Alternative Portfolio Research Articles

Revisiting the Discussion on the Effectiveness of Alternative Portfolio Models: Out-of-sample Analysis of Brazil’s Equity Market, 2015-23

Revisiting the Discussion on the Effectiveness of Alternative Portfolio Models: Out-of-sample Analysis of Brazil’s Equity Market, 2015-23

A Sparse Approximate Factor Model for High-Dimensional Covariance Matrix Estimation and Portfolio Selection

Abstract We propose a novel estimation approach for the covariance matrix based on the l1-regularized approximate factor model (AFM). Our sparse approximate factor (SAF) covariance estimator allows for the existence of weak factors and hence relaxes the pervasiveness assumption generally adopted for the standard AFM. We prove the consistency of the covariance matrix estimator under the Frobenius norm as well as the consistency of the factor loadings and the factors. Our Monte Carlo simulations reveal that the SAF covariance estimator has superior properties in finite samples for low and high dimensions and different designs of the covariance matrix. Moreover, in an out-of-sample portfolio forecasting application, the estimator uniformly outperforms alternative portfolio strategies based on alternative covariance estimation approaches and modeling strategies including the 1/N-strategy.

Flexibility in Enhancing Supply Chain Resilience: Developing a Resilience Capability Portfolio in the Event of Severe Disruption

The current study aimed to determine alternative configurations of supply chain resilience strategies for managing supply chain performance (SCP) during a severe disruption. To do this, a multi-method and multi-study approach was adopted. Phase 1 of the study employed a qualitative approach to explore supply chain risk factors and their mitigation strategies during the COVID-19 pandemic. In Phase 2, the quality function deployment technique was used alongside quantitative case studies to determine the most critical risk factors and most crucial resilience strategies. The final phase, Phase 3, used fuzzy set qualitative comparative analysis to determine the alternative portfolio of strategies for SCP to create flexibility when employing resilience strategies. The results revealed that implementing resilience strategies alone is ineffective in improving SCP, while resilience strategies combined with the nullification of risk factors enhance SCP. The study also revealed two alternative configurations of resilience strategies to tackle the relevant risk factors. These findings can be used to guide managers toward identifying the most suitable configuration of resilience strategies to manage severe and unprecedented supply chain risk. The alternative configurations of resilience strategies can also provide flexibility to managers in deciding the best course of action for their firms.

The effect of the 2008–09 short selling sales ban on UK security equities in relation to market metrics of volatility, liquidity, and price discovery

In this study we look at the Financial Services Authority imposed short sale ban of UK financial equities in the 2008–2009 financial crisis to explore the effects of the short selling ban on liquidity, volatility, and price discovery on alternative portfolios of UK equity stocks. To this end we employ a control portfolio as well a portfolio of banned stocks. By using a GARCH model to explore the effects of short selling on portfolios’ returns volatility, we find a decline of volatility for selected UK financial stocks during the short sale restriction period. Further, we employ a bid-ask spread model to explore the effects of short selling on liquidity and find that liquidity deteriorates during the short sale ban period. We also employ a Wald-Wolfowitz Runs Test to investigate the effects of the short sale ban on price discovery and find that price discovery deteriorated during the short sale ban period. Finally, we examine the causal relationships among the market metrics in order to provide additional insight into the structure of the UK equity market during the period under study.

НАУЧНО-МЕТОДИЧЕСКИЙ ПОДХОД К ОБОСНОВАНИЮ ВЫБОРА СТРАТЕГИИ КОНКУРЕНТНОГО ПОВЕДЕНИЯ ПРЕДПРИЯТИЙ СФЕРЫ ТЕЛЕКОММУНИКАЦИОННЫХ УСЛУГ

The article reveals the content of a scientific and methodological approach to justifying the choice of a strategy for the competitive behavior of enterprises in the telecommunications services sector, which is based on methodological tools for analyzing the influence of environmental factors, assessing the state of competition in the product market and determining the level of competitiveness of telecommunications enterprises. The proposed approach is adapted to the industry specifics of the activities of enterprises in the telecommunications services sector and allows us to quantitatively describe the algorithm for constructing an optimal portfolio of strategic alternatives for competitive behavior.

Performance of Risk Parity Strategy in Developed Markets

This study examines the performance and effectiveness of the Risk Parity (RP) strategy in strategic asset allocation specifically utilizing the inverse volatility (IV) approach. The study utilizes data from 13 developed countries, encompassing stocks and government data, to analyze the performance of RP portfolio. A comparative analysis is conducted between the RP portfolio, traditional 60/40 and equal-weighted portfolios across significant market crises, including the dot-com crisis, financial crisis, sovereign debt crisis, and COVID-19 pandemic. This study reveals that inverse volatility approach or a portfolio with a higher weight towards safer assets compared to riskier assets delivers superior risk-adjusted returns. It presents evidence that challenges the notion of the market or 60/40 portfolio as an efficient portfolio and suggests that an alternative portfolio such as RP may offer superior efficiency in terms of risk and return. Moreover, the study examines the role of leveraging in RP portfolios, highlighting how investors can align risk levels with benchmark portfolios while achieving higher risk-adjusted returns. The findings highlight the substantial impact of leveraging on RP portfolio performance, demonstrating their resilience and superiority even in challenging market conditions.

Contextual combinatorial bandit on portfolio management

Portfolio optimization is a classic problem in finance, and assumptions are generally accepted about the stochastic dynamics of prices and market information. The Linear Upper Confidence Bound (LinUCB) algorithm based on bandit learning is a typical reinforcement learning approach that can be a data-driven approach to the portfolio problem. Most investors may have inconsistent preferences for different assets; therefore, the reward function needs to be designed to balance exploration (allocating part of the investment to searching among other alternative portfolios) and exploitation (concentrating the investment on the historically best portfolio). The study consists of developing a two-stage investing strategy that uses the supervised adaptive decision tree approach to build a pool of candidate portfolios and the reinforcement LinUCB algorithm to derive a portfolio update rule with respect to a specific utility function. The two-stage strategy has been tested on CSI300 constitute stocks listed on the Shanghai Stock Exchange. The accumulated returns, as well as other performance measures (e.g., maximum drawdown, Sharpe ratio, and information ratio) at different testing periods achieved by the model, can well outperform the benchmark index.

Assessing the sustainability of the city-port transformations: Multi-Criteria Decision Analysis (MCDA) for alternatives portfolio selection

In recent years, the EU has sought to define sustainable transition pathways towards more equitable, prosperous, and inclusive urban and territorial models, capable of responding to the rapid degradation of ecosystems, and improving quality of life of citizens. In this context, ports have been recognised as key strategic hubs not only for economic and logistical competitiveness, but also to generate employment and investment opportunities, and to address the challenges of the climate change. The research presents a multi-scale, multi-dimensional and multi- group methodological framework to support decision-making processes related to the development of sustainable transformations of port cities, capturing the complexity of interactions and conflicts. Integrating Multi-Criteria Decision Analysis (MCDA) approaches and Problem Structuring Methods (PSM), the proposed methodology aims to address the following gaps identified in the literature: (i) a scattered application of multi-group methods; (ii) the lack of social instances within the decision problem; (iii) a weak sustainability perspective; (iv) the use of one-dimensional scale assessment in sectoral studies. The case study of the city-port of Gela in Sicily (Italy) provided an opportunity to test the proposed methodology and to integrate multi-dimensional sustainability issues into feasibility studies, promoting a more balanced relationship between city and port. The interdependencies between environment, society and economy allowed MCDA to be identified as a suitable approach to address complex decision-making and support the sustainability assessment of port areas transformation. Two multi-criteria and multi-group evaluation methods guided the decision-making process to select a portfolio of preferred alternatives by assessing technical, environmental, and economic impacts and analysing stakeholder conflicts and coalitions. The process was carried out as follows: on the one hand, a multidimensional impact matrix integrating Key Performance Indicators (KPIs) divided into technical, financial-economic, and environmental categories through the application of the multi-criteria method EVAMIX; on the other hand, a social assessment with a dendrogram of coalitions derived from the application of the multi-group method NAIADE by modelling stakeholders’ preferences regarding a portfolio of alternatives related to the decision problem.

Cost of trading factor strategies

Recent academic literature claims that style premia are not robust to trading costs. Several papers written by practitioners counter that the assumptions made by academics on how style portfolios rebalance are too conservative. In this paper we analyse the cost of trading factor strategies using a market impact model estimated from actual transactions data, realistic and multiple portfolio construction techniques and up-to-date data. Our results suggest that, for realistic portfolio sizes, the typical costs of rebalancing a single-factor strategy are unlikely to erode the factor premium.
 In addition, by building portfolios across different factors and with alternative portfolio construction techniques, we are able to assess the degree of similarity amongst strategies over time, which can be viewed as a measure of crowding. The properties of our new crowding indicator are illustrated through an extensive empirical investigation. We then assess its relation to volatility, liquidity and market impact and find that increases in our crowding measure tend to be followed by increases in the volatility of a strategy’s returns.

Petroleum exploration portfolios generated with different optimization approaches: Lessons for decision-makers

Petroleum explorers recognize the value of portfolio management, but many have not seen it effectively applied in the industry or do not understand how subsurface assessments by geoscientists link to portfolio analysis and decision-making by managers. Here, we constructed a fictitious but realistic inventory of 50 prospects distributed across plays of various maturity and then generated portfolios with rank and cut, efficient frontier and portfolio filtering approaches. Different analysis and optimization techniques have varying degrees of use and success in aligning portfolio selections with specific strategic goals and available drilling budget. Portfolios ranked and cut by mean risked volumes, economic probability of success (PoS) and expected monetary value can deliver good exploration results. However, they are sub-optimal when compared with similar efficient frontier portfolios optimized for expected total recoverable resource volume and its standard deviation. Portfolios ranked and cut by geological PoS values can deliver the greatest geological success rate among all portfolios, but not much resources and monetary value when the low-risk prospects have relatively small success-case volumes. Portfolio filtering helps find suitable alternative portfolios. Better portfolios include prospects from the emerging play setting characterized by greatest geological and economic PoS values and success-case volumes, but exclude high-risk prospects from frontier play settings. The formal portfolio optimization approaches are blind to the fact that companies have to drill high-risk frontier prospects to de-risk plays and provide access to prospects in emerging plays. Portfolio analysis and optimization techniques must be coupled with good understanding of exploration business and long-term strategy to deliver excellent sustainable exploration results.

Risk-Efficient Coverage Selection Strategies for the Pasture, Rangeland, Forage (PRF) Insurance Program

AbstractThe Pasture, Rangeland, Forage (PRF) insurance program is aimed to assist producers to manage the risk of forage loss due to the lack of precipitation. However, limited attention has been given to understanding the implications of policyholders’ coverage selection decisions. In this study, three alternative risk-efficient portfolio selection strategies are assessed in to the context of the PRF program. Proposed methods consider all the decision parameters and program restrictions, and they highlighted the underlying relationships between expected revenue, risk, and choice of the coverage parameters. Selection strategies are illustrated by examining the optimal coverage for a grid in South Texas.

The Portfolio Composition Effect

Does the evaluation of a portfolio of stocks depend on its composition of winner and loser stocks? To test this, we define a simple, counting-based measure of performance - the number of winner relative to the number of loser stocks in a portfolio - and examine how this composition measure affects individuals' willingness to invest in a portfolio. We derive testable predictions for the proposed composition measure from a framework which combines category-based thinking with mental accounting. Consistent with our predictions, we find across all experiments that individuals allocate larger investments to portfolios with more winner than loser stocks relative to alternative portfolios with more loser than winner stocks, although both portfolios (1) have realized identical overall portfolio returns and (2) show identical expected risk-return characteristics. Building on our experimental findings, we analyze fund flows of exchange-traded funds on leading equity market indices. We identify that the proposed portfolio composition measure is positively related to future net fund flows.

Maximum Drawdown, Recovery, and Momentum

We empirically test predictability on asset price using stock selection rules based on maximum drawdown and its consecutive recovery. In various equity markets, monthly momentum- and weekly contrarian-style portfolios constructed from these alternative selection criteria are superior not only in forecasting directions of asset prices but also in capturing cross-sectional return differentials. In monthly periods, the alternative portfolios ranked by maximum drawdown measures exhibit outperformance over other alternative momentum portfolios including traditional cumulative return-based momentum portfolios. In weekly time scales, recovery-related stock selection rules are the best ranking criteria for detecting mean-reversion. For the alternative portfolios and their ranking baskets, improved risk profiles in various reward-risk measures also imply more consistent prediction on the direction of assets in future. Moreover, turnover rates of these momentum/contrarian portfolios are also reduced with respect to the benchmark portfolios. In the Carhart four-factor analysis, higher factor-neutral intercepts for the alternative strategies are another evidence for the robust prediction by the alternative stock selection rules.

The Impact of Covid-19 on First Year Undergraduate Nuclear Medicine Students' Practical Skills Training.

RATIONALE: Clinical placement is an important component of any undergraduate nuclear medicine program. For first year students, it is an introduction to clinical nuclear medicine which helps them better understand the profession as well as consolidate their learning to date. At The University of Newcastle, Australia part of the clinical placement course includes radiopharmacy laboratory sessions in a simulated environment to develop necessary skills and confidence. Due to the Covid-19 pandemic, restrictions were put in place that meant that clinical placements for first year students were cancelled and time in the radiopharmacy laboratory was reduced from two hours to one hour per session. The aim of this study is to evaluate whether a clinical alternative portfolio in lieu of clinical placement was effective in increasing the students' knowledge and skills in nuclear medicine practice and if specifically developed instructional videos for preparation of the radiopharmacy laboratories compensated for the reduced time. METHODS: A paper-based survey was given to the 50 students enrolled in the first-year professional practice course. This survey containing 56 questions consisted of both open questions and closed Likert-scale questions about the changes to the radiopharmacy laboratories and the clinical alternative portfolio in two separate sections. Quantitative and qualitative analysis was performed on the resulting data. RESULTS: There was a 94% response rate to the survey. The majority of students watched the preparatory radiopharmacy videos at least once and strongly agreeing that each video adequately prepared them for the associated lab session. Just over half (51%) of the students though the reduced time in the lab was sufficient to complete the required tasks. The majority of students agreed that the modules included in the clinical alternative portfolio increased their knowledge of nuclear medicine practice. CONCLUSION: In spite of the restrictions put in place due to Covid-19, the learning outcomes of the first-year nuclear medicine professional practice course were met. The preparatory videos for the radiopharmacy laboratories and the clinical alternative portfolio was positively received and gave the students a good introduction to clinical nuclear medicine.

Copula methods for evaluating relative tail forecasting performance

PurposeThe authors apply their method to analyze which portfolios are capable of providing superior performance to those based on the Sharpe ratio (SR).Design/methodology/approachIn this paper the authors illustrate the use of conditional copulas for identifying differences in alternative portfolio performance strategies. The authors analyze which portfolios are capable of providing superior performance to those based on the SR.FindingsThe results show that under the Gaussian copula, both expected tail ratio (ETR) and skewness-kurtosis ratio portfolios exhibit remarkably low correlations respecting the SR portfolio. This means that these two portfolios are different respecting the SR one. The authors also find that copulas which focus on either the upper tail (Gumbel) or the lower tail (Clayton) render significant differences. In short, the copula analysis is useful to understand what kind of equity-screening strategy based on its corresponding performance measure (PM) performs better in relation to the SR portfolio.Practical implicationsCopula methods for evaluating relative tail forecasting performance provide an alternative tool when forecast differences are very small or found non statistically significant through standard tests.Originality/valueOur copula methods to evaluate models' performance differences are significant because when models' performance is rather similar, conclusions on statistical differences, can be defective as they may hinge on the subsample type or size used, leading to inefficient investment decisions. Our method based in copula is novel in this research topic.

Portfolio assessment in the frame of multi-layered peer coaching: An alternative portfolio for pre-service teachers

Literature has shown that portfolio assessment is meaningful for supporting student-teachers� language and teaching skills. However, integrating portfolio assessment and Multi-layered Peer Coaching (MPC) is still scarcely done. To fill in this empirical void, this study elaborates the implementation of portfolio assessment in Micro Teaching class integrated with MPC. This is a case study with 22 pre-service teachers and one teacher educator as data sources. The data were collected through observation and documentation, and analyzed by using interactive model of analysis. It reveals that the artifacts of portfolio are dominated by collaboration-based products, comprising 13 artefacts. This portfolio may be developed to accommodate alternative assessment in Micro Teaching Class.

Portfolio Optimization in DSE Using Financial Indicators, LSTM & PyportfolioOpt

Due to its suitable power to anticipate using Non-Linear forecasting methodologies, LSTM (Long Short-Term Memory) has changed the approach to time series prediction several folds. Process compatibilities of technical identifiers and various financial benchmarks that are defining financial decision-making in international markets are affecting Bangladesh Market as well. Issues like MACD and RSI as a technical investigator and financial ratio aspects of EPS and PE Ratio play an important role in the selection of assets in DSE. Given adequate training in line with intended functionality models, RNN has the potential to think through in a similar manner and the probable results are exhibited in this paper. Because of the Gated Structure, which refers to retaining important information and discarding irrelevant information through diminishing gradient and exploding gradient, LSTM has achieved significant advances in nonlinear forecasting that is based on human behavior. In this study, we compared two alternative portfolios that will be dependent on LSTM's future forecasting capabilities in terms of projecting the greatest potential output, which is demonstrated using Portfolio Optimization principles.

MODELLING ON-FARM DIVERSIFICATION THROUGH PORTFOLIO OPTIMIZATION AND GOAL PROGRAMMING: A CASE STUDY FROM BOLIVIA

Modern Portfolio Theory provides a theoretical framework for agricultural risk reduction. Powerful yet accessible tools have been developed to optimize scarce capital/labor allocation to increase returns and reduce correlated risks via diversification. Such tools are used to assess rural livelihood diversification induced by an incentive-based program for watershed conservation piloted between 2003 and 2011 in a context of rural poverty in Bolivia. The tools assembled and tested in this study may provide low-cost diagnostics to improve implementers’ understanding of risks and returns in a specific rural context. Comparing alternative portfolio frontiers may represent a useful and transformative tool to understand socio-ecological systems such as watersheds, facilitating regime shifts that benefit ecosystem services and livelihoods.

Integrated assessment modeling reveals near-channel management as cost-effective to improve water quality in agricultural watersheds

Despite decades of policy that strives to reduce nutrient and sediment export from agricultural fields, surface water quality in intensively managed agricultural landscapes remains highly degraded. Recent analyses show that current conservation efforts are not sufficient to reverse widespread water degradation in Midwestern agricultural systems. Intensifying row crop agriculture and increasing climate pressure require a more integrated approach to water quality management that addresses diverse sources of nutrients and sediment and off-field mitigation actions. We used multiobjective optimization analysis and integrated three biophysical models to evaluate the cost-effectiveness of alternative portfolios of watershed management practices at achieving nitrate and suspended sediment reduction goals in an agricultural basin of the Upper Midwestern United States. Integrating watershed-scale models enabled the inclusion of near-channel management alongside more typical field management and thus directly the comparison of cost-effectiveness across portfolios. The optimization analysis revealed that fluvial wetlands (i.e., wide, slow-flowing, vegetated water bodies within the riverine corridor) are the single-most cost-effective management action to reduce both nitrate and sediment loads and will be essential for meeting moderate to aggressive water quality targets. Although highly cost-effective, wetland construction was costly compared to other practices, and it was not selected in portfolios at low investment levels. Wetland performance was sensitive to placement, emphasizing the importance of watershed scale planning to realize potential benefits of wetland restorations. We conclude that extensive interagency cooperation and coordination at a watershed scale is required to achieve substantial, economically viable improvements in water quality under intensive row crop agricultural production.

Expected return, volume, and mispricing

We find that expected return is related to trading volume positively among underpriced stocks but negatively among overpriced stocks. As such, trading volume amplifies mispricing. Our results are robust to alternative mispricing and trading volume measures, alternative portfolio formation methods, and controlling for variables that are known to have amplification effects on mispricing. By attributing trading volume to investor disagreement, we show that our results are consistent with the recent theoretical model of Atmaz and Basak (2018) in that investor disagreement predicts stock returns conditional on expectation bias.