The Experts below are selected from a list of 108 Experts worldwide ranked by ideXlab platform

Judith A Chevalier - One of the best experts on this subject based on the ideXlab platform.

  • identifying investor sentiment from price paths the case of football Betting
    Social Science Research Network, 2000
    Co-Authors: Christopher Avery, Judith A Chevalier
    Abstract:

    We examine the hypothesis that sentimental bettors can affect the path of prices in football Betting markets. We hypothesize that sentimental traders follow the advice of false experts, believe excessively in momentum strategies, bet excessively on teams that are well known and covered in the media. We generate proxies for these sources of sentiment and show that point spreads move predictably over the course of the week, partially in response to variables known prior to the opening of Betting. We show that a Betting Strategy of Betting against the predicted movement in the point spread is borderline profitable.

  • identifying investor sentiment from price paths the case of football Betting
    The Journal of Business, 1999
    Co-Authors: Christopher Avery, Judith A Chevalier
    Abstract:

    We examine the hypothesis that sentimental bettors can affect the path of prices in football Betting markets. We hypothesize that sentimental traders follow the advice of false experts, believe excessively in momentum strategies, bet excessively on teams that are well known and covered in the media. We generate proxies for these sources of sentiment and show that point spreads move predictably over the course of the week, partially in response to variables known prior to the opening of Betting. We show that a Betting Strategy of Betting against the predicted movement in the point spread is borderline profitable. Copyright 1999 by University of Chicago Press.

Christopher Avery - One of the best experts on this subject based on the ideXlab platform.

  • identifying investor sentiment from price paths the case of football Betting
    Social Science Research Network, 2000
    Co-Authors: Christopher Avery, Judith A Chevalier
    Abstract:

    We examine the hypothesis that sentimental bettors can affect the path of prices in football Betting markets. We hypothesize that sentimental traders follow the advice of false experts, believe excessively in momentum strategies, bet excessively on teams that are well known and covered in the media. We generate proxies for these sources of sentiment and show that point spreads move predictably over the course of the week, partially in response to variables known prior to the opening of Betting. We show that a Betting Strategy of Betting against the predicted movement in the point spread is borderline profitable.

  • identifying investor sentiment from price paths the case of football Betting
    The Journal of Business, 1999
    Co-Authors: Christopher Avery, Judith A Chevalier
    Abstract:

    We examine the hypothesis that sentimental bettors can affect the path of prices in football Betting markets. We hypothesize that sentimental traders follow the advice of false experts, believe excessively in momentum strategies, bet excessively on teams that are well known and covered in the media. We generate proxies for these sources of sentiment and show that point spreads move predictably over the course of the week, partially in response to variables known prior to the opening of Betting. We show that a Betting Strategy of Betting against the predicted movement in the point spread is borderline profitable. Copyright 1999 by University of Chicago Press.

Frank Stephan - One of the best experts on this subject based on the ideXlab platform.

  • kolmogorov loveland randomness and stochasticity
    Annals of Pure and Applied Logic, 2006
    Co-Authors: Wolfgang Merkle, Joseph S Miller, Andre Nies, Jan Reimann, Frank Stephan
    Abstract:

    Abstract An infinite binary sequence X is Kolmogorov–Loveland (or KL-) random if there is no computable non-monotonic Betting Strategy that succeeds on X in the sense of having an unbounded gain in the limit while Betting successively on bits of X . A sequence X is KL-stochastic if there is no computable non-monotonic selection rule that selects from X an infinite, biased sequence. One of the major open problems in the field of effective randomness is whether Martin-Lof randomness is the same as KL-randomness. Our first main result states that KL-random sequences are close to Martin-Lof random sequences in so far as every KL-random sequence has arbitrarily dense subsequences that are Martin-Lof random. A key lemma in the proof of this result is that for every effective split of a KL-random sequence at least one of the halves is Martin-Lof random. However, this splitting property does not characterize KL-randomness; we construct a sequence that is not even computably random such that every effective split yields two subsequences that are 2-random. Furthermore, we show for any KL-random sequence A that is computable in the halting problem that, first, for any effective split of A both halves are Martin-Lof random and, second, for any computable, nondecreasing, and unbounded function g and almost all n , the prefix of A of length n has prefix-free Kolmogorov complexity at least n − g ( n ) . Again, the latter property does not characterize KL-randomness, even when restricted to left-r.e. sequences; we construct a left-r.e. sequence that has this property but is not KL-stochastic and, in fact, is not even Mises–Wald–Church stochastic. Turning our attention to KL-stochasticity, we construct a non-empty Π 1 0 class of KL-stochastic sequences that are not weakly 1-random; by the usual basis theorems we obtain such sequences that in addition are left-r.e., are low, or are of hyperimmune-free degree. Our second main result asserts that every KL-stochastic sequence has effective dimension 1, or equivalently, a sequence cannot be KL-stochastic if it has infinitely many prefixes that can be compressed by a factor of α 1 . This improves on a result by Muchnik, who has shown that were they to exist, such compressible prefixes could not be found effectively.

  • kolmogorov loveland randomness and stochasticity
    Symposium on Theoretical Aspects of Computer Science, 2005
    Co-Authors: Wolfgang Merkle, Joseph S Miller, Andre Nies, Jan Reimann, Frank Stephan
    Abstract:

    One of the major open problems in the field of effective randomness is whether Martin-Lof randomness is the same as Kolmogorov-Loveland (or KL) randomness, where an infinite binary sequence is KL-random if there is no computable non-monotonic Betting Strategy that succeeds on the sequence in the sense of having an unbounded gain in the limit while Betting successively on bits of the sequence. Our first main result states that every KL-random sequence has arbitrarily dense, easily extractable subsequences that are Martin-Lof random. A key lemma in the proof of this result is that for every effective split of a KL-random sequence at least one of the halves is Martin-Lof random. We show that this splitting property does not characterize KL-randomness by constructing a sequence that is not even computably random such that every effective split yields subsequences that are 2-random, hence are in particular Martin-Lof random. A sequence X is KL-stochastic if there is no computable non-monotonic selection rule that selects from X an infinite, biased sequence. Our second main result asserts that every KL-stochastic sequence has constructive dimension 1, or equivalently, a sequence cannot be KL-stochastic if it has infinitely many prefixes that can be compressed by a factor of α<1 with respect to prefix-free Kolmogorov complexity. This improves on a result by Muchnik, who has shown a similar implication where the premise requires that such compressible prefixes can be found effectively.

Siem Jan Koopman - One of the best experts on this subject based on the ideXlab platform.

  • a dynamic bivariate poisson model for analysing and forecasting match results in the english premier league
    Journal of The Royal Statistical Society Series A-statistics in Society, 2015
    Co-Authors: Siem Jan Koopman, Rutger Lit
    Abstract:

    Summary We develop a statistical model for the analysis and forecasting of football match results which assumes a bivariate Poisson distribution with intensity coefficients that change stochastically over time. The dynamic model is a novelty in the statistical time series analysis of match results in team sports. Our treatment is based on state space and importance sampling methods which are computationally efficient. The out-of-sample performance of our methodology is verified in a Betting Strategy that is applied to the match outcomes from the 2010–2011 and 2011–2012 seasons of the English football Premier League. We show that our statistical modelling framework can produce a significant positive return over the bookmaker's odds.

  • a dynamic bivariate poisson model for analysing and forecasting match results in the english premier league
    2012
    Co-Authors: Siem Jan Koopman, Rutger Lit
    Abstract:

    This discussion paper led to a publication in Journal of the Royal Statistical Society Series A , 2015, 178(1), 167-186. Attack and defense strengths of football teams vary over time due to changes in the teams of players or their managers. We develop a statistical model for the analysis and forecasting of football match results which are assumed to come from a bivariate Poisson distribution with intensity coefficients that change stochastically over time. This development presents a novelty in the statistical time series analysis of match results from football or other team sports. Our treatment is based on state space and importance sampling methods which are computationally efficient. The out-of-sample performance of our methodology is verified in a Betting Strategy that is applied to the match outcomes from the 2010/11 and 2011/12 seasons of the English Premier League. We show that our statistical modeling framework can produce a significant positive return over the bookmaker's odds.

  • a dynamic bivariate poisson model for analysing and forecasting match results in the english premier league
    2012
    Co-Authors: Siem Jan Koopman, Rutger Lit
    Abstract:

    Attack and defense strengths of football teams vary over time due to changes in the teams of players or their managers. We develop a statistical model for the analysis and forecasting of football match results which are assumed to come from a bivariate Poisson distribution with intensity coefficients that change stochastically over time. This development presents a novelty in the statistical time series analysis of match results from football or other team sports. Our treatment is based on state space and importance sampling methods which are computationally efficient. The out-of-sample performance of our methodology is verified in a Betting Strategy that is applied to the match outcomes from the 2010/11 and 2011/12 seasons of the English Premier League. We show that our statistical modeling framework can produce a significant positive return over the bookmaker's odds.

Rutger Lit - One of the best experts on this subject based on the ideXlab platform.

  • a dynamic bivariate poisson model for analysing and forecasting match results in the english premier league
    Journal of The Royal Statistical Society Series A-statistics in Society, 2015
    Co-Authors: Siem Jan Koopman, Rutger Lit
    Abstract:

    Summary We develop a statistical model for the analysis and forecasting of football match results which assumes a bivariate Poisson distribution with intensity coefficients that change stochastically over time. The dynamic model is a novelty in the statistical time series analysis of match results in team sports. Our treatment is based on state space and importance sampling methods which are computationally efficient. The out-of-sample performance of our methodology is verified in a Betting Strategy that is applied to the match outcomes from the 2010–2011 and 2011–2012 seasons of the English football Premier League. We show that our statistical modelling framework can produce a significant positive return over the bookmaker's odds.

  • a dynamic bivariate poisson model for analysing and forecasting match results in the english premier league
    2012
    Co-Authors: Siem Jan Koopman, Rutger Lit
    Abstract:

    This discussion paper led to a publication in Journal of the Royal Statistical Society Series A , 2015, 178(1), 167-186. Attack and defense strengths of football teams vary over time due to changes in the teams of players or their managers. We develop a statistical model for the analysis and forecasting of football match results which are assumed to come from a bivariate Poisson distribution with intensity coefficients that change stochastically over time. This development presents a novelty in the statistical time series analysis of match results from football or other team sports. Our treatment is based on state space and importance sampling methods which are computationally efficient. The out-of-sample performance of our methodology is verified in a Betting Strategy that is applied to the match outcomes from the 2010/11 and 2011/12 seasons of the English Premier League. We show that our statistical modeling framework can produce a significant positive return over the bookmaker's odds.

  • a dynamic bivariate poisson model for analysing and forecasting match results in the english premier league
    2012
    Co-Authors: Siem Jan Koopman, Rutger Lit
    Abstract:

    Attack and defense strengths of football teams vary over time due to changes in the teams of players or their managers. We develop a statistical model for the analysis and forecasting of football match results which are assumed to come from a bivariate Poisson distribution with intensity coefficients that change stochastically over time. This development presents a novelty in the statistical time series analysis of match results from football or other team sports. Our treatment is based on state space and importance sampling methods which are computationally efficient. The out-of-sample performance of our methodology is verified in a Betting Strategy that is applied to the match outcomes from the 2010/11 and 2011/12 seasons of the English Premier League. We show that our statistical modeling framework can produce a significant positive return over the bookmaker's odds.