I am looking forward to your future post as well.

Recognized your nom de plume immediately and so picked mine, and rather appropriately, I think!

thanks for the additional…upon re-reading my post @December 14th, 2008 at 9:10 pm, I see I was less than clear.

I was meaning that 2. and 4. Should be explained, b/c 1. and 3. were less difficult, more accessible..

tho, seeing this: December 14th, 2008 at 11:55 pm, thanks again~

Is not TMA merely another way to model the future based on the dependent variables of price and volume?

It may not be as precise as these more “sophisticated” automated trading algorithms, but the premise is the same, and it suffers from the same fallibility.

Looking for a method of predicting movements in a hugely complex system of human interactions is fine, but don’t bet your future on it. You can back-test algorithms to the Big Bang, but there’s no guarantee that the future will conform. I suspect that one could wrap this into the context of The Halting Problem and logically show it to be impossible.

Instead, design hedging strategies that always work — which is no small feat in itself.

as you note, 1 and 3 are the easy ones.

#1 basically killed Wall St. Once you switch from a partnertship to a LLC, the game is over: yes, increased access to capital markets lets you do bigger deals, but 1) the compensation time-horizon shrinks from 30+ years to 5 years or so, and 2) the people at risk become a diffuse pool.

Without a pool of 1000 or so of partners and limited partners (the retired MDs,) it’s all too obvious what happens next: people lever the one big asset (firm reputation,) and make great returns for a decade. Which brings us to point#3:

Few people were trying to be evil, they just optimized given the structure in place. GS, for example, lined up a $40 billion short-term credit facility: seemed prudent and conservative. Then they paid their top producers really well: again, very reasonable, theys guys made the money for the firm.

But the firm completely missed where the real risk was: you can’t lever reputation! Traditionally, most partnerships fall into one of four categories: doctors, laywers, accountants, and investment banks: all charge high fees because the service is opaque and thus putting the senior guy’s money at risk is a good way to signal and ensure quality.

The model only works if owners have real skin in the game: give them an option (e.g. LLC protection on the downside,) and they will lever to the max.

—-

In going back previous editions of the spreadsheet, somehow, they managed to send completely idiotic numbers to their customers for three full years (at least). Not a single customer, not a single manager ever noticed the inconsistency for what were supposed to be trivial multiplications; not a single one of them noticed that “The-most-important-figure-on-this-chart-we-base-all-our-decisions-on” was random garbage.

In the end, he found out that whenever Helen needed to create a new row, she would simply copy and paste some random row and then adjust the values. At some point, however, she must have messed up and the spreadsheet ended up in a weird state, with formulas referring to cells in other rows, or sometimes even referring to nothing, creating a whole bunch of inconsistent values.

Excited at the chance to clear his name, Maxim revealed his findings to the lead analyst. However, instead of relief, he only shrugged and responded “Hmph… well, we usually just use our gut for recommendations, anyway.”

Most models would have told a portfolio manager who was at 0% in Nortel when the stock was at 120$ that he was being reckless.

I rest my case.

2. and 4. are prob. the 200-level ones, out of that bunch, the other two are more readily accessible.

as a +, they’ll lend insight into the ‘brittleness’ of the pricing edifice..