scholarly journals Restrição de Liquidez para Modelos de Seleção de Carteiras

2015 ◽  
Vol 13 (2) ◽  
pp. 288
Author(s):  
Gabriel Matos Pereira ◽  
Leonardo Riegel Sant'Anna ◽  
Tiago Pascoal Filomena ◽  
João Luiz Becker
Keyword(s):  
Stock Market ◽  
Portfolio Optimization ◽  
Financial Institutions ◽  
Portfolio Management ◽  
Regulatory Agency ◽  
Liquidity Constraint ◽  
Maximum Percentage ◽  
Monetary Value ◽  
High Influence

Liquidity is an important issue in portfolio management. In 2012, the Brazilian market regulatory agency (CVM) started to require all banks and brokerages to maintain liquidity control of their portfolios. This study presents a liquidity constraint which is endogenously incorporated to portfolio optimization to Brazilian Financial Institutions. The proposed constraint incorporates endogenously some practical issues such as: portfolio value, monetary volume traded, maximum percentage of monetary value, liquidation term date and liquidation level. This constrain is applied to the Brazilian Stock Market. The selected constraint parameters have high influence on the liquidity level of the portfolio.

The Black-Litterman Model for Portfolio Optimization on Vietnam Stock Market

2020 ◽  
Vol 28 (Supp01) ◽  
pp. 99-111
Author(s):  
Bao Quoc Ta ◽  
Thao Vuong
Keyword(s):  
Stock Market ◽  
Portfolio Optimization ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Market Equilibrium ◽  
Management Model ◽  
Optimal Portfolios ◽  
Allocation Model ◽  
Allocation Process ◽  
Allocation Decisions

The Black-Litterman asset allocation model is an extended portfolio management model to construct optimal portfolios by combining the market equilibrium with investor views into asset allocation decisions. In this paper we apply Black-Litterman model for portfolio optimization on Vietnames stock market. We chose ARIMA methodology utilized in financial econonometrics to predict the views of investor which are used as inputs of the Black-Litterman asset allocation process to find optimal portfolio and weights.

PORTFOLIO MANAGEMENT OF ENERGY SAVING PROJECTS BASED ON THE MARKOVITS THEORY

2021 ◽  
pp. 79-91
Author(s):  
S. Kiyko ◽  
L. Deineha ◽  
M. Basanets ◽  
D. Kamienskyi ◽  
A. Didenko
Keyword(s):  
Energy Saving ◽  
Portfolio Optimization ◽  
Portfolio Management ◽  
Quadratic Function ◽  
Subject Area ◽  
Linear Constraints ◽  
Point Of View ◽  
Investment Portfolio ◽  
Financial Investment ◽  
Software Application

The goal of the work was to identify research and compare methods of portfolio management of energy saving projects and to develop software for optimizing portfolio investments using several methods. The key elements and strategies of creating an effective investment portfolio are considered: diversification, rebalancing, active portfolio management, passive portfolio management. Given the basic principles of investment theory, the task of portfolio investment is to form an investment portfolio with known shares of certain assets to maximize returns and minimize risk. To solve this problem, the method of Harry Markowitz, known as modern portfolio theory, was chosen. This is the theory of financial investment, in which statistical methods are used to make the most profitable risk distribution of the securities portfolio and income valuation, its components are asset valuation, investment decisions, portfolio optimization, evaluation of results. From a mathematical point of view, the problem of forming an optimal portfolio is the problem of optimizing a quadratic function (finding the minimum) with linear constraints on the arguments of the function. Methods of optimization of portfolios of energy saving projects taking into account the specifics of the subject area are analyzed. According to the results of the analysis, the methods of finding the maximum Sharpe’s ratio and the minimum volatility from randomly generated portfolios were chosen. A software application has been developed that allows you to download data, generate random portfolios and optimize them with selected methods. A graphical display of portfolio optimization results has also been implemented. The program was tested on data on shares of energy saving companies. The graphs built by the program allow the operator to better assess the created portfolio of the energy saving project.

scholarly journals Foreign Ownership Effect to Stock Market Liquidity in Indonesia

MANAJERIAL ◽  
2021 ◽  
Vol 8 (01) ◽  
pp. 01
Author(s):  
Annisa Yasmin
Keyword(s):  
Stock Market ◽  
Financial Institutions ◽  
Capital Market ◽  
Foreign Ownership ◽  
Market Liquidity ◽  
Linear Effect ◽  
Non Linear Effect ◽  
Non Linear ◽  
Stock Market Liquidity ◽  
Research Period

Background – One of economic indicators of a country is the capital market. Liquid capital market can attract investors, both foreign and domestic investors, to invest their ownership in that country, which in turn can improve the country’s economic growth. Aim – This research aims to examine the influence foreign ownership on stock market liquidity in Indonesia. Design / methodology / approach – This research splits foreign ownership into two groups, the first one is foreign ownership by financial institutions, and the second one is foreign ownership by non-financial corporations. The type of data used is panel data using fixed effect model (FEM). The technique for examining the influence of foreign ownership on liquidity used multiple regression analysis. Findings – The result found that foreign ownership by financial institutions and non-financial corporations negatively affect liquidity.  The study also found a positively non-linear effect between foreign ownership by financial institutions to liquidity and a negatively non-linear effect between foreign ownership by non-financial institutions to liquidity. Research implication – This research can assist investors in determining investment in the Indonesian capital market by pay attention to variables such as foreign ownership, return, turnover, market capitalization and standard deviation. Limitation – The research period was short, which was only 21 months due to limited data and the research period that has passed too long, that is January 2012 to September 2013.

scholarly journals Portfolio Optimization for Stock Market in Ghana Using Value-at-Risk (VaR)

2020 ◽  
Vol 5 (3) ◽  
pp. 61
Author(s):  
Eric Kwame Austro Gozah ◽  
Eric Neebo Wiah ◽  
Albert Buabeng ◽  
Paul Yaw Addai Yeboah
Keyword(s):  
At Risk ◽  
Stock Market ◽  
Portfolio Optimization ◽  
Value At Risk

Global financial architecture: possible directions of structural transformation

2021 ◽  
pp. 4
Author(s):  
Vladimir Andrianov
Keyword(s):  
Stock Market ◽  
Structural Transformation ◽  
Financial Institutions ◽  
Capital Market ◽  
Shadow Banking ◽  
International Financial Institutions ◽  
Financial Architecture ◽  
The World ◽  
Financial Infrastructure

The article examines the main trends in the transformation of the global financial infrastructure. The influence of shadow banking and the bubble of derivatives on the development of the world capital market and the stock market is investigated. Possible options for reforming international financial institutions and financial regulators are proposed.

scholarly journals Neural Network Predictive Modeling on Dynamic Portfolio Management—A Simulation-Based Portfolio Optimization Approach

2020 ◽  
Vol 13 (11) ◽  
pp. 285
Author(s):  
Jiayang Yu ◽  
Kuo-Chu Chang
Keyword(s):  
Portfolio Optimization ◽  
Portfolio Management ◽  
Value At Risk ◽  
Machine Learning Algorithms ◽  
Conditional Value At Risk ◽  
Portfolio Performance ◽  
Optimization Approach ◽  
Macroeconomic Factors ◽  
Simulation Based ◽  
Portfolio Managers

Portfolio optimization and quantitative risk management have been studied extensively since the 1990s and began to attract even more attention after the 2008 financial crisis. This disastrous occurrence propelled portfolio managers to reevaluate and mitigate the risk and return trade-off in building their clients’ portfolios. The advancement of machine-learning algorithms and computing resources helps portfolio managers explore rich information by incorporating macroeconomic conditions into their investment strategies and optimizing their portfolio performance in a timely manner. In this paper, we present a simulation-based approach by fusing a number of macroeconomic factors using Neural Networks (NN) to build an Economic Factor-based Predictive Model (EFPM). Then, we combine it with the Copula-GARCH simulation model and the Mean-Conditional Value at Risk (Mean-CVaR) framework to derive an optimal portfolio comprised of six index funds. Empirical tests on the resulting portfolio are conducted on an out-of-sample dataset utilizing a rolling-horizon approach. Finally, we compare its performance against three benchmark portfolios over a period of almost twelve years (01/2007–11/2019). The results indicate that the proposed EFPM-based asset allocation strategy outperforms the three alternatives on many common metrics, including annualized return, volatility, Sharpe ratio, maximum drawdown, and 99% CVaR.

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.

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