How much is it worth to know the future in online conversion problems?

Mohr, Esther; Schmidt, Günter

doi:10.1016/j.dam.2013.01.001

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Mohr and Schmidt [32] address the question 'How much is it worth to know the future in online conversion problems?' investigating the value of the a-priori information available.…”

Section: Resultsmentioning

confidence: 99%

“…Mohr and Schmidt [32] show that the competitive ratios of the four variants of Algorithm 3 introduced by El-Yaniv et al [11,17] can be used as an indicator for the quality of a-priori information about the future. 6 They conclude that some a-priori information is more valuable than other: The better the information the smaller are the competitive ratios.…”

Section: Problem: Uni|pmtn|φmentioning

confidence: 99%

See 1 more Smart Citation

Online algorithms for conversion problems: A survey

Mohr

Ahmad

Schmidt

2014

Surveys in Operations Research and Management Science

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“…Mohr and Schmidt [32] address the question 'How much is it worth to know the future in online conversion problems?' investigating the value of the a-priori information available.…”

Section: Resultsmentioning

confidence: 99%

Section: Problem: Uni|pmtn|φmentioning

confidence: 99%

Online algorithms for conversion problems: A survey

Mohr

Ahmad

Schmidt

2014

Surveys in Operations Research and Management Science

Self Cite

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Competitive difference analysis of the one-way trading problem with limited information

Wang

Lan

et al. 2016

European Journal of Operational Research

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Optimal negative weight moving average for stock price series smoothing

Raudys

2014

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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Moving averages (MAs) are widely used in finance by trend followers. Negative weight (corrective) moving averages negatively weight old history and attach more weight to recent history in order to achieve better fit. After analysing such methods we propose an optimal weighting scheme for smoothing stock price data. For a given smoothness level we minimise fitting error. Differently from existing methods, which have predefined weights, optimal weights are optimised for a set of stocks to achieve the best smoothness and fit ratio. Empirical evaluation of around 2000 real-world stocks from the NASDAQ and NYSE exchanges demonstrate that a novel moving average is better than other moving averages in 90% of cases. Some additional improvements can be made to improve it further, especially for longer periods. Additionally we discovered that negative weights have quite a small influence on the overall performance of moving averages. Optimised moving average weights consist of only 0% to 12% of negative weights.

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How much is it worth to know the future in online conversion problems?

Cited by 3 publications

References 22 publications

Online algorithms for conversion problems: A survey

Online algorithms for conversion problems: A survey

Competitive difference analysis of the one-way trading problem with limited information

Optimal negative weight moving average for stock price series smoothing

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