CFA Level II Foreign Currency Translation

As usual the accounting portion of the CFA is taking me the longest to learn. In fact it currently accounts for 38% of the hours I've studied so far and I'm still having trouble consistently scoring over 70% on Stalla's tests.

In an effort to hammer some of the concepts home I've decided to mind map some of the decision rules, so here is the Foreign Currency Translation section in 3 slides.

Choosing the right method

Differentiating between Current and Temporal

 How to balance (what to plug)

Guide to my shorthand.
BS = Balance Sheet
IS = Income Statement
DNE = Does Not Exist
NI = Net Income
REb = Retained Earnings Beginning
Div = Dividend
Dep = Depreciation
MV = Market Value
G/L = Gain or Loss
Inf = Inflation
CHG = Change in

Common Financial Modeling Mistakes

A working list of things that everyone assumes right, that is actually wrong.

Beta Lever / Unlever
Right out of the gate students are taught about the perils of leverage. M&M one - leverage irrelevance, M&M two - full leverage is optimal, M&M three -  optimal leverage rests on earnings volatility assumptions (ie bankruptcy risk). Let's focus on M&M three, as this is closest to reality.

Theory suggests the optimal capital structure should be found by taking the limit of WACC, where Ke changes with respect to D/A via beta, and that debt should get more expensive under higher leverage ratios. Bankrupcy costs are nonlinear, changing from 1% debt to 2% debt has no effect, going from 90% debt to 91% debt has material effect. The graph below outlines what theory might expect a typical optimal capital structure curve to look like.

Here my bankruptcy multiplier would be suitable for a pretty stable firm, perhaps a utility company. Riskier firms would have higher multiples, and the minimum would be found closer to the left axis.

Now here is the catch

This is what is approximated by the beta unlever, lever formula (that is based off of M&M two) where Beta_levered = Beta_Unlevered*(1+(D/E)(1-t)). Notice this is a linear relationship.

Now look at this.

This is what my finance professor suggests is found in practice (highly recommend taking Prof. Jarrell, his class is where the bulk of this post comes from). The main takeaway from above is that bankruptcy costs seem only to kick in at the end, and that firms actually have a wide region of leverage ratios in which to achieve their minimal WACC.

So what's the problem? Analysts are constantly using the lever unlever formula to adjust a firms beta and thus their WACC in comparable analysis. This crops up in all sorts of places, but nowhere is it worse than WACC estimation for private firms. Private firms looking to IPO will usually have very different capital structures than the firms in the industry they are about to enter. IB analysts don't have a cost of equity because the firm has no publicly traded stock, so the discount rate for the company comes nearly entirely from peers. Trouble is they adjust for the different leverage using the beta unlever, lever formula. Relatively debt light firms will be heavily penalized and relatively debt heavy firms will get a unwarranted premium. Analysts also do this in relative valuation when firms have different leverage ratios.

The simple rule should be, when it's close don't adjust, when it's not close, try not to use it. I am very interested to see if these horror stories pan out in reality when I enter the industry. My hope is that a PhD has taken the time to develop a more robust leverage adjustment ratio that accounts for bankruptcy costs, just like the CAPM now is commonly used with add-on's like size risk, liquidity risk, and country risk.


Terminal CapEx and Depreciation Assumptions
A firm must build faster than it falls apart right? It's funny how many DCF's assume this not to be the case with implied depreciation larger than CapEx in perpetuity.

This is what I take to be the best DCF framework, (again it is also what is taught by Jarrell). When you have granular information about specific revenues and expenses doing cash flows by division or product definitely has value, however the general spirit of the valuation should be as above.

I have also had some success with the CFO - Chng in Op Cash + CFI(usually negative) - Pref Divs + Pref issuance + Debt = Free Cash Flow to Equity. The major downside with this method is how much information is impounded into CFO and CFI, here the burden really lies on your economic intuition in your pro forma statements you are pulling from. The trouble with the terminal forecast is it often sticks out as unreasonable when put next to the year that proceeds it, because it needs to encompass all years after forecasting, not just the "steady state" year after the growth phase. The CFO model lends itself to be a function of projection, with a black box effect on many crucial assumptions. This is what the forecast period is for, the terminal period is altogether a different beast and needs to be delt with appropriately.

In the forecast period your margins will lie heavily on company guidance and the extension of historical trends. Your CapEx and Depreciation assumptions can be stripped out the footnotes and MD&A. Getting the forecast period right is not rocket science and most practitioners will end up with similar results.

The terminal period however sees some interesting solutions and some very wrong solutions.

Error 1
The real egregious error that can be made is to forecast NCF in the terminal period as simply the NCF of the last year in the forecast period times the perpetuity growth assumption. Here you imbed all of the forecast assumptions into the terminal period, including assuming all terminal years will have the exact CapEx/Depreciation relationship as the last year.

Error 2
Some will crawl up to NOPAT and multiply it by the perpetuity growth and then use the plowback method. The plowback method is derived from economics. A firm will need to spend it's steady state growth (real) divided by its return on investment (real also) on PPE, and the rest of its NOPAT income can be returned to shareholders. The exact derivation of the formula can be found on the internet, or in Jarrell's class notes. The reason why forecasting off of NOPAT is wrong is because you imbed the profitability and efficiency assumptions from the forecast period into the terminal period.

Solution 1
The above solution works by forecasting revenue to grow at perpetuity and then making purposeful assumptions all the way down to NCF. COGS and SG&A margin can be gleaned by looking at mature firms in the industry. Change in working capital is forecasted the same way it was in the forecast period; steady state ((A/R+Inv-A/P)/Rev) multiplied by the change in Revenue from year to year. Finally the plowback ratio is used to end at NCF and then you find perpetuity figure through the Gordon Growth Model using a mature discount assumption.

Solution 2
Not shown above, but often used is to simply forecast the terminal cash flow using multiples valuation. (NCF of the firm * AveNCF of industry/Average MVE = Terminal MVE). The trouble here is the quality of the peer multiple (quality = standard deviation within the peer group), and its propensity to change overtime (volatility or the standard deviation of the median over time). Of course as with most things in finance, it is usually a good idea to do them both, and do a sensitivity analysis on top of each method.

Trading Size

Check out this video, I was reminded of it while studying for my trading exam this Saturday. After taking Burnside's class I have a lot more appreciation for what this teddy bear does, and the hurdles he faces. 

Types of Traders

Traders come in three main types (so say the academics), Noise, Informed, or Liquidity. Informed traders trade because they have valuable information about a security that isn't impounded in the price yet, and they place the position accordingly. Noise traders are traders who think they are informed, but are mistaken and are actually taking a bet against the true price. Liquidity providers are traders who step in and buy and sell a security with the goal to only make a spread between their buys and sells, or they are traders who place limit orders to buy or sell at certain prices. The former liquidity trader is usually a market maker, and the latter can be anybody,. 

The size trading teddy say's he's a liquidity trader, but what he really means is that he is a informed trader. Chances are most of his orders are "market orders" and so he is actually liquidity taking, not liquidity providing. 

Motivations to Trade

There are five main reasons people trade in the market.

• Information: Investors may have "slow ideas" based on fundamental analysis that suggest the stock is mispriced, and they will buy or sell hoping it reverts to the true price over a period of time. Traders may have "fast ideas" based on a wide variety of methods that may suggest the stock is either temporarily mispriced, or is about to move in a certain direction, and they will buy or sell hoping to catch that move.
• Liquidity: Most liquidity trades are made for reasons that have nothing to do with future outlook of the stock. Index’s that track a benchmark need to trade to avoid tracking error, mutual funds need to rebalance holdings, hedge funds need to adjust their holdings to keep certain risks neutral. Any trade that is of this nature can be labeled a liquidity trade.
• Noise: As mentioned above the market is full of traders who think they know something but actually don't. Their volume helps informed traders move size, and so they are an essential part of the markets efforts to decrease transaction costs. 
• Tax: Many individuals and funds sell at strategic times to push capital gains taxes around or to avoid dividends.
• Agency Conflict: Mutual funds hate to show their holder's stocks that have had big negative returns, and so before the report goes out many funds sell their losers. This is called "window dressing"

Transaction Costs

I had always thought of transaction costs as that commission fee I pay every time I buy or sell a stock. Turns out that is part of it, but there are two other costs that can be much harder to see that also play a role. The total transaction cost or "implementation shortfall" is made up of commissions and fees, the execution cost, and the opportunity cost. Execution cost can be thought of the difference between the average price per share you receive and the midpoint between the bid and ask right before you traded, and opportunity cost can be thought of the difference between the midpoint when you decided you wanted to trade the stock, and the midpoint right before you started trading. For small traders these extra costs are small, but they can be enormous for large traders, and the trouble with trading is that if you do well you get bigger.

Think of it this way. Let's say you have decided to spend a lot of time researching oil junior stocks to look for mispricing’s, you figure (correctly) that this small cap sector of the market won't have as much coverage and the chance to find a mispriced stock will be easier. Let's say you find a stock that is priced at $1.00/$1.05 (bid/ask) and you think it should be priced at $1.25. You want to buy 10000 shares of it. On paper you expect to make (1.25-1.025)*10000=$2250, but how much might trading cost you? As an oil junior the stock is thinly traded, average volume of 10000 per day, so you know there is no way you can buy all 10,000 shares at once. Let's say you decide to break it into 4 blocks, and trade in 4 consecutive days. Possible trades could go as follows:

• Day 1 Stock at 1.00/1.05, Buy 2500 @ 1.075
• Day 2 Stock at 1.05/1.10, Buy 2500 @ 1.10
• Day 3 Stock at 1.12/1.17 Buy 2500 @ 1.20
• Day 4 Stock at 1.15/1.20 Buy 2500 @ 1.20

On day 2 and 4 I assume enough liquidity that you can fill at the ask, but on days 1 and 3 you need to eat into the limit order book to fill the entire order. The spread is assumed at 0.05 which is a bit wide, but definitely not unheard of for small thinly traded securities. The costs break down as follows, your average price is $1.14375 and so the total implementation shortfall (assuming no commission) is (1.14375-1.025)*10000 = $1187.5. This is more than half of your expected profit, and we have only gotten half way through the transaction, we still have to sell! The opportunity cost is captured by how the security moved up as you scaled in over 4 days (very typical with heavy buying), and the execution cost is captured by the difference between the execution price and the midpoint. 

Problems with Trading Size

• Latent Demand Problem - When you are trading a significant portion of a stocks average daily volume, it can be tough to find enough counterparties to fill your trade. In searching for these parties you are forced to show the market your intended order size, or experience 'order exposure'.
• Order Exposure - Leakage about your order causes the market to move away from you because traders expect your liquidity needs to creep through the limit order book and they want the best price, and traders may think you are informed (what do you know that makes you want to take such a large position). 
• Price Discrimination - Traders will assume that because you are trading a large block of stock, you will trade another block shortly afterwards. This is very common in sell orders, panicking investors (informed or noise) will try to hide their costs by breaking up huge sell orders into smaller blocks, so traders learn to treat a block of any size with suspicion.
• Asymetric Information - If you know you will suffer the costs described above and you are still willing to trade size, you must know something (or so the market assumes). Even before doing their own homework, the market will side with your bet and the price will move away from you, because traders will assumed you are informed. 

The Winners Curse and Where Trading Still Works

As you can see from above, if you are a successful trader transactions costs will quickly become the nemesis of your strategy. As you get larger you will have trouble with your block orders, and if you start to gain a reputation of being successful the market will move as soon as you place your first order. This winners curse has an interesting selection aspect to it, if you are too good you kill your strategy, if you are bad you go bankrupt, if you are just okay you might be able to float along undetected at a reasonable size and scratch a living. 

There are ways to trade size, and a huge mutual find called Dimensional Fund Advisers manages to do it in the toughest of markets. Talking about how they do it is a blog within itself, but the essence is simple. They invest entirely on a passive strategy that rests on the principles on the Fama-French 4 factor model; assets have risk that is characterized by beta, size, value, and momentum. Small companies are riskier than large companies, and value is riskier than growth. The genius of the strategy from a trading sense, is they are proudly uninformed. They don't care what they buy (to a certain extent) and so they happily take hard to trade small cap stocks off active investors hands for a small haircut, when otherwise the market would fleece both with transaction costs. DFA gets the stock with negative transaction costs (profit), and the active investor saves a bundle on shortfall. The real interesting part is that through the 90's DFA made more money on this trading strategy, than it make on its investment strategy - and it's a passive fund!

What schools have traction with i-banks?

I ran across this graph for recent analyst hiring by a major BB IBD for North America. It's interesting how much of a chance the 'little guys' actually have.

Connecting Valuation with Entrance Timing

My Investment Management and Trading Strategies class has moved into the trading strategies portion of the curriculum, and we have been talking a lot about the tradeoff between execution costs and opportunity costs. This discussion has helped flesh out some of the classic arbitrage examples behavioral investors like to point out, as we quantify the frictions in the efficient market system which can give rise to these mispricings. While the big mispricings are interesting to study, I think the magic lies in all of the small mispricings.

It struck me in class that the biggest weakness of any valuation activity is the time period of applicability. On one had you have the trade-off every science deals with. The more exact the measurement, the more time the measurement takes, and thus the staler it gets. But finance struggles with another application hurdle; even if you can solve for the 'true' price of an asset instantaneously, you have no idea when the market price will move to that value or if in fact it ever will. You are subject to two independent risks, the market can stay wrong or get 'wronger' (move against you), or your true price estimate becomes untrue as information enters the market about the asset and the valuation changes.

You might say, sure but we do know that the market moves to the true price in the long run. I'd agree, but that true price is always changing. If the market price of X is higher than you think it should be, you might short X. Let’s say X is trading at 15, and you think it's worth 12. Your expectation is that X will drop from 15 to 12. You might even hedge your short with a long on the S&P to protect from systematic changes in the stock’s value, and only short the 'firm specific' valuation of the stock. What is to say the following doesn't happen: positive idiosyncratic information enters the market, and the market and true price rise to 17. The S&P doesn't change (by much) because only the stock reacted to the firm specific information, your short is a loss of $2, and your hedge didn't protect you. Even with absolute knowledge about the true price, you can lose money.

I think this is why some very successful asset managers seem to know very little about valuation, or sometimes even seem to care about it. Successful investment seems to be much more about the interaction between the market price and the true price over time. Decent money managers can get by with only knowing how one of the sides works. Traders only focus how the market price changes over time, 'deep value' investors focus intently on the derivation of the true price and somewhat haphazardly jump in when they feel the spread between the market price and the true price is big enough to warrant the risk of jumping in. A true master of investments should know both.

I'm trying to develop a checklist for investments that will focus my efforts on balancing these two fields. It will need to balance both fundamental and behavioral considerations. As luck would have it some much brighter minds are working on accomplishing exactly that!