Softbank shows the way in quantifying telecom M&A synergies

As a follower of the TMTE sector firms, I happened to browse through the Japanese telco Softbank offering presentation for its 70% acquisition for the USA wireless telecom operator Sprint. It had interesting facts on the USA market like AT&T/Verizon duopoly wrt subscribers and EBITDA(67% subscribers and 82% EBITDA), low mobile data speeds of 1Mbps vs markets like UK/Australia, high postpaid base etc. The comparison of USA/UK/Japan with BRIC was an eye opener. Read the entire presentation at http://www.sec.gov/Archives/edgar/data/101830/000119312513228092/d541834d425.htm

However, what really caught my attention were these two graphs breaking down estimated deal synergies. Many talk about synergies but few walk the talk while publicly stating it.


What is really stunning is the estimated opex synergies of $2bn from 2014-2017, and then $3bn beyond. of this, they estimate nearly 40% from device procurement-really I would not have thought devices margins are so high.Another 49% rests from their knowledge transfer on network opex, churn and customer care-all performance drivers of telecom. IT is probably non incremental since vendors are quite efficient there unlike for other components.

For capex, purchasing synergies again constitute 42% of estimated synergies, while knowledge management of traffic and core building would give another 42%.

Here, they have built on their core strengths of networks and smart management while deriving the synergies. The next time a telecom CEO wants to defend that pricy acquisition, then such graphs are a good start,

Not even twins are comparable-the difficulties in transfer pricing and valuation

I've taken an elective on transfer pricing taught by PwC tax partner Sanjay Tolia, and he jokingly said once that Not even twins are comparable. That inspired me to write this post, given the similarities between transfer pricing and equities valuation.

Although transfer pricing is a particular subset of valuation, it has many unique issues related to perceived equity, performance measurement, global agreements/standards and other aspects which valuation itself does not have. There are no universally accepted standards for valuing equities/real assets(though credit securities/derivatives are now valued using widely accepted models/curves/metrics). But for transfer pricing itself, there is a lot of guidance available in form of the 2010 OECD guidelines and the United Nations guidance on the same. And many tax administration rulings are publicly available for giving valuers an idea of the latest trends, in opposition to  valuation where valuations themselves are not publicly disclosed-at best a boilerplate apology for a valuation a.k.a fairness opinion, is disclosed. Though activist investors & securities regulators prod companies to disclose their valuation opinions/deal rationale, there is not enough public data on the same, and thus little consistency.

But the purpose of this post was to highlight the difficulties in finding comparable transactions/situations for transfer pricing, and finding peer ratios for valuation. In both cases, one desires to understand the arms length transaction valuation benchmark put by the external market. The main difficulties in my view are

1. Lack of timely updated publicly available data
2. Inherent bias in picking peers-you start with the end in mind to maximize or minimize valuations. Hence, those using the valuations must be mindful of that bias
3. Differences not evident on the surface and needs indepth analysis to justify differences. For example, gross margins are influenced by marketing spend(which can support higher selling price) and so operating net margins better explain differences.
4. Everyone will claim their case is unique and therefore not amenable to comparable-especially those unfavorable to that cause. 

Other points exist but these are the main ones in my opinion

DCF valuation is relevant in India despite information issues & high growth

As any finance student would know, DCF valuations are easier learnt than done. In markets like India, where financials are not too reliable and companies are quite diversified and high growth, the DCF valuations may be unreliable and tend to understate the company valuations(nice, ain't it). But high growth brings with it high capex/debt, and that can destroy a company fast. Hence, DCF is not easy even then. But Indian investors should not give up on DCF in my opinion because

1. Value of information is maximum under uncertainty.In inefficient markets like India, that is much more important.
2. Analysts often take the easy way out by doing relative valuation. And even if they perform DCF valuations, few retail investors get to see the assumptions(and never the spreadsheets), which are shared with the big daddies. Hence, free riding on analyst recommendations is not safe.
3. Since individual investors would invest larger sums(otherwise index funds make sense), they should do the ground work of understanding stocks and making the basic investing model.
4. Under high growth situations as promised in many Indian companies, it is easy to get carried away and give triple digit P/E multiples to stocks, and overprice them for future growth. A DCF valuation helps to understand the sensitivity of future growth assumptions on the firm value, and allows an investor to see whether he's comfortable with that or not.Also, it forces the investor to think about WHEN would growth slow down/end, and that itself is an exercise in critical thinking.
5. Few Indian companies upload spreadsheets, making investors manually extract numbers from PDF annual reports. While some aggregators like moneycontrol.com offer the free download facility, that may go in the future as sites are under pressure to show profitability. Still, investors CAN download the basic numbers from those sites till then for their models. 
6. In the high inflation environment, stocks are NOT neutral to inflation. Indeed, many 'lifestyle' sectors suffer slowdowns and commodities players may see margin erosion. Hence, valuation MUST incorporate the estimated inflation effect on revenues and costs(especially for companies with uncontrollable cost base like power transmission, coal based power plants etc), and thus doing a DCF model helps
7. Mental models as advised by Charlie Munger etc are a poor substitute for analysis. After all, no good company is a buy at ANY price, nor are bad companies a sell at ALL prices. Excel features like Data Tables help to understand the implied growth/margins at which the prices would reflect the intrinsic value as per model. 

Despite all this however, DCF is not relevant for momentum driven investing. And in the increasing cases of stocks with price to book under 1, DCF is relevant for finding upside, not so much as downside. 

Corporate Finance-some complied loose ends for analysts and managers

NYU Stern Professor Ashwath Damodaran's homepage is full of material(notes, spreadsheets, ratios, data) which he generously supplies for free. This post is a humble effort to cull some insights from that, which I feel relevant for analysts, whether equity or credit.While they are inspired by what I read there, do note that they often include my original insights also, and so one may not get

1. Long term accounts receivables:-In long term contracts(like IT industry), this pattern is seen. If analysts narrowly define accounts receivables as only short term ones, they may miss poor quality earnings, as happened in some earlier USA dot com crashes in telecom space.
2. Alpha or model error? Outperformance may be just error in risk/return model, for example understating the risk in the small stocks in CAPM model. 
3. Multiples in valuation:-Either use one with best explanatory power(high R-square) OR one most logical given the value creation/measurement in the sector
4. Inherent valuation model assumptions:-a perception that markets are inefficient and make mistakes in assessing value • an assumption about how and when these inefficiencies will get corrected. In an efficient market, the market price is the best estimate of value. The purpose of any valuation model is then the justification of this value.
5. Where variability can INCREASE value:-Instead of incurring penalty under DCF models(higher risk premium), In cases where an asset is deriving it value from its option characteristics, for instance, more risk or variability can increase value rather than decrease it. And as As Damodaran puts it, The rights to a “bad” project can still have value
6. Real options:-Things like right of first refusal, vacant land etc can be valued using the real options framework. And even for content libraries, that is a useful valuation method. As Damodaran puts it, "Having the exclusive rights to a product or project is valuable, even if the product or project is not viable today. The value of these rights increases with the volatility of the underlying business.  The cost of acquiring these rights (by buying them or spending money on development - R&D, for instance) has to be weighed off against these benefits".
7. Excess capacity DOES have an opportunity cost:-Sounds very counter intuitive but as explained by Prof Rajendar Patel and Damodaran, using excess capacity means we may run out of capacity faster. Hence, a suggested framework is If I do not add the new product, when will I run out of capacity?If I add the new product, when will I run out of capacity When I run out of capacity, what will I do?
   1. Cut back on production: cost is PV of after-tax cash flows from lost sales
   2. Buy new capacity: cost is difference in PV between earlier & later investment
8. The golden median in peer comps:-Especially for companies in fragmented industries, this is a good principle to apply because outliers may distort the mean, and weighting by assets/marketcap/sales may induce another kind of bias. Of course, median has equal weighting, and should be carefully checked with mean to see for skewness in the ratios.
9. Trailing price averages more reliable? Like how we use Trailing Twelve Months(TTM) earnings, it is time to use trailing 12 months stock price as well to see long term P/E ratio trend? That data is much more difficult to compute as not routinely available hence would need manual work. But to avoid getting entirely deceived in boom/bust market, that seems an idea.
10. EBITDA is NOT FCF:-Firms seldom mention their FCF in those snazzy investor relations presentations, and there is good reason for that. EBITDA does not cover the capex spend, which can be substantial. Hence, investors should look at the FCF metric, which is cashflows from assets,
    prior to any debt payments but after firm has reinvested to create growth assets. Some companies may create fancy proforma metrics excluding those very payments for growth assets(content costs, capex etc) but investors should not be fooled. Another reason to distrust EBITDA and prefer FCF is that if companies classify operating expenses as capex, that inflares EBITDA but nullified in FCF.  For companies with doubtful financial integrity/asset intensive ones, FCF is a useful sanity check.

Why DCF fails on valuing banks

In his research paper on valuation of financial firms(http://people.stern.nyu.edu/adamodar/pdfiles/papers/finfirm.pdf),  the NYU Stern Professor and renowned valuation authority Prof Ashwath Damodaran gives a few insights on why DCF is not practical here. Besides the heavy regulatory dependence(on revenues, costs, leverage), banks also suffer from the handicap of classifying cash flows. Simple looking metrics like interest, capex, debt all need to be abandoned or redefined in case of banks.

But DCF has been the holy grail of valuation, so even if we abandon it how can we take anything else without some base? Well, for a going concern, the book value is the very minimum that the share price can fall to. Not coincidently, one of the most common metrics used is price to book value, where the excess>1 is defended based on intangibles, earning power, P/E and other factors. But it is agreed that these all are relative, and you won't see a DCF for a bank anytime soon. So is that model fundamentally flawed? Given that valuation is inherently subjective, this is not too bad a bargain.

Some useful excel features for fundamental valuation

The old school investing story(Graham/Buffet) would be to take up a balance sheet, calculate the breakup/minimum value at large margin of safety, and then buy the shares if trading below that price. While academically found reliable, the DCF valuation used to be too computationally cumbersome. So for those well versed at mental number crunching(I still know a few of the oldschool CAs who could whip any newbie at this) or with an army of assistants, DCF was possible. Otherwise for those with limited resources and time, this was not possible. But now, with a spreadsheet on every computer, DCF is no longer that difficult, if you know the right features/methods to configure. Below are some tips for the same

1. Assumptions in separate cell/worksheet:-This ensures that one can change them en-masse, and the linked cells(and therefore valuations will be updated)
2. Currencies/Rounding:-For different investors or purposes, one may desire to vary the currency/rounding(crores or millions). That is possible at a click
3. Conditional formatting:-This allows to highlight, colour or format cells which are meeting the criteria. For example, if one needs to highlight the years with negative cash flow, conditional formatting does that for you at a click. 
4. Extend the formula across cells:-This is probably the most time saving tool. Just use relative/fixed referencing properly, and a single formula often be dragged to fill the time periods. It is then easy to change the time period/formula without much ado.
5. Data Table for Sensitivity Analysis:-Excel's feature data table allows one to present a sensitivity analysis without much computational burden. 
6. Writing macros for data mining:-Often, large data sets('bhav copy' etc) are freely downloadable in Excel. One can write macros to automate the daily stock screens for price, trading volume, F&O liquidity etc. Of course, brokerage houses often do this better, so this is for those investors without access to those good brokerages.
7. Goal Seek/Solver:-For those wishing to find out a breakeven projection(via goal seek!) or to fill the cash flow statement consistently(via Solver), these tools remove the requirement for endless iterations. Personally, I've found this most useful to calculate implied fundamental values for market prices, and to fill the cash flow statement after income statement and balance sheet are readied.
8. Cross referencing and automatic recalculations:-This ensures that cascading effect of changes are applied consistently in the calculation
9. Facility for external data feed to update:-For those with CIQ feeds etc, this allows dynamic updation of valuations

Valuing companies whose business is based on OPM(Other People's money)

All entities use their owner's(equity holders) money initially. Some rely on heavy debt financing(for example infrastructure companies have D/E of around 4x). But for some(mainly banks, insurance companies), the whole business model revolves around using other people's money(depositors, policy holders) to whom they have a fiduciary responsibility(and not merely borrower-lender relationship). It is this class of companies which are the subject of discussion in this post.

Banks have historically created value for their shareholders. Barring the subprime blip(post which they were the first to post super profits), banking stocks have a secular trend of going up. For insurance companies, this is even more pronounced where the most legendary value investors alive today(USA's Warren Buffet and Canada's Prem Watsa) have made fortunes for themselves and their shareholders. Which begs the question, to what can we attribute the high valuations of those businesses.

Leverage is one argument. If the banks/insurance cos apply conservative standards of capital acceptance(tight underwriting norms, low deposit rates etc), they can safely leverage(sometimes called 'lazy banking') OPM to make outsized profits for their narrow equity base.

1. So using a ROE metric would be flawed because though banks/insurance companies need to maintain an equity base for regulatory comfort/keeping their license, their actual profits are made from income on that OPM float(net interest income for banks, investment income for insurance companies). If that float is persistent in the form of loyal long term depositors or policyholders, one could theoretically do a proforma ROE calculation considering float as equity. But since the present disclosure standards do not permit this, we need to get other metrics. 
2.  Where OPM outstrips equity,  ROA could be a good proxy for return on float. Banks with high ROA are given higher valuations(better P/BV; higher P/E). Insurers with higher investment income also get a premium abroad(no insurer is listed in India).
3.  Using the old workhorse DCF may pose a difficulty because investors just do not have the information/expertise to value the returns generated by float(or indeed the possible MTM in the float itself). DCF may still be possible for banks but is difficult for insurers. 
4. Barring these metrics, we can use hybrid metrics like P/BV, multiples of premium etc. But these would at best serve as triangulation measures and not for doing the primary calculations. 

One should remember that OPM dies to a trickle at the sniff of reputation risk. Bank runs start and policyholders get concerned. So which doing a valuation, it would be a good thing to embedd a discount to keep that crucial margin of safety for such events.