CCMV Portfolio Optimization with Stocks and Options using Forecasted Data

Although the future of a financial market is ambiguous and mysterious, historical data play a key role to forecast the future of the market. Along with all the advantages of these data, they may result to some errors and consequently, some losses. In this paper, we consider the cardinality constraints mean-variance (CCMV) portfolio optimization model in the presence of short selling, risk-neutral interest rate and transaction costs. We insure the investment using options against unfavorable outcomes. The Geometric Brownian Motion model is utilized to forecast the stocks prices. Also, to improve the results, we calibrate its parameters using historical data by the maximum likelihood estimation method. We perform numerical experiments using historical and forecasted data on the S&P 500 index, to assess the efficiency of the GBM model in forecasting stocks prices. Also, to examine the effect of options in the portfolio, we compare the portfolio with stocks only versus the portfolio with stocks and options using historical and forecasted data in terms of returns and Sharpe ratios.

Portfolio optimization model with uncertain returns based on prospect theory

When investing in new stocks, it is difficult to predict returns and risks in a general way without the support of historical data. Therefore, a portfolio optimization model with an uncertain rate of return is proposed. On this basis, prospect theory is used for reference, and then the uncertain return portfolio optimization model is established from the perspective of expected utility maximization. An improved gray wolf optimization (GWO) algorithm is designed because of the complex nonsmooth and nonconcave characteristics of the model. The results show that the GWO algorithm is superior to the traditional particle swarm optimization algorithm and genetic algorithm.

Artificial Intelligence Approach to Secure Pension Fund

Pension fund needs to produce a high-income return to face actuarial expectations of different kinds of benefits. An asset allocation management model of a pension fund must consider a large planning horizon because of its long-term obligations. Asset allocation controls the solvency of the fund by suitable investments and contribution policies to secure the pensioner’s future liabilities. Artificial intelligence approaches given by experts and accepted by decision-makers, provide a powerful tool for describing uncertainty. A portfolio optimization model is introduced based on variance minimization at a required return level that secures the fund against insolvency risk. This method uses an artificial Bee ColonyOptimizationApproach to the mean-variance defined by Markowitz so that future returns of the stocks are predicted where the ability of AI to improve predictive and prescriptive financial forecasting processes will change the world of finance management.

Prosumer Community Portfolio Optimization via Aggregator: The Case of the Iberian Electricity Market and Portuguese Retail Market

The participation of household prosumers in wholesale electricity markets is very limited, considering the minimum participation limit imposed by most market participation rules. The generation capacity of households has been increasing since the installation of distributed generation from renewable sources in their facilities brings advantages for themselves and the system. Due to the growth of self-consumption, network operators have been putting aside the purchase of electricity from households, and there has been a reduction in the price of these transactions. This paper proposes an innovative model that uses the aggregation of households to reach the minimum limits of electricity volume needed to participate in the wholesale market. In this way, the Aggregator represents the community of households in market sales and purchases. An electricity transactions portfolio optimization model is proposed to enable the Aggregator reaching the decisions on which markets to participate to maximize the market negotiation outcomes, considering the day-ahead market, intra-day market, and retail market. A case study is presented, considering the Iberian wholesale electricity market and the Portuguese retail market. A community of 50 prosumers equipped with photovoltaic generators and individual storage systems is used to carry out the experiments. A cost reduction of 6–11% is achieved when the community of households buys and sells electricity in the wholesale market through the Aggregator.

Scenario generation by selection from historical data

In this paper, we present and compare several methods for generating scenarios for stochastic-programming models by direct selection from historical data. The methods range from standard sampling and k-means, through iterative sampling-based selection methods, to a new moment-based optimization approach. We compare the models on a simple portfolio-optimization model and show how to use them in a situation when we are selecting whole sequences from the data, instead of single data points.

A Personalized Mean-CVaR Portfolio Optimization Model for Individual Investment

Investment as an important issue in daily life is accompanied by the occurrence of various financial assets, such as stocks, bonds, and mutual funds. However, risk tolerances vary across individuals. Individual investors have to select corresponding personalized investment portfolios to satisfy their own needs. Moreover, it is difficult for ordinary people to select a personalized investment portfolio by themselves, and it is too expensive and inefficient to look for professional consultation. Therefore, the objective of this research is to propose a personalized portfolio recommendation model, which can build the personalized portfolio based on investors’ risk tolerances. In this research, investors’ risk tolerance is determined by the fuzzy comprehensive evaluation method based on investors’ demographic characteristics. The CVaR is used as the risk measurement of financial assets. The dynamics of the distribution of returns are described in the combined Copula-GARCH model, and the future scenarios of returns are generated by the Monte Carlo simulation based on the combined Copula-GARCH model to estimate CVaR. The mean-CVaR portfolio optimization model is used to find out the best personalized portfolio. Finally, experiments are conducted to validate the applicability and feasibility of the personalized investment portfolio optimization model. Results show that the proposed investment portfolio optimization model can recommend personalized investment portfolio according to investor’s risk tolerance.

Portfolio Optimization Using an Alternative Approach. Towards Data Mining Techniques Way

In the light of the current budget constraints, the investors face a challenge when building their stock portfolios that should lead to a minimized risk for an expected level of return. Mathematical tools have become essential for portfolio theory formulation in the last decades. In this article, our main objective is to illustrate the utility of some data mining tools and techniques, with a focus on principal components analysis and cluster analysis. The case study reveals a comparative empirical results analysis of the classical Markowitz portfolio optimization model and a combined data mining techniques model. The results show how useful data mining techniques can be for the finance area, with positive implications for investors’ strategy design and implementation. Our study reveals that stocks selection requires the use of modern techniques that take in account the multidimensional perspective of investment decision. Henceforth, we propose that a debate should be launched concerning the design of stock markets design, which generally focus on simple design oriented to the stocks liquidity. In order to help investors, those indices should combine multiple dimensions of stocks definition, as the return, the risk and the liquidity of a stock are at least of the same importance from an investment decision perspective.