Macro Themes for 2013 (part I)

1. Man vs. Machine
• Productivity gains will outstrip pace of consumption – technology such as automation, robotics and 3D printing will destroy jobs faster than it create jobs
• Up to 50 million current jobs could be automated in the future (and thus destroyed)
• How many jobs will be created through this automation? The answer is however many AI programmers you need (probably far less than 50 million)
• Firm's labor demand will be for a few skilled workers rather than many unskilled workers
• Hiring - firms are spending on capex instead of hiring; 75% of US manufacturing firms already employ <20 workers
2. US Manufacturing Renaissance
• Localization, or Anti-Globalization
• Production is being re-shored to be closer to the huge US consumer market and take advantage of local logistics
• EM (emerging market) are becoming less competitive
• EM currencies are appreciating such as CNY (Chinese Yuan)
• China could enter the "middle income trap"
• Overseas transportation are too high due to energy prices, incentivizing firms to repatriate
• Underinvestment and low capex spending in US means pent-up demand
• Theme 1 - automation and robotics override low labor costs
3. US Energy Boom
• EIA forecasts
• US will become energy independent by 2020
• Largest natural gas producer by 2015, surpassing Russia
• Oil output poised to surpass Saudi Arabia’s by 2019
• Consumption - 87% will be from domestic sources of energy by 2020, up from 79% today 
• Imports - 13% of consumption by 2020, will be primarily supplied by Canada & Mexico, increasing from 36% of imports today to 62% by 2020.
• Competitiveness
• EU suffers from expensive gas contracts with Russia
• Latam has moved plants to US due to low natural gas and electricity prices
• Electricity - prices are 50% cheaper in the US than in Europe
• Roughly 30% of US electricity is generated by burning cheap domestic natural gas
4. DM (Developed Markets) Aging Demographics
• Population - baby boomers outnumber millenials due to decreasing fertility rates
• Labor - baby boomers are retiring later due to recession, crowding out young from workforce
• Gov't debt - millenials inherit high gov't debt caused by spending on entitlements towards baby boomers
• Other - high student debt, tight credit, skills mismatch, high job turnover
• "Peter Pan" generation - millenials reliant on parents, delay adulthood, live at home
5. DM Big Gov't Socialism
• Political sentiment will lean towards fairness and equality
• Theme 1 - high unemployment and inequality will be balanced by redistribution through increased taxes and spending
• Theme 4 - baby boomers dominate gov't and are biased towards increasing gov't healthcare, pensions, social security, etc.
6. DM Central Bank Printing
• Currencies - race to the bottom means depreciation
• Inflation - will stay low due to tight lending and low velocity
• Financial repression - captive investors ensure low rates
7. "Peak Car"
• Urbanization, high fuel prices, increasing youth insurance premiums
• Car-sharing schemes - 1 rental equals 15 owned cars; e.g. 700k Zipcar members share only 9k cars
• Theme 4 - tight credit depresses auto-ownership

Betting/trading strategies- Sizing

One of the mysteries facing finance professionals is how to reconcile the quantitative with the qualitative/discretionary. I actually think most gambling professionals do this quite well (eg. bet sizing on poker)- this may be because the risk/reward is much more well-defined (vs investing). I would like to propose a system to conduct position sizing that integrates the qualitative with the quantitative.


this is dependent on 3 things. 

(1) how risky the thing is on a daily basis (ie. can go up/down by $1 vs $100)
(2) how much conviction you have (ie. i would do something like, 3 conviction levels, lowest = believe can get 5% Return on Risk, mid = 10%, high = 20%)
(3) what is your intended time horizon (or alternative way to say this is take profit/stop loss level)

Then (4) plug into kelly's criterion and take 1/2 kelly as position size

So taking aapl as an example:
(1) daily range is say $15
(2) say you have high conviction (ie. you believe you can make $0.2 vs every $1 you risk, as an average of many bets with this level of conviction. this could mean you make $1.2 half the time vs lose $1 half the time, or that 60% of the time you make $1, and 40% of time you lose $1). I think these conviction levels make sense. 5% = any lower and you should definitely just put it in cash/ST bonds. 20% = anything higher and this is a once in a lifetime/decade type trade, where you really just plunge as much as possible (and sizing is to make sure you can maintain exposure in face of MTM losses)
(3) let's say your intended time horizon is 1yr. then yearly vol is $15 *sqrt(252) ~= 240. this sounds about right (eg; this yr aapl range was from 380-700) 
(4) so every share of aapl (550), you may make +290 (240*1.2) vs lose -240. kelly's = EV/win = 50/290 = 0.17. which means that you should risk 17% of your portfolio.
taking half kelly to be conservative, that is 8.5% of portfolio. which means amt of AAPL shares to buy = your total portfolio value * 8.5% / 240

so eg: on 1mil portfolio, you should buy 355 shares of aapl (195k) if you intended to hold it for 1yr+ and have medium conviction on it. this is about 20% of your portfolio, which is very aggressive sizing already. for long/short equity, anything 10%+ would be considered concentrated. the reason why it is high here is because you have super high conviction assumptions. 


note that
(1) we havn't looked at portfolio correlation yet, which would involve dialing down sizing if you have similar exposures.
(2) this # that we got is the MAX risk you should ever take. ie. anything more is theoretically bad for you (ie. your LT returns will be lower than if you just took less risk). so depending on how risk averse you are, you should be sizing significantly less than kelly. (eg: you could always size 1/4 kelly)

(3) can play around with the skew/kurtosis of returns to get a different sizing. In fact, all the steps above are actually asking you to describe a probability distribution of your return for this trade. (1) is asking for stdev, (2) is asking for mean, (3) is looking at how returns scale with time (is there autocorrelation?) which is also going to be related to kurtosis in this case (+ve autocorrelation = higher kurtosis compared to standard assumptions when scaled up with time) (4) is asking about the skew (are you 50% to win 1.2 and 50% to lose $1, or are you 60% to make $1 and 40% to lose $1)

Betting/trading strategies- Chunking

X%- a concept to unify different betting strategies

Following last post, a similar trading cousin for the Martingale gambling strategy is the sit and wait strategy (buy and hold with no stop losses and exit when you reach a predefined take profit level). This trading strategy is similar to the gambling strategy in the sense that both depend on the market/game to eventually do something (if I wait for long enough/try enough times it will eventually happen and I will be profitable overall). Sit and wait is like a more conservative version of Martingale. Lets say for the Martingale, instead of doubling down each time you lose, you increase your bet by x%. For example, x could be 200% (triple down) or 0% (sit and wait for casino bets). The stock market equivalent is re-balancing and increasing your original exposure by x%. (ie. let's say you had invested $100 in stock, it goes to $75. For Martingale strategy you invest another $125 to make it 2x original exposure = $200) This rebalancing assumes you are still under the same return distribution situation.

As we increase x, we are more likely to win/get back to breakeven when we are losing. For example, let's say we just lost k in a row. For doubling down on a 50/50 bet, we are 50% likely to get back to winning $1 overall by the next turn, but for sit and wait, we are only (1/2)^k likely to get back to breakeven.

At the same time, our losses increase at an exponential rate (1+x%) if we keep losing. This creates an embarrassing problem- how do you avoid bankruptcy if your losses increase exponentially? This goes back to the "chunking" mentioned in the first post. In trading this would be how much extra margin/loss buffer should you budget for this trade before it starts to win. And if you are going to double down/add, at what point should you do this? ie. if you go bankrupt/can't take the pain/extreme exposure after the third time you are wrong, then you need to split the potential worst case scenario into 3 segments and add as you cross between segments. In general you would split these segments by P&L, but it could also be possible to split it by underlying price movements, or even time or a function of all of the above. In the case of the stock mkt, it is also meaningful to vary x as a function of the underlying stock price movement, p&l change, time and other parameters.

Chunking- trade planning/margin budgeting

Let's derive the formula for how to chunk. For gambling, this is relatively easy. You lose b, b*(1+x), b*(1+x)^2,...,b*(1+x)^(n-1) So our total loss is a geometric series and equal to b*[(1+x)^n-1]/x. So given n (how many times you lose before you bankrupt) and x (the double down amt), you can calculate how big your bankroll has to be relative to bet size b:  bankroll is [(1+x)^n-1]/x times that of bet size b. For example, if x = 100% (martingale; doubling down each time), n = 10 (will go bankrupt if wrong 10 times in a row), you need 2^10 - 1 = 1023x initial bet.

It is however more complicated for trading, since you don't lose everything that you bet. Let's define a set {p_0, p_1...p_n} to stand for the stock price at which we would dial up our exposure another 1+x. We could have the set be in arithmetic progression (eg: keep adding per every $1 price drop), or it could be a function of P&L (eg: keep adding per every $100 loss), or time (eg: keep adding every 1min- here we would not be able to predict what p_i is before time t).

Let's start with the common “Martingale-like” case where p_i is an arithmetic sequence, we have a defined stop loss pt p_sl determined by the stock price terms (not P&L), and we rebalance by doubling the # of shares that you own each time. Then p_i = p_0 + (p_sl - p_0) * i / n. And the shares you buy each time b_i = b_0 * 2**i. (note ** means exponential).

Your worst loss is sum[b_i * (p_sl - p_i)] where i is the set of integers from 0 to n-1. Courtesy ofWolfram Alpha, we see:

Since m = n-1, this simplifies to b_0 * (p_sl - p_0) * [2**(n+1) / n - 1 - 2/n]. So let's say you had 1 mil budgeted to buy this stock, and you wanted to bet 4x before you go bankrupt, and you expect p_sl - p_0 = 100-70 = $30. Then we can look at how much (# of shares) to bet initially if we want to be able to rebuy n times.

 

We can also create a table showing how the intial bet size (both in terms of shares- b_0 and also as a % of total risk budgeted/worst case loss) increases with n.

	Initial Bet
N	Shares	% of Budget
1	33,333	333%
2	16,667	167%
3	9,091	91%
4	5,128	51%
5	2,924	29%
6	1,667	17%
7	945	9%
8	531	5.3%
9	296	3.0%
10	164	1.6%

Note that, for example, for n = 1 (just bet once and don't redouble ever), then you would be buying stocks with a notional amt that is 3x your risk budget. Notice that this does not necessarily mean that you are "taking on leverage"- for example, if you had 10mil, and budgeted 1mil for this trade, and so bought ~3mil, then you are not going over your total account equity. The traditional concept of "leverage" is insufficient to describe your trades/risk levels when it comes to more complicated trading strategies.

A more useful version for trading is perhaps this:

	Initial Risk	Loss
N	(as % of budget)	Multiplier
1	100%	1x
2	50%	2x
3	27%	4x
4	15%	7x
5	8.8%	11x
6	5.0%	20x
7	2.8%	35x
8	1.6%	63x
9	0.9%	113x
10	0.5%	204x

And then to get how much notional to invest, you simply divide this percentage by the worst case scenario return. eg: N = 2, worst case is -25%, then you should buy notional amt of 0.50 / 0.25 = 2x that of risk allocation initially. The loss multiplier is simply the inverse of the risk %. You can also use this to figure out what your max loss will be if you bought some amt and you are not sure how many times you will redouble:

eg: you bought 100 shares, and prices went from 120 to 110. You have been redoubling every $5 (so this will your third buy). If you are wrong again, then by 105, your total loss will be 1 / 0.27 * (100 * 15) = 5.5k. Let's check this:

Price	Shares	Loss
120	100	-1500
115	200	-2000
110	400	-2000
Current Price	105
Total Loss	-5500

So this checks out.

Food for Thought- Thinking about Risk

This x% concept can be thought of as a trading style/bias. People who trade by x% (ie. doubling down on losses and taking profits vs cutting losses and adding to winners) are essentially trying to convert a payoff distribution that is linear to stock prices (ie. if stock goes up $1, you make b, if stock goes down $1, you lose b, where b = # of shares), to a distribution that is non-linear and path dependent. It is more complicated to understand and incorporate the risks involved with this sort of dynamic trading strategy when you are evaluating your portfolio's risk characteristics. You might realize that what I described is very similar to a description of options. There are interesting ways to integrating traditional methods of looking at options risk and concepts presented here (eg: chunking and taking into account your trading style):

(1) Bringing options concepts to x%. Your trading style (especially in the extreme case where you preset limit/stoploss orders religiously) can essentially be converted into discretized chunks of gamma. Using this, you can do standard risk analytics taking into account your intended future trades (eg: the increased tail risk? the expected theta?). Another possibility is to use x% as a way to normalize for some portfolio measures. eg- if you use a high +x% for this bucket of trades, then you would expect high success rates and probably equal-ish avg profit vs avg loss but also significant long tail losses. In fact, you could also back out an implied x% from your success rates or your avg profit vs avg loss.

(2) Bringing risk budgeting to portfolio analytics on derivative positions. One problem with current scenario analysis and stress testing techniques is that they is usually purely based on past data, and that they are used as a control- ie. someone/some software will give us this number and let's just keep our losses under this threshold. One thing to do is to be more logical when it comes to estimating the risk budget/max loss. For example, we developed a formula for max loss (given n) above. I believe that a model based on fundamental characteristics (eg- for stocks this could be using a P/E range to derive a max loss) could be an interesting supplement to standard risk models nowadays. Unfortunately, traditionally, the risk manager is not involved in the investment research process and so lacks the in-depth fundamental knowledge required to create such a model.

Two Schools of Quants

I recently read this question off the Quant Finance subforum of StackExchange, a Q&A website for programmers: "Which approach dominates? Mathematical modeling or data mining?"

Basically, it seems that there are two schools of quants. This is my possibly over-simplified and generalized interpretation of the distinction.

1. Background: mathematicians / theoretical physicists / computer scientists / academic economists

Inference: deductive

Epistemology: rationalist

Type of knowledge: a priori

Beliefs: "There exist absolute immutable truths of the markets."

"I can discover these truths by superior thinking."

"With the right theories, I can make money."

2.  Background: scientists / statisticians / engineers / programmers / business economists

Inference: inductive

Epistemology: empiricist

Type of knowledge: a posteriori

Beliefs: "I cannot know if there exist any immutable truths of the markets."

"However, I can asymptotically approximate these truths through superior observation."

"With the right models, I can make money."

I think that's the essence of it, although the commenters in that post seem to be using many more words than I am. To boil it down to even simpler terms (at the risk of evaporating some meaning), the theoretical dominates for the former whereas the data dominates for the latter.

How would you begin to identify your "school" of quant strategy? I guess the first thing to do is to ask yourself, why did I make that trade? If you say, in historically similar situations, the price of this security responded this way, and I'm going to assume that this will continue to happen in the future, then you're probably in the latter camp. On the other hand, if you respond with, there's this theory which proves that security prices moves this way, given certain assumptions and axioms, then you're probably in the former.

However, there's problems with this answer. On the StackExchange post, "Quant Guy" makes a good observation about the Fama-French models: 

As more complex/realistic theories are devised, there is also the concern whether the theory itself was formed after peeking at the data - i.e. devising theories to explain persistent patterns or anomalies which an earlier theory could not 'explain' away. In this context, Fama-French's model is not a theory - it spotted an empirical regularity which was not explained by CAPM, but it is not a theory in the deductive sense.

Some background: CAPM (Capital Asset Pricing Model) explains the return of an asset as a function of a single factor: "beta", or more specifically, "market beta". As time went by, however, market participants noticed that this single factor couldn't explain all asset returns, as some low beta stocks outperformed and high beta stocks underperformed. In stepped Eugene Fama and Kenneth French of the University of Chicago, who noticed that even after correcting for market beta, small cap stocks and cheap stocks tend to outperform large cap stocks and expensive stocks: hence the Fama–French three-factor model, which adds a small cap factor and a value factor.

There is even a four-factor extension called the "Carhart four-factor model", which adds a momentum factor. We can quickly see the problem with the progression of such theories: they are not theories at all! The reason is simple: they were not conceived independently in the mind of the theoretician from logical principles and axioms, but rather, were given birth by the data. With each new market anomaly that cannot be proven by existing models, we can explain it away with a new factor, creating an n+1 factor model.

I often give the hypothetical example of a Keynesian who believes in the Phillips curve, which is the inverse relationship between unemployment and inflation, being confronted by a disbeliever. The disbeliever shows to the Keynesian a counterexample: the US in the 1970s, in which stagflation - the co-occurrence of high unemployment and inflation - is rampant, and exclaims triumphantly, "HA! There is no way that the Phillips curve can explain this! Now you must throw away your theories!" To which the Keynesian calmly replies, "On the contrary, this gives me a new theory, which is the Phillips2 curve. It's exactly the same as the old Phillips1 curve, and indeed the inverse relationship between unemployment and inflation still holds everywhere and always. Except with one important modification: when Nixon is president."

Of course, this is a ridiculous example, but it shows exactly how Fama-French is not a theory. In statistics, we call this overfitting. One might asks which is the better approach, theory or data, but I'm not sure if there is an answer. I'm not even sure if you can be strictly in one camp and not the other, as there seems to be more of a continuum than a strict dichotomy. In the real world, it is hard to really make the distinction between theory and models, as theories are often suggested by the data. Indeed, it's impossible NOT to be influenced by real world, unless you live in a cave with no Bloomberg terminals or something.

I guess ideally, you should be a quant who finds the middle way and melds the two approaches, but I'm not sure what that would look like... pragmatism?

Betting/trading strategies- Martingale

Introduction to betting strategies
Something I have been thinking about recently has to do with different trading/betting systems. I'll probably be doing a series of blog posts on this. Let me first define what I mean by a trading/betting strategy- this is a set of rules that dictate what you should do next based on your prior streak of wins/losses and your present P&L in the game (eg: gambling game, stock market...). The goal of this strategy may be to make money, but may also be to minimize variance (risk), avoid losing too much on money that has already been laid on the table (ie. successfully exiting losing positions), or to exploit a certain effect (eg: mean reversion, autocorrelation)...

For background, look at these 3 strategies:

• Martingale strategy
• Daily re-balancing strategy
• Kelly's criterion

Martingale is generally considered fatally flawed; daily re-balancing is a hot topic right now in finance (I'll explain more later); and Kelly's is actually a mathematically proven betting strategy.

In this post I will examine Martingale and a close cousin of it (trading version) and return to other strategies in later posts.

Mathematics behind the Martingale strategy
The Martingale basically says this: bet $1. if you lose, double down. if you lose again, double down again. So  you are "guaranteed" to win $1 at the end (unless you just keep losing ad infinitum). The major problem with this is that you only have a limited bankroll. eg: say after you've lost 10x in a row, you may be out of money and the casino/brokerage firm won't let you double down anymore (because you won't be able to pay if you lose again) At this point, you are close to bankrupt and you can't double down to recoup your losses anymore.

So the hidden downside is actually that you might go bankrupt before you win once. So the question is actually what is the probability that you would go bankrupt? Let's say in a 50/50 game you budget 1023x the initial bet (ie. you could lose 10 times and then be bankrupt). Then the chance of you going bankrupt per attempt to win $1 is around 0.1% (precisely 1/1024). Now let's look at the probability that you will go bankrupt as a function of how many times you attempt:

Note that while the curve (first graph) is bounded by 1, initially (say first 100 attempts- second graph), your chance of bankruptcy is almost linear to your # of attempts n. This is because Pr(bankruptcy) = 1-(1023/1024)^n  = 1 - (1 - 1/1024)^n ≃ 1 - (1 + n/1024)  = n/1024. This result is from first order Taylor series approximation (given small enough x = 1/1024 around the point a =1)

So for example, if you plan to attempt to win $500 (try 500 times), there is a 1-(1023/1024)^500 = 38.6% chance that you will "go bankrupt' before you reach 500 (ie. lose $1023- notice that you would still have the $499 or however much you collected before you go bankrupt). If you did go bankrupt before you reach 500, on average you would have gone bankrupt on attempt #230 (you get this by doing sum[Pr(n) * n] / sum[Pr(n)]. where Pr(n) = Pr(bankrupt on n-th turn) = (1023/1024)^(n-1) * (1/1024). I just summed this over excel- is there another way to do this?)

Soooo. let's see if this all makes sense.
EV = 38.65% * (-1023+230.2) + 61.35% * (+500) = 0

Betting strategies = transform your return distribution
So as you can see, by following the Martingale betting strategy and stopping at 500 wins, we can actually consider this whole strategy as one single bet of [win $500 with 61% probability or lose $793 with 39% probability], which is still 0 EV. ie. We have not gone anywhere from the original single bet of [win $1 with 1023/1024 probability or lose $1023 with 1/1024 probability] which is also 0 EV. Essentially the betting strategy has allowed us to change the distribution of returns while keeping your expected profit (EV) the same. This is an interesting topic that we will revisit later (probably when we talk about Kelly's criterion).

The interesting thing about this is that you can "see" your P&L progress over time. ie. instead of one single bet where you have 0 info about if you are going to win or not and that becomes 100% certain right after the dice roll, here, as you make successful attempts, you are slowly but surely moving towards the +$500 outcome (instead of the -$800 outcome. ie. the odds are slowly tilting in favor of you. One significant difference between gambling and trading is that trading is more continuous. ie. even though your trades are discrete, the market price changes rather continuously during trading hours. This is similar to what is happening here where you can see your P&L slowly accumulate before you finally exit the trade.

In trading, I would would look at this strategy as having positive theta (ie. as time passes, you start to make money). In this case, the reason why there exists positive theta is because you are taking extra risks on every iteration/bet/time period. Here, the risk you are taking is the 1/1024 chance that you lose 1023. When these risks are not realized, you make money. This is the case for say short gamma positions in trading. Short gamma positions basically mean that large price movements are bad for you. (vs long gamma positions ie. the people betting against you are betting on large price movements) So every second/minute/hour/day (remember trading is more continuous) that there is no large price movements materializing, these mini bets for large price movements are expiring worthless for the long gamma guy and you are making money because you have lived to fight another day/minute/second.

Further topics (to be discussed next)
So strats like these are highly dependent on budgeting. On a totally unrelated note, in poker, there's this concept called chunking- basically say you expect 3 more rounds of betting till showdown. Then you size up your opponent's (assuming he has less chips than you) stacks and figure out how to size the bets to get him all-in at the third bet. So for example, in a $15 pot right  now and your opponent having $100 stack left, you would say bet $15, $30, $60. The bets scale up every time because the pot has gotten larger. You for a small pot, it might turn out that you need 4 bets to be all-in- in which case you need some action (ie. a raise) to be able to go all-in. But the point of this discussion is that you need to do the same thing when you are budgeting for Martingale betting. ie. if I am going to bet 1,2,4,8,16 then give up, then I need to budget $31. And this $31 hopefully lasts me for the craziest price swings ever. ie. Let's say AAPL trades in $10 range 60% of time, $30 range 90% of time, and $50 range 99% of time. You should be betting say $1 for a $5 deviation from the median (whatever that means), $2 for a $10 deviation, $4 for a $15 deviation, $8 for a $25 deviation, and really really hold tight before you make your $16 bet at the end. ie. at what pt you double down is highly dependent on how rare of an outcome this pt is.

ok- back to martingale and its cousin trading strategy. The cousin trading strategy that I wanted to talk about was the following:

Buy the stock. Set take profit level at say +10%. If stock trades down, sit and wait. If it keeps trading down, sit and wait more. Since variance increases with time and is not bounded, this means that eventually stock will trade to +10% and you can exit at a profit. So this is similar to the Martingale in some respects. What I mean is this:

If you look at the chart above, the range of estimates for the stock increases the further you go into the futures. This is in part because the stock is expected to trade in a bigger and bigger range given longer and longer time periods. This means that it is easier for you to reach any pre-set level as time goes on, potentially even if the drift (general trend) of the stock is downwards (ie. the opposite direction to your bet).

This post is getting pretty long, so I'm actually going to talk about this strat more in the next post before going on to other strats. In the mean time, key thoughts about this strat:

It is also highly dependent on the return distribution. Need to compare how much increased variance over time contributes to probability of hitting target take profit level  (eg: 50% chance of touching x in y days, -> 50% chance of touching sqrt(2)x in 2y days) to "breakeven drag". ie. how much stock will have to trend down for you to have deteriorating chances of hitting target level (have negative theta).

Thoughts of the Week - Big Bank Inefficiencies

Here is a recent article by Zerohedge on big bank inefficiencies.

I usually try not to cite ZH, but this has a lot of good links. It reminds me of an HBS article I read recently about bees and risk management. The tendencies of bees towards decentralization is a risk management tool to prevent TBTF.

Take, for example, their approach toward the "too-big-to-fail" risk our financial sector famously took on. Honeybees have a failsafe preventive for that. It's: "Don't get too big." Hives grow through successive divestures or spin-offs: They swarm. When a colony gets too large, it becomes operationally unwieldy and grossly inefficient and the hive splits. Eventually, risk is spread across many hives and revenue sources in contrast to relying on one big, vulnerable "super-hive" for sustenance.

In fact, when hives divide, it is the old queen who departs, leaving the old hive to a young virgin queen. Contrast this with normal corporate M&A and spin-off behavior in which risk remains concentrated.

One classic argument in favor of TBTF is that big banks are in a better position to capitalize on economies of scale, and the fact that breaking up large banks simply doesn't prevent bank runs. The ZH article attacks the first point through a discussion of diseconomies of scale (which I quoted almost verbatim from here):

1. atmospheric consequences due to specialization - as firms expand there will be increased specialisation, but also less commitment on the part of employees. In such firms, the employees often have a hard time understanding the purpose of corporate activities, as well as the small contribution each of them makes to the whole. Thus, alienation is more likely to occur in large firms.
2. bureaucratic insularity - as firms increase in size, senior managers are less accountable to the lower ranks of the organisation and to shareholders. They thus become insulated from reality and will, given opportunism, strive to maximise their personal benefits rather than overall corporate performance. This problem is most acute in organisations with well-established procedures and rules and in which management is well-entrenched.
3. incentive limits of the employment relation - the structure of incentives large firms offer employees is limited by a number of factors. First, large bonus payments may threaten senior managers. Second, performance-related bonuses may encourage less-than-optimal employee behavior in large firms. Therefore, large firms tend to base incentives on tenure and position rather than on merit. Such limitations may specially affect executive positions and product development functions, putting large firms at a disadvantage when compared with smaller enterprises in which employees are often given a direct stake in the success of the firm through bonuses, share participation, and stock options.
4. communication distortion due to bounded rationality - Because a single manager has cognitive limits and cannot understand every aspect of a complex organisation, it is impossible to expand a firm without adding hierarchical layers. Information passed between layers inevitably becomes distorted. This reduces the ability of high-level executives to make decisions based on facts and negatively impacts their ability to strategize and respond directly to the market. Even under static conditions (no uncertainty) there is a loss of control.

The Alternative Banking working group of OWS (Occupy Wall Street) prefers TIBACO to TBTF. It stands for Too Interconnected, Big and Complex to Oversee, which I think is a more apt description of what exactly is wrong with these banks.

Things I am up to:

- I recently downloaded Anki, a flashcard program, to help me study for the CFA (Chartered Financial Analyst) exam. I've always wanted to try Anki, but I never got around to it. It displays flashcards on increasing time intervals based on the principle of spaced repetition. One example of this is the following image, where successive "boxes" show their cards less frequently, e.g. the flashcard program will draw from box 1 half of the time, box 2 one quarter of the time, etc.

- Learning R. There is a lot of vibrant community support (especially in finance) for R. Some of my favorite quant finance bloggers - Timely Portfolio, Systematic Investor, Milktrader, CSS Analytics - use R. Non-finance R bloggers can be found on the excellent R-bloggers. So far, I have already been able to link my R environment to Bloomberg terminal data as well as free data sources such as Yahoo Finance and FRED (Federal Reserve Economic Data) using community packages.

Books I would buy my friend if he had my exact interests and his birthday were coming up very soon (say, in 3 days):

Haidt, Jonathan - The Happiness Hypothesis
Koo, Richard - The Holy Grail of Macroeconomics
Postman, Neil - Amusing Ourselves to Death
Schelling, Thomas - Micromotives and Macrobehavior
Singer, Peter - Animal Liberation
Strausse, William & Howe, Neil - Generations
Wolfram, Stephen - A New Kind of Science