Suboptimal Portfolio Research Articles

Behavioral Finance: The Impact of Investor Expectation on Financial Decision-Making

Behavioral finance challenges traditional financial theories by questioning the assumption of investor rationality, highlighting the influence of cognitive biases, emotions, and psychological factors on decision-making. This deviation from conventional models like the Efficient Market Hypothesis recognizes the complexity of human decision-making in financial contexts. The field focuses on key phenomena: loss aversion, market anomalies, the framing effect, and the endowment effect. Loss aversion reveals a stronger reaction to losses than equivalent gains, influencing overly cautious decisions. Market anomalies, such as momentum and trend reversals, challenge efficient market expectations. The framing effect shows how decision-making is swayed by information presentation. The endowment effect impacts perceived asset value, affecting financial decisions. These biases significantly impact investor behavior, potentially leading to suboptimal portfolio choices. Recognizing these biases is crucial for making informed financial decisions. Behavioral finance research has profound implications, emphasizing the integration of behavioral insights with traditional models. Future studies should explore cumulative behavioral effects, conduct cross-cultural analyses, and investigate diverse market conditions for more effective investment strategies and regulations. Understanding global financial behavior through behavioral finance is essential for comprehensive insights into market dynamics and decision-making processes.

Developing a hybrid system for stock selection and portfolio optimization with many-objective optimization based on deep learning and improved NSGA-III

Portfolio management is a critical aspect of investment strategies, with the goal to balance the low-risk and high-return investments. Despite this, existing portfolios frequently overlook the integration of stock selection outcomes and underutilize data from listed companies, leading to suboptimal portfolio performance. Addressing these shortcomings, this paper introduces a hybrid system involving stock selection and portfolio optimization. In stock selection, the system employs a combination of convolutional neural network and bi-directional recurrent neural network to predict stock trends. This approach enables the identification of stocks likely to appreciate in value, setting the stage for their inclusion in the subsequent optimization process. For portfolio optimization, the study formulates a five-objective optimization problem that incorporates mean, variance, skewness, kurtosis, and distance-to-default as key considerations. To solve the many-objective constrained optimization problem, an advanced strategy employing a static penalty function and an improved Non-dominated Sorting Genetic Algorithm III (NSGA-III) based on tent chaotic mapping is utilized. The efficacy of the proposed hybrid system is rigorously tested through three sets of ablation experiments alongside two discussions focused on its robustness and computational efficiency. The findings from these investigations reveal that the hybrid system outperforms traditional approaches, reducing risks and improving returns for investors.

Optimization of large portfolio allocation for new-energy stocks: Evidence from China

Investment in the fast-growing new-energy of China has gradually attracted great attention. At present, more than 100 new-energy stocks are listed on China’s stock markets. How to effectively manage the investment risk of these stocks and achieve better returns is of great interest to investors and policymakers. In finance, mean–variance portfolio (MVP) is often applied to solve portfolio allocation problems. However, when investing in numerous stocks, the classical MVP is no longer applicable. For large portfolio allocation, this study constructs a comprehensive MVP model, called an unconstrained regression model with latent factors (URELAF). It applies a factor structure with latent factors to modify the covariance matrix estimation in the classical MVP, using an unconstrained penalized regression to estimate the allocation. In addition, to more realistically simulate the investment of stock markets, transaction costs are considered. The empirical results show that URELAF model can control the risk with Sharpe ratio reaching 6 times as many as MVP model, and even improve about 26% compared with the sub-optimal portfolio model. URELAF also outperforms other portfolios with transaction costs. Finally, hypothesis tests further verify that URELAF can significantly improve portfolio performance compared with other portfolio methods for China’s new-energy market.

Behavioural Biases in Equity, Futures, and Options Trading: Implications for Market Efficiency

This research paper presents a comprehensive analysis of behavioural biases among traders operating in the equity, futures, and options segments of financial markets. The study aims to understand the psychological factors influencing trading decisions, risk perception, and trading strategies. By exploring the impact of behavioural biases on market efficiency and stability, this research contributes to the growing field of behavioural finance. Using a mixed-method approach, the study collected data through surveys, interviews, and behavioural experiments from a diverse group of traders, including retail investors and institutional participants. The analysis focused on identifying common behavioural biases such as overconfidence, loss aversion, herding behaviour, anchoring, and the disposition effect. The findings reveal that behavioural biases are prevalent among traders in all three segments. Overconfidence leads to excessive trading and underestimation of risks, while loss aversion influences risk aversion and reluctance to exit losing positions. Herding behaviour results in the formation of market bubbles and crashes, and anchoring affects traders' reliance on past information to make decisions. The disposition effect influences traders to hold onto losing positions longer than profitable ones. The study also highlights the impact of behavioural biases on trading strategies and risk management practices. Traders tend to exhibit risk-seeking behaviour during periods of market euphoria and become risk-averse during times of uncertainty and volatility. Moreover, behavioural biases can lead to suboptimal portfolio allocation and performance outcomes. The implications of these findings for market efficiency and stability are significant. Behavioural biases can contribute to market inefficiencies, distorted price patterns, and increased market volatility. As a result, market participants and regulators must address these biases to foster more informed decision-making and improve market outcomes. To mitigate the impact of behavioural biases, the study recommends trader education programs focusing on behavioural finance principles and risk management techniques. Additionally, market regulators should implement measures to promote transparency and discourage excessive speculative trading. In conclusion, this research underscores the importance of understanding behavioural biases in equity, futures, and options trading. By shedding light on the psychological drivers of trading behaviour, this study provides valuable insights for traders, financial institutions, and policymakers to enhance market efficiency and stability in the face of behavioural challenges.

The Media's Role in Fostering Knowledge of Financial Management: A Case of Pakistan

This study investigates how behavioural biases affect how individual investors make investing decisions. Drawing upon the discipline of behavioral finance, which integrates psychological insights into financial decision-making, we investigate the presence and effects of various biases on investment behavior. The study aims to contribute to understand the factors thoroughly that shape individuals' investment choices and their subsequent financial outcomes. Using a mixed-methods approach, including surveys and interviews, we explore the prevalence and magnitude of biases for instance loss aversion, overconfidence, and framing effects among individual investors. By analyzing real-world investment decisions, we assess the influence of these biases on portfolio composition, trading frequency, and performance outcomes. According to the preliminary findings, behavioral biases play a significant role in shaping investment decisions. Overconfident investors prefer to trade more often, leading to higher transaction costs and lower returns. Loss aversion biases lead to suboptimal portfolio allocation, as investors disproportionately allocate funds to low-risk assets. Additionally, framing effects impact decision-making by altering risk perceptions and preferences. The study underscores the importance of recognizing and mitigating behavioral biases in investment decision-making. By raising awareness and providing insights into the specific biases influencing individual investors, this research offers valuable implications for financial education, regulatory policies, and investment advisory services. Ultimately, understanding the role of behavioral biases can contribute to more informed and rational investment decision-making, leading to improved financial outcomes for individual investors.

Computing optimal portfolios of multi-assets with tail risk: the case of bitcoin

ABSTRACT Assets with tail risk may produce a suboptimal portfolio under mean-variance optimization when asset returns are not normally distributed. We provide a new Monte Carlo simulation method for computing and attaching tails to observed empirical return distributions. We find that a combination of stochastic optimization and the new method for simulating tails in returns with expected shortfall utility function produces optimal portfolios that have better return and risk characteristics than those of mean-variance optimal portfolios. Results from this study suggest that bitcoin can be a diversifier in a multi-asset portfolio when optimization takes all moments of return into consideration.

Is there a diversification “cost” of Shari’ah compliance? Empirical evidence from Malaysian equities

Islamic equity portfolios work with a smaller investment universe given the filtering of non-Shari’ah compliant stocks. It has been theoretically argued that this culminates in suboptimal portfolio diversification, which in turn adversely affects risk-adjusted returns. We offer empirical evidence that such a conceived portfolio diversification “penalty” is far from a foregone conclusion, at least empirically. Our results tend to indicate that Islamic portfolios are not invariably handicapped in terms of portfolio diversification. We also explored dimensions that may account for differences in the relative investment performance between Islamic and conventional portfolios, such as portfolio constraints, short selling and market conditions. We believe this paper is among the first to apply substantial empirical analysis specifically with respect to the portfolio diversification perspective on Islamic equity investments.

Practical Applications of Skew and Trend Aversion: The Impact of Positive Skew and Behavioral Biases on Allocation Decisions

Practical Applications Summary In Skew and Trend Aversions: The Impact of Positive Skew and Behavioral Biases on Allocation Decisions, from the Winter 2020 issue of The Journal of Alternative Investments, Zachary Dugan (International Standard Asset Management, Johns Hopkins University) and Alexander Greyserman (International Standard Asset Management) investigate potential reasons for small portfolio allocations to trend-following strategies that are observed in most portfolios. Trend-following strategies can deliver “crisis alpha,” which is a positive return when markets are down. Their returns are positively skewed with a low correlation with the S&P 500, but their average returns are close to the S&P 500. These characteristics indicate that most portfolios could benefit from a healthy allocation to this strategy. The authors explain that investors are particularly prone to behavioral biases when evaluating strategies that have positively skewed returns. By quantifying biases implied by suboptimal portfolio allocations to trend-following strategies, they expose commonly chosen poor allocation decisions so that the reader can avoid making suboptimal choices. TOPICS:In portfolio management, risk management, wealth management, performance measurement

Suboptimal investment behavior and welfare costs: A simulation based approach

We propose a representation of suboptimal investment behavior based on the stochastic discount factor (SDF) paradigm. Suboptimal investment behavior is rationalized as being the investor’s optimal decision under a wrong SDF, while wealth trajectories and budget constraints are based on the true SDF. We develop a novel Monte Carlo simulation approach to compute the welfare costs for this suboptimal behavior.We study the suboptimal portfolio choice under CRRA preferences using two financial market models. The Monte Carlo simulation delivers comparable welfare losses to those computed in the original studies, which are based on partial differential equations (PDE) and - finite-difference schemes.

A Cautionary Note on the Use of Benefit Metrics for Cost-Effective Conservation

Cost-effective land conservation techniques, such as optimization, have the potential to contribute substantially to the provision of many important environmental benefits, such as biodiversity protection, flood control, food security, water quality, and reduction of greenhouse gas emissions. There has been a recent push for conservation organizations to adopt project selection optimization approaches such as binary linear programming. The metrics used to measure the benefits of a project however, are often poorly defined in that they do not directly compute a value. These scores represent normalized measurements of underlying values that are likely log-normally distributed. Applying such metrics in optimization will tend to undervalue high-benefit projects and select a suboptimal portfolio of projects relative to simpler approaches. This suboptimal performance can lead to losses in efficiency as high as 30%. We propose a hybrid optimization heuristic that can improve performance and, additionally, provide conservation professionals with more flexibility and freedom to select conservation projects at their discretion—potentially overcoming a substantial real-world adaptation hurdle.

Multi-Objective Portfolio Optimization by Mixed Integer Programming

In this PhD dissertation the mathematical programming methods of operations research for multi-criteria optimization are presented. The PhD dissertation deals with the problem of selection of methods and numerical tools for solving portfolio optimization problems with different objectives. In particular, the research efforts were concentrated on mixed integer programming formulations. The need for solving multi-objective portfolio optimization models by mixed integer programming can be illustrated for the portfolio models with Value-at-Risk (VaR) as a risk measure, as well as, when the number of assets (investment ventures) is one of the optimality criteria. An alternative, multi-objective portfolio optimization problems is formulated with Conditional Value-at-Risk (CVaR) as a risk measure or with symmetric measure of risk - covariance (variance) of return - as in Markowitz portfolio.The portfolio models with CVaR and with covariance (variance) of historical return were being solved with the use of mathematical programming with the continuous variables. The proposed multi-objective portfolio models are constructed with the expected return as a performance measure and the expected worst-case return as a risk measure, using Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). These measures allow the evaluation of worst-case return and shaping of the resulting return distribution through the selection of the optimal portfolio. The mathematical programming models are constructed and solved using weighting, lexicographic and reference point approach. The presented portfolio models are single-, bi- and triple-objectives and the optimization criteria considered are risk, return and number of stocks.The main research problem considered in this Ph.D. dissertation is the way for finding the best multi-objective portfolio formulation with risk. The additional research problem is to find the relation between the optimization results with Value-at-Risk solved by mixed integer programming and the results obtained with the use of linear and quadratic programming portfolio models (Conditional Value-at-Risk, Markowitz).Computational experiments have been conducted for multi-criteria portfolio models of stock exchange investments. The input data for computations consist of historical daily returns of stocks quoted on Warsaw Stock Exchange. The number of selected securities for input data varies from 46 to 240 assets. The historical stocks quotations come from the period from March 10th, 1997 to February 2nd, 2009. This time period includes data from the increase of stock exchange quotations, as well as the economic crisis period. The considered number of data in historical time series is from 500 to 3000 days with assets quoted each day in the whole historical horizon. The portfolios were optimized in an increased time window, which was helpful in evaluating the results of optimization (time-varying optimal portfolio).The multi-criteria portfolio optimization models with Conditional Value-at-Risk (CVaR) as a risk measure can be used to support on-line stock market investments, since the computational times required to find the optimal solution is relatively short, regardless of the size of the input data. The presented models provide a decision maker with a tool for evaluating the relationship between expected and worst-case returns.The results obtained from computational experiments proved, that multi-objective portfolio optimization models with Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) could be used to shape the distribution of portfolio returns in a favorable way for a decision maker. The portfolios obtained with both methods (mixed-integer or linear programming) are often similar results, which shows their capability of solving the corresponding problems. It means that a suboptimal portfolio for the integer program with Value-at-Risk (VaR) as optimality criterion can be found by solving the corresponding linear program for the portfolio problem with Conditional Value-at-Risk (CVaR) as an optimality criterion. The proposed scenario-based portfolio optimization problems under uncertainty, formulated as a single- or multi-objective mixed integer program were solved using commercially available software (AMPL/CPLEX) for mixed integer programming.In addition to the multi-objective approach for portfolio optimization of securities (e.g. stocks) from stock exchanges presented in this dissertation, the selected multi-objective mixed integer programming models are shown for supporting services in medical care institutions, based on an assignment problem.

Investor sentiment and portfolio selection

We provide a theoretical framework to examine how investor sentiment impacts the mean-variance tradeoff. We derive a sentiment-adjusted Markowitz efficient frontier in which investor sentiment alters the first two moments of asset returns, the minimum-variance frontier as well as the Capital Market Line. Our theoretical results are consistent with empirical findings that heightened sentiment-related noise trading activity drives perceived prices away from fundamental and increases market volatility. A rational investor neglecting the effect of investor sentiment may end up selecting a sub-optimal portfolio.

From Research Project to Research Portfolio: Meeting Scale and Complexity

The article considers how the past and present tendency to focus on selecting the best projects based on the sole criterion of meritorious science may result in a sub-optimal portfolio. The authors argue that scientists need to proactively engage in the discussion over the need to improve the efficiency and effectiveness of societal investments to ensure that the next generation of the management and decision-making process for our science, technology and innovation system is rooted in sound principles.The classic peer review process tends to provide unintended overlap and allows for an ill fit between some of the pieces and unwanted gaps to occur. Areas of high risk and high return can be missed due to their controversial nature and split decisions typically resulting in negative funding decisions. In general, high risk and a high frequency of split decisions tend to be replaced with lower risk initiatives. The authors propose herein supplementing peer review with research portfolio evaluation approaches and decision-making tools that can better assess research uncertainties and other special features of the transformation of the resulting knowledge into improved social well-being. A coupling of research quality review by peers with more systematic portfolio meta-analysis of recommended projects is both possible and essential.

Performance analysis of log-optimal portfolio strategies with transaction costs

In this paper we introduce an empirical approximation of the log-optimal investment strategy that guarantees an almost optimal growth rate of investments. The proposed strategy also considers the effects of portfolio rearrangement costs on growth optimality and recommends a suboptimal portfolio for discrete investment periods. We do not assume any parametric structure for the market process, only a first-order Markov property. The model introduced is based on kernel-based agents' (experts') approximation of the maximum theoretical growth rate with transaction costs. Although the optimal solution is theoretically a complex Bellman programming problem, our suboptimal empirical result appears to be attractive for Dow Jones 30 shares. The paper presents a performance analysis where the return of the empirical log-optimal portfolio is compared with passive portfolio counterparts compiled from similar components using the CAPM, the three-factor model and the four-factor model. The proposed methods, in the presence of transaction costs, provide a significant positive abnormal return compared with the preceding equilibrium models, and is even a survivorship bias-free setup.

Jump Mis-Specification and International Portfolio Selection

In this paper we make a distinction between systemic co-jumps and independent idiosyncratic jumps, and examine the impact of their mis-specification on asset allocation. We discuss how jumps mis-specification may lead to jumps mis-estimation and to a suboptimal portfolio. Specifically, we develop a framework where security prices have three distinct parts: a diffusion component, a systemic co-jump component, and an independent idiosyncratic jump component. In our model, the intensity and the timing of systemic co-jumps is the same across all assets whilst the timing and intensity of the idiosyncratic jump is, obviously, asset specific. The recognition that stock market returns jump together as well as separately is crucial for cross-market asset allocation policy. Univariate jump diffusion is estimated through a Markov Chain Monte Carlo (MCMC) method for weekly MSCI index returns of eleven developed and emerging markets from 1988 to 2009. The MCMC procedure produces sample paths of the latent univariate jump processes which were used in estimating the systemic and idiosyncratic jumps between series. Our results suggest that in developed markets, the omission of (idiosyncratic) jumps has little impact on portfolio selection. Developed markets are more homogeneous and their stock returns tend to jump together. The covariance matrix absorbed a large part of the linear co-jump risks producing an optimised portfolio that is not too dissimilar to one produced by a diffusion model with only the possibility for co-jumping. However, we find that, for emerging markets that tend to have idiosyncratic jumps, the assumption of co-jumps produced biased jump estimates. Losses in portfolio certainty equivalence due to this wrong jump dependence assumption are economically significant.

Dynamic Portfolio Rebalancing with Transaction Costs and Time-Varying Expected Return

We consider the problem of tracking the optimal allocation between a risky and a risk-free asset when the expected return is stochastic and trading incurs transaction costs. The rebalancing policy optimises the tradeoff between the opportunity cost of holding a suboptimal portfolio and the transaction cost. Using a Markovian approximation of the portfolio mismatch process, we derive the target function analytically and obtain closed-form approximations for the optimal rebalancing policy. Numerical simulations are used to confirm the outperformance of the optimal control band policy of partial adjustment compared with the policy of full rebalancing to the contemporaneous optimal portfolio at a pre-determined frequency.

Dynamic Optimal Pension Fund Portfolios when Risk Preferences Are Heterogeneous among Pension Participants

AbstractIn this paper, we consider the problem of an optimal pension fund portfolio given the heterogeneous risk preferences of pension fund participants. The relative risk aversion of a pension fund tends to be a decreasing function of the level of aggregate wealth. We find that the dynamic optimal portfolio is simply characterized as the weighted sum of the optimal portfolio for each participant. Our model helps successfully establish the microfoundation of asset liability management models. A numerical example using recent Japanese data indicates the significant total welfare losses of adopting a suboptimal portfolio strategy and an inefficient risk‐sharing rule.

Performance Analysis of Log-Optimal Portfolio Strategies with Transaction Costs

In the paper we introduce an empirical approximation of the log-optimal investment strategy that guarantees an almost optimal growth rate of investments. The proposed strategy also considers the effects of portfolio rearrangement costs on growth optimality and advises a suboptimal portfolio for discrete investment periods. We do not assume any parametric structure for the market process, only first order Markov property. The model introduced is based on kernel-based agents' (experts') approximation on the maximum theoretical growth rate with transaction costs. Although the optimal solution is theoretically a complex Bellman programming problem, our sub-optimal empirical result seems to be attractive on Dow Jones 30 shares. The paper presents a performance analysis where the return of the empirical log-optimal portfolio is compared to passive portfolio counterparts compiled from similar components using the CAPM, the three-factor model and the four-factor model. The proposed methods in the presence of transaction costs gain a significant positive abnormal return as compared with the preceding equilibrium models, and is even a survivorship bias free setup.

Optimal portfolio choice using the maximum Sharpe ratio

Choosing a portfolio from among the enormous range of assets now available to an investor would be facilitated if we could locate the return– risk ratio of a particular allocation along a spectrum of possibilities. A comparison between portfolio choices can tell us, for example, whether it is better to select a suboptimal portfolio from a large class of assets or to perform a Markowitz optimal procedure on a subset of the assets. A common criterion for this assessment is the expected return-to-risk tradeoff as measured by the Sharpe ratio. Given that the ideal, maximized Sharpe ratio must be estimated, we develop, in this paper, an approach that enables us to assess ex ante how close a given portfolio is to this ideal. For this purpose, we derive the large-sample distribution of the maximized Sharpe ratio, as obtained from sample estimates, under very general assumptions. This distribution then represents the spectrum of possible optimal return–risk trade-offs that can be constructed from the data. We illustrate applications of the theory by analyzing a large sample of US companies, comparing constant-correlation and momentum strategies with the optimal strategy. Simulations based on this data are also given for illustration.

Dynamic Optimal Portfolio of Pension Funds When Risk Preferences are Heterogeneous Among Pensioners

In many developed countries, pension funds are growing its sizes because of dwindling birthrate and aging population. Portfolio management of their reserved funds arouses public attention. Since agents who participate in pension fund, in particular public pension fund, are heterogeneous, it is not easy in general to agree on a particular portfolio strategy. When we take heterogeneity of risk preferences among members into account, portfolio management problems of pension funds are considered as syndicate problems. Objective functions of pension funds do not tend to be constant absolute or relative risk aversion but tend to be decreasing relative risk aversion. The strategic asset allocation strategy (or constant-mix strategy) that is widely used in pension's asset allocation decision is inconsistent with decreasing relative risk aversion utility function. We formulate a security market model and an optimal portfolio problem for pension fund with heterogeneous risk preferences. The optimal portfolio is characterized as a weighted average of each member's optimal portfolio. Using Japanese public pension fund as an example, it is shown that losses of social welfare by using inefficient risk sharing rules and suboptimal portfolio strategies could be significant.