Universal algorithm for trading in stock market based on the method of calibration

Vladimir V'yugin

Результат исследований: Глава в книге, отчете, сборнике статейМатериалы для конференциирецензирование

3 Цитирования (Scopus)

Аннотация

We present a universal method for algorithmic trading in Stock Market which performs asymptotically at least as well as any stationary trading strategy that computes the investment at each step using a continuous function of the side information. In the process of the game, a trader makes decisions using predictions computed by a randomized well-calibrated algorithm. We use Dawid's notion of calibration with more general checking rules and some modification of Kakade and Foster's randomized rounding algorithm for computing the well-calibrated forecasts. The method of randomized calibration is combined with Vovk's method of defensive forecasting in RKHS. Unlike in statistical theory, no stochastic assumptions are made about the stock prices.

Язык оригиналаАнглийский
Название основной публикацииAlgorithmic Learning Theory - 24th International Conference, ALT 2013, Proceedings
Страницы53-67
Число страниц15
DOI
СостояниеОпубликовано - 2013
Опубликовано для внешнего пользованияДа
Событие24th International Conference on Algorithmic Learning Theory, ALT 2013 - Singapore, Сингапур
Продолжительность: 6 окт. 20139 окт. 2013

Серия публикаций

НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том8139 LNAI
ISSN (печатное издание)0302-9743
ISSN (электронное издание)1611-3349

Конференция

Конференция24th International Conference on Algorithmic Learning Theory, ALT 2013
Страна/TерриторияСингапур
ГородSingapore
Период6/10/139/10/13

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