scholarly journals A deep Q-learning portfolio management framework for the cryptocurrency market

2020 ◽  
Vol 32 (23) ◽  
pp. 17229-17244
Author(s):  
Giorgio Lucarelli ◽  
Matteo Borrotti
Keyword(s):  
Portfolio Optimization ◽  
Portfolio Management ◽  
Dynamic Decision Making ◽  
Financial Assets ◽  
Management Framework ◽  
Q Learning ◽  
Reward Function ◽  
Dynamic Portfolio ◽  
Definition Of ◽  
Dynamic Portfolio Optimization

AbstractDeep reinforcement learning is gaining popularity in many different fields. An interesting sector is related to the definition of dynamic decision-making systems. A possible example is dynamic portfolio optimization, where an agent has to continuously reallocate an amount of fund into a number of different financial assets with the final goal of maximizing return and minimizing risk. In this work, a novel deep Q-learning portfolio management framework is proposed. The framework is composed by two elements: a set of local agents that learn assets behaviours and a global agent that describes the global reward function. The framework is tested on a crypto portfolio composed by four cryptocurrencies. Based on our results, the deep reinforcement portfolio management framework has proven to be a promising approach for dynamic portfolio optimization.

scholarly journals Dynamic portfolio optimization with transaction costs and state-dependent drift

2015 ◽  
Vol 243 (3) ◽  
pp. 921-931 ◽  
Author(s):  
Jan Palczewski ◽  
Rolf Poulsen ◽  
Klaus Reiner Schenk-Hoppé ◽  
Huamao Wang
Keyword(s):  
Transaction Costs ◽  
Portfolio Optimization ◽  
State Dependent ◽  
Dynamic Portfolio ◽  
Dynamic Portfolio Optimization

Distribution Characteristics of Multivariate Financial Assets Return based on Mathematical Model

2021 ◽  
Vol 7 (5) ◽  
pp. 2244-2259
Author(s):  
Han Wang
Keyword(s):  
Mathematical Model ◽  
Stock Return ◽  
Time Variability ◽  
Financial Assets ◽  
Return Distribution ◽  
Proposed Model ◽  
Dynamic Portfolio ◽  
T Distribution ◽  
Dynamic Portfolio Optimization ◽  
Fluctuation Model

For the non-normality and time variability of the distribution of multivariate financial assets return, a dynamic model of the distribution of multivariate financial assets return based on mathematical model is constructed in this paper. AR(1)-DCC(1,1)-GARCH(1,1) model reflects dynamic characteristics of conditional expectation and conditional variance of multivariate financial assets return. It solves the problem that restricts the in-depth research on high order dynamic portfolio optimization, which is the estimation of conditional coskewness matrix and conditional cokurtosis matrix. By constructing a multi-dimensional fluctuation model with biased t distribution, conditional asymmetric parameter and conditional free degree parameter, the distribution of multivariate financial assets return is researched. Experimental results show that the proposed model can reasonably reflect the time-varying characteristics of the multivariate stock return distribution in China’s stock market.

Dynamic Portfolio Optimization with Expected Value-Variance Criteria 1

2001 ◽  
Vol 34 (8) ◽  
pp. 295-300 ◽  
Author(s):  
Przemysław Magiera ◽  
Andrzej Karbowski
Keyword(s):  
Portfolio Optimization ◽  
Expected Value ◽  
Dynamic Portfolio ◽  
Dynamic Portfolio Optimization

scholarly journals Portfolio optimization for cointelated pairs: SDEs vs Machine learning

2021 ◽  
Vol 8 (3-4) ◽  
pp. 101-125
Author(s):  
Babak Mahdavi-Damghani ◽  
Konul Mustafayeva ◽  
Cristin Buescu ◽  
Stephen Roberts
Keyword(s):  
Machine Learning ◽  
Portfolio Optimization ◽  
Maximization Problem ◽  
Financial Mathematics ◽  
Pairs Trading ◽  
Power Utility ◽  
Dynamic Portfolio ◽  
Dynamic Portfolio Optimization ◽  
Mean Variance

With the recent rise of Machine Learning (ML) as a candidate to partially replace classic Financial Mathematics (FM) methodologies, we investigate the performances of both in solving the problem of dynamic portfolio optimization in continuous-time, finite-horizon setting for a portfolio of two assets that are intertwined. In the Financial Mathematics approach we model the asset prices not via the common approaches used in pairs trading such as a high correlation or cointegration, but with the cointelation model in Mahdavi-Damghani (2013) that aims to reconcile both short-term risk and long-term equilibrium. We maximize the overall P&L with Financial Mathematics approach that dynamically switches between a mean-variance optimal strategy and a power utility maximizing strategy. We use a stochastic control formulation of the problem of power utility maximization and solve numerically the resulting HJB equation with the Deep Galerkin method introduced in Sirignano and Spiliopoulos (2018). We turn to Machine Learning for the same P&L maximization problem and use clustering analysis to devise bands, combined with in-band optimization. Although this approach is model agnostic, results obtained with data simulated from the same cointelation model gives a slight competitive advantage to the ML over the FM methodology1.

scholarly journals Dynamic portfolio optimization with liquidity cost and market impact: a simulation-and-regression approach

2018 ◽  
Vol 19 (3) ◽  
pp. 519-532 ◽  
Author(s):  
Rongju Zhang ◽  
Nicolas Langrené ◽  
Yu Tian ◽  
Zili Zhu ◽  
Fima Klebaner ◽  
...  
Keyword(s):  
Portfolio Optimization ◽  
Market Impact ◽  
Regression Approach ◽  
Dynamic Portfolio ◽  
Liquidity Cost ◽  
Simulation And Regression ◽  
Dynamic Portfolio Optimization

Dynamic Portfolio Optimization with Bounded Shortfall Risks

2005 ◽  
Vol 23 (3) ◽  
pp. 579-594 ◽  
Author(s):  
A. Gabih ◽  
W. Grecksch ◽  
R. Wunderlich
Keyword(s):  
Portfolio Optimization ◽  
Dynamic Portfolio ◽  
Dynamic Portfolio Optimization
Sign in / Sign up

Export Citation Format

Share Document