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

Financial portfolio management is the process of constant redistribution of afund into different financial products. This paper presents afinancial-model-free Reinforcement Learning framework to provide a deep machinelearning solution to the portfolio management problem. The framework consistsof the Ensemble of Identical Independent Evaluators (EIIE) topology, aPortfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL)scheme, and a fully exploiting and explicit reward function. This framework isrealized in three instants in this work with a Convolutional Neural Network(CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory(LSTM). They are, along with a number of recently reviewed or publishedportfolio-selection strategies, examined in three back-test experiments with atrading period of 30 minutes in a cryptocurrency market. Cryptocurrencies areelectronic and decentralized alternatives to government-issued money, withBitcoin as the best-known example of a cryptocurrency. All three instances ofthe framework monopolize the top three positions in all experiments,outdistancing other compared trading algorithms. Although with a highcommission rate of 0.25% in the backtests, the framework is able to achieve atleast 4-fold returns in 50 days.

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem