Dec

19

Q: A problem I seem to be having when I play hold 'em is that I am chip leader by a lot, but then somehow it always seems my chips disappear, as was the case in the last tournament I played with my friends. I know the basic rules of what the chip leader should do, like being aggressive and betting a lot. But it seems that every time I do this, I eventually lose all my chips.

Jim Sogi responds:

As we come to the end of the year, the issues of risk and return and performance metrics are of great interest. Absolute dollar returns, yield and risk measures are the most important measures. Diebold said risk is a part and parcel of return, and understanding the type of risk as measured by the new measure of realized volatility is key. He mentioned that other forms of risk are as important as market risk, but are less discussed, such as operations risks. Here is where performance metrics enter. Thank you to erudite specs for great contributions on the subject, In studying performance metrics the issue of the relationship between such things as return and Sharpe or Sortino ratios arises. What is the sweet spot in a particular market cycle to achieve high returns and high Sharpe ratios or high Sortino ratios? What is the performance metric that has the greatest effect on maximizing the 'sweet spot'? Dr. Phil mentioned that a high percent win/loss has a very high leverage effect on yield and risk measures. he trade off is average gain per trade drops. Chair advises to hold longer and go for greater average gain, but the price is larger drawdowns. Diebold said that the Sharpe itself is a time series and will vary across the cycles. How can one adjust trading parameters to maximize metrics for varying market cycles? In other words how does one milk the maximum out of the current market, and how does this change as cycles or days changes. Can one boil this down to expected yield targets for a particular volatility regime, day, hour? How many percent, points, days, weeks, hours should one stay in the trade or use as goals to get the maximum expected utility? This will all vary according to preference as discussed under the Bayes Utility hypothesis, but within the preference, what is the best key to maximum utility for maximum return, and lowest risk? This relationship should be able to be quantified and to act accordingly, as athletes do to win. How would this relationship be quantified? It appears to be the relationship of more than just one metric.


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