Islamic religiosity and portfolio allocation: the Malaysian context

Purpose This paper aims to examine the relationship between Islamic religiosity and Islamic financial asset holdings (IFAH) among Muslim individuals in Malaysia. Design/methodology/approach Data for this study were collected through a survey questionnaire, and a sample of 751 working Muslims in Kuala Lumpur was obtained. Islamic religiosity was measured via religiosity levels and religiosity dimensions. IFAH was measured as the fraction of Islamic financial assets held in a financial portfolio. A generalized linear model (GLM) was used to estimate the relationships. Findings The results show that religiosity level influences the IFAH. Devout Muslims held more Islamic financial assets than casual Muslims. All religiosity dimensions influenced IFAH, with faith having the greatest influence and virtues the least. Educational level strongly influenced IFAH. Research limitations/implications The sample consisted of working Muslims in Kuala Lumpur; hence, generalization cannot be made to all Malaysian Muslims. This study only focused on Islamic financial assets and did not consider other types of Islamic financial products, such as financing. Practical implications Efforts to encourage Muslims to opt for Islamic financial assets may be more effective if they begin from the core of religious education. Educating individuals on Islamic principles, including the values and concepts of Islamic finance, is important to encourage the Islamic banking industry to prosper among Muslims. Originality/value The paper provides an extension of current literature on spirituality and religion by incorporating a comprehensive measure of Islamic religiosity and its relationship with financial asset holdings.

PurposeThe purpose of this study is to test Islamic religiosity scale’s reliability and validity and to evaluate the impact of Islamic religiosity on materialism in Turkey.Design/methodology/approachUsing a quantitative research method, a self-administered survey was distributed to the students of two faculties – Faculty of Theology and Faculty of Business – of the same university in Izmir, Turkey. In total, 400 questionnaires for each faculty were distributed and a total of 529 completed questionnaires – 326 from the Faculty of Theology and 203 from the Faculty of Business – were returned. Exploratory and confirmatory factor analyses were conducted via SPSS and AMOS.FindingsIslamic religiosity and materialism have been found to be negatively correlated, supporting the existing literature. However, not all the dimensions of Islamic religiosity (behavioral religiosity, spiritual religiosity and necessity of religion) had this negative effect on materialism (possession-defined success, acquisition centrality and acquisition as the pursuit of happiness). Negative influence of “behavioral religiosity” on all three dimensions of materialism was observed. However, the “spirituality” dimension of religiosity was found to have a positive impact, while the “necessity of religion” had no significant impact on the dimensions of materialism.Research limitations/implicationsA sample consisting of undergraduate students might limit the generalizability of the findings to Turkish Islamic population in general. This study is one of the first attempts to test the Islamic religiosity scale which needs to be further analyzed and developed.Originality/valueThe present study will contribute to the literature by testing an Islamic religiosity scale developed by a Turkish researcher. Besides, this is the only study undertaken on the impact of Islamic religiosity on materialism using an Islamic religiosity scale.

A note on portfolio selection with restrictions on leverage

We use data from the PSID to investigate how households' portfolio allocations change in response to wealth fluctuations. Persistent habits, consumption commitments, and subsistence levels can generate time-varying risk aversion with the consequence that when the level of liquid wealth changes, the proportion a household invests in risky assets should also change in the same direction. In contrast, our analysis shows that the share of liquid assets that households invest in risky assets is not affected by wealth changes. Instead, one of the major drivers of households' portfolio allocation seems to be inertia: households rebalance only very slowly following inflows and outflows or capital gains and losses.

The two objectives of this paper are to examine the effect of financial literacy, risk aversion and expectations on retirement planning; and, to investigate the effect of these antecedents on the retirement portfolio allocation. Data was collected via a self-administered questionnaire from a sample of 270 working individuals in Kuala Lumpur, Malaysia. Logistic and ordered probit regressions were employed to analyse the first and second objective, respectively. The results from the logistic regression indicate that future expectations significantly influence the probability of planning for retirement. Meanwhile, individuals with higher financial literacy and lower risk aversion are more likely to hold risky assets in their retirement portfolios. Subsequently, two-sample t-test and one-way ANOVA tests were conducted to further examine the differences in financial literacy, risk aversion and expectations, across demographic subgroups. The study contributes to the literature by holistically incorporating the behavioural aspects that affect retirement planning and by exploring an uncharted issue of retirement planning—namely, the retirement portfolio allocation.

As a result of the worldwide pandemic, many industries are in need of growth stimuli, and the US equity markets, as the main growth engine, have intrigued investor interest. This article renders a performance analysis using the weights portfolio optimization given as a focal process that is skillfully implemented by financial practitioners. Each portfolio which includes Apple, IBM, Microsoft, Home Depot, Starbucks, NIKE, P&G, QUALCOMM, and JPM, gathers weekly return data from January 2017 to December 2020 and performs a Monte Carlo simulation. The amount of portfolio alternatives will be defined by the strategies being utilized, and it will choose ten stocks from a pool of 100 stocks to form a portfolio. Asset allocation strategies with Mean-Variance and Minimum Variance were constructed using the efficient frontier. Comparing a portfolio's asset allocation performances to that of the benchmark, the S&P 500 index, and an equally weighted strategy portfolio, with respect to asset allocation, the portfolio with the highest Sharpe ratio is the most optimal. The results in this study benefit investors and industry stakeholders in the post-covid era.

Jump-Diffusion International Asset Pricing with Nontraded Consumption Goods

In <b>Portfolio Optimization with Active, Passive, and Factors: <i>Removing the Ad Hoc Step</i></b>, from the March 2020 issue of <b><i>The Journal of Portfolio Management</i>, Roger Aliaga-Diaz, Giulio Renzi-Ricci, Ankul Daga</b>, and <b>Harshdeep Ahluwalia</b>, all with <b>The Vanguard Group</b>, discuss a more streamlined way to optimize portfolios. They present a quantitative asset-allocation framework that incorporates active, passive, and factor strategies—and propose a utility optimization model that concurrently allocates assets among investments in these three dimensions. Their approach condenses portfolio optimization into one step that integrates investors’ risk tolerance into asset allocation. The model incorporates investor risk preferences shaped by the uncertainties inherent with active, passive, and factor investment returns. By clarifying investors’ decision-making, this approach steers them away from ad hoc portfolio allocations and permits them to customize their portfolios more deliberately. The authors highlight practical applications, including how the approach can be used to substitute lower-cost factor strategies for higher-cost active strategies and to create factor-tilted portfolios aligned with investors’ varying risk thresholds. <b>TOPICS:</b>Analysis of individual factors/risk premia, factor-based models, portfolio theory, portfolio construction

Portfolio Optimization with Ambiguous Correlation and Stochastic Volatilities

Investment in Education and Strategic Asset Allocation

By this study, we want to provide tools for an economic agent who wants to maintain her maximized intertemporal consumption rate over time period regularly by trading activity in capital market. She would use the diversification principle to compose portfolio that is consisted of risky and risk free assets, with initial wealth by using principle of dynamic programming in stochastic control problem. While our target function is to maximize the discounted utility function over consumption rate, the agent may use this portion of wealth for hedonistic lifestyle without thinking the bequest aim. Motivated by observation that seminal work of Samuelson (1969)’s optimal investment on risky asset was independent of time and wealth process, we were replacing the source of uncertainty in the risky asset by binomial process in order to improve Samuelson's solution. The risky asset price evolution is determined to move in two possible directions of up and down in the next time instantly with certain probability, following binomial trees scheme. Working within this setting, we manage to obtain trading strategies as explicit solution for the optimal investment and consumption decisions which were being expressed as time-dependent functions as well as wealth process feedback form. This solution goes along with inter-temporal concept that our consumption and investment strategies tomorrow will be affected with our wealth today. We exemplify the results by numerical examples and perform Monte Carlo simulation.

Beyond expected utility: Subjective risk aversion and optimal portfolio choice under convex shortfall risk measures

THE DEVELOPMENTS OF the last twenty years in the theory of choice under conditions of uncertainty have provided economists with the theoretical tools to handle problems of portfolio selection by individuals. These newlyfashioned tools have been applied subsequently to financial institutions such as commercial banks.2 This paper is an addition to the increasing stock of studies based on the framework provided by the relatively recent works of Markowitz [6], Tobin [9], and Farrar [2] in the field of asset choice. More particularly, this study provides an analysis of the observed aggregate commercial banks' portfolio of assets. The first part of the paper provides the theoretical development which leads to the identification of the variables determining the bankers' aggregate portfolio. The banker's view of the various assets is conceptualized in terms of expectations about future prices of assets. Then, from these expectations about prices, we derive the expectations about future returns from each asset. The banker's views of these returns are then transformed into a view of his expectations about linear combinations of assets, i.e., alternative portfolios. We assume that the banker determines which is the best, or optimal, portfolio from among all those available under prevailing market conditions on the basis of maximum expected utility. While the development so far enables us to identify the specific variables which explain the optimal portfolio, the link between these variables and the observed (as opposed to the optimal) portfolio is still missing. To provide this connection between the optimal and observed portfolio, we introduce a Markov process which describes the dynamic adjustments of the aggregate banks' portfolio as it approaches the optimal, or equilibrium, portfolio. We then derive the equations determining the aggregate portfolio composition at each point of time. The second part of this paper deals with the estimation and analysis of the regression equations. Using the estimated coefficients we calculate the

An extension of portfolio theory in selecting projects to construct a preferred portfolio of petroleum assets

Rather than investing in a single asset, most investors put their money into a portfolio of assets. This raises the question of how to calculate the expected return and risk of a portfolio. Furthermore, it has to be determined which portfolios of risky assets are most efficient in terms of expected return and risk. Markowitz’s portfolio theory demonstrates how to construct the efficient frontier on which the most efficient risky portfolios lie with regard to expected return and risk. The efficient frontier is created from capital market data, which are used to estimate the expected return and standard deviation of the returns of individual assets, as well as the covariance or correlation coefficient between the returns of a pair of assets. This chapter describes how to calculate the expected return and risk of a portfolio of risky assets. It then demonstrates how the efficient frontier can be determined using historical return data. The chapter ends with a discussion of the diversification effect and the number of stocks required for a well-diversified portfolio.