scholarly journals Dynamic portfolio rebalancing through reinforcement learning

2021 ◽  
Author(s):  
Qing Yang Eddy Lim ◽  
Qi Cao ◽  
Chai Quek
Keyword(s):  
Reinforcement Learning ◽  
Prediction Model ◽  
Global Market ◽  
Alternative View ◽  
Minimal Risk ◽  
Market Conditions ◽  
Portfolio Rebalancing ◽  
Risk Levels ◽  
Portfolio Returns ◽  
Dynamic Portfolio

AbstractPortfolio managements in financial markets involve risk management strategies and opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim to have a minimal risk with highest accompanying investment returns, regardless of market conditions. This paper focuses on providing an alternative view in maximising portfolio returns using Reinforcement Learning (RL) by considering dynamic risks appropriate to market conditions through dynamic portfolio rebalancing. The proposed algorithm is able to improve portfolio management by introducing the dynamic rebalancing of portfolios with vigorous risk through an RL agent. This is done while accounting for market conditions, asset diversifications, risk and returns in the global financial market. Studies have been performed in this paper to explore four types of methods with variations in fully portfolio rebalancing and gradual portfolio rebalancing, which combine with and without the use of the Long Short-Term Memory (LSTM) model to predict stock prices for adjusting the technical indicator centring. Performances of the four methods have been evaluated and compared using three constructed financial portfolios, including one portfolio with global market index assets with different risk levels, and two portfolios with uncorrelated stock assets from different sectors and risk levels. Observed from the experiment results, the proposed RL agent for gradual portfolio rebalancing with the LSTM model on price prediction outperforms the other three methods, as well as returns of individual assets in these three portfolios. The improvements of the returns using the RL agent for gradual rebalancing with prediction model are achieved at about 27.9–93.4% over those of the full rebalancing without prediction model. It has demonstrated the ability to dynamically adjust portfolio compositions according to the market trends, risks and returns of the global indices and stock assets.

scholarly journals Kelly's Strategy Analysis in Optimizing Investment Portfolios in Foreign Exchange

2019 ◽  
Vol 8 (2S7) ◽  
pp. 245-250
Keyword(s):  
Financial Markets ◽  
Foreign Exchange ◽  
Optimal Portfolio ◽  
Minimal Risk ◽  
Investment Portfolio ◽  
Strategy Analysis ◽  
Average Value ◽  
Portfolio Returns ◽  
Strategy Model ◽  
Investment Portfolios

Investments in financial markets not only pay attention to promising profits, but also need to consider the risks that follow. Risks can be minimized by establishing an investment portfolio. This research was conducted with the aim of analyzing optimal portfolios on foreign exchange investments, so that investments made provide maximum returns at certain risks, or minimal risk on certain returns. The data analyzed in this study are foreign exchange traded at Bank Indonesia. Data analysis is carried out quantitatively using the Kelly Strategy model. The steps: (i) Calculation of individual foreign exchange returns, (ii) Determine the average value of individual foreign exchange returns, (iii) Determine the optimal portfolio using the Kelly strategy approach, and (iv) Determine portfolio returns and risks. Based on the results of the analysis obtained the allocation of weights that provide returns and risks to the optimal portfolio. A 95% USD currency is an optimal portfolio of the five currencies used. So that it can be used as a consideration for investors, in making investment decisions in the foreign exchange being analyzed.

Arbor City Community Foundation (A): The Foundation

2017 ◽  
pp. 1-3 ◽  
Author(s):  
Karl Schmedders ◽  
Russell Walker ◽  
Michael Stritch
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Value At Risk ◽  
Management Policy ◽  
City Region ◽  
Portfolio Rebalancing ◽  
City Community ◽  
Portfolio Returns ◽  
The Impact ◽  
Community Foundation

The Arbor City Community Foundation (ACCF) was a medium-sized endowment established in Illinois in the late 1970s through the hard work of several local families. The vision of the ACCF was to be a comprehensive center for philanthropy in the greater Arbor City region. ACCF had a fund balance (known collectively as “the fund”) of just under $240 million. The ACCF board of trustees had appointed a committee to oversee investment decisions relating to the foundation assets. The investment committee, under the guidance of the board, pursued an active risk-management policy for the fund. The committee members were primarily concerned with the volatility and distribution of portfolio returns. They relied on the value-at-risk (VaR) methodology as a measurement of the risk of both short- and mid-term investment losses. The questions in Part (A) of the case direct the students to analyze the risk inherent in both one particular asset and the entire ACCF portfolio. For this analysis the students need to calculate daily VaR and monthly VaR values and interpret these figures in the context of ACCF's risk management. In Part (B) the foundation receives a major donation. As a result, the risk inherent in its portfolio changes considerably. The students are asked to evaluate the risk of the fund's new portfolio and to perform a portfolio rebalancing analysis.Understanding the concept of value at risk (VaR); Calculating daily and monthly VaR by two different methods, the historical and the parametric approach; Interpreting the results of VaR calculations; Understanding the role of diversification for managing risk; Evaluating the impact of portfolio rebalancing on the overall risk of a portfolio.

scholarly journals Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning

2020 ◽  
Vol 11 (40) ◽  
pp. 10959-10972
Author(s):  
Xiaoxue Wang ◽  
Yujie Qian ◽  
Hanyu Gao ◽  
Connor W. Coley ◽  
Yiming Mo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Prediction Model ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Value Network ◽  
Synthesis Routes

A new MCTS variant with a reinforcement learning value network and solvent prediction model proposes shorter synthesis routes with greener solvents.

scholarly journals Hidden Link Prediction in Criminal Networks Using the Deep Reinforcement Learning Technique

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 8 ◽  
Author(s):  
Marcus Lim ◽  
Azween Abdullah ◽  
NZ Jhanjhi ◽  
Mahadevan Supramaniam
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Network Analysis ◽  
Prediction Model ◽  
Supervised Learning ◽  
Link Prediction ◽  
Supervised Machine Learning ◽  
Criminal Networks ◽  
Criminal Network ◽  
Learning Technique

Criminal network activities, which are usually secret and stealthy, present certain difficulties in conducting criminal network analysis (CNA) because of the lack of complete datasets. The collection of criminal activities data in these networks tends to be incomplete and inconsistent, which is reflected structurally in the criminal network in the form of missing nodes (actors) and links (relationships). Criminal networks are commonly analyzed using social network analysis (SNA) models. Most machine learning techniques that rely on the metrics of SNA models in the development of hidden or missing link prediction models utilize supervised learning. However, supervised learning usually requires the availability of a large dataset to train the link prediction model in order to achieve an optimum performance level. Therefore, this research is conducted to explore the application of deep reinforcement learning (DRL) in developing a criminal network hidden links prediction model from the reconstruction of a corrupted criminal network dataset. The experiment conducted on the model indicates that the dataset generated by the DRL model through self-play or self-simulation can be used to train the link prediction model. The DRL link prediction model exhibits a better performance than a conventional supervised machine learning technique, such as the gradient boosting machine (GBM) trained with a relatively smaller domain dataset.

The Use of Hematological Effects in the Development of Minimal Risk Levels

1998 ◽  
Vol 28 (1) ◽  
pp. 61-66 ◽  
Author(s):  
Henry G. Abadin ◽  
H.Edward Murray ◽  
John S. Wheeler
Keyword(s):  
Minimal Risk ◽  
Risk Levels ◽  
Hematological Effects

scholarly journals Global Market Conditions and Systemic Risk

2011 ◽  
Vol 10 (2) ◽  
pp. 227-252 ◽  
Author(s):  
Brenda González-Hermosillo ◽  
Heiko Hesse
Keyword(s):  
Systemic Risk ◽  
Global Market ◽  
Market Conditions

scholarly journals ESTIMATING THE HEDGE RATIOS

2016 ◽  
Vol 4 ◽  
pp. 229-234
Author(s):  
Mária Bohdalová ◽  
Michal Greguš
Keyword(s):  
Quantile Regression ◽  
Likelihood Estimation ◽  
Equal Weight ◽  
Minimal Risk ◽  
Hedge Ratio ◽  
Portfolio Returns ◽  
Hedge Ratios ◽  
Hedging Portfolio ◽  
T Copula ◽  
T Distribution

This paper examines the problem of hedging portfolio returns. Many practitioners and academicians endeavor to solve the problem of how to calculate the optimal hedge ratio accurately. In this paper we compare estimates of the hedge ratio from a classical approach of a linear quantile regression, based on selected quantiles as medians, with that of a non-linear quantile regression. To estimate the hedge ratios, we have used a calibrated Student t distribution for the marginal densities and a Student t copula of the portfolio returns using a maximum likelihood estimation. We created two portfolios of the assets, one for equal weight and another for optimal weight in respect of minimal risk. Our findings show that an assumption of Student t marginal leads to a better estimation of the hedge ratio.

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