Wednesday, November 11, 2015

Time-Value Vs Expected-Value Or Likely Poverty Vs Average Riches

In his excellent book, A Mathematician Plays The Stock Market, John Allen Paulos outlines a potentially disastrous, trading strategy that clearly illustrates the difference between expected value and time value summary statistics or, in his case, mean (expected value = +10% -> average riches) and median (time value = –16.43% -> likely poverty) performance.

This extreme example points to the obvious advantage of knowing the most likely outcome of an investment and raises the interesting question of how to summarize a trading portfolio in time value terms?
By way of illustration, assume a trading portfolio (illustration only) that includes just two sports, baseball and horse-racing, with the following profiles:

It is now immediately apparent that, even though both profiles have positive expected values, they are losing propositions as reflected by their evens-equivalent, negative time values.
In summary, expected value summarizes the average performance across all traders and is of critical importance to the bookmaker whereas time value best reflects the most likely, individual outcome and is of paramount value to the individual sports trader!

Monday, September 21, 2015

Volatility Drag As Time Averaging (Ole Peters)

Ole Peters outlines his idea of time averaging in contrast to ensemble averaging in a discussion of the St Petersburg Paradox. Note the parallels with volatility drag, median outcomes, and expected values!

Sunday, August 16, 2015

Doubling Rate Entropy And Kullback-Leibler Divergence

Cover and Thomas (2006) show that, in a horse race, a handicapper has an expected wealth growth-rate equal to that of an investor who wins every race minus a measure of uncertainty of the race and minus the difference in the win probability distribution used by the handicapper and the distribution of true win probabilities. Intuitively, this makes sense. To reduce the race uncertainty, we should focus on open betting markets with as few runners as possible. To reduce the win estimates difference, we should meld our betting line estimates with that of the crowd (Benter, 2004). In terms of a simplistic equation:

   Long-Term Profit = Clairvoyance – Race Uncertainty – Win Estimate Divergence.

My experience is that most handicappers focus too much on trying to become clairvoyant and not enough on selecting open races and factoring in the “wisdom of crowds”.

Friday, June 05, 2015

Euro-Style Handicapping

As a long-distance, handicapper of Euro races, I look for events in England, France, and Ireland (Grade 1 Horse-Racing Countries) with a high degree of chaos (I have entropy scores for all race-types). From this initial list, I have selected seven race-types that I focus on exclusively. Because of the high level of uncertainty in these races, the market (wisdom of crowds) is a less successful predictor. With a Bayesian-based, Elo-Class algorithm, I generate my own performance figures. Using the performance figures for all entrants in a chosen race, I run a Monte-Carlo simulation of 1000 races that automatically generates a realistic odds-line as final output. Critically, as long as there is at least one overlay (almost always) in the chosen race, I finally run a Haigh-like, Kelly-variant algorithm that selects the final contenders. (As I have stated elsewhere, this list may include both overlays and underlays).

Monday, March 09, 2015

Cheltenham Supreme Novices Hurdle (G1)

Though not my discipline (National Hunt Racing), Cheltenham's four-day graded stakes meeting is an exception. Effectively, this is the Breeders Cup of jumps racing. Lacking the in-depth expertise required to handicap this form of thoroughbred racing, I seek to focus on novice races where historical knowledge is as informative as current form. To that end, the Supreme Novices Hurdle has many similarities to the Kentucky Derby - young horses, many attempting a graded stakes, championship race for the first-time with little form in the book. Using dosage analysis of the "in-the-money" finishers over the last ten years and ranking the current field against this metric to identify "live" outsiders, shortlisted two interesting contenders - Shaneshill (14/1) and Tell Us More (25/1). Though not necessarily the most likely winners, these two selections are the best matched to previous contenders on dosage and, therefore, worthy of punting in both the win and show markets!