Portfolio Decisions Research Articles

Computational insights into optimal household portfolio decisions: a stochastic approach with heston model and finite difference scheme

Computational insights into optimal household portfolio decisions: a stochastic approach with heston model and finite difference scheme

Robustifying Conditional Portfolio Decisions via Optimal Transport

We propose a data-driven portfolio selection model that integrates side information, conditional estimation, and robustness using the framework of distributionally robust optimization. Conditioning on the observed side information, the portfolio manager solves an allocation problem that minimizes the worst-case conditional risk-return tradeoff, subject to all possible perturbations of the covariate-return probability distribution in an optimal transport ambiguity set. Despite the nonlinearity of the objective function in the probability measure, we show that the distributionally robust portfolio allocation with a side information problem can be reformulated as a finite-dimensional optimization problem. If portfolio decisions are made based on either the mean-variance or the mean-conditional value-at-risk criterion, the reformulation can be further simplified to second-order or semidefinite cone programs. Empirical studies in the U.S. equity market demonstrate the advantage of our integrative framework against other benchmarks. Funding: The material in this paper is based on work supported by the Air Force Office of Scientific Research [Award FA9550-20-1-0397]. Additional support is gratefully acknowledged from the National Science Foundation [Grants 1915967, 1820942, and 1838676], the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2016-05208], and the China Merchant Bank. V. A. Nguyen gratefully acknowledges the generous support from the Chinese University of Hong Kong [Improvement on Competitiveness in Hiring New Faculties Funding Scheme] and the Chinese University of Hong Kong [Direct Grant 4055191]. S. Wang is partially supported by the National Natural Science Foundation of China [Grant 72371022]. Finally, this research was enabled in part by support provided by Compute Canada. Supplemental Material: The computer code and data that support the findings of this study and the online appendix are available within this article’s supplemental material at https://doi.org/10.1287/opre.2021.0243 .

A simheuristic for project portfolio optimization combining individual project risk, scheduling effects, interruptions, and project risk correlations

This paper introduces a simheuristic method to the Project Portfolio Selection Problem, designed to maximize the net present value of the portfolio while considering uncertain costs, schedules, interruptions, and inter-project risk correlations. The novel approach combines techniques from Monte Carlo simulation, critical path analysis, queuing theory, and optimization, integrating baseline schedules, project-level uncertainties, budgetary constraints, and risk correlations in a single model. A computational experiment is conducted on a realistic set of ten candidate projects and validated respect to the deterministic version of the problem, demonstrating its ability to select near optimal portfolio proposals with varying combinations of risk and net present value. The findings highlight the significant impact of factors such as contingency reserve allocation policies, operational interruptions, and project risk correlations on portfolio decisions, constituting a helpful framework for the decision-makers at portfolio level.

A Comparison of Financial Risk-Tolerance Assessment Methods in Predicting Subsequent Risk Tolerance and Future Portfolio Choices

This study explores the effectiveness of various methods for measuring risk tolerance, with the aim to better understand the risk-taking attitudes and behaviors of financial decision-makers. Using data collected between October 2020 and March 2021, the research investigates three key areas: (a) the stability of risk tolerance over a six-month period, (b) the individual and household characteristics that predict future risk tolerance, and (c) the predictive accuracy of various risk-tolerance assessment methods in relation to portfolio choices made by financial decision-makers. The results show that risk-tolerance scores derived from a psychometrically developed scale provide the most accurate insights into future risk-taking attitudes and portfolio decisions. For those looking for a simple way to assess both current and future risk tolerance and portfolio choices, a stated-preference item can be effective. Although less consistent, a revealed-preference test can also be used to predict risk tolerance and risk-taking behavior. Findings provide guidance for financial decision-makers and financial advisors by comparing the key features of the three primary risk-tolerance assessment methods evaluated in this study. The study also establishes a foundational basis for selecting the most appropriate evaluation approach, based on the variables identified in the findings.

Evaluation of real estate investment performance of insurance companies by NHF Act in curbing housing deficit in Nigeria

PurposeThe purpose of this study is to evaluate the real estate investment performance portfolio decision-making of the insurance firms by the National Housing Fund (NHF) Act in reducing the housing deficit in Nigeria. The insurance companies' gross premium, real estate investment and return on investment trends for the period 2014–2019 were evaluated, to determine the extent of their investment in real estate in Nigeria, this ought to have reduced the shortfall in housing.Design/methodology/approachBoth primary and secondary sources were used. Cronbach’s alpha was used for testing the reliability. The Friedman mean rank with Chi-square was used for different types of real estate investment properties and for reasons for investing in real estate by insurance companies in Nigeria. The test for normality was conducted using the Shapiro–Wilk and Spearman correlation for the significance. The percentage and the data envelopment analysis frontier model were used for measuring performance and efficiency.FindingsThe results showed that the majority of real estate investments made by insurance companies were in commercial and land buying and reselling and that their performance was below average. However, their motivation for investing is not toward the NHF Act but rather for diversification and increasing their capital.Originality/valueTo the best of the authors’ knowledge, this is one of the first studies in Nigeria that looked at how well insurance firms have performed investing in real estate as required by the NHF Act. However, the new Act, the National Housing Fund (Establishment) Act of 2018 which was put on hold due to flaws, affected data availability beyond 2019.

PREDICTION INDONESIA COMPOSITE INDEX USING INTEGRATION DECOMPOSITION- NEURAL NETWORK ENSEMBLE DURING VUCA ERA

The Volatility, Uncertainty, Complexity, and Ambiguity (VUCA) era causes turmoil in the capital markets, stocks, commodities, etc. The impact is a decline in the Composite Stock Price Index (IHSG) in 2020. Therefore, future data is needed to inform investors and business people when making portfolio decisions. This paper develops a decomposition and Neural Network (NN) integration model to predict ICI during the VUCA era. The results are presented empirically to show the model's effectiveness in reducing prediction errors. First, the actual data is converted into three components; second, with the Neural Network Ensemble (NNE) approach where the initial step of decomposition results is trained using artificial NN with architecture, training data, and topology to produce individual networks; The output is selected using Principal Component Analysis (PCA) and becomes input to the ensemble model, then combined using a simple average and weighted average. The empirical results from ICI predictions illustrate: (1) decomposition has the potential to overcome data that is characterized by high volatility; (2) NNE is able to reduce errors (MSE≤0.100e-4, MAE≤0.01) compared to individual networks (MSE=0.0024 MAE=0.0376); (3) ensemble combinations using weighted averages (MSE≤3.00e-5,MAE≤0.002) are superior to simple averages (MSE≤5.00e-5,MAE≤0.01); (4) the integration carried out shows effectiveness in predicting ICI and provides better prediction results.

Volatility Ambiguity, Portfolio Decisions, and Equilibrium Asset Pricing

Volatility Ambiguity, Portfolio Decisions, and Equilibrium Asset Pricing

The role of health in consumption and portfolio decision-making: Insights from state-dependent models

In this paper, we study an optimal consumption and asset allocation problem accounting for the fact that individuals’ utility differs across various health states. Our study is based on the assumption that an individual’s health status evolves through a semi-Markov process, where the transition probabilities are contingent on both the present state and the duration spent in that state. The optimal form of stock investment and of consumption is determined analytically. The optimal consumption level is significantly shaped by the integration of health-related factors and can be represented as the inverse of the marginal utility function with respect to time and state price density. Additionally, we introduce a Lagrangian multiplier that can be derived by solving a fixed point problem. While our optimal solutions are applicable to a wide range of utility functions, we provide numerical illustrations specifically using the power utility function in a model encompassing three distinct health states. Transition probabilities are estimated from real data collected by the China Insurance Regulatory Commission (CIRC), using a high-order polynomial Perks formula. We find that the investor’s consumption is higher when health is good, but lower when care is needed.

Frequency domain cross-quantile coherency and connectedness network of exchange rates: Evidence from ASEAN+3 countries

This study examines the frequency domain connectedness and synchronization between the exchange rates of Association of Southeast Asian Nations (ASEAN) member countries and those of China, Japan, and South Korea across quantile levels. We propose a quantile cross-spectrum of exchange rates to establish the coherency of connectedness and synchronization measurements. Our empirical results are as follows: First, the return connectedness between the exchange rates is heterogeneous, being stronger in the long run than in the short run and more pronounced under normal market conditions than under extreme market conditions. Second, the dynamic return connectedness among the exchange rates follows a similar trend in the monthly and yearly cycles. Third, exchange rate returns and volatility exhibit long-term synchronization. However, short-term heterogeneity persists across market conditions and investment horizons. Overall, these findings offer valuable insights for monetary authorities in their efforts to maintain exchange rate stability and for investors in making informed portfolio decisions.

Selection of biomass and bioenergy applications from rice production waste: an integrated method of a circular bioeconomy-based fuzzy inference system and portfolio decision analysis

Selection of biomass and bioenergy applications from rice production waste: an integrated method of a circular bioeconomy-based fuzzy inference system and portfolio decision analysis

Ex-ante expected changes in ESG and future stock returns based on machine learning

This study has two primary objectives. Firstly, it enhances the reliability and transparency of machine-learning-based models for predicting future changes in environmental, social and governance (ESG) performance. Secondly, it explores the relationship between ex-ante expected changes in ESG and future stock returns. This study collects 3258 STOXX Europe 600 firm-year observations. In the ESG prediction phase, two machine learning algorithms (logistic regression and random forest) are utilised to develop ESG forecasting models. Hyperparameter optimisation and walk-forward validation techniques are employed to address issues of underestimation and information leakage. The machine-learning-based ESG forecasting models are evaluated using three metrics: accuracy, area under the curve (AUC) and area under the precision-recall curve (AUPR). Subsequently, this study investigates the relationship between ex-ante expected ESG changes and future stock returns using the predicted ESG changes. A positive correlation is found between ex-ante expected ESG changes and future stock returns. The supplementary tests also reveal that this positive relationship is highly and statistically significant among large firms and after the COVID-19 pandemic. Moreover, this study introduces a robust and transparent approach for constructing effective machine-learning-based ESG forecasting models using hyperparameter optimisation and walk-forward validation. Additionally, traditional regression analyses are modernised by incorporating machine-learning-predicted independent variables. Furthermore, the findings provide empirical support for stakeholder, agency and resource-based theories. Finally, practical insights are provided to facilitate ESG-focused investment portfolio decision making.

COVID-19 and US females’ portfolio decisions

This paper uses a panel survey (micro-level) dataset to explore the impact of the COVID-19 pandemic crisis on females’ portfolio decisions. Using 150,000 US annual female portfolio choice observations from the Census Bureau database over the period 2001–2021, as well as Difference-in-Difference (DiD) methodologies, the analysis documents that female investors significantly reduce their market investments in risky assets during the pandemic period. The findings survive certain robustness checks, while they clearly illustrate the rising effect of the pandemic uncertainty on such portfolio decisions. The results carry substantial implications by suggesting that the pandemic acts as a negative externality in the risky asset type of portfolio choices and has high significance for portfolio managers, as well as for regulators and policymakers.

Retirement satisfaction as a quantified outcome of superior financial literacy: An empirical investigation

The purpose of the study is to create a viable and customised retirement planning system with financial literacy as the driver to achieve retirement adequacy and retirement satisfaction, taking into account the increasing financial needs of retirees. Perception regarding retirement choices necessitates sufficient self-efficacy to implement retirement portfolio decisions. Despite transformation in the way retirement is looked at, planning for retirement has taken a backward stride as individuals concentrate on their present needs rather than a holistic approach to life. The study implemented an online self-administered questionnaire for 770 participants using convenience sampling from India’s two major IT hubs, Bangalore and Chennai. The data samples were analysed using Partial Least Squares Structural Equation Model (PLS-SEM). The study evidenced a strong association between superior financial literacy and retirement planning and identified five essential financial behaviours, such as savings attitude, borrowing pattern, investment decision, insurance allocation, and spending behaviour, that strongly influence individual retirement planning. These behaviours lay the foundation for retirement readiness, lowered debt anxiety, and greater risk-bearing capacity. The results further reveal that individual’s satisfaction levels are significantly influenced by retirement adequacy. Therefore, it is imperative to formulate a well-structured retirement plan to attain retirement adequacy and satisfaction.

Extracting Stock Predictive Information in Mutual Fund Managers’ Portfolio Decisions Through Machine Learning with Hypergraph

Extracting Stock Predictive Information in Mutual Fund Managers’ Portfolio Decisions Through Machine Learning with Hypergraph

EXPLORING THE LINKAGES BETWEEN GREEN BONDS AND INTERNATIONAL FINANCIAL MARKETS: AN INTERNATIONAL PORTFOLIO PERSPECTIVE

The aim of study is to examine the dynamic linkages between green bond & international financial markets. In particular, the study investigates the inter-connections among green bonds, world’s developed and emerging equity markets, commodities market (gold & oil) and the foreign exchange market. Sample period ranges from July 1, 2014, to June 30, 2023 encircling numerous important events including COVID-19 pandemics. For empirical investigation, study used combination of several econometric techniques including Johansen Cointegration model and Granger Causality test and analysis for examining short & for long-term associations among markets. Results of study show that these markets are linked in varying degrees in long-run as evident from cointegration results that show two cointegrating vectors. This infers that there are limited diversification benefits specially in long-run in selected markets. In short-run, results show diversification potential as showed by results from Granger Causality tests. Results have important implications for investors & fund managers in making portfolio decisions, & policy makers in devising various strategies for risk mitigation during turbulent periods.

RELATIONSHIP BETWEEN COMMODITIES MARKET AND STOCK MARKETS EVIDENCE FROM MALAYSIA AND CHINA

The stock-commodity link affects market participants, commodity producers, policymakers, and, in emerging nations, the entire economy. The lead-lag relationship between market representative indices can be studied using many methodologies. This initiative examines the Indian stock-commodity market interaction. This project will leverage secondary data from the National Stock Exchange, Multi Commodity Exchange of India Limited, and Bombay Stock Exchange websites. Project results can be used to create an investment plan. Thus, market participants must draw crucial inferences or discover important relationships between the Indian Stock Market and Commodity Market. Statement of the Problem Market players and investors face difficult portfolio decisions. People struggle to choose stocks and commodities like gold, copper, etc. Due to a lack of credible market data on the Indian Stock Market-Commodity Market connection. The lack of research on the Indian stock-commodity market link is the issue. Such a study is needed, using some of the top marketplace contributors as examples.The project's results should help regulators and market participants understand the problem and adopt appropriate laws, rules, policies, and methods. Investors and individuals have confirmed the issue. .Objectives of the Study The following objectives are the focus of the project study. · To make important inferences about the way the Indian Stock Market and Commodity Market interact; · to ascertain the type, scope, and direction of the link between the Indian Commodity Market and the Indian Stock Market.

Does childhood adversity affect household portfolio decisions? Evidence from the Chinese Great Famine

We employ the 1959–1961 Chinese Great Famine as a quasi-natural experiment to examine the relationship between experiencing adversity in childhood and financial decisions in adulthood. Using data from the 2017 China Household Finance Survey and the intensity of excess deaths during the Great Famine, results from our preferred two-part fractional regression model suggest that, for an additional excess death per thousand people during the Famine, cohorts who were in their infancy and early childhood during the Famine are 0.2–0.3 percentage points less likely to hold risky financial assets than other cohorts. We also find that, conditional on the decision to hold risky assets, those who experienced an additional excess death per thousand people during the Famine in their middle to late childhood hold 1.1 percentage points higher share of risky assets in their household financial portfolios than those who did not experience the Famine. We explore several potential mechanisms and find that financial literacy, risk-taking preferences and locus of control are channels through which childhood adversity in the famine years affects household portfolio decisions. Our findings are robust to a series of sensitivity checks.

Management Students' Perception Towards Investment in Equity Markets

The fascination with the equity market, which is renowned for its ability to generate exponential wealth, has captivated individuals from different generations. However, for those who are new to the financial landscape, the idea of investing can be overwhelming. This is especially true for management students who possess business acumen but have limited experience navigating the complexities of the stock market. As these students prepare to enter the professional world, where making personal financial decisions is crucial, it becomes essential to understand their perceptions of equity market investment. This research project aims to thoroughly explore the attitudes, beliefs, and concerns of management students regarding their participation in the equity market. By carefully examining their financial knowledge, risk tolerance, and motivations for investing, the project seeks to create a detailed understanding of how this important demographic perceives the dynamic and influential financial realm of equity markets. This exploration is vital as it provides valuable insights into potential obstacles and opportunities for stakeholders such as financial institutions, policymakers, and educators. The findings obtained from this study have the potential to guide specific interventions and initiatives in various fields. Armed with these insights, financial institutions can develop investment products and services that are tailored to the specific needs and preferences of management students. Policymakers can also benefit from these findings by using them to design targeted financial literacy programs that aim to empower future business leaders with the necessary knowledge and confidence to navigate the financial landscape responsibly. Additionally, educators can seamlessly incorporate the project's discoveries into their curriculum, ensuring that management students acquire the essential financial literacy skills needed to make informed and responsible investment choices. Ultimately, the goal of this research project is to make a significant contribution towards shaping a generation of financially knowledgeable individuals who can actively participate in and meaningfully contribute to the economic ecosystem. Key Words: · Equity Market · Management Students · Portfolio Decision-making · Financial Literacy · Investment Behavior

Human-Robot Interactions in Investment Decisions

We study the introduction of robo-advising on a large set of employee saving plans. Different from many services that fully automate portfolio decisions, our robo-advisor proposes investment and rebalancing strategies, leaving investors free to follow or ignore them. The resulting human-robot interactions occur both at the time of the subscription and over time, as the robot sends alerts when the investor’s portfolio gets too far from the target allocation. We show that the robo-service is associated with an increase in investors’ attention and trading activities. Following the robot’s alerts, investors change their rebalancing behaviors so as to stay closer to their target allocation, which results in larger portfolio returns. Counterfactual returns induced by automatic rebalancing by the robot would be only slightly higher, suggesting that, on average, the financial cost of letting investors retain control is not large. This paper was accepted by Jean-Edouard Colliard, special issue on the human-algorithm connection. Funding: This work was supported by Observatoire epargne europeenne, as well as LTI@Unito and Agence Nationale de la Recherche [Grant ANR-17-EURE-0010] to M. Bianchi. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03886 .

Bayesian Learning in an Affine GARCH Model with Application to Portfolio Optimization

This paper develops a methodology to accommodate uncertainty in a GARCH model with the goal of improving portfolio decisions via Bayesian learning. Given the abundant evidence of uncertainty in estimating expected returns, we focus our analyses on the single parameter driving expected returns. After deriving an Uncertainty-Implied GARCH (UI-GARCH) model, we investigate how learning about uncertainty affects investments in a dynamic portfolio optimization problem. We consider an investor with constant relative risk aversion (CRRA) utility who wants to maximize her expected utility from terminal wealth under an Affine GARCH(1,1) model. The corresponding stock evolution, and therefore, the wealth process, is treated as a Bayesian information model that learns about the expected return with each period. We explore the one- and two-period cases, demonstrating a significant impact of uncertainty on optimal allocation and wealth-equivalent losses, particularly in the case of a small sample size or large standard errors in the parameter estimation. These analyses are conducted under well-documented parametric choices. The methodology can be adapted to other GARCH models and applications beyond portfolio optimization.