English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In online portfolio optimization the investor makes decisions based on new,continuously incoming information on financial assets (typically their prices).In our study we consider a learning algorithm, namely the Kiefer--Wolfowitzversion of the Stochastic Gradient method, that converges to the log-optimalsolution in the threshold-type, buy-and-sell strategy class.  The systematic study of this method is novel in the field of portfoliooptimization; we aim to establish the theory and practice of StochasticGradient algorithm used on parametrized trading strategies.  We demonstrate on a wide variety of stock price dynamics (e.g. withstochastic volatility and long-memory) that there is an optimal threshold typestrategy which can be learned. Subsequently, we numerically show theconvergence of the algorithm. Furthermore, we deal with the typicallyproblematic question of how to choose the hyperparameters (the parameters ofthe algorithm and not the dynamics of the prices) without knowing anythingabout the price other than a small sample.

Learning Threshold-Type Investment Strategies with Stochastic Gradient Method