Risky Portfolio Research Articles

Modeling risky portfolios with guaranteed maximize returns under a patent race

This paper studies the patent race problem of communication enterprises investing in communication technologies, and constructs a portfolio optimization model which considers the expected returns, investment risks, and replacement costs, in order to achieve the dual goals of maximizing the net investment income of backward enterprises and minimizing the expected investment risk. Through numerical experimental analysis, the optimal investment portfolio strategy under different risk levels and the impact of different risk levels on the net income of lagging company are obtained. The research results show that due to the backward research in the first stage of the backward enterprises, when their own investment decision-making power is relatively high, they can focus on the development of self-interested key technology areas in order to achieve the victory of the patent race.

A buy-hold-sell pension saving strategy

We propose a ‘buy, hold, sell’ (BHS) deterministic lifecycle strategy that involves buying and holding assets until they are sold to generate income. Savings are invested entirely into a risky portfolio until a pre-specified ‘switch age’ and then entirely into a risk-free portfolio after the switch age, followed by withdrawing during the payout phase from both portfolios based on annuitization factors that vary with age. We also allow for access to mortality credits through an insurance market. We analytically derive the dynamics of the investment strategy and show that the strategy is optimal for a range of investors with HARA risk preferences. We demonstrate numerically that the BHS strategy delivers limited loss of utility versus an optimal solution for investors with CRRA preferences and low-moderate levels of risk aversion while significantly outperforming deterministic strategies commonly seen in practice. The BHS strategy offers an attractive alternative for practical applications as it is straightforward to apply while avoiding the need for dynamic optimization and portfolio rebalancing.

CAPM Model and Optimal Risky Portfolio for American Stock Market

The stock market has high risks. The purpose of this project is to calculate the beta coefficients and build the optimal risky portfolio consisting of eight different stocks each representing different significant industries (which includes information technology, electric cars etc.) to diversify risk and gain a high return. Putting more stocks into the portfolio can help analysts carry out comprehensive analysis on different situations, periods, and types of investment portfolio, so as to disperse risks and obtain high returns, also, ensure the diversity of portfolio and a lower risk. The empirical results in this paper shows that by allocating their money appropriately in different stocks, investors can gain returns multiple times higher than putting all of them in merely one single stock, which proves the validity of this paper

Rating disclosure analysis: compensation reform consequence on counteracting biased credit ratings

This study analyzes a compensation reform proposal designed to prevent inaccurate ratings produced by credit rating agencies (CRAs). Specifically, the CRA’s incentive to exert effort to observe the portfolio’s signal and adopt the rating disclosure policy is investigated under the rating-contingent and incentive-based contract. The issuer has a risky portfolio and solicits a rating from the CRA, which endogenously observes a signal and decides on a rating disclosure policy during the rating production process. The findings reveal that the CRA exerts no effort to observe a signal and inflate the rating under the rating contingent contract. Under the incentive-based contract, the CRA always exerts an optimal effort to observe a signal and adopt the full disclosure regime. Hence, the incentive-based contract better incentivizes the CRA to exert more effort to improve rating accuracy and implement the full disclosure policy than the rating contingent contract.

Portfolio Allocation, Risk Aversion, and Digital Literacy Among the European Elderly

Abstract This study contributes to the literature on portfolio choices of elder persons by examining the influence of digital literacy on the propensity to own risky assets, when considering individual- and country-level variables. Our empirical analysis is based on data from Survey of Health, Ageing and Retirement in Europe and a set of macroeconomic indicators retrieved from international databases. To capture the impact of individual- and country-level factors, we have utilized multilevel models with a two-level sampling design. The results obtained provide evidence for a positive relationship between individual- and country-level digital literacy and portfolio riskiness, as well as heterogeneity in the portfolio choices of older individuals across the European Union (EU) countries. In addition, age is found to influence differently the portfolio riskiness of elder persons across the EU countries. Bridging the digital divide may provide access to balanced-risk portfolios for elderly persons.

Strategies for effective management of loans: A case study of keystone Bank plc

In this study, strategies for effective loans management using keystone bank plc as a case study was investigated. Survey research methodology was adopted via structured questionnaire containing both open and closed ended questions administered to 120 respondents who were management staff of Keystone bank at both head and corporate offices. Likert scale was used in the questionnaire formulation and Hypothesis was tested using chi square at 0.05 level of significance. The result revealed that corporate loan portfolio diversifications is the most effective technique of loan portfolio management. Also, preference function allows banks compare risky portfolios and rank them according to the degree of satisfaction. Problems encountered with management of loan portfolio were identified to include insider abuse, increased perception of risk regarding the solvency of other banks within the banking system and anticipated decline in the value of the collateral used by the banks to secure the loans. The result obtained indicated that there is significant relationship between loan portfolio management and productivity. It is recommended that Keystone bank should focus on exposure assessment involving the credit review and a valuation of the collateral based on the data provided by the credit applicant. Also, effective risk management based on the risk regarding the solvency of other banks within the banking system should be implemented. Adequate provisions for bad loans that have very little chance of being recovered should be provided. In conclusion, there is significant relationship between loan portfolio management and productivity.

Measuring Discrete Risks on Infinite Domains: Theoretical Foundations, Conditional Five Number Summaries, and Data Analyses

To accommodate numerous practical scenarios, in this article we extend statistical inference for smoothed quantile estimators from finite domains to infinite domains. We accomplish the task with the help of a newly designed truncation methodology for discrete loss distributions with infinite domains. A simulation study illustrates the methodology in the case of several distributions, such as Poisson, negative binomial, and their zero-inflated versions, which are commonly used in the insurance industry to model claim frequencies. Additionally, we propose a very flexible bootstrap-based approach for use in practice. Using automobile accident data and their modifications, we compute what we have termed the conditional five number summary (C5NS) for the tail risk and construct confidence intervals for each of the five quantiles making up C5NS and then calculate the tail probabilities. The results show that the smoothed quantile approach classifies the tail riskiness of portfolios not only more accurately but also produces lower coefficients of variation in the estimation of tail probabilities than those obtained using the linear interpolation approach.

Dynamic prediction of portfolio riskiness in financial markets based on multi-factor quantitative models

Dynamic prediction of portfolio riskiness in financial markets based on multi-factor quantitative models

Enhancing portfolio resilience during crisis periods: Lessons from BRICS indices and multi asset strategies

This paper uses Markowitz’s mean-variance model to construct an investment portfolio incorporating multiple assets – BRICS equity indices, Gold, crude oil, bonds, and cryptocurrencies. The optimally created risky portfolios outperform alternative portfolio optimization methods – the naive portfolio and the equal risk contribution portfolio; and established indices – the S&P 500 and the MSCI Emerging Equity Index in terms of metrics – adjusted Sharpe ratio, modified Sharpe ratio, and the modified Value at Risk. The findings are validated across different periods, including the COVID-19 period and the Russian invasion of Ukraine, including various in and out of sample periods. The findings highlight the benefits of portfolio diversity, mainly using BRICS indices, Gold, and Brent Crude oil, and challenge the notion of limited diversification benefits in BRICS indices found in previous studies. This paper further suggests the potential of emerging market bonds ETF as a diversification option during turbulent economic periods and highlights the limitations of cryptocurrencies in optimizing multi asset portfolios. By adopting the recommended multi asset portfolios, investors can enhance their risk-return trade-offs and achieve superior performance compared to the S&P500 and MSCI emerging indices. Lastly, the paper recommends future research opportunities in measuring portfolio performance and hedging strategies considering risk-adjusted return measurements, transaction expenses, and dynamic rebalancing techniques.

Demonstrating Portfolio Frontier Formation and the Capital Allocation Line Using Interactive Graphing

This paper shows how to use interactive graphing via scroll bars to create a two-security portfolio frontier and the Capital Allocation Line. The frontier is enhanced via the use of spinners to control the correlation coefficient and the weight of risky assets as well as data labels that track the weight of the stocks along the frontier as a scroll bar is incremented. The VLOOKUP function is incorporated to lookup the calculated expected return and standard deviation of the risky portfolio at a stock weight controlled by a spinner. The purpose of this paper is to aid instructors in explaining the concepts of investment opportunity sets or frontiers, including the efficient and inefficient portions and riskless lending and borrowing to students of investing.

A study of risk free and risky portfolio investment optimisation strategies under multi-objective constraints

The purpose of portfolio investment is to maximise returns while minimizing risks. For investors with different risk attitudes, this research uses Economic Model Predictive Control (EMPC) to study the multi-objective securities portfolio problem and obtains a multi-objective securities strategy that guarantees the maximum return and the minimum risk under different risk attitudes through the tracking method, considering the return, risk, and other factors of the securities portfolio. Finally, the effectiveness of the strategy is verified by simulation, and an analytical method for selecting the optimal portfolio of securities is proposed.

Tensors Associated with Mean Quadratic Differences Explaining the Riskiness of Portfolios of Financial Assets

Bound choices such as portfolio choices are studied in an aggregate fashion using an extension of the notion of barycenter of masses. This paper answers the question of whether such an extension is a natural fashion of studying bound choices or not. Given n risky assets, the question of why it is appropriate to treat only two risky assets at a time inside the budget set of the decision-maker is handled in this paper. Two risky assets are two goods. They are two marginal goods. The question of why they always give rise to a joint good inside the budget set of the decision-maker is addressed by this research work. A single risky asset is viewed as a double one using four nonparametric joint distributions of probability. The variability of a joint distribution of probability always depends on the state of information and knowledge associated with a given decision-maker. For this reason, two variability tensors are defined to identify the riskiness of the same risky asset. A multilinear version of the Sharpe ratio is shown. It is based on tensors. After computing the expected return on an n-risky asset portfolio, its riskiness is obtained using mean quadratic differences developed through tensors.

Investment Portfolio Optimization in Indonesia (Study On: Lq-45 Stock Index, Government Bond, United States Dollar, Gold and Bitcoin)

In forming their portfolios, investors should analyze the risk and return of each investment instrument. This is aimed at preventing investors from speculating and gambling with their investments. Conducting an investment portfolio optimization study on LQ-45 stock index, government bond, USD, gold, and Bitcoin can provide valuable insights due to unique market characteristics in Indonesia. This research analyzes the formation of investment instruments over the last 60 months, specifically from January 2018 to December 2022. The research method used in this study is quantitative research aimed at selecting several investment instruments for a portfolio in Indonesia. The portfolio aims to minimize risk and maximize return using the Markowitz method, also known as the optimal portfolio. To fulfill the objectives of this research, data on the prices of each instrument are required. An optimal portfolio can be obtained by combining two instruments: 18% bitcoin and 82% gold. This optimal portfolio can achieve an expected return of 1.29% with a risk level of 5.15%. Considering a risk-free rate of 0.375%, this portfolio forms a slope of 0.1775, which is the largest slope formed between the combination of risk-free instruments and risky portfolios. Investors should allocate their funds more wisely, considering not only the highest return but also the associated risk. High returns often come with high risks, so investors need to assess the risk-return trade-off before making investment decisions.

Evidence of the Impact of Coskewness on the Low Risk Anomaly in European Stocks

This paper investigates the low risk anomaly, which suggests that less risky stocks outperform riskier ones. Focusing on the European stock market, the present study examines the influence of coskewness, a measure of asymmetry in stock returns with respect to the market return. Stocks are sorted into 2x5 quintile portfolios based on coskewness and beta volatility. Regression analysis using Fama-French three and five factor models reveals a significant low risk anomaly in the low coskewness category, where less risky portfolios consistently outperform riskier ones. However, as coskewness increases, the low risk anomaly weakens and loses significance. In the high coskewness category, less risky portfolios no longer consistently outperform riskier ones. In other words, accounting for coskewness significantly lowers the profitability of low risk and betting-against-beta strategies in Europe. These findings enhance the understanding of the relationship between risk and returns in the European market.

Farmers’ risk rating and crop portfolio choice in Kewot Woreda, North Ethiopia

Production risk and marketing risks are considered as critical factors in shaping farm decisions. This study is concerned with assessing crop risk rating by farmers and its impact on their crop portfolio choice. 392 farmers who were selected using “Multi-stage Random Sampling” technique and qualitative data from focus group discussions were employed in the analysis. The risk rating was done using a three-stage rank and Ordinary Least Square econometric regression was applied to identify the determinant factors. The study found sorghum, teff, onion and mung bean as major crops covering 95% of the total cultivated land. Results from descriptive statistics revealed that 73% of the respondents prioritize marketing over consumption in their crop choice decisions. Mung bean was identified as a riskier crop while onion risk rating showed higher variation among respondents. Findings on determinants of riskiness of the farmers’ crop portfolio choice showed that livestock ownership, education and number of crop types found positively associated with riskier crop portfolio while irrigation use and gender of the household head associate with portfolio riskiness negatively. The major contribution of this study is its explicit treatment of farmers own risk rating and farmers also rated crops not grown by them. The key policy implication is that to manage marketing issue better and achieve optimal crop choice, there is a need to develop marketing insurance and promote pre-contract for riskier crops. Keywords: Risk variation, consumption decision, contract, farm household, insurance

Role of Variable Renewable Energy Penetration on Electricity Price and its Volatility across Independent System Operators in the United States

The US electrical grid has undergone substantial transformation with increased penetration of wind and solar forms of variable renewable energy (VRE). Despite the benefits of VRE for decarbonization, it has garnered some controversy for inducing unwanted effects in regional electricity markets. In this study, the role of VRE penetration is examined on the system electricity price and price volatility based on hourly, real-time, historical data from six independent system operators (ISOs) in the US using quantile and skew t-distribution regressions. After correcting for temporal effects, we found an increase in VRE penetration is associated with a decrease in system electricity price in all ISOs studied. The increase in VRE penetration is associated with a decrease in temporal price volatility in five out of six ISOs studied. The relationships are nonlinear. These results are consistent with the modern portfolio theory where diverse volatile assets may lead to more stable and less risky portfolios.

Performance Analysis of Gold- and Fiat-Backed Cryptocurrencies: Risk-Based Choice for a Portfolio

This study aims to investigate the performance and behavior of fiat- and gold-backed cryptocurrencies to support stakeholders through the preparation of a portfolio from 1 January 2021 to 30 June 2022. Moreover, while searching for a hedge or a diversifier to construct a less risky portfolio with handsome returns, the prices of fiat-backed cryptocurrencies report high fluctuation during the sample period. ARIMA-EGARCH models have been employed to examine the volatile behavior of these cryptocurrencies. The empirical results are mixed as Bitcoin has been highly volatile during the economic recession. Due to its volatility, investors seek a safe haven. Ripple, on the other hand, shows low risk compared to Bitcoin. The results further reveal that PAX gold is more volatile than PM gold, while Bitcoin, being a highly traded cryptocurrency, is significantly correlated to other cryptocurrencies. The implications of this research showing the volatility of gold- and fiat-backed cryptocurrencies are equally important to stakeholders, such as investors, and policymakers.

Emotional Biases and Equity Investment Decision of Individual Investors

Emotional bias is the presentation of felling of the people about acting something. It affects the human behavior which ultimately influence the decision-making behavior of the individual investors. This study focuses on examining the influence of emotional biases on equity investment decision of individual investors. This study was based on quantitative approach of research with the sample size of 385 individual investors. However, the response rate of the study was 79% while the usable response was only 68.31% of the sample. Emotional biases are measured by loss aversion bias, overconfidence bias, endowment bias, self-control bias, regret aversion bias and status quo bias. Evidence indicates that loss aversion bias, overconfidence bias, self-control bias and regret aversion bias had significant positive influence on equity investment decision. However, endowment bias and status quo bias had no significant contribution to the equity investment decision of the individual investors in Chitwan. Hence, the equity investment decision of investors in Chitwan is broadly depend upon easily and readily available information and strongly based on their own beliefs and emotions without resorting to deep research, investigation, information processing and analysis of the securities and the market with fundamental and technical analysis that leads to overtrading and overreaction to the market, holding on too losing stock enormously, holdings on to enormous risky portfolios, selling off gaining portfolio to early and finally, making investment decisions without proper analysis of the information, decisions are based on their own moods, beliefs, emotions, past experience and stock of knowledge.

Portfolio Optimization for US. Stock with Mean-variance Model, CAPM, Fama French Three-factor Model

Applying modern investment theories to construct investment portfolios is a crucial way for investors to reduce risks and obtain high returns in the investment market. This paper selects eight stocks from eight industries of the US stock market and constructs a portfolio based on their historical return data over the past five years. This study uses the CAPM model and the Fama French three-factor model to estimate the expected returns of these eight stocks, and then applies the mean-variance model to construct the optimal risky portfolio under both expectations. Under the CAPM model, the expected monthly returns for these eight stocks are BKNG (1.05%), EQR (0.80%), MPC (1.38%), NTDOY (0.64%), ON (1.44%), PFE (0.68%), SBUX (0.86%) and FCX (1.60%) respectively. Under Fama-French three factor model, the expected monthly returns for these eight stocks are BKNG (1.21%), EQR (0.90%), MPC (2.06%), NTDOY (0.55%), ON (1.82%), PFE (0.60%), SBUX (0.82%) and FCX (1.96%) respectively. The optimal portfolio weights obtained by using CAPM model estimation results are BKNG (7.97%), EQR (23.72%), MPC (6.02%), NTDOY (11.61%), ON (8.24%), PFE (18.66%), SBUX (10.61%) and FCX (13.16%) respectively. Under Fama French three factor model, the weight distribution of the portfolio is as follows BKNG (3.01%), EQR (27.04%), MPC (33.89%), NTDOY (2.40%), ON (15.85%), PFE (4.98%), SBUX (0.00%) and FCX (12.83%). These results can provide a reference for investors who are willing to invest in US stock market, which will help them reduce investment risks and increase returns.

Improving the Process of Early-Warning Detection and Identifying the Most Affected Markets: Evidence from Subprime Mortgage Crisis and COVID-19 Outbreak-Application to American Stock Markets.

Stock-market-crash predictability is of particular interest in the field of financial time-series analysis. Famous examples of major stock-market crashes are the real-estate bubble in 2008 and COVID-19 in 2020. Several studies have studied the prediction process without taking into consideration which markets might be falling into a crisis. To this end, a combination analysis is utilized in this manuscript. Firstly, the auto-regressive estimation (ARE) algorithm is successfully applied to electroencephalography (EEG) brain data for detecting diseases. The ARE algorithm is employed based on state-space modelling, which applies the expectation-maximization algorithm and Kalman filter. This manuscript introduces its application, for the first time, to stock-market data. For this purpose, a time-evolving interaction surface is constructed to observe the change in the surface topology. This enables tracking of the stock market's behavior over time and differentiates between different states. This provides a deep understanding of the underlying system behavior before, during, and after a crisis. Different patterns of the stock-market movements are recognized, providing novel information regarding detecting an early-warning sign. Secondly, a Granger-causality time-domain technique, called directed partial correlation, is employed to infer the underlying interconnectivity structure among markets. This information is crucial for investors and market players, enabling them to differentiate between those markets which will fall in a catastrophic loss, and those which will not. Consequently, they can make successful decisions towards selecting less risky portfolios, which guarantees lower losses. The results showed the effectiveness of the use of this methodology in the framework of the process of early-warning detection.