Optimizing Lotus365 ID Risk-Adjusted Returns
This section of Lotus365 ID is based on an earlier piece called “Maximizing Expected Return to Risk Ratio.” Most of the information came from chapter 20 of Paul Wilmott's book Paul Wilmott explains quantitative finance.
Getting the highest expected return
Most gamblers' unspoken goal is to get the best predicted return, which works like this. Think about the 'high bat' chance for Australia's first innings in a test match. We look at the choices and decide how likely we think it is that each player will win. Then, we use (ODDS * PERCEIVED_PROBABILITY) – 1 to figure out what each pick is likely to earn.
Batsman | Bookmaker Odds | Perceived Probability | Expected Return |
Ricky Ponting | 4.00 | 20% | -20% |
Simon Katich | 5.00 | 23% | 15% |
Phillip Hughes | 5.25 | 15% | -21% |
Michael Clarke | 5.50 | 22% | 21% |
Michael Hussey | 8.00 | 8% | -52% |
Marcus North | 7.50 | 6% | -55% |
Brad Haddin | 10.00 | 5% | -50% |
Mitchell Johnson | 31.00 | 1% | -69% |
Nathan Hauritz | 81.00 | 0% | -100% |
Peter Siddle | 201.00 | 0% | -100% |
Ben Hilfenhaus | 251.00 | 0% | -100% |
Total probability | 100% | Â | |
We would back Michael Clarke based on these probabilities since he gives the biggest projected return. Clarke has a 21% predicted return with a standard deviation of 228% (the process for calculating standard deviation will be detailed momentarily). It is important to note that if none of the predicted returns are favourable, we should avoid placing a wager.