Investment Portfolio Selection Research Articles

Prediction of Stock Price Volatility Using the Long Short Term Memory (LSTM) Model for Investment Portfolio Selection Strategy

Volatility is an important variable in financial data models. Predicting volatility in financial data is helpful for investors to make good decisions to reduce risk and to gain investment returns. In predicting volatility, many researchers have conducted research in building prediction models using data mining. This research uses a deep learning algorithm, namely Long Short Term Memory (LSTM) which has high accuracy compared to other models. The research aims to predict stock price volatility and for investment portfolio selection. The object of this study is the historical stock price of PT. Unilever Indonesia Tbk. (UNVR), PT. Fast Food Indonesia Tbk. (FAST) which manages KFC and PT. MAP Boga Adiperkasa Tbk. (MAPB) which manages Starbucks in the period 2023 to 2024, when there was a boycott caused by the war between countries that occurred in the Middle East. The data is analysed using the LSTM model where stock price volatility was determined by the variance of the return and log return on the next seven days, then using LSTM the stock price volatility data was predicted. The results show that the MSE and RMSE values are very small, which means that the volatility prediction results are almost the same as the actual data. And the average volatility prediction results in UNVR stock of 0.00841, MAPB stock of 0.01717, and FAST stock of 0.01323. From these results can be used as a reference for the selection of investment portfolios.

A comparative study of fuzzy multi-objective investment project portfolio selection and optimization based on financial return and different risk measurements

Competitiveness in the global market is getting more intense. Due to resource and budget constraints, firms need to achieve their expected goals and satisfy all investment constraints under uncertainty. Selecting the set of projects among other candidates to get the most efficient portfolio requires a lot of attention from the Decision Makers (DMs) as this consideration no longer relies purely on the financial term. This problem becomes a multi-objective problem under uncertainty where the financial return and risk from uncertainty are required into the trading off consideration. Due to the financial uncertainty, the chance-constrained programming has been employed in this study for defuzzifying and solving uncertain optimization problems at a specified confidence level that is defined by the DMs. Then, various kinds of investment or financial risk measures, Lower-Semi Variance Index (LSVI), the absolute deviation with the expected FNPV, and the absolute mean-Conditional Value at Risk (CVaR) gap are provided in the selection of such risk measures to show their differences in characteristics and performances in the obtained results. Since, such problems can consist of many project candidates and complex constraints, which may grow beyond the application of the exact optimization approach, a meta-heuristic method, Genetic Algorithm (GA), is introduced to optimize this problem through designing and constructing a decision support tool for the investment portfolio selection and optimization. The applicability of the proposed comparative approach and the constructed tool are illustrated through examples.

Research on the Optimal Strategy of Investment Portfolio Based on Markowitz Model

Nowadays, investment is becoming increasingly common. Under the globalization of the economy, investors are given more investment opportunities and choices. Investors need to select excellent assets and allocate the selected asset portfolio on weight during the investment process. The mean-variance model proposed by Markowitz plays an important guiding role in investment and risk management. This model can effectively evaluate investors portfolio risk and return decisions and significantly impact their decision-making choices. This study uses the data of the annual average return and variance from 11 risky assets and 1 risk-free asset in about 20 years to construct an investment portfolio in a fixed group based on the Markowitz Model. The study calculates the changes in the allocation of the maximum Sharpe ratio and the minimum variance portfolios under three different constraints and one condition with the addition of risk-free assets, respectively, and then analyzes changes in the outcome of data. The capital allocation lines are introduced to analyze the outcome of an investment portfolio with risk-free assets. Compared with the changes from three constraints, reasons are explained why these changes happen under three constraints. Then, the above limitations are taken as a premise, and the study proposes recommendations for the optimal investment portfolio selection for different investors according to the investment cycle, the preference of investors based on the situation of different investors, and market constraints. The study guides investors future investment decisions.

Population intensity, location choice, and investment portfolio selection: A case of emerging economies

Rapid population growth is often considered a barrier to economic advancement and strategic corporate investment. However, the nuances of this impact remain insufficiently explored. This study aims to explore the impact of population density and location choice on firms' investment decisions, elucidating how these elements shape enterprises' portfolio selections. To investigate this impact, data spanning ten years (2010–2019) from non-financial sector firms in China and Pakistan were scrutinized. Utilizing 2SLS and GMM models, we measured the external influence of these factors on both long-term and short-term investment strategies. The statistical analysis reveals that corporate firms originating from countries with high population growth rates, situated in densely populated areas, exhibit a predilection for long-term investments. This favorable trend can be attributed to heightened product demand and convenient access to local markets. Nevertheless, a comparative assessment between Pakistan and China delineates contrasting effects of location on short-term investments—positively impacting in the case of Pakistan and negatively affecting in the case of China. These findings carry significant implications for crafting regional development policies aimed at fostering industrial growth. Furthermore, beyond conventional determinants, this study introduces fresh perspectives on the role of demographic and geographic factors in shaping industrial investments, addressing a gap in existing literature where similar analyses are absent.

Analysis of a portfolio selection model at three-moments

This work performs a numerical simulation for an investment portfolio selection model that considers the three first moments of asset returns distribution – mean return, variance, and skewness. The application of the model, based on data collected on the platform of a Brazilian stockbroker, allowed obtaining portfolios of maximum skewness for fixed values of expected return and weighted variance of the portfolio. The results are analyzed and presented graphically, giving rise to an optimal surface for triples of moments associated with portfolios of maximum skewness. Furthermore, this experiment allowed us to confirm the relevance of considering higher-order moments in the selection of investment portfolios and verifying the efficiency of the Three-Moments model, having as reference the Markowitz solution in his Mean-Variance model.

ChatGPT-Based Investment Portfolio Selection

ChatGPT-Based Investment Portfolio Selection

Exploration on Portfolio Selection and Risk Prediction in Financial Markets Based on SVM Algorithm

In order to cope with the complex risk environment of the current financial market, achieve portfolio optimization and accurate risk prediction, this paper conducts effective research using SVM algorithm. This article uses stock data as a sample to empirically analyze the risk return and risk prediction performance of investment portfolio strategies based on SVM algorithm. Compared with traditional index fund investment strategies, the risk resistance of investment portfolio strategies is significantly improved, and the risk return is also stable at a high level. In addition, with the support of SVM algorithm, the risk prediction error level in the financial market remains within a relatively low range. From the perspective of practical applications, the financial market investment portfolio selection and risk prediction based on SVM algorithm has strong feasibility.

Neutrosophic Decision Making Model for Investment Portfolios Selection and Optimizing based on Wide Variety of Investment Opportunities and Many Criteria in Market

Investment portfolio selection is a difficult subject due to the presence of competing factors. Choosing a portfolio for one's investments is a major choice that may have far-reaching effects on one's financial well-being. Risk tolerance, time horizon, investing objectives, asset allocation, and investment selection are only a few of the factors that will be studied in this article. The Stable Preference Ordering Towards Ideal Solution (SPOTIS) technique is the basis for our proposed integrated multi-criteria decision-making (MCDM) model. This paper used the single-valued neutrosophic set as a framework to deal with uncertain data. The purpose of the suggested SPOTIS–Neutrosophic model is to choose the most promising investment possibilities by considering several financial variables. Because of the wide variety of investment opportunities and the many elements (political unpredictability, news, economic circumstances, etc.) that may affect the market, investors often worry about selecting and optimizing their investment portfolios.

Dynamic volatility contagion across the Baltic dry index, iron ore price and crude oil price under the COVID-19: A copula-VAR-BEKK-GARCH-X approach

The international dry bulk shipping market is closely related to the commodity and crude oil markets. At the same time, the Baltic Dry Index (BDI) is usually considered as the main indicator of economic activities. In order to clarify the correlations between the three markets, this paper employs the Copula-VAR-BEKK-GARCH-X model to explore the dynamic dependence and volatility spillovers between the Baltic Dry Index, iron ore price and Brent crude oil price. In addition, the influence of exogenous variables (BDI/Iron ore/Brent) on market volatility and co-volatility is further discussed. The empirical results indicate that: first, the dependence between BDI, iron ore price and Brent crude oil price is time-varying and time-lag, especially when suffering from major crises. Second, dynamic dependencies and volatility spillovers between BDI, iron ore price and Brent crude oil price have significantly strengthened during COVID-19, manifesting that the impact of market turmoil has reinforced the linkages between markets. Third, this paper reaffirms the role of BDI as an intra and inter market indicator, while finding indication that iron ore is beginning to emerge its predictive indicator post-COVID-19. The results are not only conducive to the investment portfolio selection of individual investors and institutional investors, but also beneficial to formulating trade policies or shipping strategies by countries or shipping organizations.

Stock selection using a multiple criteria hierarchical process in the Dow Jones index

One of the problems that investors often face is deciding on the stocks to include in an investment portfolio. The objective of the paper is the selection of investment portfolios considering the 30 main companies listed on the New York Stock Exchange 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 analyses the performance of the shares in a multiple criteria hierarchical process (MCHP). The ordering is generated with the hierarchical version of the ELimination Et Choix Traduisant la REalité (ELECTRE) III method using different decision criteria based on financial ratios.

Proposal of a diversified investment portfolio in stocks: An approach to AHP-Gaussian method

The purpose of this article is to assist decision-making in the selection of diversified investment portfolios in Brazilian stocks, proposing alternatives in a hierarchical manner within each sector of the Stock Exchange. Among various multicriteria decision methods, the AHP-Gaussian method, a new approach to the traditional AHP that does not require prior evaluation by a decision-maker, was chosen. Using criteria from fundamental analysis of companies, the methodology hierarchizes alternatives within each sector of the Stock Exchange. As a result, the proposal suggests the best companies for investment in each sector, which can be viewed through a web application that allows filters according to the investor's needs. Thus, the methodology presented in this study can provide valuable support for decision-making in the financial market.

A framework for treating model uncertainty in the asset liability management problem

The problem of asset liability management (ALM) is a classic problem of the financial mathematics and of great interest for the banking institutions and insurance companies. Several formulations of this problem under various model settings have been studied under the Mean-Variance (MV) principle perspective. In this paper, the ALM problem is revisited under the context of model uncertainty in the one-stage framework. In practice, uncertainty issues appear to several aspects of the problem, e.g. liability process characteristics, market conditions, inflation rates, inside information effects, etc. A framework relying on the notion of the Wasserstein barycenter is presented which is able to treat robustly this type of ambiguities by appropriate handling the various information sources (models) and appropriately reformulating the relevant decision making problem. The proposed framework can be applied to a number of different model settings leading to the selection of investment portfolios that remain robust to the various uncertainties appearing in the market. The paper is concluded with a numerical experiment for a static version of the ALM problem, employing standard modelling approaches, illustrating the capabilities of the proposed method with very satisfactory results in retrieving the true optimal strategy even in high noise cases.

Optimization of Investment Portfolio Selection Using Time Series Algorithms

In contemporary financial analytics, precise forecasting and optimizing investment portfolios are paramount. Traditional diversification methods have limitations in volatile markets, necessitating innovative approaches. This article proposes leveraging time series algorithms to enhance the analysis of market data and improve forecasting accuracy. The algorithm's development and implementation offer a breakthrough, significantly improving investment decision accuracy. The focus is on adapting algorithms to dynamic market conditions, utilizing new technologies for effective risk management, and achieving a balance between risk and return. This article addresses gaps in existing research, emphasizing the importance of time series algorithms in optimizing investment portfolios for robust decision-making in diverse market scenarios.

ChatGPT-based Investment Portfolio Selection

ChatGPT-based Investment Portfolio Selection

Solving Periodic Investment Portfolio Selection Problems by a Data-Assisted Multiobjective Evolutionary Approach

Classic portfolio selection problems mainly focus on high-risk financial markets with tradeoffs between returns and risk. However, more risk-averse investors pursue long-term portfolio planning with the objectives of maximizing final returns and maximizing flexibility. This article addresses a new type of the portfolio problem, called periodic investment portfolio selection problems (PIPSPs), in which investors periodically allocate resources to financial products with different periods. A multiobjective model for PIPSPs is first presented. With a mechanism for utilizing the data generated during the implementation of multiobjective evolutionary algorithms (MOEAs), a data-assisted MOEA (DA-MOEA) is proposed to solve PIPSPs. The main idea of a DA-MOEA is to combine a MOEA with a data-assisted process that consists of three components: 1) feature construction; 2) data fusion model development; and 3) obtained information utilization. To solve the addressed PIPSPs, two versions of DA-MOEAs with baselines of nondominated sorting and decomposition-based mechanisms are implemented, namely, the data-assisted NSGA-II (DA-NSGA-II) and data-assisted MOEA/D (DA-MOEA/D). In the developed DA-MOEAs for PIPSPs, a feature construction process and a data fusion model are well designed for mining data with different formats. To validate the algorithms, two sets of test instances are generated. The experimental results demonstrate the efficacy of the data-assisted process. Furthermore, the effects of the algorithm components, such as the data source sizes, information types, and information utilization strategies, are investigated.

Investment portfolio tracking using model predictive control

AbstractThe composition of an investment portfolio aiming to increase the financial returns while reducing the exposure to risk is a topic of growing interest in the world. In this direction, we propose a model predictive control (MPC) strategy in order to optimize the investment portfolio selection taking into account the tracking of a reference portfolio with desired return. In addition, an analysis comparing different sizes of the prediction horizon according to VPH‐MPC (Varying Predictive Horizon‐MPC) and FPH‐MPC (Fixed Predictive Horizon‐MPC) algorithms is conducted. Finally, we propose an optimal control problem using the tracking error as a function of loss of CVaR (Conditional Value at Risk) measurement. Numerical experiments are run based on Brazilian Stock Exchange data. The experimental results are compared with the Markowitz portfolio optimization model, a conventional tracking strategy, and benchmarks from the Brazilian financial market. This comparison indicates a good tracking performance obtained by the proposed MPC in the two versions while satisfying the constraints.

Portfolio Optimization using Artificial Intelligence: A Systematic Literature Review

Artificial intelligence (AI) models can help investors find portfolios in which the focus is to optimize the risk-return relationship. There are several algorithms and techniques in the literature that allow the application of tests to a set of historical data for the selection and validation of investment portfolios. Based on this, this research intends to examine the contribution of the main machine learning techniques used in portfolio management through a systematic literature review. By using the Methodi Ordinatio for selection and ranking of articles, we classified papers considering object of study, type of AI used, period of analysis, data frequency, balance and cardinality. In addition, we detail the main contributions and trends conceived until the year 2020. Therefore, our findings reveal gaps and suggest future works on the topic.

Fuzzy Multi-Objective Chance-Constrained Portfolio Optimization Under Uncertainty Considering Investment Return, Investment Risk, and Sustainability

Effective portfolio management can increase business value by maximizing investment return, reducing possible risk and, making the best use of limited resources. Achieving this goal is constrained by multiple conflicting objectives and uncertainty, which are inherent in all projects. The investment return, investment risk, and sustainability have been simultaneously evaluated in this study by fuzzy multi-objective chance-constrained portfolio optimization, aligned with an organization’s strategic direction. Through the fuzzy chance-constrained programming, the obtained portfolio under uncertainties can be optimized with a specified confidence level imposed by decision makers. With such an NP-hard problem, a Genetic Algorithm (GA) is embedded in the developed decision support tool for sustainable investment portfolio selection and optimization. The applicability of the proposed approach and the constructed tool are illustrated through an example.

Selection of investment portfolios with social responsibility: a multiobjective model and a Tabu search method

In this study, a model for the selection of investment portfolios is proposed with three objectives. In addition to the traditional objectives of maximizing profitability and minimizing risk, maximization of social responsibility is also considered. Moreover, with the purpose of controlling transaction costs, a limit is placed on the number of assets for selection. To the best of our knowledge, this specific model has not been considered in the literature to date. This model is difficult (NP-Hard), and therefore, only very small instances may be solved in an exact way. This paper proposes a method based on tabu search and multiobjective adaptive memory programming (MOAMP) strategies. With this method it is possible to obtain sets of nondominated solutions in short computational times. To check the performance of our method it is compared with adaptations of the nondominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA-II) and multiobjective particle swarm optimization (MOPSO). The results of different computational experiments show that our tabu search-MOAMP method performed best. The quality of the sets of solutions that were obtained and the speed of execution mean that our tabu search-MOAMP can be used as a tool for financial assessment and analysis (including online services). This tool, as we can see in this work with some examples, can take into account the social concerns of many clients and their overall risk profile (very conservative, conservative, moderate, or fearless). This approach is also in line with current legal regulations that oblige financial advisors to take the client profile into account to provide greater protection and propose good financial advice.

Econometric Modeling to Measure the Efficiency of Sharpe’s Ratio with Strong Autocorrelation Portfolios

Sharpe’s ratio is the most widely used index for establishing an order of priority for the portfolios to which the investor has access, and the purpose of this investigation is to verify that Sharpe’s ratio allows decisions to be made in investment portfolios considering different financial market conditions. The research is carried out by autoregressive model (AR) of the financial series of returns using Sharpe’s ratio for evaluations looking over the priority of financial assets which the investor can access while observing the effects that can cause autocorrelated series in evaluation measures for financial assets. The results presented in this study confirm the hypothesis proposed in which Sharpe’s ratio allows decisions to be made in the selection of investment portfolios under normal conditions thanks to the definition of a robustness function, whose empirical estimation shows an average 73% explanation of the variance in the degradation of the Spearman coefficient for each of the performance measures; however, given the presence of autocorrelation in the financial series of returns, this similarity is broken.