Matches in SemOpenAlex for { <https://semopenalex.org/work/W2912019150> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W2912019150 abstract "Allocation of liquid capital to the financial instruments in a portfolio is typically done using a two-step process. In the first step, predictive techniques are used to determine the future risk and rewards for the instrument. In the subsequent step, a quadratic optimization problem is solved to obtain the allocation that maximizes a relevant measure of the portfolio performance computed using a combination of the risks and the rewards. Deep Reinforcement Learning (DRL) eliminates the need for a two step process to find the allocation across the instruments that will optimize a measure of portfolio performance obtained from the market. DRL based portfolio construction autonomously adjusts to a change in the environment unlike traditional machine learning algorithms used in prediction. The existing DRL methods suffer from the challenges of stability, and do not lend themselves well to the portfolio construction problem that has a continuous action space. Proposed in 2015, Deep Deterministic Policy Gradients (DDPG) is a type of actorcritic DRL algorithm that provides support for continuous action space which is encountered in portfolio construction. This paper evaluates the use of DDPG to solve the problem of risk aware portfolio construction. Simulations are done on a portfolio of twenty stocks and the use of both Rate of Return and Sortino ratio as a measure of portfolio performance are evaluated. Results are presented that demonstrate the effectiveness of DDPG for risk aware portfolio construction. The simulation results presented in this paper show that having a risk-aware measure of portfolio performance such as Sortino ratio give a portfolio with superior return and lower variance." @default.
- W2912019150 created "2019-02-21" @default.
- W2912019150 creator A5024278085 @default.
- W2912019150 creator A5060974647 @default.
- W2912019150 creator A5062478344 @default.
- W2912019150 date "2018-11-01" @default.
- W2912019150 modified "2023-10-02" @default.
- W2912019150 title "Risk aware portfolio construction using deep deterministic policy gradients" @default.
- W2912019150 cites W1484695179 @default.
- W2912019150 cites W1757796397 @default.
- W2912019150 cites W2064675550 @default.
- W2912019150 cites W2106214155 @default.
- W2912019150 cites W2131773668 @default.
- W2912019150 cites W2169015875 @default.
- W2912019150 cites W2173248099 @default.
- W2912019150 cites W2279759792 @default.
- W2912019150 cites W2286163714 @default.
- W2912019150 cites W2615790994 @default.
- W2912019150 cites W2731083990 @default.
- W2912019150 cites W3124028146 @default.
- W2912019150 doi "https://doi.org/10.1109/ssci.2018.8628791" @default.
- W2912019150 hasPublicationYear "2018" @default.
- W2912019150 type Work @default.
- W2912019150 sameAs 2912019150 @default.
- W2912019150 citedByCount "5" @default.
- W2912019150 countsByYear W29120191502019 @default.
- W2912019150 countsByYear W29120191502021 @default.
- W2912019150 countsByYear W29120191502022 @default.
- W2912019150 countsByYear W29120191502023 @default.
- W2912019150 crossrefType "proceedings-article" @default.
- W2912019150 hasAuthorship W2912019150A5024278085 @default.
- W2912019150 hasAuthorship W2912019150A5060974647 @default.
- W2912019150 hasAuthorship W2912019150A5062478344 @default.
- W2912019150 hasConcept C10138342 @default.
- W2912019150 hasConcept C124101348 @default.
- W2912019150 hasConcept C126255220 @default.
- W2912019150 hasConcept C154945302 @default.
- W2912019150 hasConcept C162324750 @default.
- W2912019150 hasConcept C202655437 @default.
- W2912019150 hasConcept C21099588 @default.
- W2912019150 hasConcept C2780009758 @default.
- W2912019150 hasConcept C2780821815 @default.
- W2912019150 hasConcept C2781472820 @default.
- W2912019150 hasConcept C33923547 @default.
- W2912019150 hasConcept C41008148 @default.
- W2912019150 hasConcept C69257216 @default.
- W2912019150 hasConcept C77913304 @default.
- W2912019150 hasConcept C97541855 @default.
- W2912019150 hasConceptScore W2912019150C10138342 @default.
- W2912019150 hasConceptScore W2912019150C124101348 @default.
- W2912019150 hasConceptScore W2912019150C126255220 @default.
- W2912019150 hasConceptScore W2912019150C154945302 @default.
- W2912019150 hasConceptScore W2912019150C162324750 @default.
- W2912019150 hasConceptScore W2912019150C202655437 @default.
- W2912019150 hasConceptScore W2912019150C21099588 @default.
- W2912019150 hasConceptScore W2912019150C2780009758 @default.
- W2912019150 hasConceptScore W2912019150C2780821815 @default.
- W2912019150 hasConceptScore W2912019150C2781472820 @default.
- W2912019150 hasConceptScore W2912019150C33923547 @default.
- W2912019150 hasConceptScore W2912019150C41008148 @default.
- W2912019150 hasConceptScore W2912019150C69257216 @default.
- W2912019150 hasConceptScore W2912019150C77913304 @default.
- W2912019150 hasConceptScore W2912019150C97541855 @default.
- W2912019150 hasLocation W29120191501 @default.
- W2912019150 hasOpenAccess W2912019150 @default.
- W2912019150 hasPrimaryLocation W29120191501 @default.
- W2912019150 hasRelatedWork W1622292463 @default.
- W2912019150 hasRelatedWork W2006574308 @default.
- W2912019150 hasRelatedWork W2082634177 @default.
- W2912019150 hasRelatedWork W2380305602 @default.
- W2912019150 hasRelatedWork W2384465097 @default.
- W2912019150 hasRelatedWork W2589738636 @default.
- W2912019150 hasRelatedWork W2912019150 @default.
- W2912019150 hasRelatedWork W4287554016 @default.
- W2912019150 hasRelatedWork W614441771 @default.
- W2912019150 hasRelatedWork W800337391 @default.
- W2912019150 isParatext "false" @default.
- W2912019150 isRetracted "false" @default.
- W2912019150 magId "2912019150" @default.
- W2912019150 workType "article" @default.