Stocks are one of the popular investment instruments traded in the capital market. The popularity of stock purchase has developed along with the massive financial literacy movement. However, the ...massiveness of this movement must be balanced with knowledge and expertise in managing stock instruments since they have a high return rate and high risk. One way to manage stocks is by developing an optimal stock portfolio based on the Markowitz model. The Markowitz model is a method that formulates the elements of return and risk in an investment, and specifically the elements of risk can be minimized through diversification and combination of various investment instruments into a portfolio. By using the Markowitz method, investors can take advantage of all available information as a basis for maximizing the portfolio. This study aimed to determine which stocks can form an optimal portfolio, especially in the era of the COVID 19 pandemic and the optimal proportion of the portfolio that is feasible to obtain from stocks listed in the LQ 45 Index. The study samples involved stocks listed in the LQ 45 Index. The data analysis technique applied in this study was portfolio optimization using the Markowitz model. The results of this study showed that the optimal portfolio consisted of BBCA with a weight of 78.09% and BRPT with a weight of 21.91% which produced an expected return of 2.35% and a standard deviation of 7.01%.
Sparse portfolio selection via the sorted ℓ1-Norm Kremer, Philipp J.; Lee, Sangkyun; Bogdan, Małgorzata ...
Journal of banking & finance,
January 2020, 2020-01-00, 2020, Volume:
110
Journal Article
Peer reviewed
We introduce a financial portfolio optimization framework that allows to automatically select the relevant assets and estimate their weights by relying on a sorted ℓ1-Norm penalization, henceforth ...SLOPE. To solve the optimization problem, we develop a new efficient algorithm, based on the Alternating Direction Method of Multipliers. SLOPE is able to group constituents with similar correlation properties, and with the same underlying risk factor exposures. Depending on the choice of the penalty sequence, our approach can span the entire set of optimal portfolios on the risk-diversification frontier, from minimum variance to the equally weighted. Our empirical analysis shows that SLOPE yields optimal portfolios with good out-of-sample risk and return performance properties, by reducing the overall turnover, through more stable asset weight estimates. Moreover, using the automatic grouping property of SLOPE, new portfolio strategies, such as sparse equally weighted portfolios, can be developed to exploit the data-driven detected similarities across assets.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The Markowitz model, a Nobel Prize winning model for portfolio analysis, paves the theoretical foundation in finance for modern investment. However, it remains a challenging problem in the high ...frequency trading (HFT) era to find a more time efficient solution for portfolio analysis, especially when considering circumstances with the dynamic fluctuation of stock prices and the desire to pursue contradictory objectives for less risk but more return. In this paper, we establish a recurrent neural network model to address this challenging problem in runtime. Rigorous theoretical analysis on the convergence and the optimality of portfolio optimization are presented. Numerical experiments are conducted based on real data from Dow Jones Industrial Average (DJIA) components and the results reveal that the proposed solution is superior to DJIA index in terms of higher investment returns and lower risks.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In this paper, a generalized Markowitz model, which is a convex kurtosis minimization under mean and variance constraints, is proposed. It has a close relationship to the classical Markowitz model. ...The corresponding optimization problem is a convex quartic polynomial minimization problem with linear and quadratic constraints, which differs from the non-convex polynomial optimization models in the literature. A numerical method with an alternating minimization framework is proposed to solve this quartic optimization problem to a global minimizer, whose global convergence is established under a mild assumption. With a careful separation of the nonlinear constraints in the alternating iterations, two subproblems are solved respectively by accelerated proximal gradient method and semidefinite relaxation method efficiently. Moreover, it is shown that the semidefinite relaxation is tight in this situation without any further assumption. A real data from eight stock indices and some synthetic data are tested for the feasibility of this model and the proposed algorithm, whose performance is quite promising.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In this paper a data mining approach for classification of stocks into clusters is presented. After classification, the stocks could be selected from these groups for building a portfolio. It meets ...the criterion of minimizing the risk by diversification of a portfolio. The clustering approach categorizes stocks on certain investment criteria. We have used stock returns at different times along with their valuation ratios from the stocks of Bombay Stock Exchange for the fiscal year 2007–2008. Results of our analysis show that
K-means cluster analysis builds the most compact clusters as compared to SOM and Fuzzy
C-means for stock classification data. We then select stocks from the clusters to build a portfolio, minimizing portfolio risk and compare the returns with that of the benchmark index, i.e. Sensex.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
This paper proposes a transformation of the portfolio selection problem into SAT. SAT was the first problem to be shown tobe NP-complete, and has been widely investigated ever since. We derive the ...SAT instances from the Portfolio Selection onesusing the concept of cover, and reduce their size via established reduction techniques. The resulting instances are based onthe use of variance as the main risk measure, and are solved via both a standard SAT solver and an adaptive genetic algorithm.Results show that adaptive genetic algorithms are effective in solving these variance-based instances. Further work will bedevoted to investigate other SAT formulations based on different risk measures.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
High-frequency trading proposes new challenges to classical portfolio selection problems. Especially, the timely and accurate solution of portfolios is highly demanded in financial market nowadays. ...This article makes progress along this direction by proposing novel neural networks with softmax equalization to address the problem. To the best of our knowledge, this is the first time that softmax technique is used to deal with equation constraints in portfolio selections. Theoretical analysis shows that the proposed method is globally convergent to the optimum of the optimization formulation of portfolio selection. Experiments based on real stock data verify the effectiveness of the proposed solution. It is worth mentioning that the two proposed models achieve <inline-formula> <tex-math notation="LaTeX">5.50</tex-math> </inline-formula>% and <inline-formula> <tex-math notation="LaTeX">5.47</tex-math> </inline-formula>% less cost, respectively, than the solution obtained by using MATLAB dedicated solvers, which demonstrates the superiority of the proposed strategies.
One of the problems investors often face is deciding on the stocks to include in an investment portfolio. Hence, this article seeks to select investment portfolios considering the 30 leading ...companies listed on the New York Stock Exchange (NYSE) of the Dow Jones Index. Portfolio selection in this index is carried out by generating a previous ranking of the shares with a novel approach that analyzes their performance using a multiple criteria hierarchical process (MCHP). The current research allows the evaluation of shares and optimizing a portfolio, while developing a procedure that supports a decision-making process for organizations or practitioners to invest in the stock market. The results of analyzing the NYSE generated a portfolio that can be used for interested investors. The main contribution of the application of MCHP and Markowitz model is related to the possibility of replicating the developed procedure to select portfolios in other stock markets.
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BFBNIB, CEKLJ, NMLJ, NUK, PNG, UL, UM, UPUK
•We develop a long- and a short-term strategy for optimal portfolio and provide a comparison between them.•We consider a market characterized by the presence of thin stocks.•We build a mixed ...continuous/discrete time model.•We provide extensive Monte Carlo experiments to derive insights under the financial perspective.
This paper deals with a mean–variance optimal portfolio selection problem in presence of risky assets characterized by low-frequency trading and, therefore, low liquidity. To model the dynamics of illiquid assets, we introduce pure-jump processes. This leads to the development of a portfolio selection model in a mixed discrete/continuous time setting. We pursue the twofold scope of analyzing and comparing either long-term investment strategies as well as short-term trading rules. The theoretical model is analyzed by applying extensive Monte Carlo experiments, in order to provide useful insights from a financial perspective.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Asset return is associated with uncertainty and always occurs during unexpected fluctuations in economic, social and political conditions, and so forth. In return on assets such as stocks. Fuzzy ...logic can be one of the best options for modelling asset returns. For this purpose, a rule based fuzzy expert system has been developed to support investment managers in their mid term investment decisions. Considering the non linearity of the portfolio selection problem and its NP Hard, the performance of the proposed fuzzy system is evaluated by the information of 157 companies that have been active in Tehran Stock Exchange between 2008 to 2018 using of Fuzzy Genetic and Fuzzy Hunting Search Algorithms. The system's performance in terms of risk taking and duration of investment was comparable to average market returns. Besides, the performance of the proposed fuzzy system for the risk averse investor in the short run yields good results.
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