Financial Portfolio Selection Research Articles

Two-stage stock portfolio optimization based on AI-powered price prediction and mean-CVaR models

With the advancement of prediction methods in the field of artificial intelligence, accurate price predictions can effectively support financial portfolio selection. This paper proposes an intelligent stock portfolio selection method based on a prediction neural network, incorporating signal processing and hyperparameter optimization techniques. The method is divided into two key stages: stock price prediction and portfolio selection. In the first stage, we apply Savitzky–Golay filtering to denoise price data and reveal its patterns, and optimize the hyperparameters of the long short-term memory network using the sparrow search algorithm to achieve high-precision stock price predictions. In the second stage, we use the mean-Conditional Value-at-Risk (mean-CVaR) model to select the optimal stock allocation, considering factors such as potential returns, prediction accuracy, and growth rate. Numerical comparisons based on multiple public financial datasets demonstrate that the proposed two-stage method significantly outperforms seven benchmark methods. Specifically, on the Shanghai and Shenzhen 300 (CSI 300) Index dataset, the proposed method achieves a determination coefficient of 0.9980 and an accuracy rate of 97.05%. Additionally, its cumulative returns reach 9.38%, 8.63%, and 7.54% at different confidence levels.

Unleashing the Power of Goal Programming: A Comprehensive Literature Review on Optimal Financial Portfolio Selection

Unleashing the Power of Goal Programming: A Comprehensive Literature Review on Optimal Financial Portfolio Selection

A multi-objective sustainable financial portfolio selection approach under an intuitionistic fuzzy framework

In recent decades, sustainable investing has caught on with investors, and it has now become the norm. In the age of start-ups, with scant information on the sustainability aspects of an asset, it becomes harder to pursue sustainable investing. To this end, this paper proposes a sustainable financial portfolio selection approach in an intuitionistic fuzzy framework. We present a comprehensive three-stage methodology in which the assets under consideration are ethically screened in Stage-I. Stage-II is concerned with calculating the sustainability scores, based on various social, environmental, and economic (SEE) criteria and an evaluation of the return and risk of the ethical assets. Intuitionistic fuzzy set theory is used to gauge the linguistic assessment of the assets on several SEE criteria from multiple decision-makers. A novel intuitionistic fuzzy multi-criteria group decision-making technique is applied to calculate the sustainability score of each asset. Finally, in Stage-III, an intuitionistic fuzzy multi-objective financial portfolio selection model is developed with maximization of the satisfaction degrees of the sustainability score, return, and risk of the portfolio, subject to several constraints. The ϵ-constraint method is used to solve this model, which yields various efficient, sustainable financial portfolios. Subsequently, investors can choose the portfolio best suited to their preferences from this pool of efficient, sustainable financial portfolios. A detailed empirical illustration and a comparison with existing works are given to substantiate and validate the proposed approach.

Tail risk adjusted clean energy portfolios in P2P transactive markets using Rachev ratio

AbstractRenewable energy sources (RES) and electric vehicles (EVs) pose ‘energy‐risk’ in peer energy commitments due to their temporal and spatial uncertainties. Thus, optimistic commitments in the peer‐to‐peer transactive energy market (P2P TEM) are improbable. This paper proposes a two‐stage master–slave portfolio optimization approach for combining energy‐risk of RES and EVs, and welfare‐risk of peers, in building clean energy portfolios. The master portfolio (MP) refers to the shares of renewable and EVs in P2P market settlement, whereas the slave portfolio (SP) gives the wind‐solar mix within renewables. Here, Rachev Ratio (RR), an index used in financial portfolio selection for tail‐risk management, is adapted and combined with Markowitz Efficient Frontier (EF) to find the optimal slave portfolio. Both the extreme tails are optimized, encouraging energy outputs far above forecast and discouraging those far below forecast. The master portfolio is obtained by maximizing the sum of the average welfare of the peers at the best (right) and worst (left) tails of the welfare distribution curve using Stochastic Weight Trade‐off Particle Swarm Optimization (SWT‐PSO). The proposed portfolio selection approach is better in terms of increased expected energy output, improved utilization of RES and EVs, and better collective peer welfare.

Empirical Analyses of OLMAR Method for Financial Portfolio Selection in Stock Markets

The OLMAR method, which stands for the on-line moving average reversion method, is reported to be one of the most powerful among portfolio selection algorithms in the stock markets. In this research, we use intensive statistical and simulation analyses of long-term data on stock market changes to uncover the secrets of why and when the superiority appears. We find that there have been long-lasting fluctuations in the stock markets and that the OLMAR method actively makes use of such characteristics. In this paper, we analyze long-term stock data from Japan and the United States. The analyses confirm the following points. 1) The OLMAR method yields superior returns. 2) By using the moving average divergence rate provided by the OLMAR method, it is possible to detect specific fluctuation characteristics in long-term stock data from Japan and the United States. 3) Superior returns cannot be obtained from data in which specific fluctuation characteristics have been corrected.

Maximum entropy approach to multivariate time series randomization

Natural and social multivariate systems are commonly studied through sets of simultaneous and time-spaced measurements of the observables that drive their dynamics, i.e., through sets of time series. Typically, this is done via hypothesis testing: the statistical properties of the empirical time series are tested against those expected under a suitable null hypothesis. This is a very challenging task in complex interacting systems, where statistical stability is often poor due to lack of stationarity and ergodicity. Here, we describe an unsupervised, data-driven framework to perform hypothesis testing in such situations. This consists of a statistical mechanical approach—analogous to the configuration model for networked systems—for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series. We showcase its possible applications with a case study on financial portfolio selection.

Goal programming for financial portfolio management: a state-of-the-art review

Over the last decades, the Goal Programming (GP) model has been applied to financial portfolio management and/or selection problem in decision-making contexts where several conflicting and incommensurable objectives are simultaneously aggregated. The aim of this paper is to identify the research trends and publication outlets for the application of GP model to portfolio management. We point out an increasing interest and affirmation of more sophisticated models. We present a characterization of the existing GP variants and provide historical data and statistical analysis.

Preservation of weak stochastic arrangement increasing under fixed time left-censoring

Both WSAI and CLOAI properties of a random vector are proved to be preserved under left-censoring at fixed times. Applications in threshold default model of financial portfolio selection and system warranty cost allocation are presented as well.

Large-sample approximations for variance-covariance matrices of high-dimensional time series

Distributional approximations of (bi--) linear functions of sample variance-covariance matrices play a critical role to analyze vector time series, as they are needed for various purposes, especially to draw inference on the dependence structure in terms of second moments and to analyze projections onto lower dimensional spaces as those generated by principal components. This particularly applies to the high-dimensional case, where the dimension $d$ is allowed to grow with the sample size $n$ and may even be larger than $n$. We establish large-sample approximations for such bilinear forms related to the sample variance-covariance matrix of a high-dimensional vector time series in terms of strong approximations by Brownian motions. The results cover weakly dependent as well as many long-range dependent linear processes and are valid for uniformly $ \ell_1 $-bounded projection vectors, which arise, either naturally or by construction, in many statistical problems extensively studied for high-dimensional series. Among those problems are sparse financial portfolio selection, sparse principal components, the LASSO, shrinkage estimation and change-point analysis for high--dimensional time series, which matter for the analysis of big data and are discussed in greater detail.

Research on regularized mean-variance portfolio selection strategy with modified Roy safety-first principle.

We propose a consolidated risk measure based on variance and the safety-first principle in a mean-risk portfolio optimization framework. The safety-first principle to financial portfolio selection strategy is modified and improved. Our proposed models are subjected to norm regularization to seek near-optimal stable and sparse portfolios. We compare the cumulative wealth of our preferred proposed model to a benchmark, S&P 500 index for the same period. Our proposed portfolio strategies have better out-of-sample performance than the selected alternative portfolio rules in literature and control the downside risk of the portfolio returns.

Fuzzy chance-constrained goal programming model for multi-attribute financial portfolio selection

The aim of this paper is to propose a fuzzy chance constrained goal programming model for solving a multi-attribute financial portfolio selection problem under two types of uncertainty namely randomness and fuzziness. The chance-constrained goals are considered as random variables. The obtained portfolio through this model is the portfolio of the best compromise where the financial decision-maker was asked to make tradeoffs among conflicting and incommensurable attributes such as the expected return, risk and the earning price ratio. The proposed model has been applied to the Tunisian stock exchange market for the period July 2003 to December 2007.

Financial portfolio management through the goal programming model: Current state-of-the-art

Since Markowitz (1952) formulated the portfolio selection problem, many researchers have developed models aggregating simultaneously several conflicting attributes such as: the return on investment, risk and liquidity. The portfolio manager generally seeks the best combination of stocks/assets that meets his/her investment objectives. The Goal Programming (GP) model is widely applied to finance and portfolio management. The aim of this paper is to present the different variants of the GP model that have been applied to the financial portfolio selection problem from the 1970s to nowadays.

Financial Portfolio Selection using the Multifactor Capital Asset Pricing Model and Imported Options Data

Diversification and portfolio selection are integral parts of a finance curriculum. In this article, a multifactor capital asset pricing model is fit for components of the Dow Jones Composite Index using data from Yahoo! Finance. Along with the capital asset pricing model's Beta, other statistics that are common criteria for portfolio selection are calculated: historic standard deviation (total risk), total return, average daily return, and Sharpe and Treynor measures. Two new commands are introduced, fetchcomponents and fetchportfolio, that automate the entire process. A third new command, fetchyahoooptions, is provided to download and parse equity options data from Yahoo! Finance webpages and, optionally, to calculate the implied volatilities for the downloaded options.

Comments on: Multicriteria decision systems for financial problems

The authors present quite a complete and exhaustive literature review of the existing models and the most recent developments related to the financial portfolio selection and the corporate performance evaluation. They showed how different MultiCriteria Decision Aid (MCDA) techniques have been applied to the field of Financial Management and Multi-Attribute Financial Portfolio Selection. The authors highlighted the contributions of the MCDA to the financial decision-making process. They made a good link between Finance and Operational Research disciplines. The survey discusses some interesting issues and challenges that the researcher within multiattributes financial portfolio management may face and should address.

Different Probability Distributions for Portfolio Selection in the Chance Constrained Compromise Programming Model

The Chance Constrained Compromise Programming model has been utilized for the multi-attributes financial portfolio selection where several conflicting and incommensurable objectives are optimized simultaneously. This model is based on the assumption that the aspiration levels of the objectives are normally distributed with known means and variances. The aim of this paper is to extend this model to other probability distributions such as uniform, triangular and skew triangular. The different formulations will be illustrated through twenty five securities from the Toronto stock exchange market.

Mean-Variance Based QoS Management in Cognitive Radio

Guaranteeing Quality of Service (QoS) in Cognitive Radio (CR) networks is a challenging task due to the random nature of radio channel conditions and primary user traffic. In this paper we analyze the recently proposed mean-variance based QoS and resource management methods, and introduce the concept of mean-variance evaluation of QoS and resource management techniques. Inspired by financial Portfolio Selection theory, mean-variance based resource management techniques for CR networks are statistical approaches and do not require instantaneous knowledge of channel state and Primary User (PU) activity, and enable the option to tradeoff between risk (QoS variance) and reward (QoS mean). Using throughput as a measure of QoS, we present a derivation of the theoretical throughput mean-variance characteristics of a CR-OFDM system employing a mean-variance based QoS management strategy. We conduct an analysis of existing Portfolio Selection based strategies in the mean-variance domain and compare them to the theoretical performance. Finally, we present a further enhancement of the approach by explicitly considering constraints on individual channel power allocation in the mean-variance optimization problem. Simulation results illustrate the effect of our enhancements in improving the risk-reward profile of the mean-variance based QoS management strategy, by moving it closer to the theoretical characteristic.

Application of fuzzy measures and interval computation to financial portfolio selection

As many data-driven fields, finance is rich in problems requiring high computational power and intelligent systems techniques. In particular, the problem of selecting an optimal financial portfolio can be conveniently represented as a constrained optimization problem or a decision-making problem. The aim of this paper is to show how to express the optimal portfolio selection problem from a decision-theoretic perspective and show how to address this problem using fuzzy measures and fuzzy integrals. © 2010 Wiley Periodicals, Inc.

A CONVEX STOCHASTIC OPTIMIZATION PROBLEM ARISING FROM PORTFOLIO SELECTION

A continuous‐time financial portfolio selection model with expected utility maximization typically boils down to solving a (static) convex stochastic optimization problem in terms of the terminal wealth, with a budget constraint. In literature the latter is solved by assuming a priori that the problem is well‐posed (i.e., the supremum value is finite) and a Lagrange multiplier exists (and as a consequence the optimal solution is attainable). In this paper it is first shown that, via various counter‐examples, neither of these two assumptions needs to hold, and an optimal solution does not necessarily exist. These anomalies in turn have important interpretations in and impacts on the portfolio selection modeling and solutions. Relations among the non‐existence of the Lagrange multiplier, the ill‐posedness of the problem, and the non‐attainability of an optimal solution are then investigated. Finally, explicit and easily verifiable conditions are derived which lead to finding the unique optimal solution.