On stock market trading and portfolio optimization: A control systems perspective (T-2)

2009 ◽  
Author(s):  
James A. Primbs ◽  
B. Ross Barmish ◽  
Daniel E. Miller ◽  
Yuji Yamada
Keyword(s):  
Stock Market ◽  
Portfolio Optimization ◽  
Control Systems ◽  
Systems Perspective ◽  
Stock Market Trading

scholarly journals Artificial Intelligence Applied to Stock Market Trading: A Review

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 30898-30917 ◽  
Author(s):  
Fernando G. D. C. Ferreira ◽  
Amir H. Gandomi ◽  
Rodrigo T. N. Cardoso
Keyword(s):  
Artificial Intelligence ◽  
Stock Market ◽  
Stock Market Trading

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.

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

Single step estimation of ARMA roots for non-fundamental nonstationary fractional models

2022 ◽  
Author(s):  
Ignacio N Lobato ◽  
Carlos Velasco
Keyword(s):  
Stock Market ◽  
Strong Evidence ◽  
Long Memory ◽  
Trading Volume ◽  
Moving Average ◽  
Single Step ◽  
Arma Process ◽  
Dow Jones Industrial Average ◽  
Fractional Models ◽  
Stock Market Trading

Abstract We propose a single step estimator for the autoregressive and moving-average roots (without imposing causality or invertibility restrictions) of a nonstationary Fractional ARMA process. These estimators employ an efficient tapering procedure, which allows for a long memory component in the process, but avoid estimating the nonstationarity component, which can be stochastic and/or deterministic. After selecting automatically the order of the model, we robustly estimate the AR and MA roots for trading volume for the thirty stocks in the Dow Jones Industrial Average Index in the last decade. Two empirical results are found. First, there is strong evidence that stock market trading volume exhibits non-fundamentalness. Second, non-causality is more common than non-invertibility.

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