Search results

The purpose of this paper is to investigate the competing effects of brand community participation, which should enhance loyalty to both the brand and to already-owned products, against switching costs, which should make consumers sensitive about the financial costs associated with new products.

Using the participation and weekly adoption data from 7,411 members in two brand communities and one product category forum over a six-month period, switching costs were computed for each member using 10 years of product release and pricing data.

Consistent with prior research, switching costs had a significant effect on reducing product adoption. Brand community participation also had a significant effect on overcoming switching costs. However, these main effects were qualified by an interaction, such that the most active participants were more likely to buy the new product when switching costs were higher.

Most importantly, these findings provide unique insights into financial switching costs and demonstrate ways in which brand community participation provides a way to mitigate switching costs for consumers who would most be affected by them.

In asset management, what if clients want to purchase protection from risk factors, under the form of variance risk premia. This paper aims to address this topic by developing a portfolio optimization framework based on the criterion of the minimum variance risk premium (VRP) for any investor selecting stocks with an expected target return while minimizing the risk aversion associated to the portfolio according to “good” and “bad” times.

To accomplish this portfolio selection problem, the authors compute variance risk-premium as the difference from high-frequencies' realized volatility and options' implied volatility stemming from 19 stock markets, estimate a 2-state Markov-switching model on the variance risk-premia and optimize variance risk-premia portfolios across non-overlapping regions. The period goes from March 16, 2011, to March 28, 2018.

The authors find that optimized portfolios based on variance-covariance matrices stemming from VRP do not consistently outperform the benchmark based on daily returns. Several robustness checks are investigated by minimizing historical, realized or implicit variances, with/without regime switching. In a boundary case, accounting for the realized variance risk factor in portfolio decisions can be seen as a promising alternative from a portfolio performance perspective.

As a new management “style”, the realized volatility approach can, therefore, bring incremental value to construct the conditional covariance matrix estimates.

The authors assess the portfolio performance determined by the variance-covariance matrices that are derived by four models: “naive” (Markowitz returns benchmark), non-switching VRP, maximum likelihood regime-switching VRP and Bayesian regime switching VRP. The authors examine the best return-risk combination through the calculation of the Sharpe ratio. They also assess another different portfolio strategy: the risk parity approach.

Uncertainties in residential property investment performance require that real estate assets are designed in a flexible manner to respond to impacts of market dynamics. Though estimating the cost of flexibility is straightforward, assessing the economic value of flexibility is not. The purpose of this study is to explore the potential practical application of real option analysis to determine the economic value of a switching output flexibility embedded in a residential property investment in Australia. The study involves the exploration of an optimal strategy for investment in a residential development through real option analysis and valuation of a mixed use investment.

The real option valuation model developed by McDonald and Siegel (1986) is adopted for the evaluation because the switching output flexibility is likened to a perpetual American call option with dividend payout.

Through real option analysis, the economic value of switching output flexibility of the mixed use building was determined to be higher than the initial upfront costs. Moreover, a payoff of about $4million was determined to be the value of the switching output flexibility, therefore justifying upfront investments in flexibility as an uncertainty and risk management tool.

This application is an important demonstration of the practical use of options pricing techniques (real options analysis) and delivers further evidence needed to support the adoption of real option valuation in practice. Flexibility can also enhance risks and uncertainty management in residential property investment better than the adjustment of discount rates.

There is limited evidence on the use of real options techniques for the valuation of switching output flexibility in practice, and this comes as an original application; both the case study and data are all initial applications of switching flexibility in the Australian property market.

The purpose of this paper is to develop a combined control (CC) technique based on the direct torque control (DTC) strategy and vector control (VC) method, to improve the overall performance of a three-phase induction machine (TPIM) drives.

The proposed control scheme includes a table-based DTC strategy in connection with a proportional-integral-sliding mode controller and pulse width modulation switching strategy. The control system has merits of DTC technique such as simple structure, less dependent on machine parameters, fast dynamic response and merits of VC technique such as high accuracy and constant switching frequency.

To validate the effectiveness of the proposed control system, simulation and experimental studies are carried out for a 0.75 kW TPIM in different operating conditions. The achieved results show the superiority of the proposed method in terms of fast dynamics and simple structure compared to the VC strategy and low speed and torque ripples and constant switching frequency compared to the DTC method.

Compared to the conventional CC strategies, the control law of the proposed method is based on DTC theory and modulation is established based on VC. In other words, the variable switching frequency which is one of the main disadvantages of the conventional CC strategies is rectified using the proposed CC scheme.

The purpose of this paper is to explore a regime switching asset allocation model that includes six major real estate security markets (USA, UK, Japan, Australia, Hong Kong and Singapore) and focuses on how the presence of regimes affects portfolio composition.

A Markov switching model is first developed to characterize real estate security markets’ risk‐return in two regimes. The mean‐variance portfolio construction methodology is then deployed in the presence of the two regimes. Finally, the out‐of‐sample analyzes are conducted to examine whether the regime switching allocation outperforms the conventional allocation strategy.

Strong evidence of regimes in the six real estate security markets in detected. The correlations between the various real estate security markets’ returns are higher in the bear market regime than in the bull market regime. Consequently the optimal real estate portfolio in the bear market regime is very different from that in the bull market regime. The out‐of‐sample tests reveal that the regime‐switching model outperforms the non‐regime dependent model, the world real estate portfolio and equally‐weighted portfolio from risk‐adjusted performance perspective.

The application of the Markov switching technique to real estate markets is relatively new and has great significance for international real estate diversification. With increased significance of international securitized property as a real estate investment vehicle for institutional investors to gain worldwide real estate exposure, this study provides significant insights into the investment behavior and optimal asset allocation implications of the listed real estate when returns follow a regime switching process.

This study sheds some light on factors that affect customer switching intention in the Malaysian mobile phone service industry. More particularly, the purpose of this paper is to examine the effect of service quality (SQ), customer satisfaction, switching cost and consumer innovativeness (CI) on service switching intention (SWI); the mediating role of customer satisfaction; and the moderating role of service switching cost on the relationship between CI and SWI.

Data were collected using a self-administered questionnaire survey that yielded 535 responses. Using structural equation modelling approach, the partial least square software, version 3 was utilised to test the study hypotheses.

Results reveal that customer satisfaction, service switching cost and CI directly affect SWI. However, no significant relationship was found between SQ and SWI. Again, data supported the mediating effect of customer satisfaction as well as the moderating effect of service switching cost.

It is expected that the findings from this study will enable policymakers, managers and marketers to formulate better strategies and effectively implement loyalty programs, preventing their customers from switching.

This study contributes to the existing literature by testing switching costs as the quasi moderator. Moreover, this is a pioneer study to consider CI as the antecedent of SWI.

This study explores company strategies for intangibles. The authors investigate whether it is reasonable for companies to intensify intangibles when the current strategy is not intangible-intensive. The purpose of this paper is to elaborate a theoretical model to describe the strategic decision making in companies.

The authors use the Bellman-equation framework to find the conditions under which a change in strategy for intangibles is reasonable.

The results determine the parameters of returns on intangibles in different strategies, the optimal intangible stock and the influence of external economic shocks. The findings of the study demonstrate that many requirements have to be met to make intangible-intensive strategy beneficial for a company. Moreover negative shocks of crises force a company to postpone a new strategy on intangibles.

This research provides an insight into strategic behavior of companies under uncertainty. The theoretical findings demonstrate under which conditions companies should decide to switch to a strategy more intangible-intensive. This model can be used to empirically test parameters of different investment strategies of companies using structural estimation techniques.

This work contributes to the theory of managerial economics giving closed form solutions for the dynamic optimization of company behavior. The findings also show how this behavior might change when economic crises are faced or expected.

Using a market model of international equity returns, which fully incorporates the regime switching and heteroskedasticity effects, we conduct an empirical study on the asymmetric behavior of 31 emerging equity markets across the different regimes of both the global and the local markets. Asymmetric correlation is found to be much weaker than that among developed markets as documented in the recent studies. There is little evidence of performance enhancement by possessing information on asymmetric correlation in international asset allocation strategies involving emerging markets.

The subject of this paper is seasonal variation in the return on stocks. The phenomenon we analyze here is known as the “Halloween effect” or the trading strategy “sell in May and go away.” The authors test the hypothesis that stock markets tend to return considerably less in the six months beginning in May than in the other half of the year. This effect has shown persistency over time and is seemingly large enough to be a candidate for economic significance.

The authors analyze monthly data from 13 countries for the period 1958–2019, using the Kruskal–Wallis test, t-test and a boot-strap based estimator. In addition, we look a sub-periods for a larger group of countries and include data on both stock returns and interest rates.

The authors find a strong seasonal effect in a large majority of the markets, with the period from November to April seeing higher returns than the other six months of the year. This result also holds for a larger sample of countries based on data from a shorter period. The effect is found to be economically significant in most countries in the sample. The authors examine one potential explanation for seasonal variation in stock returns, i.e. seasonal affective disorder (SAD). The authors find some, albeit weak, support for this hypothesis.

This paper uses a rich dataset that has not been used for this purpose before and robust tests of statistical and economic significance to shed light on an important aspect of global financial markets.

In this paper, the previously developed idea of generalized optimization of circuits for deterministic methods has been extended to genetic algorithm (GA) to demonstrate new possibilities for solving an optimization problem that enhance accuracy and significantly reduce computing time.

The disadvantages of GAs are premature convergence to local minima and an increase in the computer operation time when setting a sufficiently high accuracy for obtaining the minimum. The idea of generalized optimization of circuits, previously developed for the methods of deterministic optimization, is built into the GA and allows one to implement various optimization strategies based on GA. The shape of the fitness function, as well as the length and structure of the chromosomes, is determined by a control vector artificially introduced within the framework of generalized optimization. This study found that changing the control vector that determines the method for calculating the fitness function makes it possible to bypass local minima and find the global minimum with high accuracy and a significant reduction in central processing unit (CPU) time.

The structure of the control vector is found, which makes it possible to reduce the CPU time by several orders of magnitude and increase the accuracy of the optimization process compared with the traditional approach for GAs.

It was demonstrated that incorporating the idea of generalized optimization into the body of a stochastic optimization method leads to qualitatively new properties of the optimization process, increasing the accuracy and minimizing the CPU time.