Portfolio Risk Levels Research Articles

Monetary policy transmission under pandemic uncertainty: Effect on banks' risk and capital adjustments

AbstractThis paper investigates the effects of monetary policy on the simultaneous adjustments in asset portfolio risk and capital of banks amidst the uncertainty of the COVID‐19 pandemic, focusing on the 12 largest economies from 2018 Q1 to 2021 Q4. Results indicate that banks show lower portfolio risk and capital levels when the monetary policy stance is eased. However, amid heightened pandemic uncertainty, the risk‐reducing effect of monetary policy on banks amplifies, while bank capital levels remain unchanged. Heterogeneity analyses reveal that banks with higher levels of diversification and herding are more responsive to interest rates amid pandemic uncertainty, exhibiting lower risk exposure in their asset portfolios. Banks in countries adopting negative interest rate policies also tend to assume greater asset risk to accommodate the intended stimulus of monetary policies.

Propensity score matching: a tool for consumer risk modeling and portfolio underwriting

Researchers and practitioners in financial services utilize a wide range of empirical techniques to assess risk and value. In cases where known performance is used to predict future performance of a new asset, the risk of bias is present when samples are uncontrolled by the analyst. Propensity score matching is a statistical methodology commonly used in medical and social science research to address issues related to experimental design when random assignment of cases is not possible. This common method has been almost absent from financial risk modeling and portfolio underwriting, primarily due to the different objectives for this sector relative to medicine and social sciences. In this application note, we demonstrate how propensity score matching can be considered as a practical tool to inform portfolio underwriting outside of experimental design. Using a portfolio of distressed consumer credit accounts, we demonstrate that propensity score matching can be used to predict both account-level and portfolio-level risk and argue that propensity score matching should be included in the methodological toolbox of researchers and practitioners engaged in risk modeling and valuation activities of portfolios of consumer assets, particularly in contexts with limited observations, a large number of potential modeling features, or highly imbalanced covariates.

Optimal Portfolio Formation Analysis vs. Single Index Model: 2018–2019 Before Crisis and 2020–21 During Crisis

This paper focuses on a portfolio comparison analysis between 30 leading board stocks and 30 development board stocks selected from the Indonesia Stock Exchange (IDX). The analysis used in this study is the single index model. The periods used in this study are 2018-2019 (before the crisis) and 2020-2021 (during the crisis). The research results concluded that the Development Board stock group generated a higher portfolio return compared to the Main Board stock group, both in the 2018-2019 period (before the crisis) and in the 2020-2021 period (during the crisis). However, even though it produces a higher portfolio return, the Development Board stock group also has a higher level of portfolio risk compared to the Main Board stock group.

Vine Copula based Portfolio Level Conditional Risk Measure Forecasting

Accurately estimating risk measures for financial portfolios and validating their robustness is critical for both financial institutions and regulators. However, many existing models operate at the aggregate portfolio level, hence they fail to capture the complex cross-dependencies between portfolio components and particularly provide no methodology to perform a sensitivity analysis on the estimates. To address both aspects, a new approach is presented that uses vine copulas in combination with univariate ARMA-GARCH models for marginal modelling to compute conditional portfolio-level risk measure estimates by simulating portfolio-level forecasts conditioned on a stress factor. A quantile-based approach is then presented to observe the behaviour of risk measures given a particular state of the conditioning asset(s). In an illustrative case study of Spanish equities with different stress factors, the results show that the portfolio is quite robust to a sharp downturn in the American market. At the same time, there is no evidence of this behaviour with respect to the European market. The novel algorithms presented are ready for use through the R package portvine, which is publicly available on CRAN.

Portfolio Selection Strategies in Bursa Malaysia Based on Quadratic Programming

The study aims to select the efficient portfolio on stock listed in Bursa Malaysia by using the quadratic programming method. It can help the investors to gain expected returns from the diversification portfolio. However, there are some problems that should be considered such as the measurement of inputs for Mean-Variance Models (MVM), use of portfolio models through time and consistency with management objectives in the portfolio. These problems will affect the performance of selected portfolio and cause the loss problem. Therefore, this study implements a quadratic programming approach to select an efficient portfolio on stocks listed in Bursa Malaysia. The study will choose 15 potential companies which have the best performance in the Bursa Malaysia. Quadratic programming (QP) model can solve any type of mathematical optimisation problem in the study. Therefore, investors can optimise the investment portfolio returns by using QP methods. However, we can observe the efficient frontier which is a graph that representing a list of portfolios that optimising expected return for a different level of portfolio risk so can help the investors make a good decision. The findings of this study will give important inputs, especially to the investors to maximise their portfolio return at different level of risks.

Pembentukan Portofolio Optimal Saham berdasarkan Model Markowitz dan Model Indeks Tunggal pada Perusahaan IDX BUMN20 Tahun 2017-2021

This study aims to analyze the formation of the optimal stock portfolio based on the Markowitz model and the Single Index model in the IDX BUMN20 company in 2017- 2021. The type of research used is applied quantitative descriptive. The secondary data used are the monthly closing price of shares and the monthly JCI as well as the interest rate of Bank Indonesia. The sample in this study were 15 companies. The model used is the Markowitz model and the Single Index model. The results of the analysis show that 1. There are 11 stocks that are included in the optimal portfolio candidates, namely ANTM, BBTN, BBNI, BBRI, BMRI, JSMR, PGAS, PTBA, SMGR, TINS, and TLKM 2. The formation of the optimal portfolio of the Markowitz model consists of eight issuers with the proportions are BMRI 20%, TLKM 20%, BBRI 18%, PTBA 15%, ANTM 11%, JSMR 9%, SMGR 4% and BBTN 3% by providing a portfolio risk level of 0.0002 or 0.02% and the expected return on the portfolio is 0.0050 or 0.5%. 3. The formation of the optimal portfolio of the Single Index model consists of three issuers with the proportions of BBRI at 48%, ANTM at 45%, TINS 7%.

A Useful (But Painful) Risk-Management Lesson from the Chilean Pension System

This article demonstrates the dangers of attempting to create investment funds with different risk-return profiles by relying only on investment policies based on asset class-limits, but without incorporating portfolio-level risk constraints. The analysis is based on a natural experiment: the Chilean pension system. The study considers monthly returns data from the five investment funds that constitute the bedrock of the Chilean system and covers the period October 1, 2002 to June 1, 2020. The analysis, which relies on the use of different rank-order metrics, shows that the five funds delivered returns incompatible with their intended risk-return profile. In short, rank-ordering the five funds based on their cumulative returns resulted in a sequence at odds with the original intention; that is, contrary to the regulator’s aim, riskier funds frequently delivered lower returns. These findings are relevant for policy makers and the private sector, as many institutional investors (not only pension funds) based their investment policies on such limits alone.

Estimation of Optimal Portfolio Return and Risk on the LQ-45 Index for the 2020-2022 Period Using the Capital Asset Pricing Model (CAPM)

Stocks are one of the financial instruments with fluctuating prices that cannot be predicted accurately, so every investor must be able to estimate returns and risks so that investment objectives can be achieved. The purpose of this study is to determine the estimated return and risk of stock investment through the formation of an optimal portfolio and show that the resulting portfolio is capable of increasing the chances of achieving investment objectives. This research uses stocks listed on the LQ-45 Index for the 2020-2022 period, which can form an optimal portfolio. The method used to form an optimal portfolio is the capital asset pricing model (CAPM) method. The results of this study indicate that there are 14 out of 30 issuers that form an optimal portfolio with an expected portfolio return of 0.52% and a portfolio risk level of 0.26%. The results obtained from this method indicate that by forming an optimal portfolio, investors can diversify the risks that will be obtained with a more optimal rate of return when compared to the rate of return generated by each stock.

ANALISIS SINGLE INDEX MODEL DALAM PEMBENTUKAN PORTOFOLIO OPTIMAL (STUDI SAHAM INDEKS LQ45 PERIODE 2017–2022)

<p class="Style4">This study examines the optimal portfolio composition of stocks and calculates the return and risk of the optimal portfolio formed using a single index model. The type of research used in this study is descriptive research with a quantitative approach. This research uses secondary data in the form of stock prices for the 2017-2022 period. The data analysis method used is stock analysis with a single index model. The results showed five stocks from 29 stock samples, including the optimal portfolio. Here are the optimal portfolio stocks and their composition: ADRO at 4.41%, ANTM at 10.02%, BBCA at 54.33%, BBRI at 8.65%, and CPIN at 22.59%. The portfolio formed will generate an expected portfolio return of 1.72% and a portfolio risk level of 1.20%.</p><p><strong>Key words:</strong> Expected Return, Optimal Portfolio, Risk, Single Index Model</p>

Analisis Expected Return Dan Risiko Portofolio Sebelum Dan Pada Saat Pandemi Covid-19 Pada Saham LQ-45

This study aims to determine whether there are differences in the Expected Return and Risk of the LQ-45 Stock Portfolio before and during the COVID-19 pandemic. This type of research is quantitative research. The object of this research is LQ-45 stock in 2019-2020 or during and before the COVID-19 pandemic. The conclusion in this study is that the portfolio in this study uses the Markowitz Portfolio model which forms a combination into 21 combinations in the 2019-2020 time interval. From the results of the combination, it was found that the Expected Return and Portfolio Risk levels in the composition of 50:50 and 70:30 experienced differences before and during the COVID-19 pandemic.

Optimal Portfolio Formation Using Single Index Model on the IDX80 Index for the Period February 2021 – May 2022

The policy of the Central Bank of the United States (US), namely the Fed, in raising its benchmark interest rate, has increased the risk of volatility in global markets and triggered negative sentiment for foreign investors in Indonesia. Foreign investors began to relinquish their ownership up to Rp 3.19 trillion. This caused the risk of falling prices. Therefore, investors need to diversify through the formation of an optimal portfolio to minimize risk and maximize return. This study aims to analyze the formation of the optimal portfolio on the IDX80 index for the period February 2021 - May 2022. This type of research is descriptive using quantitative methods. The sample of this research used a purposive sampling technique. This study uses secondary data with data collection methods, namely documentation and literature study. The data analysis method in this study is a single index model. The results showed that of the 53 stock samples, there were 19 optimal stocks. The expected return portfolio level is 0.20% in one day, while the portfolio risk level is 0.05%.

The Appropriate Age for Transition to Managed Accounts in a Qualified Default Framework: It Might Be Earlier than You Think

In this article, we review evidence of retirement under-saving and potential solutions, as well as empirical evidence regarding investor reaction to negative market returns. We present new corroborating evidence from fund flows around the recent volatile market events in 2020 that indicates a rising loss aversion, just as asset levels become significant with investors approaching retirement. We surmise that recent trading activity implies an aggregate investor preference for target-date fund (TDF) portfolios within a 20-year time horizon of retirement, with a volatility profile that is potentially too conservative for requisite savings in later years, due to concomitant lower portfolio return potential combined with the aggregate under-saving problem. This mismatch in investor preferences is compounded by a reactionary tactical investor trading strategy that essentially buys high and sells low. We propose that the rising investor loss-aversion problem can be mitigated by including guaranteed retirement income products in the asset mix in a defined contribution qualified default investment alternative setting to provide for the desired downside risk protection against extreme events, or else by shifting investors from a TDF to a managed account (MA) setting in later years to accomplish the same goal, or by utilizing both approaches in tandem. Given the evidence of MA utilization in increasing aggregate savings rates, reducing portfolio risk levels and its potential to meaningfully address the serious measured return drag from investor return-chasing behavior, the potential benefits can outweigh the higher average fee burden for managed advice in many cases. The question then becomes at what age do the benefits outweigh the potential costs, on average. Based upon the recent empirical evidence of TDF flow data from 2020 and data on average account balances, we propose that the appropriate age for automatic transition to managed accounts could be as early as 40.

The Financial Technologies Utilization to Increase the Competitive Advantage of Product Marketing

The purpose of this study is to analyze the formation of an optimal stock portfolio and provide the expected rate of return and minimum portfolio risk. This type of research is descriptive with a quantitative approach. The data used in this study is secondary data, consisting of closing prices, market index (LQ45 index), seven-day repo rate, beta and alpha for the periods June 2019-June 2020 and June 2020-June 2021. The data analysis method uses Microsoft Excel program to calculate LQ45 stock index in optimal portfolio formation. The results of the study show that there are seven stocks that perform well and form an optimal portfolio, which can be sorted by stock performance, namely: MIKA (44%); JPFA (24%); ITMG (12%); INKP (11%); INDF (6%); PGAS (1%), and PTPP (2%). which produces an expected portfolio return of 13.00% and a portfolio risk level of 19.22%. Of the ten sample companies used in the study, there were three issuers that were not optimal, namely:INTP;MNCN;and PTBA, due to the value of ERB C.

Finances, accounting and taxation

Methods. Economic and mathematical modeling was used in the process of revealing the specifics of the portfolio of banking products in Ukraine and its optimization in the conditions of Fintech development. Methods of economic and statistical analysis, nonlinear multiparameter optimization, and methods of dynamic stochastic programming were used to solve the tasks. Results. Based on the results of modeling the optimal structure of the investment portfolio, it was established that the use of a dynamic model is a fairly effective tool and allows to increase the profitability of the optimal portfolio of banking products in Ukraine. With the help of this tool, in the course of the research, a new, more optimal distribution of securities in the investment portfolio of banking products was obtained and recommendations were made to optimize its structure. Novelty. The scientific novelty of this work lies in the simplification of the dynamic model of investment portfolio formation with long planning horizons. Practical value of the research is that the innovation, namely, the simplification of the dynamic model of investment portfolio formation with long planning horizons, makes the model more accessible for use in practice. The practical significance of this work also lies in the possibility of wider use of this effective model, which will increase the profitability of the portfolio of banking products and reduce the level of portfolio risk.

Practical Applications of Income Investing in a Low-Yield Environment

In <b><i>Income Investing in a Low-Yield Environment</i></b>, from the Winter 2021 issue of <b><i>The Journal of Wealth Management</i></b>, <b>David M. Blanchett</b> of <b>PGIM</b> addresses the lack of relevant investment strategies aimed at income-focused investors. Drawing on historical financial data regarding portfolio returns as well as bond and equity yields, he estimates optimal portfolio risk levels for investors focused on income return rather than price return. Although Blanchett notes that bond yields have exceeded dividend yields by approximately 1.5% on average historically, in market conditions where dividend yields equal or exceed bond yields—as is true in the current market—higher equity allocations are more favorable. This is particularly true for income-driven investors with a neutral or low risk aversion, as higher equity allocations increase portfolio risk. Thus, in a low-bond-yield environment, it may be appropriate for investors looking to generate income to consider increasing equity allocations.

Analisis Portofolio Optimal CAPM dan Single Index Model pada Perusahaan IDX30

The purpose of this study is to analyze the optimal stock portfolio using the Capital Asset Pricing Model and Single Index Model, then look at the portfolio performance formed on IDX30 stocks for the period February 2017 – July 2021 with a sample of 16 stocks. This research includes quantitative descriptive. The results showed: (1) CAPM method, obtained 2 stocks, namely: BBCA and ICBP. With a portfolio return rate of 0.26%, and a portfolio risk level of 5.32%. (2) SIM method, only one share is obtained, namely: BBCA. With a portfolio return rate of 1.38%, and a portfolio risk level of 0.57%. (3) the performance of the CAPM portfolio is worth 0.0485 Sharpe index, 0.0034 Treynor, and 0.00515 Jensen. (4) SIM portfolio performance has an index of Sharpe 2.4280, Treynor 0.0154, and Jensen 0.01690. (5) stock performance in CAPM, namely BBCA and ICBP with Sharpe indexes 1.09286 and 0.17500, Treynor 0.00342 and 0.00319, and Jensen 0.00615 and 0.00116. (6) stock performance on SIM, namely BBCA with Sharpe index of 0.07857, Treynor 0.00025, and Jensen 0.00331

Treynor Ratio guided risk-return model for TSO-DSO mutualism considering energy and price volatility

Treynor Ratio (TR), a reward to volatility measure in finance, is used to quantify excess return per unit risk taken by the investor, where risk implies volatility in returns relative to market portfolio return. In this paper, TR is modified (TREP) to suggest the hourly weight of each risky energy asset at which it is worth using in the transmission and distribution levels to sustain the volatility of the market and to yield reasonable returns to both TSO and DSO. Uncertainties in the wind and solar energy sources pose energy-risk and fluctuations in grid power price poses financial risk, and both impact the TSO-DSO profit trade-off. Here, beta is used as the risk measure that evaluates the effect of uncertainty in each energy source on operators’ profit relative to the collective effect of all the energy sources available in the system. When risk is modelled using absolute measures like standard deviation or variance which are independent of market portfolio returns, it discourages risky assets more, to keep a risk-averse profile. By co-optimizing TREP values of TSO and DSO, more realistic portfolios are built by maximizing their expected profits per unit risk in relative terms. The resultant portfolio utilizes higher share of distributed energy resources (DERs) and renewable energy, thereby improving the role of DSOs in the market. The operators’ returns are found to be closer to the market portfolio returns at risks well within the market portfolio risk level.

Analisis Single Index Model dalam Membentuk Portofolio Optimal pada Saham Perusahaan Jakarta Islamic Index (JII) Periode 2016 - 2020

This study aims to determine the Jakarta Islamic Index (JII) stocks which are included in the optimal portfolio, the size of the composition of the shares in the optimal portfolio; as well as the optimal expected return and portfolio risk. The type of research used in this research is descriptive research with a quantitative approach. This study uses secondary data in the form of stock prices for the period 2016-2020. The data analysis method used is stock analysis with a single index model. The results showed that five stocks from 30 stock samples is included in the optimal portfolio. Following are the stocks that make up the optimal portfolio and their composition: INCO (29.91%), ADRO (29.60%), UNTR (16.25%), INDF (12.26%), and ICBP (11.98%). The formed portfolio will produce an expected portfolio return of 1.53% and a portfolio risk level of 1.23%.

Comparative Analysis of Expected Return and Portfolio Risk in Formation of Optimal Portfolios Using the Capital Asset Pricing Model (Capm) and Single Index Methods

&#x0D; This study aims to determine how the ratio of expected return and portfolio risk in the formation of an optimal portfolio using the Capital Asset Pricing Model (CAPM) and Single Index methods. This study uses a quantitative approach. The object of this research is the LQ45 stock for the 2017-2019 period. The research sample amounted to 30 companies selected through purposive sampling technique. The results showed that the optimal portfolio formed using the Capital Asset Pricing Model (CAPM) and Single Index methods resulted in 7 candidate stocks consisting of ICBP, BBCA, JSMR, SMGR, BBRI, MNCN, INCO. Where the results of the calculation of the level of the expected return of the portfolio using the Capital Asset Pricing Model (CAPM) method is 0.0241 and the portfolio risk level is 0.010. Meanwhile, the level of portfolio expected return using the single index method is 0.0372 and the portfolio risk level is 0.0136. After the independent t-test was carried out, there was no difference in the expected return of the portfolio between the Capital Asset Pricing Model (CAPM) and Single Index methods, while in portfolio risk testing there were differences between the CAPM and Single Index methods.

The Impact of the Exchange Rate Volatility on Stock Markets Dynamics in Tunisia and Turkey: An Artificial Neural Network Analysis

The present research provides an overview of links between exchange rate volatility and the dynamics of stock market returns in order to identify the influence of several macroeconomic variables on the volatility of stock markets, useful for political decision makers as well as investors to better control the portfolio risk level. More precisely, this research aims to identify the impact of exchange rate volatility on the fluctuations of stock market returns, considering two countries that belong to the Middle East and North Africa (MENA) zone: Tunisia and Turkey. Previous works in the literature used very specified and short periods of study, many important variables were neglected, and most of the earlier research was concentrated on the developed countries. In this research, we integrate several control variables of stock market returns that have not been simultaneously studied before. In addition, we spread out our research period up to 15 years including many events and dynamics. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and multiple regression models are first employed. Then, an Artificial Neural Network (ANN) is used and compared with the results of the multiple regression. Hence, the results show that for both Tunisia and Turkey, exchange rate volatility has a significant effect on stock market fluctuations.