Search results

This study attempts to conduct a comparative analysis between dynamic and static asset allocation to achieve the long-term target return on asset liability management (ALM). This study conducts asset allocation using the ex ante expected rate of return through the outlook of future economic indicators because past economic indicators or realized rate of returns which are used as input data for expected rate of returns in the “building block” method, most adopted by domestic pension funds, does not fully reflect the future economic situation. Vector autoregression is used to estimate and forecast long-term interest rates. Furthermore, it is applied to gross domestic product and consumer price index estimation because it is widely used in financial time series data. Based on asset allocation simulations, this study derived the following insights: first, economic indicator filtering and upper-lower bound computation is needed to reduce the expected return volatility. Second, to reach the ALM goal, more stocks should be allocated than low-yielding assets. Finally, dynamic asset allocation which has been mirroring economic changes actively has a higher annual yield and risk-adjusted return than static asset allocation.

The purpose of this paper is to compare simple dynamic withdrawal strategies with the static withdrawal method, examining not only failure rates and ending wealth but also spending. All withdrawal strategies are adjusted for the Internal Revenue Service’s (IRS) required minimum distribution (RMD). In addition, this study investigates the use of small company stocks (SCS) in place of large company stocks (LCS). Results indicate SCS portfolios are superior to large. When returns are poor, some dynamic strategies will not ensure income for life. This study demonstrates that the simplest dynamic strategy is superior to two popular dynamic strategies.

Using historical overlapping periods, different withdrawal strategies are examined. Previous studies focused on failure rates and ending wealth. As discussed in Milevsky (2016) different statistical distributions can have similar tail properties (prob of failure) but dissimilar risk and return profile. The detailed examination of both spending and use of small stocks advances the literature in this area.

Results indicate that use of small stocks is superior to using large stocks in the portfolios. When US historical stock returns are adjusted downward, there is the potential that some dynamic strategies will not ensure income for life. This study demonstrates that the simplest dynamic strategy is superior to two popular dynamic strategies.

This paper is the first to examine, in detail, annual spending results for the retiree. Second, it is shown that, overall, SCS are superior to LCS for all stock/bond allocations. Even though absolute downside risk increases slightly, this increase in downside risk is dominated by the upside potential. In other words, the positive skewness of small stock returns along with the cumulative effects of compounding at a higher rate increases both the available wealth for spending and ending wealth. Third, IRS’s RMDs are taken into account for every withdrawal strategy examined. Lastly, it demonstrates that the simplest dynamic strategy is superior to two popular dynamic strategies.

The purpose of this paper is to investigate whether asset allocation across various industries listed on the Ghana Stock Exchange (GSE) varies across different monetary policy states.

This paper adopts the Markov Chain technique to split monetary policy into three different states. The authors further adopt the Markowitz portfolio optimization technique to find the minimum variance and optimum portfolio for the industries listed on the GSE.

The finding reveals a dynamic asset allocation, which varies the industry’s weight mix across the various monetary policy states enhance excess returns compared to the static asset allocation. Specifically, the authors find risk-return trade-off among industries listed on the GSE. Financial and Food and Beverage industries portfolios record high returns relative to the Government of Ghana 91-day Treasury bill. The Food and Beverage portfolio is the only portfolio that records relatively high excess returns across all the monetary policy states. The authors also find that, during expansionary state (high monetary policy rates) of the monetary policy, investors are to allocate about 69 and 30 percent of their investment into food and beverages and financials, respectively. Corner solution is found in the transient state where 100 percent of wealth is allocated to financial to obtain the optimum portfolio. The optimum portfolio in the contraction state assigns 52 percent to financials and 42 percent to manufacturing. In summary, the result supports the dependence of investors’ asset allocation decisions on monetary policy.

Therefore, the authors propose an investment strategy which is dynamic and takes into consideration the monetary policy states rather than static asset allocation which maintains the same industry weight mix over the investment period.

In sum, the authors interpret the result as support for the dependence of investors’ asset allocation decisions on monetary policy. In Ghana, an increase in the monetary policy appears to support industries listed on the equity market. The result also gives knowledge about investors’ asset allocation decisions on the GSE, which is practical balanced source of information for investors’ risk and return choices. For a prudent monetary policy framework, the monetary policy committee should monitor industries listed on the GSE. The result from the analysis has also an implication for investors, portfolio managers and fund managers to consider the state of the monetary policy in Ghana when making investment decisions.

The study differs from earlier research on asset allocation by breaking new grounds on two levels. First of all, based on the notion that different industries have different exposures to monetary policy states, the authors extend the portfolios by grouping the equities listed on the GSE into their industrial sectors. Second, the authors examine how investors’ optimal portfolio allocation may change depending on the state of monetary policy.

The ratio between share price and current earnings per share, the Price Earning (PE) ratio, is widely considered to be an effective gauge of under/overvaluation of a corporation’s stock. Arguably, a more reliable indicator, the Cyclically Adjusted Price Earning ratio or CAPE, can be obtained by replacing current earnings with a measure of permanent earnings i.e. the profits that a corporation is able to earn, on average, over the medium to long run. In this study, we aim to understand the cross-sectional aspects of the dynamics of the valuation metrics across global stock markets including both developed and emerging markets. We use a time varying DCC model to exploit the dynamics in correlations, by introducing the notion of value spread between CAPE and the respective Market Index from 2002 to 2014 for 34 countries. Value spread is statistically significant during the 2008 crisis for asset allocation. The signal can be utilized for better asset allocation as it allows one to interpret the common movements in the stock market for under/overvaluation trends. These estimates clearly indicate periods of misvaluation in our sample. Furthermore, our simulations suggest that the model can provide early warning signs for asset mispricing in real time on a global scale and formation of asset bubbles.

The purpose of this paper is to prove the effectiveness of minimum semi‐absolute deviations (MSAD) method in dynamic portfolio investment.

In financial investment, the classical static portfolio theory of Markowitz type lacks the dynamic adaptability to the changing market situations. This paper proposes a dynamic portfolio theory which uses MSAD criterion on a moving window to replace the Markowitz mean‐variance analysis.

Two specific models are developed to test the validity of the MSAD method: the first model constructs a portfolio consisting of Shanghai‐Shenzhen 300 Index and a national debt as two contrarian assets; the second model constructs a portfolio consisting of a complete set of 18 Chinese stock sector indices and a national debt. The empirical results of the test using six‐year monthly data (2005 to 2010) provide significant evidence that the MSAD method is valid, producing superior returns of investment over the stock index during the test period.

The findings in this study clearly highlight the validity of the MSAD method in determining the weights of assets in Chinese stock markets.

In order to resolve the problem of portfolio investment in Chinese stock markets, the MSAD method with stop loss control strategy can be used for investors to obtain the weights of assets and control the risk.

This study analyzes and verifies the effectiveness of the MSAD method in dynamic portfolio investment. The stop loss control strategy designed and used in the MSAD method is a pioneering and exploratory experiment.

The author proposes an update to the mean variance (MV) framework that replaces a constant risk aversion parameter using a dynamic risk aversion indicator. The contribution to the literature is made through making the static risk aversion parameter operational using an indicator of market sentiment. Results suggest that Sharpe ratios improve when the author replaces the traditional risk aversion parameter with a dynamic sentiment indicator from the behavioral finance literature when allocating between a risky portfolio and a risk-free asset. However, results are mixed when using the behavioral framework to allocate between two risky assets.

The author includes a dynamic risk aversion parameter in the mean variance framework and back test using the traditional and updated behavioral mean variance (BMV) framework to see which framework leads to better performance.

The author finds that the behavioral framework provides superior performance when allocating between a risky and risk-free asset; however, it under performs when allocating between risky assets.

The research is based on back testing; therefore, it cannot be concluded that this strategy will perform well in real-time circumstances.

Portfolio managers may use this strategy to optimize the allocation between a risky portfolio and a risk-free asset.

An improved allocation between risk-free and risky assets that could lead to less leverage in the market.

The study is the first to use such a sentiment indicator in the traditional MV framework and show the math.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

It is crucial to find a better portfolio optimization strategy, considering the cryptocurrencies' asymmetric volatilities. Hence, this research aimed to present dynamic optimization on minimum variance (MVP), equal risk contribution (ERC) and most diversified portfolio (MDP).

This study applied dynamic covariances from multivariate GARCH(1,1) with Student’s-t-distribution. This research also constructed static optimization from the conventional MVP, ERC and MDP as comparison. Moreover, the optimization involved transaction cost and out-of-sample analysis from the rolling windows method. The sample consisted of ten significant cryptocurrencies.

Dynamic optimization enhanced risk-adjusted return. Moreover, dynamic MDP and ERC could win the naïve strategy (1/N) under various estimation windows, and forecast lengths when the transaction cost ranging from 10 bps to 50 bps. The researcher also used another researcher's sample as a robustness test. Findings showed that dynamic optimization (MDP and ERC) outperformed the benchmark.

Sophisticated investors may use the dynamic ERC and MDP to optimize cryptocurrencies portfolio.

To the best of the author’s knowledge, this is the first paper that studies the dynamic optimization on MVP, ERC and MDP using DCC and ADCC-GARCH with multivariate-t-distribution and rolling windows method.