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

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.

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.

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

scholarly journals Transfer Entropy Approach for Portfolio Optimization: An Empirical Approach for CESEE Markets

2021 ◽  
Vol 14 (8) ◽  
pp. 369
Author(s):  
Tihana Škrinjarić ◽  
Derick Quintino ◽  
Paulo Ferreira
Keyword(s):  
Portfolio Optimization ◽  
Main Idea ◽  
Second Step ◽  
Portfolio Performance ◽  
Portfolio Strategies ◽  
Dynamic Portfolio ◽  
Using Data ◽  
Methodological Aspects ◽  
Dynamic Portfolio Optimization ◽  
Over Time

In this paper, we deal with the possibility of using econophysics concepts in dynamic portfolio optimization. The main idea of the research is that combining different methodological aspects in portfolio selection can enhance portfolio performance over time. Using data on CESEE stock market indices, we model the dynamics of entropy transfers from one return series to others. In the second step, the results are utilized in simulating the portfolio strategies that take into account the previous results. Here, the main results indicate that using entropy transfers in portfolio construction and rebalancing has the potential to achieve better portfolio value over time when compared to benchmark strategies.

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