Dynamic Portfolio Research Articles

Optimal investment strategies and profit shares distributions: A stochastic control approach

With the advent of Solvency II regulations in 2016, European insurance companies are tasked with calculating technical provisions by either using a standard formula given by regulators or developing their own internal model. This paper first reviews a model adapted to Solvency II regulations for an insurance company’s participating contracts, which was proposed by Hainaut [1]. The objective of the insurer is to optimize profit shares distributions and asset allocation strategies. In this setting, a stochastic control approach is used to formulate a dynamic portfolio optimization problem. The insurer invests in an account or bond that is risk-free, she also invests in a risky asset that is based on the Black-Scholes model. A closed form solution to the problem under a power utility function on future profit shares and the insurer’s future economic wealth is calculated. The paper ends with a numerical example, where Tesla and Apple share prices are used as the insurer’s risky asset, in order to test the usability of the model in industry.

Higher Moments Actually Matter: Spillover Approach for Case of CESEE Stock Markets

The interconnectedness of stock markets is an important topic in empirical research, as spillovers on financial markets matter for asset pricing, portfolio allocation, financial stability, and risk management. This research focuses on all four moments of return distributions on stock markets and their spillovers between CESEE (Central, Eastern, and South-Eastern Europe) stock markets. Higher moments analysis needs to be explored more deeply, but can provide detailed insights into distribution shifts of market returns due to shocks in other markets. This research fills such a gap in the literature by estimating spillover effects between the four moments of stock market return distributions. Based on data from January 2013 to September 2022, the VAR (vector autoregression) model is estimated for individual moments across stock markets as a base for the calculation of spillover indices. The main findings indicate that it is difficult to track all the spillovers at once as the net emitter of shocks to one or other of the countries involved often change to being a net receiver and vice versa. Moreover, higher moments spillovers matter for individual markets, which has important implications for dynamic portfolio selection.

Dynamic portfolio rebalancing with lag-optimised trading indicators using SeroFAM and genetic algorithms

Some common technical indicators, such as moving average convergence divergence (MACD), relative strength index (RSI), and MACD histogram (MACDH) are used in technical analyses and stock trading. However, some of them are lagging indicators, affecting the effectiveness in the stock trading and portfolio management. A forecasted MACDH (fMACDH) indicator for predicting next day price by a neuro-fuzzy network, Self-reorganizing Fuzzy Associative Machine (SeroFAM) which has been reported in the prior research work. In order to further reduce the lagging effect, two trading indicators are proposed in this paper: the optimised fMACDH indicator and the fMACDH-fRSI indicator. The optimised fMACDH indicator is derived to extend price forecasting to 1–5 days ahead as the prediction depth, using 1–5 days of historical price data as the input depth. The fMACDH-fRSI indicator is derived by combining the optimized fMACDH indicator and the forecasted RSI (fRSI) indicator. A genetic algorithm (GA) and the fitness functions are designed with the SeroFAM in this paper, which are utilised for optimising parameters of these two proposed indicators. Experiments have been conducted to evaluate and benchmark of the proposed trading indicators optimised by the GA. Two rule-based portfolio rebalancing algorithms are then proposed using the optimised fMACDH trading indicator tuned by the GA: the Tactical Buy and Hold (TBH) and the Rule-Based Business Cycle (RBBC) portfolio rebalancing algorithms. The TBH algorithm takes advantage of relative differences in risk levels to perform rebalancing during trend reversals. The RBBC portfolio rebalancing algorithm takes advantage of the offsets between the business cycles of different market sectors. Experiments have been conducted to evaluate the performance of both algorithms using two sets of portfolios consisting of different assets. The TBH portfolio rebalancing algorithm outperforms the equally weighted portfolio strategy by about 26 % − 27 %; as well outperforms the Buy and Hold strategy by 5 % − 40 %. The RBBC portfolio rebalancing algorithm outperforms the equally weighted portfolio strategy by 54 % − 55 %; it also outperforms 12 out of the 13 assets with the Buy and Hold strategy, by an average performance of about 166 %. The results are highly encouraging with consistent performances achieved in dynamic portfolio rebalancing.

Dynamic portfolio selection with linear control policies for coherent risk minimization

This paper is concerned with a linear control policy for dynamic portfolio selection. We develop this policy by incorporating time-series behaviors of asset returns on the basis of coherent risk minimization. Analyzing the dual form of our optimization model, we demonstrate that the investment performance of linear control policies is directly connected to the intertemporal covariance of asset returns. To mitigate overfitting to training data (i.e., historical asset returns), we apply robust optimization. For this optimization, we prove that the worst-case coherent risk measure can be decomposed into the empirical risk measure and the penalty terms. Numerical results demonstrate that when the number of assets is small, linear control policies deliver good out-of-sample investment performance. When the number of assets is large, the penalty terms improve the out-of-sample investment performance.

Green transition, investment horizon, and dynamic portfolio decisions

This paper analyzes the implications of investors’ short-term oriented asset holding and portfolio decisions (or short-termism), and its consequences on green investments. We adopt a dynamic portfolio model, which contrary to conventional static mean-variance models, allows us to study optimal portfolios for different decision horizons. Our baseline model contains two assets, one asset with fluctuating returns and another asset with a constant risk-free return. The asset with fluctuating returns can arise from fossil-fuel based sectors or from clean energy related sectors. We consider different drivers of short-termism: the discount rate, the nature of discounting (exponential vs. hyperbolic), and the decision horizon of investors itself. We study first the implications of these determinants of short-termism on the portfolio wealth dynamics of the baseline model. We find that portfolio wealth declines faster with a higher discount rate, with hyperbolic discounting, and with shorter decision horizon. We extend our model to include a portfolio of two assets with fluctuating returns. For both model variants, we explore the cases where innovation efforts are spent on fossil fuel or clean energy sources. Detailing dynamic portfolio decisions in such a way may allow us for better pathways to empirical tests and may provide guidance to some online financial decision making.

Improved Dynamic Portfolio Selection Model with DCC-GARCH: Evidence from the U.S. Stock Market

As an important financial instrument in real financial market, the selection and management of portfolio has always been a significant and hot issue in the field of economics. This study collects data of stocks daily returns in the real US stock market to test whether DCC-GARCH model can improve the performance of portfolio return compared with the original static model when the assumption of fixed covariance matrix is relaxed. This study also compares the performance of improved portfolio with that of S&P500. The result shows that portfolio returns obtained by the dynamic model outperform the original static portfolio and market performance by a large margin. This shows that, without taking into account transaction costs, an effective way to enhance the traditional mean-variance model is to fit and predict the dynamic covariance using DCC-GARCH model.

Health insurance, portfolio choice, and retirement incentives

We study optimal portfolio choice and labor market participation in a continuous time setting in which agents face health shocks, medical expenses, and random lifetimes. We explore the implications of different forms of health coverage and study their impact on dynamic portfolios and labor supply decisions. We characterize these effects in semi-closed form, providing tools to measure retirement incentives as a function of relevant state variables and health cover arrangements. A calibration of the model matches empirically observed labor market participation patterns and portfolio decisions of US workers during the last phase of their working lives, while offering insights into the interlinkage between labor market participation, health insurance provision and portfolio choice.

Dynamic portfolio optimization with inverse covariance clustering

Market conditions change continuously. However, in portfolio investment strategies, it is hard to account for this intrinsic non-stationarity. In this paper, we propose to address this issue by using the Inverse Covariance Clustering (ICC) method to identify inherent market states and then integrate such states into a dynamic portfolio optimization process. Extensive experiments across three different markets, NASDAQ, FTSE and HS300, over a period of ten years, demonstrate the advantages of our proposed algorithm, termed Inverse Covariance Clustering-Portfolio Optimization (ICC-PO). The core of the ICC-PO methodology concerns the identification and clustering of market states from the analytics of past data and the forecasting of the future market state. It is therefore agnostic to the specific portfolio optimization method of choice. By applying the same portfolio optimization technique on a ICC temporal cluster, instead of the whole train period, we show that one can generate portfolios with substantially higher Sharpe Ratios, which are statistically more robust and resilient with great reductions in the maximum loss in extreme situations. This is shown to be consistent across markets, periods, optimization methods and selection of portfolio assets.

A Generalized Entropy Approach to Portfolio Selection under a Hidden Markov Model

This paper develops a dynamic portfolio selection model incorporating economic uncertainty for business cycles. It is assumed that the financial market at each point in time is defined by a hidden Markov model, which is characterized by the overall equity market returns and volatility. The risk associated with investment decisions is measured by the exponential Rényi entropy criterion, which summarizes the uncertainty in portfolio returns. Assuming asset returns are projected by a regime-switching regression model on the two market risk factors, we develop an entropy-based dynamic portfolio selection model constrained with the wealth surplus being greater than or equal to the shortfall over a target and the probability of shortfall being less than or equal to a specified level. In the empirical analysis, we use the select sector ETFs to test the asset pricing model and examine the portfolio performance. Weekly financial data from 31 December 1998 to 30 December 2018 is employed for the estimation of the hidden Markov model including the asset return parameters, while the out-of-sample period from 3 January 2019 to 30 April 2022 is used for portfolio performance testing. It is found that, under both the empirical Sharpe and return to entropy ratios, the dynamic portfolio under the proposed strategy is much improved in contrast with mean variance models.

Formulating Cryptocurrencies Dynamic Portfolio with Consumption Sectors’ Stocks

This study was conducted to analyze the performance of the portfolio formed with different asset classes. The instrument used is the consumption sector index with 5 cryptocurrencies. Does the formed portfolio have a better performance than the portfolio that is only formed from the consumption sector index. The type of data in this study uses secondary data in the form of a daily frequency time series with a research period from January 2019 to January 2021. The data in this study used quantitative data. Portfolio performance measurement in this study was measured using the ratio of Sharpe, Treynor, Jensen, Sortino, and Omega. Based on the results of the study, it shows that the performance of the consumption sector index portfolio that is hedged with cryptocurrency produces a higher rate of return in the period during the pandemic than in the period before the pandemic. However, there is 1 crypto that produces negative values in each ratio and research period, namely Tether. Overall, the results of this study can be concluded that adding cryptocurrency to the formation of a portfolio will get a better portfolio performance<em>.</em>

Portfolio diversification possibilities between the stock and housing markets in G7 countries: Evidence from the time-varying Granger causality

Stock and housing assets are the most important forms of wealth held by households and firms and so how to reduce their portfolio risk is a major concern. However, modern financial markets are characterized by a significant degree of fluctuations. We therefore propose a new time-varying Granger causality model (Shi et al., 2020) to monitor a time-dependent relationship between stock and housing prices in G7 member countries over the 1970–2021 period. Although no evidence of causality, namely the two-market segmentation is found in the UK, unidirectional causality may reveal informational content from one market to the other in the cases of Canada, Italy Japan and the USA, while a bi-directional relationship points to perfect integration in France and Germany before the launch of the euro. To sum up, there is a large amount of evidence to prove that this new causal method can assist in the allocation of a dynamic portfolio over time.

Quantitative trading portfolio optimization based on machine learning

The capital asset pricing model (CAPM) is to study the quantitative relationship between the expected return rate of assets and risky assets in the securities market. With the development of computer network technology and the trading market, quantitative trading has gradually become a new way of investment. Its essence lies in the decision of trading strategy. This paper obtains the relative price data of the subsequent short-term investment period through the time series method, then uses the PA algorithm in machine learning to update the portfolio and verify the effectiveness of the strategy in the data set. Based on the exchange rate change trend of the US dollar, gold, and bitcoin within five years given in the question, we constructed dynamic portfolio models for the three assets to ensure maximum profits. First, we use the time series model to predict the relative price data of the next period in a continuous trading period. Then, we implement the PA passive attack algorithm by calling the Sklearn package in Python to update the asset components in real-time every day and calculate the average annual return rate in 5 years. Based on the above analysis, we used the ADF unit root to test the stationarity of the time series. Meanwhile, we also carried out a white noise test of time series. The specific test indexes include: According to CAPM's theory, this product has a high yield, with an annual interest rate of 195.849%, which is entirely objective. However, the annual standard deviation reached 0.919164, and the maximum retractable rate exceeded 50%, indicating a sizeable overall risk. Despite this, the composite product's Calmar ratio is 3.556, Sharpe ratio is 2.104623, and its return per unit risk is still high. Compared with other quantitative trading models, this paper combines time series, machine learning, Monte Carlo, and other algorithms to effectively solve the portfolio investment optimization problem of nonlinear time-varying correlation structure among multiple assets. It enriches the results of portfolio investment structure decisions.

Dynamic sparse portfolio rebalancing model: A perspective of investors’ behavior-related decisions

By using the elaboration likelihood model (ELM) and prospect theory (PT) to model investors’ behavior-related decisions in portfolio optimization, we propose a novel dynamic behavior-based sparse portfolio selection model (BPSM) operating over multiple periods. With the BPSM model, we complement recent research that involves only investors’ sentiments by also considering market sentiments to model investors’ portfolio rebalancing behavior. Market sentiments are obtained by analyzing the online information through deep learning text analysis algorithms based on the Bi-directional Long Short-Term Memory (Bi-LSTM) model. The stochastic neural networks-based algorithm is designed to solve the BPSM. We demonstrate the effectiveness of the BPSM model on the Shanghai 50 and Hushen 300 data sets. The frame of experiments includes a dynamic portfolio rebalancing model, in which both the investors’ sentiments and the market sentiments are modeled to analyze investors’ dynamic portfolio rebalancing behavior. The experiment results show that, first, by updating the expected return rate of each period according to investors’ sentiments and market sentiments, in all cases, the BPSM model achieves a higher investment return per unit risk (Rpr) than the conventional Mean–variance (MV) model to minimize investment risk. Second, compared with the two baseline models that include only investors’ sentiments, the BPSM realizes a portfolio policy that improves investment return per unit risk (Rpr) in 70% of situations. These results reveal that incorporating investors’ behavior-related signals into the portfolio selection model is beneficial to investors’ investment results, which offers implications for financial stakeholders.

Portfolio optimisation of Shariah-compliant samples in the USA

PurposeThis paper aims to investigate the dynamic portfolio optimisation performance of numerous samples of Shariah-compliant firms in the USA vis-à-vis the overall conventional sample.Design/methodology/approachThis paper constructs efficient frontiers and subsequently the capital market line using the ovport set of commands in STATA. From the capital market line, the tangent portfolio is found, and the Sharpe ratio of the tangent portfolio is the primary measurement of the dynamic portfolio optimisation performance of the samples of Shariah-compliant samples in this study.FindingsThis paper finds that the overall conventional sample will outperform the Shariah-compliant samples in most cases. However, there exists a consistent trend whereby the performance of the overall conventional sample will converge towards the performance of the Shariah-compliant samples (and even be lower at times), as the market approaches a looming crisis suggesting that the Shariah-compliant samples do not experience significant deteriorations in their performance as compared to the conventional sample and that they provide stability during such times.Research limitations/implicationsThis paper assumes no transaction costs, illiquidity, bid-ask spread and non-compliant revenue purification all of which may negatively affect portfolio performance.Practical implicationsThe findings of this paper suggest that Shariah-compliant samples should be included in portfolios during times of crisis because they are less affected by market-wide volatility.Social implicationsThe stability of Shariah-compliant samples reflects the conservativity of the contemporary Shariah stock screening methodologies and the Shariah itself.Originality/valuePortfolio optimisation studies on Shariah-compliant samples are usually static in nature and are conducted in selected Muslim countries. This paper studies the dynamic portfolio optimisation in the USA where a liquid Islamic capital market is non-existent.

Dynamic portfolio decisions with climate risk and model uncertainty

ABSTRACT We study the effect of investment horizon on the optimal stock–bond–cash portfolio in a dynamic model with uncertainty about climate change. The stock risk premium is assumed to be an affine function of the average global temperature and an unobserved factor which is estimated via Bayesian learning. We assume that the probability distribution of future temperature is uncertain. The optimal investment strategy, robust to the uncertainty about climate change, is derived in closed form and analyzed for returns on the S&P500 index and the S&P500 ESG index. We find that stock market investment is quite sensitive to climate uncertainty with allocation to the S&P500 index being the most sensitive. We also show that, even for relatively short time horizons, welfare losses from climate uncertainty could be large for investments in either the S&P500 index or the S&P500 ESG index.

Dynamic portfolio formulation using bitcoin and LQ45 stocks

This research aims to evaluate whether dynamic portfolios consisting of bitcoin and LQ45 stocks outperform portfolios composed solely of LQ45 stocks, especially during the Covid-19 pandemic. Accordingly, we use the time-series data of eight stocks and bitcoin from January 1, 2020, to December 31, 2020. We then run the DCC-GARCH method to analyze better the dynamic correlation between assets and the abnormalities of stock return distributions. The findings demonstrate that bitcoin is negatively correlated with LQ45 stocks, and hence, it can be used to hedge against stock assets. Further, we measure the portfolio performance of bitcoin-hedged and unhedged stock portfolios using the Jensen Index, Treynor Index, Sharpe Index, Sortino Ratio, and Omega Ratio. These measures consistently indicate that bitcoin-hedged stocks outperform unhedged stocks. In sum, our study concludes that incorporating bitcoin into portfolio formation improves portfolio performance.

Emerging Market Investing: A Multi-Asset, Granular, and Dynamic Portfolio Approach

Investors seldom include emerging markets in their strategic asset allocations. The authors argue that much of this skepticism is due to a period of weak returns, exacerbated by the suboptimal construction of the traditional market-cap benchmarks. Such indexes concentrate risk in single countries, are inconsistent across asset classes, and do not reflect the fundamentals of the underlying economies. Hence, the authors propose an asset allocation framework comprising two modules. The first module constructs a portfolio with balanced risk targeted across regions, countries, and asset classes. By geographically balancing risk, the portfolio is less vulnerable to the idiosyncratic shocks prevalent in emerging markets. Moreover, the resulting portfolio overweights lower-volatility bond markets, which not only exhibit higher risk-adjusted returns but are also less correlated with riskier assets. The second module introduces tilts based on expected returns within a tracking error budget. The authors demonstrate the benefit of incorporating expected return assumptions via systematic value- and carry-based tilting.

ASEAN-5 and Crypto Hedge Fund: Dynamic Portfolio Approach

This study aims to compose a portfolio consisting crypto hedge fund and ASEAN-5 stock market and to examine the hedging effect of crypto hedge fund against those stock markets. This study employs dynamic portfolio approach using data from the period of July 2013 to August 2021. This analysis finds that crypto hedge fund can provide hedging effect against ASEAN-5 stock portfolio resulting in hedging effectiveness with positive value. Crypto hedge fund is also proven to be able to increase the risk adjusted performance of all ASEAN-5 portfolio observed under this study, shown by an increase in Sharpe ratio and Sortino ratio.

Dynamic portfolio optimization using technical analysis‐based clustering

An accurate prediction of asset prices is perhaps the biggest challenge of any study in portfolio optimization. Asset prices are affected by several random and nonrandom factors, which makes them difficult to forecast. This paper proposes a two-phase dynamic portfolio optimization approach. In the first phase, assets are clustered into buy, sell, and hold groups using technical indicators. We provide a methodology to integrate the investor attitude (optimistic, pessimistic, or neutral) during the clustering phase. In the second phase, we input the clustered groups into a portfolio optimization model to obtain the optimum asset allocations. We use coherent fuzzy numbers to model the asset returns to integrate the investor attitude in this phase. The optimization model is solved using a genetic algorithm. The portfolios are rebalanced at regular intervals as new data becomes available. We illustrate the proposed methodology on a 100-asset problem of the US stock market. We analyze the real-world performance of the obtained portfolios. We compare the performance of the proposed approach with the mean–variance model, and other portfolios, such as the naïve portfolio and the NASDAQ-100 index.

A Tail Measure With Variable Risk Tolerance: Application in Dynamic Portfolio Insurance Strategy

A Tail Measure With Variable Risk Tolerance: Application in Dynamic Portfolio Insurance Strategy