scholarly journals Application and Comparative Study of Optimization Algorithms in Financial Investment Portfolio Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuai Liu ◽  
Chenglin Xiao
Keyword(s):  
Genetic Algorithm ◽  
Comparative Study ◽  
Capital Markets ◽  
Asset Allocation ◽  
Optimization Algorithms ◽  
Economic Benefits ◽  
Investment Portfolio ◽  
Financial Investment ◽  
Financial Products ◽  
Financial Portfolio

Portfolio theory mainly studies how to optimize the allocation of assets under the premise of maximizing expected returns and minimizing investment risks. In view of the instability of the financial market, a diversified investment portfolio can help control the loss of the investment portfolio. In addition to paying attention to the safety and return of asset allocation, we cannot ignore the liquidity of assets, that is, their liquidity. Adding high-liquidity products to asset allocation, such as equity investment, can better control the financial cash flow in response to emergencies. One of the ways to make assets flow is to securitize assets and sell them to the market. In order to revitalize the stock assets, good investment efficiency is a necessary choice for financial investment. Various financial products and their derivatives continue to enter people’s vision. There are many financial products in reality, and optimizing the investment portfolio can bring high economic benefits. The purpose of this paper is to study the application of optimization algorithms in financial portfolio problems. (1) Monetary policy remains prudent and neutral. It is not easy to expect flooding, but flexibility is required in complex situations. (2) Financial resources are tilted towards innovation and transformation and capital markets, which is beneficial to the development of capital markets in the medium and long term. (3) Unblocking the transmission mechanism is conducive to lenient credit and tapping the wrong killing opportunities in private enterprise debt. (4) Banks and other financial institutions have moderate pressure to give benefits to entities, but in the long run, the interests of the two are consistent. (5) Finance risk prevention will continue, orderly breaking the rigid exchange and reshaping the financial structure and ecology. (6) The pace of opening up of the financial industry has accelerated, and the bond market investor structure has improved. In this paper, we establish different optimization schemes to compare and study the portfolio problem and then use MATLAB to solve the modeling and programming problem, calculate the highest return rate and the lowest risk value before and after optimization, and then make a comparative analysis to get a better optimization scheme. The results show that the genetic algorithm model is superior to the quadratic programming method in terms of risk control. The minimum risk of portfolio optimization through genetic algorithm has been reduced by about 40%, and the maximum return has increased by about 25%. The comprehensive optimization effect is better than the quadratic planning method and ultimately can obtain higher economic benefits. It can be seen that the optimization algorithm is of great significance for the comparative study of financial portfolio problems.

scholarly journals Riskmetrics™ Methodology in Assessment of Investment Risk on Capital Markets

2010 ◽  
Vol 9 (1) ◽  
pp. 34-51
Author(s):  
Grzegorz Mentel
Keyword(s):  
Capital Markets ◽  
Stock Exchange ◽  
Risk Measures ◽  
Market Risk ◽  
Investment Risk ◽  
Financial Instruments ◽  
Investment Portfolio ◽  
Warsaw Stock Exchange ◽  
Financial Portfolio

Riskmetrics™ Methodology in Assessment of Investment Risk on Capital Markets In the article the author has presented the methodology of assessment of market risk connected with investing in all sorts of financial instruments such as: shares, bonds and other derivatives, e.g. RiskGrade (RG). The measure has been introduced by RiskMetrics. The article presents the application of RiskGrades methodology while choosing the optimum investment portfolio for a Polish investor who invests in shares in the Warsaw Stock Exchange. Moreover, some other risk measures have been discussed which describe the efficiency of the optimum financial portfolio.

scholarly journals OPTIMAL CONTROL OF THE DECUMULATION OF A RETIREMENT PORTFOLIO WITH VARIABLE SPENDING AND DYNAMIC ASSET ALLOCATION

2021 ◽  
pp. 1-34
Author(s):  
Peter A. Forsyth ◽  
Kenneth R. Vetzal ◽  
Graham Westmacott
Keyword(s):  
Asset Allocation ◽  
Asset Returns ◽  
Retirement Savings ◽  
Allocation Strategy ◽  
Investment Portfolio ◽  
Dynamic Asset Allocation ◽  
Dynamic Strategy ◽  
Prior Literature ◽  
Weighted Combination ◽  
Over Time

Abstract We extend the Annually Recalculated Virtual Annuity (ARVA) spending rule for retirement savings decumulation (Waring and Siegel (2015) Financial Analysts Journal, 71(1), 91–107) to include a cap and a floor on withdrawals. With a minimum withdrawal constraint, the ARVA strategy runs the risk of depleting the investment portfolio. We determine the dynamic asset allocation strategy which maximizes a weighted combination of expected total withdrawals (EW) and expected shortfall (ES), defined as the average of the worst 5% of the outcomes of real terminal wealth. We compare the performance of our dynamic strategy to simpler alternatives which maintain constant asset allocation weights over time accompanied by either our same modified ARVA spending rule or withdrawals that are constant over time in real terms. Tests are carried out using both a parametric model of historical asset returns as well as bootstrap resampling of historical data. Consistent with previous literature that has used different measures of reward and risk than EW and ES, we find that allowing some variability in withdrawals leads to large improvements in efficiency. However, unlike the prior literature, we also demonstrate that further significant enhancements are possible through incorporating a dynamic asset allocation strategy rather than simply keeping asset allocation weights constant throughout retirement.

A new asset allocation technique to reduce financial portfolio risk

2011 ◽  
Vol 12 (6) ◽  
pp. 418-425
Author(s):  
Gino Gandolfi ◽  
Antonella Sabatini ◽  
Monica Rossolini
Keyword(s):  
Asset Allocation ◽  
Portfolio Risk ◽  
Financial Portfolio

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.

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