scholarly journals Numerical Solution of Dynamic Portfolio Optimization with Transaction Costs

2013 ◽  
Author(s):  
Yongyang Cai ◽  
Kenneth Judd ◽  
Rong Xu
Keyword(s):  
Transaction Costs ◽  
Numerical Solution ◽  
Portfolio Optimization ◽  
Dynamic Portfolio ◽  
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

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 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.

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