Dynamic Portfolio Research Articles

Machine learning with kernels for portfolio valuation and risk management

We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is given in closed form thanks to a suitable choice of the kernel. We show asymptotic consistency and derive finite-sample error bounds under conditions that are suitable for finance applications. Numerical experiments show good results in large dimensions for a moderate training sample size.

Performance of the Car Carrier Shipping Sector in the Iberian Peninsula under the COVID-19 Scenario

The Iberian Peninsula represents the second European producer and the eighth world producer of vehicles in 2020. The pandemic of SARS-CoV2 introduced severe challenges for the worldwide population and for the industrial production and supply chains. The car carrier shipping sector has not been studied in depth in the Maritime Transportation and Port Logistics literature. This research pays special attention to the performance of this traffic in the Iberian Peninsula in the pre-pandemic era and under COVID-19 pandemic conditions, in which seven ports with car-carrier ship traffic in the Iberian Peninsula are analyzed. First, a dynamic portfolio analysis about how the COVID-19 pandemic affected the evolution of competitive positions of Iberian Peninsula ports is performed. Second, studies of the seasonality patterns of vehicle movements in ports of the Iberian Peninsula were carried out using time series of the periods from 2012 to 2019 and from 2012 to 2020. The Seasonal Variation Index (SVI) was employed to determine the seasonality of vehicle traffic in the periods considered and analyses were performed independently for both embarking and disembarking traffic. Important conclusions are revealed, e.g., during a year of COVID-19, the seven ports had decreased vehicle movements for disembarking traffic and only one port increased the traffic for embarking traffic. Furthermore, COVID-19 introduced important changes in the seasonality patterns of vehicle movements during the first months of the pandemic.

Bank maturity, income diversification, and bank stability

The purpose of this research is to examine the influence of bank life cycle or bank maturity on income diversification (ID) and stability. In addition, this research investigates the ID relationship with bank stability. Drawing on the dynamic resource-based view and modern portfolio theory, this research examines the influence of a paramount internal factor i.e. bank life cycle or bank maturity on income diversification (ID) and stability consequence. Data were collected from the Pakistani’s commercial banks’ financial statements over the period 2005 to 2019. This research relied on the fixed effect and generalized method of moments (GMM) model to empirically test the proposed relationships. Core findings of the research reveal that bank maturity leads to enhanced ID and ID strongly influences the bank stability consequence, moreover, research findings are robust to use different measures of bank stability and GMM estimation techniques. To the authors’ best knowledge, this research is the first to report specific evidence about bank maturity as an internal driver of income diversification and stability and advances the literature seeking to understand the determinants of ID. This research also shows managers to recognize the importance of internal drivers to diversify effectively into non-interest income, and how such an effective ID translates into stability consequence.

Dynamic rebalancing portfolio models with analyses of investor sentiment

This study extends the risky portfolio models that use historical returns while incorporating investor sentiments in optimizing asset allocations. To ensure the practicality, the portfolio models that are suitable to strategize a large number of assets are developed by advancing two linearized objectives, the Omega ratio and Conditional Value-at-Risk (CVaR). Transaction costs, short selling, and adjustment of lower bounds of weights are considered in dynamic portfolio rebalancing. More than ten million messages from Twitter are analyzed to generate sentiment scores in managing portfolio rebalancing. Using the data of the S&P 500 composite stocks, our empirical results show the sentiment-triggered dynamic rebalancing portfolios ex post outperform their corresponding fixed-period rebalancing models and the naïve diversification portfolio. The flexibility in adjusting the asset allocations according to investor sentiments improves realized portfolio performance. The proposed models demonstrate self-correction by detecting investor sentiments, resulting in more effective asset management.

Operational resilience as a new concept and extension of operational risk management

In March 2021, the Basel Committee on Banking Supervision published new ‘Principles for Operational Resilience’, which define resilience as a bank’s ability to respond to and recover from disruptions, and the Bank of England published a ‘Statement of Policy on Operational Resilience’ that financial services firms should be able to prevent disruption occurring to the extent practicable, adapt systems and processes to continue to provide services and functions in the event of an incident, return to normal running promptly when a disruption is over and learn and evolve from both incidents and near misses. Both publications use the concept of a ‘disruption’ for a rare/plausible/severe event, which can be viewed through the prism of Frank Knight’s distinction between ‘known’ risk and ‘unknown’ uncertainty. We know that a pandemic, a financial crisis or even a global cyberattack can happen within a lifetime, but we do not know the probability and cannot estimate a frequency by number. A more general approach for risk — as applied in risksensitive industries — extended the traditional view of risk in ‘repeated games’ to rare events with catastrophic impact and includes our ‘strength of knowledge’ as a crucial factor in determining how much the past can be forecasted into the future. This approach and best practices from risksensitive industries such as power grids can help to integrate operational resilience into existing operational risk management in the financial services industry. Nonetheless, any precautionary measure of redundancy, flexibility and adaptivity requires additional investments and is antagonistic to the paradigm of economies of scale with minimisation of buffers. Therefore, the governance of operational resilience will require a fundamental and new understanding about rare ‘severe but plausible scenarios’, which might happen beyond typical timescales of management in a bank and require an intertemporal investment, which transcends usual economic reporting timescales.

CSR portfolio characteristics and performance outcomes: examining the impacts of CSR portfolio diversity and dynamism

PurposeThe purpose of this paper is to introduce and test a portfolio view of a firm’s corporate social responsibility (CSR) activities. Drawing from stakeholder theory and the dynamic capabilities literature, the authors introduce CSR portfolio diversity and dynamism as key portfolio characteristics that have differential impacts across short- and long-term performance contexts.Design/methodology/approachThe study draws from the Kinder, Lydenberg and Domini database to examine CSR portfolio diversity and dynamism across seven dimensions of CSR activities. The authors test the direct and indirect relationships between CSR portfolio characteristics and both short- and long-term performance outcomes to assess the opportunities and challenges associated with managing a diverse and dynamic CSR portfolio.FindingsThe findings suggest that a diverse portfolio of CSR activities positively impacts long-term performance; however, CSR portfolio diversity yields negative performance outcomes in the short-term. The authors also find that CSR portfolio dynamism moderates the relationship between CSR level and firm performance, such that a dynamic portfolio of CSR positively moderates the relationship between a firm’s CSR level and long-term performance; however, it negatively moderates the relationship between CSR level and short-term performance.Originality/valueThis study integrates insights from the literature that examine the independent effects of individual CSR activities and the broader perspective that assesses the aggregated summation of CSR activities in relation to firm performance. By taking a portfolio perspective, the present study provides a unique integration of these two research streams to examine the performance implications of engaging in a diverse and dynamic range of CSR activities.

Identifying the Role of Gold on Sustainable Investment in Indonesia: The DCC-GARCH Approach

This research investigated the performance of a dynamic portfolio that consists of sustainable/ethical stocks and gold. The main purpose of this study is to prove that the inclusion of gold in sustainable/ethical stocks portfolios could produce better performance. Therefore, the method used in this research, DCC-GARCH, was relaxing the basic assumptions in the theory of modern portfolio that is under the assumption of the normality of stock return and securities would have constant correlation. This research used data such as SRI-KEHATI Index (SKI) and Jakarta Islamic Index (JII) in Indonesia as a proxy for sustainable investments. Additionally, this research used gold from 2013 to 2019. This study is able to provide evidence regarding the ability of a dynamic portfolio to minimize the level of portfolio risk. However, this led a lower rate of return. Based on the OLS regression, gold is also proven as a weak safe haven for sustainable investment in Indonesia. Investors who believe in ethical investment may include gold in this time-varying approach when formulating the portfolio to reduce risk significantly. The inclusion of gold in portfolios could produce hedging effectiveness. Overall, this study supports some previous findings regarding the ability of gold as an instrument, which could reduce investment risk if involved in a portfolio.

Constrained Dynamic Mean-Variance Portfolio Selection in Continuous-Time

This paper revisits the dynamic MV portfolio selection problem with cone constraints in continuous-time. We first reformulate our constrained MV portfolio selection model into a special constrained LQ optimal control model and develop the optimal portfolio policy of our model. In addition, we provide an alternative method to resolve this dynamic MV portfolio selection problem with cone constraints. More specifically, instead of solving the correspondent HJB equation directly, we develop the optimal solution for this problem by using the special properties of value function induced from its model structure, such as the monotonicity and convexity of value function. Finally, we provide an example to illustrate how to use our solution in real application. The illustrative example demonstrates that our dynamic MV portfolio policy dominates the static MV portfolio policy.

Formulating the Concept of an Investment Strategy Adaptable to Changes in the Market Situation

The study aims to develop a dynamic model for the management of a strategic investment portfolio, taking into account the impact of crisis processes on asset value. A mathematical model of a dynamic portfolio strategy is developed, and guidelines for framing a long-term investment strategy based on the current state of the investment market are formalized. An efficient method of long-term ensemble forecasting to increase the accuracy of predicting financial time series is elaborated. A methodology for constructing and rebalancing a dynamic strategic investment portfolio based on a changing portfolio strategy that results from assessing the current market state and forecast is developed. The obtained strategic portfolio model has been estimated empirically based on historical data and its rate-of-return characteristics have been compared with those of the existing conventional models used in strategic investment.

Efficiency of Dynamic Portfolio Choices: An Experiment

Abstract We study the efficiency of dynamic portfolio choices using the nonparametric methods of Dybvig (1988) and Post (2003). We compare a dynamic portfolio task against an equivalent static Arrow-Debreu problem under two alternative environments: (1) nonpooled with $2^T$ terminal states and (2) pooled with $T+1$ unique terminal states. The results suggest that, within each environment, efficiency is lower in a static format and when the number of final states is larger. In the nonpooled dynamic task, which allows for path dependent strategies, we find that a form of stop-loss strategy drives efficiency losses.

The Formulation of a Dynamic Portfolio between Gold and Stocks on the Indonesia Stock Exchange during the COVID-19 Pandemic

COVID-19 pandemic made investor to be careful to choose the portfolio instrument. A portfolio that is formed by the right instrument certainly can minimize risks and maximize the return. This research analyzes the dynamic portfolio formed by gold and leading stocks in COVID-19 period, which is better than the the portfolio formed by stocks alone. The data used is secondary data, which is the LQ-45 index daily closing price data and world gold prices. This research also uses risk free rate data taken from bi.go.id. The analysis technique in this research is DCC-GARCH. Findings. This research concludes that stock portfolios with gold showing a good performance result with variability-based measurement.

Dynamic Portfolio Selection in Gas Transmission Projects Considering Sustainable Strategic Alignment and Project Interdependencies through Value Analysis

Selecting a sustainable cross-country natural gas pipeline project portfolio plays a vital role in enhancing energy security and national self-reliance. The interdependencies between projects augment the complexity of project portfolio selection. Hence, the selection must be done with sustainable strategic alignment and adjustment of ongoing projects to determine the most suitable project portfolio. This is while they have barely been addressed simultaneously in the literature. The aim of the present study is to fill in the mentioned gap by establishing an integrated framework incorporating the organization strategies, project interdependencies, and ongoing projects in the project portfolio selection problem. This presented framework uses network mapping to visualize project interdependencies and improve the quality of the resulting decision. The decision-aid approach of Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) was employed to tackle multi-criteria value measurement in project portfolio selection. Applicability and validity of the proposed framework were tested using the case study of the Iranian Gas Engineering and Development Company (IGEDC). The pipeline project portfolios were analyzed on the basis of experts’ opinions with regard to technical and sustainability strategic criteria (economic, environmental, and social pillars).

Risk management with expected shortfall

This article studies optimal, dynamic portfolio and wealth/consumption policies of expected utility-maximizing investors who must also manage market-risk exposure which is measured by expected shortfall (ES). We find that ES managers can incur larger losses when losses occur, compared to benchmark managers. A general-equilibrium analysis reveals that the presence of ES managers increases the market volatility during periods of significant financial market stress. We propose weighted shortfall, a coherent and moreover spectral risk measure, that can rectify the shortcomings of ES.

Modeling Price Dynamics, Optimal Portfolios, and Option Valuation for Cryptoassets

The authors demonstrate the construction of an optimal dynamic portfolio of cryptoassets that minimizes either return variance or conditional value at risk. One can view such a portfolio as a minimum-risk index for this asset class. They carefully backtested the dynamic portfolio model and developed a fair valuation model for options based on a dynamic pricing model for the underlying cryptoasset index. They obtain the valuation by passing from the natural world to the equivalent martingale measure via the Esscher transform. The work underscores the need for a cryptoasset index–based exchange-traded fund, the development of derivatives, particularly for cryptoportfolio insurance purposes, and the development of (nearly) riskless rates for this asset class. <b>TOPICS:</b>Currency, options, statistical methods, VAR and use of alternative risk measures of trading risk <b>Key Findings</b> ▪ This article provides a methodology for constructing an optimal dynamic portfolio of major cryptoassets that minimizes either return variance or conditional value at risk. ▪ The authors develop a fair valuation model for options based on a dynamic pricing model for the underlying cryptoindex. ▪ The work emphasizes the need for a cryptoasset index–based exchange-traded fund and the development of derivatives, in particular for cryptoportfolio insurance purposes.

Time-Varying Risk Aversion and Dynamic Portfolio Allocation

Despite the overwhelming evidence of time-varying risk aversion documented in recent literature, standard dynamic portfolio theories often adopt an assumption of constant (relative) risk aversion due to analytical tractability. In “Time Varying Risk Aversion and Dynamic Portfolio Allocation,” Li et al. explicitly consider the implications of time-varying risk aversion for dynamic portfolio allocation under the framework of regime-switching models. An investor with regime-dependent utility exhibits a decreasing relative risk aversion (DRRA) and has higher risk aversion when a bear market regime is more likely in the future. They develop an efficient dynamic programming algorithm that overcomes the challenges imposed by regime-dependent preference in obtaining time-consistent portfolio policies. The empirical results show that VIX is an important predictor of regime shifts and that investors with regime-dependent risk aversion achieve better investment performance than those with constant risk aversion.

DYNAMIC ASSET ALLOCATION FOR TARGET DATE FUNDS UNDER THE BENCHMARK APPROACH

ABSTRACTTarget date funds (TDFs) are becoming increasingly popular investment choices among investors with long-term prospects. Examples include members of superannuation funds seeking to save for retirement at a given age. TDFs provide efficient risk exposures to a diversified range of asset classes that dynamically match the risk profile of the investment payoff as the investors age. This is often achieved by making increasingly conservative asset allocations over time as the retirement date approaches. Such dynamically evolving allocation strategies for TDFs are often referred to as glide paths. We propose a systematic approach to the design of optimal TDF glide paths implied by retirement dates and risk preferences and construct the corresponding dynamic asset allocation strategy that delivers the optimal payoffs at minimal costs. The TDF strategies we propose are dynamic portfolios consisting of units of the growth-optimal portfolio (GP) and the risk-free asset. Here, the GP is often approximated by a well-diversified index of multiple risky assets. We backtest the TDF strategies with the historical returns of the S&amp;P500 total return index serving as the GP approximation.

Modern Approaches to Dynamic Portfolio Optimization

Although appealing from a theoretical point of view, empirical assessments of dynamic portfolio optimizations in a mean-variance framework often fail to reach the high expectations set forth by analytical evaluations. A major reason for this shortfall is the imprecise estimation of asset moments and in particular, the expected return. This work levers recent advancements in the field of machine learning and employs three types of artificial neural networks in an attempt to improve the accuracy of the asset return estimation and the expected associated portfolio performances. After an introduction of the dynamic portfolio optimization framework and the artificial neural networks, their suitability for the considered application is analyzed in a two asset universe of a market and a risk-free asset. A comparison of the corresponding risk-return characteristics and those achieved using a more traditional exponentially weighted moving average estimator is subsequently drawn. While outperformance of the artificial neural networks is found for daily and monthly estimated returns, significance can only be established in the latter case, especially in light of trading costs. Multiple robustness checks are performed before an outlook for subsequent research opportunities is given. Keywords: Portfolio optimization; machine learning; multilayer perceptron; convolutional neural network; long short-term memory.

Dynamic Portfolio Management with Machine Learning

We present a structured portfolio optimization framework with sparse inverse covariance estimation and an attention-based LSTM network that exploits machine learning (deep learning) techniques. We shrink Wishart volatility towards a Graphical Lasso initial covariance estimator and solve the portfolio optimization using a fast coordinate descent algorithm with regularization determined using a genetic algorithm. We further introduce a novel portfolio shrinkage rule using an attention-based Long-Short-Term-Memory (LSTM) network, allowing a formal selection of reference portfolios where the network forecasts future performance based on predetermined out-of-sample monthly certainty equivalent return. We reduce the dimension of successful candidates and then linearly combine them. When nested within a minimum-variance, Bayes-Stein shrinkage, Black-Litterman portfolio framework with four types of weight constraints based on no-short-selling, upper, lower-generalized variance-based restrictions, our approach delivers a clear improvement over the baseline sample-based minimum-variance portfolio and claims superiority over 11 GARCH models used to forecast covariances, as well as a minimum-variance combination of all dynamic optimization models. We provide an illustrative example based on optimal diversification across hedge fund strategies. Robustness checks show our application of sparse covariance dominates the use of a dimension reduction algorithm for Wishart covariance forecasting.

Dynamic optimal portfolio choice under time-varying risk aversion

In this paper, we empirically analyze the possible advantages of modelling a time-varying risk aversion that best fits investors’ behavior in the context of the optimal portfolio choice. We build optimal dynamic portfolios by focusing on the estimation of a time-varying relative risk aversion parameter (RRA). Conditional univariate and multivariate models, such as GARCH, GARCH-M and DCC-GARCH, for modelling the optimal portfolio choice and the RRA parameter are implemented. As a model validation tool, the realized performance and downside risk exposure of these portfolios one month ahead is compared to that resulting from implementing a constant risk aversion parameter. The Ledoit and Wolf (2008) test provides robustness to our results and reveals the average outperformance of the dynamic risk aversion strategy over others as the constant risk aversion or the passive management strategies.

Correction to: Solving High-Dimensional Dynamic Portfolio Choice Models with Hierarchical B-Splines on Sparse Grids

The original publication has been updated. In the original publication of this article, under the Introduction heading section, the corrections to the second paragraph’s inline equation were not incorporated. The author’s additional corrections have also been incorporated. The publisher apologizes for the error made during production.