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Filter trading is a technical trading rule that has been used extensively to test the efficient market hypothesis in the context of long-term trading. In this paper, the authors adopt the rule to analyze intraday trading, in which an open position is not left overnight. This paper aims to explore the relationship between intraday filter trading profitability and intraday realized volatilities. The bivariate thin plate spline (TPS) model is chosen to fit the predictor-response surface for high frequency data from the Hang Seng index futures (HSIF) market. The hypotheses follow the adaptive market hypothesis, arguing that intraday filter trading differs in profitability under different market conditions as measured by realized volatility, and furthermore, the optimal filter size for trading on each day is related to the realized volatility. The empirical results furnish new evidence that range-based realized volatilities (RaV) are more efficient in identifying trading profit than return-based volatilities (ReV). These results shed light on the efficiency of intraday high frequency trading in the HSIF market. Some trading suggestions are given based on the findings.

Among all the factors that affect the profit of filter trading, intraday realized volatility stands out as an important predictor. The authors explore several intraday volatilities measures using range-based or return-based methods of estimation. The authors then study how the filter trading profit will depend on realized volatility and how the optimal filter size is related to the realized volatility. The bivariate TPS model is used to model the predictor-response relationship.

The empirical results show that range-based realized volatility has a higher predictive power on filter rule trading profit than the return-based realized volatility.

First, the authors contribute to the literature by investigating the profitability of the filter trading rule on high frequency tick-by-tick data of HSIF market. Second, the authors test the assumption that the magnitude of the intraday momentum trading profit depends on the realized volatilities and aims to identify a relationship between them. Furthermore, the authors consider several intraday realized volatilities and find the RaV have the higher prediction power than ReV. Finally, the authors find some relationship between the optimal filter size and the realized volatilities. Based on the observations, the authors also give some trading suggestions to the intraday filter traders.

The purpose of this paper is to identify a list of underlying considerations in choosing the appropriate economic valuation method for use in the conservation of historic property and to highlight the importance of non‐use values in making decisions.

A thorough literature review is conducted to provide a concise overview of the most commonly used economic valuation methods in the cultural heritage field. The stated and revealed preference methods were analyzed. Their theoretical basis, methodology and analysis procedures are described. By highlighting the strengths and limitations of these evaluation methods for use in the different context, a list of underlying factors for choosing the appropriate method for different decision‐making problems in managing historic properties were deduced.

The underlying considerations in choosing the appropriate evaluation method in historic properties include “Matching the objectives ”, “Evaluate use or non‐use values ”, “Scope of evaluation ”, “availability of data”, “Time and cost of conducting the methods”, “Methodological procedures”, “Analysis of the methods”, and “Local contexts where the techniques will be applied”.

The long‐term significance of this study is to enhance a holistic understanding of the quantitative approach to evaluate the value of historic properties. This enhanced understanding should help to inform the decision‐makings on comparing and prioritizing the management of heritage facilities when confronted with limited resources.

Ecotourism is a burgeoning sector of the tourism industry offering a relatively guilt-free environment in which to satisfy the desire for travel and adventure. The discourse is firmly entrenched within the dominant conception of sustainability where nature is seen as a privileged ‘other’, untouched by humans. This ideology is also prevalent in the design of ecotourism facilities, which are generally predicated on a model of minimal intervention. This low-impact approach is not problematic in itself, but it misses the opportunity to engage in a more productive and ‘regenerative’ relationship with place. Conversely, Philip Cox Richardson Taylor's design for the resort town of Yulara in central Australia sought a more constructive relationship with place and questioned the conventional notion of ‘resort’. Although this resort, constructed in 1984, predates the current ecotourism industry and certification programs, it remains an early exemplar of innovations in this area and offers the benefits of hindsight. Through an exploration of the ideals and realities of the design and subsequent occupation of Yulara, this paper questions the potential challenges and opportunities of the design of ecotourism facilities to engage in a more ‘regenerative’ agenda. In particular, it identifies the social context and consideration of spatial practice as a key area of opportunity for the built environment to contribute to the ecotourism goal of interpretation and education through a more reflexive form of environmental awareness.

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.

We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates local hemodynamic response functions (HRFs) and activation parameters, as well as global effective and functional connectivity parameters. Existing methods assume identical HRFs across brain regions, which may lead to erroneous conclusions in inferring activation and connectivity patterns. Our approach addresses this limitation by estimating region-specific HRFs. Additionally, it enables neuroscientists to compare effective connectivity networks for different experimental conditions. Furthermore, the use of spike and slab priors on the connectivity parameters allows us to directly select significant effective connectivities in a given network.

We include a simulation study that demonstrates that, compared to the standard generalized linear model (GLM) approach, our model generally has higher power and lower type I error and bias than the GLM approach, and it also has the ability to capture condition-specific connectivities. We applied our approach to a dataset from a stroke study and found different effective connectivity patterns for task and rest conditions in certain brain regions of interest (ROIs).

New information technologies such as IoT and big data analytics have reshaped the development of smart green products. These products exhibit two important features that are not seen in traditional products: environmental friendliness and data network effect. Based on these unique features, the authors investigate a firm's optimal selling strategy of smart green products from both the profitability and environmental perspectives.

The authors establish stylized models to consider the optimality of three selling strategies: (1) traditional strategy – only offering traditional products, (2) green strategy – only offering smart green products, and (3) hybrid strategy – offering both traditional and smart green products.

The authors’ analysis shows that in the absence of data network effect, there will always be a conflict between profit maximization and environmental protection. However, a strategy that benefits both the firm and the environment exists when data network effect is present. Interestingly, hybrid and traditional strategies can be win-win strategies, but the green strategy cannot. Also surprisingly, the green strategy may harm the environment more as smart products become greener.

This study examines the economic and environmental implications of selling smart green products, and contributes to existing literature on sustainable operations and green product design by incorporating the impact of both consumer environmental awareness and data network effect. The authors’ findings shed light on how to coordinate the profitability and environmental impact of selling smart green products in the era of big data and IoT.