Optimal portfolio

Daily price limits are adopted by many securities exchanges in countries such as the USA, Canada, Japan and various other countries in Europe and Asia, in order to increase the stability of the financial market. These limits confine the price of the financial asset during all trading stages of any trading day to a range, usually determined based on the previous day’s closing price. In this paper we study the portfolio optimization problem with impose the price limit constraint. The dynamic programming technique is applied to derive the Hamilton–Jacobi–Bellman equation and the method of Lagrange multiplier is used to tackle the constraint. Optimization problem solution results, using numerical method show that the equilibrium path of wealth and investment in risky assets has a different way than in the absence of price limits.

One of the most important aspects of investment is determining the “optimal investment portfolio”. To date, scientific research has been conducted to determine the optimal portfolio with “artificial intelligence” and “Fuzzy logic”. However, we determine the optimal portfolio based on Dynamic Stochastic General Equilibrium (DSGE) model. On the other hand, several factors affect returns, which is one of the most important issues in investment decision-makings, and various models have been developed to analyze the return of “capital” and “other assets”. In this regard, some of the most important models include linear and non-linear models, artificial neural network models, Fama–French model, Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, and optimal stable model. All of these models are indicative of the use of quantitative methods and models in the investment industry. One of the causes of using these models is developing the financial economy. In fact, the development of financial economy has more increased with the concept of “portfolio optimization”. In fact, the portfolio optimization and diversification concept is the basis of developing and expanding classical finance and financial decision-making. Financial markets, especially the capital market, can have a great deal of relevance to other sectors of the economy. In the present study, we design and calibrate a new Keynesian DSGE model in relation to the optimal investment portfolio and effectiveness of shocks (e.g., productivity and foreign exchange earnings fluctuation) on macroeconomics variables.

In this study, we examine the correlation between stock returns of Export-oriented (EOIs) and Import-oriented (IOIs) industries and exchange rates, to derive stock-exchange optimal weights, attempting to manage the risk of investors in the capital market. To do so, the ADCC and DCC models are used. The data consists of the stock return of the listed industries, and the daily exchange rate from 2008 to 2020. The results suggest that EOIs have a dynamic asymmetric conditional correlation, and IOIs have a dynamic symmetric conditional correlation with the exchange rate. Moreover, the results indicate that in both currency crises, the weight of optimal portfolio in all industries except pharmaceuticals, in non-crisis period is over 50% and in the crisis period is less than 50%. Accordingly, and to reduce the risk of the portfolio, in the non-crisis period, investors should invest more than half of a one-Rial portfolio to dollar exchange, and in the crisis period, they should allocate less than half of a one-Rial portfolio to this currency. In case of the currency crisis, it is suggested that investors invest in the stock of basic metals, because this industry is a pioneer in attracting currency crisis and increasing stock value of the industry through future cash flow and replacement value, and reduce the stock of pharmaceuticals and computers in their portfolio, due to attracting negative effects of the exchange market.

In this paper, we use fuzzy numbers and possibility theory to model possibility. The purpose of this work is to determine the optimal investment model based on the neural network method for fuzzy LR, trapezoidal and triangular numbers in an optimal portfolio. It is listed on the Tehran Stock Exchange to maximize "returns" and reduce "risk" to find the optimal portfolio. Therefore, to achieve this goal, the problem of multi-objective nonlinear programming is addressed. Also, by substituting the mean-variance model and the standard mean deviation instead of the Markowitz mean-variance model, the selection of the optimal portfolio in the possible space is examined. Finally, after calculating the model of the possibility of fuzzy numbers, we reach the optimal stock portfolio, which can be used to set the stock portfolio that has the highest returns and the lowest risk.

Project portfolio management is a comprehensive framework for decision making and selecting the portfolio of projects to achieve the goals of the organization by considering resource constraints. The importance of this issue in Iran's oil and gas industry is even more remarkable than ever due to its unique position in the country's economy, capital-intensive and capital budget constraints that have been intensified in recent years. Identifying and defining different scenarios for each oil and gas field, determining the parameters of the mathematical model, the required data to calculate the parameters of the model and the process and methods of identifying this data, indicate the distinction and necessity of this research. This study is an applied research in terms of objective, using mathematical modeling approach, has provided a pattern to determine the optimal portfolio of capital plans of oil and gas companies. The research method is case study which has studied one of the most important oil and gas producing companies in the country and the only offshore company. In this study, a framework for selecting the optimal portfolio of capital projects is determined and after gathering required data, the zero-one integer linear mathematical programming model with the objective function of maximizing the net present value from fields (as the strategic goal of company) by considering investment constraints was designed and solved by GAMS software. Finally, according to the defined constraint, the best investment mode for each field was identified and the optimal portfolio was defined.

Existing models do not adequately capture how changes in the external environment (systematic risk) affect corporate returns. This study addresses this gap by identifying explanatory variables and an experimental model design. The sample includes 16 investment companies over two periods, 2006-1 and 2020-4. We inputted 69 systematic risk variables into the model and identified the 1-12 non-fragile variables affecting investment company weighted averages using a Bayesian model averaging approach. The findings show that the non-official hard currency exchange rate is the most robust variable influencing the Tehran Stock Exchange. Thus, stocks with the highest correlation to the foreign exchange rate should be selected when forming a portfolio. Moreover, fiscal policy variables directly impact investment company weighted average returns. Consequently, portfolios of quasi/semi-government-owned companies will see higher return fluctuations.