dimpy.handa
Dimpy Handa
There is this strange notion out there that the greater the complexity of the mathematical process, the greater the accuracy of the results. The underlying notion seems to be that gains can be increased and losses minimized if only we can increase the complexity of the mathematics employed.
Warren Buffett is famous for his refusal to employ these complex mathematical models. He prefers the simple mathematical formulas. His refusal to resort to catalactic contortions in order to achieve extraordinary profits does not seem to have hurt him none.
To illustrate the point, allow me to compare two mathematical processes that I have employed as a risk arbitrageur.
The first is very commonly used and is almost naive in its simplicity:
CG-L(1-C)/YP
That's it. Any algebra student can understand it, and any idiot can profitably employ it. Want proof? I have never lost money using this arbitrage equation and my annualized returns ran from 18%-35% per year.
The second, which is a more complex version of the the above, takes into consideration everything from market volatility to variations in support levels and correlations with the price changes of an appropriate index.
I am not going to post the formulas here. It is this string of catalactic contortions which attempt to take into consideration an assortment of interrelated variables which may, or may not, effect risk adjusted rates of return as the closing date approaches.
In the end, the more complex mathematical models that I have employed created dismal returns. Shifts in market indexes would increase or decrease the risk levels of a position, thus resulting in a failure to take profitable positions. The incorporation of beta, a measure of historical stock price volatility relative to a stock index, also muddied the waters because the target does not trade on fundamentals again until long after the deal has fallen through.
Far from improvement by resorting to a more complex mathematical model, the increase in complexity of the mathematical models had the opposite effect. Is it because Austrians can't do math? Perhaps. But I have my own understanding that has nothing to do with Austrian mathematical incompetence.
The reason is not due to a failure of the complex mathematics involved. The failure was in the assumptions which lead to the increase in complexity of the mathematics used in the first place. For example, if it were true that a decline in the index would by necessity lower the support level of a target company in case of a failed acquisition and thus lower the risk adjusted rate of return, our enhanced mathematical models should work better - but it's not the case. Even if a deal falls through, targets can trade in a range well above its traditional relationship to the index indefinitely as it no longer trades on its own business fundamentals but as a potential acquisition target.
This is not true only in arbitrage - it applies to many of the attempts to apply complicated mathematical models to economic phenomena. Once again, the math is sound - it is the erroneous assumptions which prompt an increase in the complexity of the equations that are the source of invalidity.
Warren Buffett is famous for his refusal to employ these complex mathematical models. He prefers the simple mathematical formulas. His refusal to resort to catalactic contortions in order to achieve extraordinary profits does not seem to have hurt him none.
To illustrate the point, allow me to compare two mathematical processes that I have employed as a risk arbitrageur.
The first is very commonly used and is almost naive in its simplicity:
CG-L(1-C)/YP
That's it. Any algebra student can understand it, and any idiot can profitably employ it. Want proof? I have never lost money using this arbitrage equation and my annualized returns ran from 18%-35% per year.
The second, which is a more complex version of the the above, takes into consideration everything from market volatility to variations in support levels and correlations with the price changes of an appropriate index.
I am not going to post the formulas here. It is this string of catalactic contortions which attempt to take into consideration an assortment of interrelated variables which may, or may not, effect risk adjusted rates of return as the closing date approaches.
In the end, the more complex mathematical models that I have employed created dismal returns. Shifts in market indexes would increase or decrease the risk levels of a position, thus resulting in a failure to take profitable positions. The incorporation of beta, a measure of historical stock price volatility relative to a stock index, also muddied the waters because the target does not trade on fundamentals again until long after the deal has fallen through.
Far from improvement by resorting to a more complex mathematical model, the increase in complexity of the mathematical models had the opposite effect. Is it because Austrians can't do math? Perhaps. But I have my own understanding that has nothing to do with Austrian mathematical incompetence.
The reason is not due to a failure of the complex mathematics involved. The failure was in the assumptions which lead to the increase in complexity of the mathematics used in the first place. For example, if it were true that a decline in the index would by necessity lower the support level of a target company in case of a failed acquisition and thus lower the risk adjusted rate of return, our enhanced mathematical models should work better - but it's not the case. Even if a deal falls through, targets can trade in a range well above its traditional relationship to the index indefinitely as it no longer trades on its own business fundamentals but as a potential acquisition target.
This is not true only in arbitrage - it applies to many of the attempts to apply complicated mathematical models to economic phenomena. Once again, the math is sound - it is the erroneous assumptions which prompt an increase in the complexity of the equations that are the source of invalidity.