Portfolio Optimization Approach Research Articles

An R2R approach for stock prediction and portfolio optimization

An R2R approach for stock prediction and portfolio optimization

Multispecies Portfolios of U.S. Marine Fisheries: Ecosystem‐Based Fisheries Management Reduces Economic Risk

Fisheries management has focused on single stocks, not directly accounting for species interactions, and usually only considering economic factors in post hoc analysis. This approach has been successfully applied for many species over many years, but may also inadvertently result in greater risks being incurred. We demonstrate a portfolio optimization approach to inform a broader set of fishery concerns as a way to emphasize species interactions and economic considerations in resource management decision making. The approach can use readily available data on landings and revenue to generate easily digestible indicators of risk, namely the risk gap (i.e., the difference between actual and optimal portfolio values). Herein, we calculate portfolio efficiency frontiers that minimize risk for target revenue outcomes and resulting risk gaps for commercial fisheries using the top 25 landed‐value species in six U.S. fisheries regions. Most regions exhibited a risk gap on the order of US$20–50 million, collectively on average over $250 million. Risk gaps can be used as ecosystem‐level indicators to inform managers of the unnecessary risk being assumed for a given level of revenue for a portfolio of fisheries stocks, which can move us towards operational ecosystem‐based fisheries management.

Portfolio optimization for sustainable investments

In mean-variance portfolio optimization, multi-index models often accelerate computation, reduce input requirements, facilitate understanding, and allow easy adjustment to changing conditions more effectively than full covariance matrix estimation in many situations. In this paper, we develop a multi-index model-based portfolio optimization approach that takes into account aspects of the environment, social responsibility and corporate governance (ESG). Investments in assets related to ESG have recently grown, attracting interest from both academic research and investment fund practice. Various literature strands in this area address the theoretical and empirical relation among return, risk and ESG. Our portfolio optimization approach is flexible enough to take these literature strands into account and does not require large-scale covariance matrix estimation. An extension of our approach even allows investors to empirically discriminate among the literature strands. A case study demonstrates the application of our portfolio optimization approach.

Safety assessment of cryptocurrencies as risky assets during the COVID-19 pandemic

This study examines the safe-haven properties of cryptocurrencies during the COVID-19 pandemic by exploring various cryptocurrency allocation strategies and their impact on downside risk compared to holding only the stock market index. Spanning from 2019 to 2021, the analysis includes seven cryptocurrencies—Bitcoin, Ethereum, Litecoin, Dash, Maker, Monero, and NEO—and six stock market indexes—SP500, FTSE, CAC40, IBEX, AEX, and DJIM. The research introduces a portfolio optimization approach focused on mitigating significant losses during crises, employing Value at Risk (VaR), Modified Value at Risk (MVaR), and Conditional Value at Risk (CVaR) metrics across different portfolio compositions. Findings highlight several key insights: higher cryptocurrency allocations correlate with increased downside risk, with none of the studied cryptocurrencies acting as a safe haven for international equity indexes during the COVID-19 pandemic. Relative MVaR exceeds CVaR across most combinations, and Maker demonstrates the highest downside risk, while Bitcoin and Litecoin exhibit the lowest risk levels based on CVaR and MVaR, respectively. Integration of the FTSE index is associated with peak risk levels, whereas incorporating DJIM and AEX shows reduced risk exposure. The study contributes novel insights into cryptocurrency risk dynamics during the COVID-19 pandemic, offering valuable implications for risk management strategies in investment portfolios.

Optimal Portfolios for Large Investors in Housing Markets Under Stress Scenarios: A Worst-Case Approach

AbstractThe study focuses on constructing a mathematical housing market threatened by a major catastrophic event or crash. It incorporates the worst-case scenario portfolio optimization problem as introduced in Korn and Wilmott (Int J Theor Appl Finance 5(02):171–187, 2002) into housing markets. The standard stochastic models for housing markets assume a geometric Brownian motion and neglect sudden housing price falls during crash times. However, the size, timing, and frequency of crashes have to be included in such models. By incorporating the worst-case portfolio optimization problem into housing markets, this study introduces a methodology to construct portfolios for large investors that are robust and resilient to extreme housing market conditions. The worst-case portfolio optimization approach can be used in housing markets to incorporate stress scenarios, minimize potential losses, utilize mathematical techniques, and manage housing investment risk effectively. This study provides valuable insights for large investors seeking to construct housing portfolios prioritizing downside protection and minimizing losses in extreme housing market conditions. Utilizing numerical illustrations, it provides insights into portfolio construction, demonstrating the effectiveness of adjusting portfolios to mitigate downside risks during housing market crises. The results highlight dynamic portfolio management’s significance in safeguarding wealth when housing prices undergo significant fluctuations.

A novel regret-rejoice cross-efficiency approach for energy stock portfolio optimization

As climate change intensifies, more and more people realize that it is necessary and urgent to optimize energy structure through constructing energy asset portfolios. However, most of existing literature on energy asset portfolio models neglects real market restrictions, and the related portfolio performance evaluation approaches ignore the impact of the psychological factors of decision makers (DMs) on evaluation results, which limit their applications. To overcome these limitations, we first present a mean-WMCVaR (MWMC) model for energy stock portfolio selection with real features. Then, we employ the proposed MWMC model and its closely related works to yield a set of investment schemes and determine their characteristic index values including mean, WMCVaR, carbon intensity and ESG. Subsequently, we present a novel regret-rejoice cross-efficiency (NRRCE) approach to evaluate the performance of these investment schemes, and then make a comparison with the ones of the other seven competitive evaluation approaches by using an empirical example. Empirical results show that the proposed MWMC model can yield an optimal investment scheme with a better trade-off between economic and ecological benefits, and our NRRCE approach can effectively provide reasonable and reliable evaluation results for different investment schemes.

Harnessing Cognitively Inspired Predictive Models to Improve Investment Decision-Making

In the last years, researchers and practitioners have focused on defining portfolio optimization approaches. This task aims to identify a suitable distribution of assets for maximizing profits and minimizing risks, also offering protection against unexpected market behaviors. Nevertheless, the state-of-the-art approaches encounter significant limitations due to the complex nature of the task: (1) forecasting of non-stationary, non-linearity and volatile stock price; (2) budget allocation over different stocks satisfying multi-objective objective function; (3) risk costs can significantly affect the effectiveness of the designed approaches. In this paper, we propose a cognitively inspired framework for portfolio optimization by integrating deep learning-based stock forecasting for maximizing the revenue and portfolio diversification and Shape Ratio for minimizing the risk. Furthermore, the cognitively inspired forecasting module relies on the LSTM-based approach which combines historical financial data and technical indicators. Hence, this approach addresses the portfolio optimization task with the aim of designing more and more cognitive agents that perform autonomous actions for supporting decision-making. To make these agents cognitive, we further integrate stock forecasting into the portfolio optimization model, also investigating the main factors affecting both stock forecasting and portfolio optimization tasks. The proposed framework has been evaluated in two stages on a real-world dataset, composed of four years of information about stocks from six different areas. Firstly, we compare the proposed forecasting models based on LSTM and GRU, pointing out that the former achieves higher effectiveness results although the latter has a shorter training time. Finally, the proposed framework has been compared with different baselines, obtaining a net difference of $168 at the maximum. Finally, we compare the proposed approach w.r.t. several baselines in terms of total revenue, also providing an ablation analysis to investigate how stock prediction might support investors in dealing with portfolio optimization task.

Portfolio optimization through hybrid deep learning and genetic algorithms vine Copula-GARCH-EVT-CVaR model

This study investigates the potential benefits of using the Conditional Value at Risk (CVaR) portfolio optimization approach with a GARCH model, Extreme Value Theory (EVT), and Vine Copula to obtain the optimal allocation decision for a portfolio consisting of Bitcoin, gold, oil, and stock indices. First, we fit a suitable GARCH model to the return series for each asset, followed by employing the Generalized Pareto Distribution (GPD) to model the innovation tails. Next, we construct a Vine Copula-GARCH-EVT model to capture the interdependence structure between the assets. To refine risk assessment, we combine our model with a Monte Carlo simulation and Mean-CVaR model to optimize the portfolio. In addition, we utilize a novel version of deep machine learning's genetic algorithm to address the optimization decision. This research contributes new evidence to the CVaR portfolio optimization approach and provides insights for portfolio managers seeking to optimize multi-asset portfolios.

A memetic-based technical indicator portfolio and parameters optimization approach for finding trading signals to construct transaction robot in smart city era

With the development of smart cities, the demand for personal financial services is becoming more and more importance, and personal investment suggestion is one of them. A common way to reach the goal is using a technical indicator to form trading strategy to find trading signals as trading suggestion. However, using only a technical indicator has its limitations, a technical indicator portfolio is further utilized to generate trading signals for achieving risk aversion. To provide a more reliable trading signals, in this paper, we propose an optimization algorithm for obtaining a technical indicator portfolio and its parameters for predicting trends of target stock by using the memetic algorithm. In the proposed approach, the genetic algorithm (GA) and simulated annealing (SA) algorithm are utilized for global and local search. In global search, a technical indicator portfolio and its parameters are first encoded into a chromosome using a bit string and real numbers. Then, the initial population is generated based on the encoding scheme. Fitness value of a chromosome is evaluated by the return and risk according to the generated trading signals. In local search, SA is employed to tune parameters of indicators in chromosomes. After that, the genetic operators are continue employed to generate new offspring. Finally, the chromosome with the highest fitness value could be provided to construct transaction robot for making investment plans in smart city environment. Experiments on three real datasets with different trends were made to show the effectiveness of the proposed approach, including uptrend, consolidation, and downtrend. The total returns of them on testing datasets are 26.53% 33.48%, and 9.7% that indicate the proposed approach can not only reach risk aversion in downtrends but also have good returns in others.

Mixture Design of Experiments as Strategy for Portfolio Optimization

Portfolio analysis is widely used by financial investors to find portfolios producing efficient results under various economic conditions. Markowitz started the portfolio optimization approach through mean-variance, whose objective is to minimize risk and maximize the return. This study is called Markowitz Mean-Variance Theory (MVP). An optimal portfolio has a good return and low risk, in addition to being well diversified. In this paper, we proposed a methodology for obtaining an optimal portfolio with the highest expected return and the lowest risk. This methodology uses Mixture Design of Experiments (MDE) as a strategy for building non-linear models of risk and return in portfolio optimization; computational replicas in MDE to capture dynamical evolution of series; Shannon entropy index to handle better portfolio diversification; and desirability function to optimize multiple variables, leading to the maximum expected return and lowest risk. To illustrate this proposal, some time series were simulated by ARMA-GARCH models. The result is compared to the efficient frontier generated by the traditional theory of Markowitz Mean-Variance (MVP). The results show that this methodology facilitates decision making, since the portfolio is obtained in the non-dominated region, in a unique combination. The advantage of using the proposed method is that the replicas improve the model precision.

First passage times in portfolio optimization: A novel nonparametric approach

This paper introduces a portfolio optimization procedure that aims to minimize the intra-horizon (IH) risk subject to a minimum expected time to achieve a target cumulative return. To estimate the first passage probabilities and the expected time a novel nonparametric method and a new Markov chain order determination approach are developed. The optimization framework proposed allows us to include novel path-dependent measures of risk and return in the asset allocation problem. An empirical application to S&P 100 companies, a risk-free asset and stock indices is provided. Our empirical results suggest that the proposed framework exhibits more consistency between in-sample and out-of-sample performance than the mean-variance model and an alternative optimization problem that minimizes the MaxVaR measure of Boudoukh et al. (2004). Overall, the portfolio optimization approach we introduce results in higher out-of-sample annualized returns for relatively low levels of IH risk.

Large portfolio allocation based on high-dimensional regression and Kendall’s Tau

In financial investments, portfolio allocation is always one of the most fundamental and challenging tasks. This paper proposes a robust portfolio optimization approach, extending the application of classical mean-variance (M-VAR) method for high-dimensional situations. The yielded assets return of the proposed method can be enhanced to some extent. It is called Kendall’s tau unconstrained shrinkage regression for M-VAR method (KUSR-MV). By some representative empirical studies, it is shown to have a more robust estimation of high-dimensional portfolio allocation compared to its competitors. Besides, its Sharpe ratio can be improved while the risk constraint can be well remained.

An improved learning automata based multi-objective whale optimization approach for multi-objective portfolio optimization in financial markets

An improved learning automata based multi-objective whale optimization approach for multi-objective portfolio optimization in financial markets

Tail risk adjusted clean energy portfolios in P2P transactive markets using Rachev ratio

AbstractRenewable energy sources (RES) and electric vehicles (EVs) pose ‘energy‐risk’ in peer energy commitments due to their temporal and spatial uncertainties. Thus, optimistic commitments in the peer‐to‐peer transactive energy market (P2P TEM) are improbable. This paper proposes a two‐stage master–slave portfolio optimization approach for combining energy‐risk of RES and EVs, and welfare‐risk of peers, in building clean energy portfolios. The master portfolio (MP) refers to the shares of renewable and EVs in P2P market settlement, whereas the slave portfolio (SP) gives the wind‐solar mix within renewables. Here, Rachev Ratio (RR), an index used in financial portfolio selection for tail‐risk management, is adapted and combined with Markowitz Efficient Frontier (EF) to find the optimal slave portfolio. Both the extreme tails are optimized, encouraging energy outputs far above forecast and discouraging those far below forecast. The master portfolio is obtained by maximizing the sum of the average welfare of the peers at the best (right) and worst (left) tails of the welfare distribution curve using Stochastic Weight Trade‐off Particle Swarm Optimization (SWT‐PSO). The proposed portfolio selection approach is better in terms of increased expected energy output, improved utilization of RES and EVs, and better collective peer welfare.

Portfolio optimization of generic energy storage-based virtual power plant under decision-dependent uncertainties

Distributed energy resources (DERs) can be integrated into a smart and aggregated entity, namely a virtual power plant (VPP). This integration is beneficial to facilitate their flexibility utilization and optimally allocate their capacity to participate in competitive electricity markets. While various types of uncertainties exist in the market operation of VPP, such as the stochastic power of renewable energy resources (RES), etc., the literature generally overlooked the decision-dependent uncertainties (DDUs) with the operation of generic energy storage (GES). To be risk-hedging, this manuscript proposes a novel approach for risk-averse portfolio optimization of VPP with special attention to DDUs. To achieve tractable optimization, we propose a two-stage chance-constrained stochastic optimization model and three reformulations of different types of uncertainties. Furthermore, a modified and iterative progressive hedging algorithm is further proposed to handle the conditional value at risk (CVaR) and DDU items and efficiently solve the problem with large-scale GES portfolios. Exhaustive case studies based on real-world data demonstrate that VPP has to face the consequence of a significant real-time penalty (6.36 % loss in average profit) and losses in GES power (about 6.5 MW) when overlooking the DDUs' consequences. Moreover, it is more beneficial for greedy decision-makers to incorporate the DDUs' impact into their less risk-aversion strategy. Additionally, comparative results prove the validity of the proposed algorithm and its superiority in solving problems with large-scale GES portfolios and high-performance computing systems (over 30 % CPU time saved).

An adaptive metaheuristic approach for risk-budgeted portfolio optimization

<div align="left"><span lang="EN-US">An investment portfolio implies the assortment of assets invested in the commodity market and equity funds across global markets. The critical issue associated with any portfolio under its optimization entails the achievement of an optimal Sharpe ratio related to risk-return. This issue turns complex when risk budgeting and other investor preferential constraints are weighed in, rendering it difficult for direct solving via conventional approaches. As such, this present study proposes a novel technique that addresses the problem of constrained risk budgeted optimization with multiple crossovers (binomial, exponential & arithmetic) together with the hall of fame via differential evolution (DE) strategies. The proposed automated solution facilitates portfolio managers to adopt the best possible portfolio that yields the most lucrative returns. In addition, the outcome coherence is verified by monitoring the best blend of evolution strategies. As a result, imminent outcomes were selected based on the best mixture of portfolio returns and Sharpe ratio. The monthly stock prices of Nifty50 were included in this study.</span></div>

COMPARISON OF INVESTMENT STRATEGIES IN INNOVATION AND NON-INNOVATION PORTFOLIOS WITH PORTFOLIO OPTIMIZATION

This study aims to examine whether asset allocation strategies to the innovation sector can provide superior portfolio performance to investors. This type of research is comparative research using secondary data sources. The sample used is ETFs (Exchange Traded Funds) from investment companies namely BlackRock and Ark Invest since 2015. The focus of this study is to compare the performance of portfolios without an allocation to innovation assets and portfolios with allocations to innovation assets. This study analyzes the difference in performance between the two portfolios for various weightings and several portfolio optimization approaches, such as 'equal weighting' (1/N), optimality Lagrange, minimum-variance (MinVar), mean-variance (MV), and market-value weighted strategy.

A mean-variance portfolio optimization approach for high-renewable energy hub

This paper proposes a high-renewable portfolio model of energy hub. In this model, geothermal-solar-wind multi-energy complementarities are fully explored based on electrolytic thermo-electrochemical effects of geothermal-to-hydrogen (GTH), which are converted, conditioned, and coupled through energy hub. The proposed high-renewable energy hub portfolio is an intractable optimization problem due to their inherent strong energy couplings and conflicted energy cost/risk. The original problem is thus characterized through the mean-variance approach to explicitly express the risk associated with the forecast uncertainties. The formulated mean-variance portfolio problem is subsequently modeled as a two-stage mixed-integer nonlinear programming (MINLP) stochastic programming to optimally determine appropriate energy generation, conversion, and storage candidates. Numerical studies on a community microgrid are implemented to verify the effectiveness and superiority of the proposed methodology over conventional wind-solar-battery scheme. Simulations results show that the portfolio cost can be reduced by at most 14.9% with a significantly higher operational flexibility.

A Synthetic Data-Plus-Features Driven Approach for Portfolio Optimization

A Synthetic Data-Plus-Features Driven Approach for Portfolio Optimization

Behavioral finance and the architecture of the asset management industry

The standard portfolio approach assumes that investors maximize expected utility, and that the Markowitz mean–variance standard portfolio optimization approach can be applied. Behavioral research, however, indicates that investors’ behavior with respect to risk or uncertainty is not consistent with expected utility. Notably, decision makers transform probabilities. Most of the academic literature integrating those aspects, however, focuses on so-called financial market anomalies but does not focus on the structural impact those behavioral aspects might have, notably in terms of industrial organization of the asset management industry. This paper provides a survey of the asset management industry and argues that the architecture of the asset management industry is inconsistent with the standard expected utility framework. The origin of the industrial organization of the asset management industry stems from the convexity of the flow–performance relationship, which is due to prospect theory effects. The convexity of the flow–performance relationship explains the inflation of mutual funds and fund families, a phenomenon inconsistent with the Mutual Fund Separation Theorem.