can be no assurance such procedures will effectively limit its credit risk and avoid losses.”

Beneish analysis also flags an unusual jump AQI to 4.46 in 2009. Annual report for 2009 indicates $16B increase in investment in long-term and short-term securities.

To summarize, examine results of these metrics for any red flags. Further analyze financial data contributing to these flags. That should lead you foot notes and explanations in financial statements. Remember that financial statements are prepared by firm and you should critically examine firm’s explanations for such changes. 

I will be delighted to hear your comments on my analysis and explanations. Any suggestions for improving this blog will be most welcome. If you will like to receive a copy of my calculator, please drop me an email.

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!