Search results

This paper introduces a new mathematical model for analyzing the economic benefits of incorporating the fourth party logistics (4PL), which is a contractor (i.e. agent) for the supply chain coordination and construction based on the division of community and the outsourcing development. Based on the physical theory and the wave-particle duality, a supply chain is the special organization whose characteristic has wave-particle duality. The mathematical model enriches the connotation of 4PL and it broadens the thought for 4PL development. Secondly, the proposed mathematical model predicated on transaction costs, is supported by Transaction Cost Theory (TCT) and acts as the theoretical analysis tool of 4PL for coordinating 3-party generic supply chain. Through the model, some trendy conclusions can be drawn to provide theoretical support for 4PL’s practices. Finally, a case illustrates our conclusions.

Multiple jobholding (MJH) is assumed to be a growing phenomenon due to working life changes. This study presents new knowledge on the MJH career paths, from the perspectives of both employers and employees.

The qualitative interview study was focused on retail trade and restaurant and food service industries in Finland, where MJH is a quite common work arrangement compared to other European countries. The data were analyzed with the concepts of the chaos theory of careers and with an abductive thematic content analysis.

According to the results, several events and intertwined factors may lead individual careers gradually to MJH. Changing personal and family situations and leisure time needs attracted the careers towards MJH. MJH was not only a financial necessity to employees, but it also served their flexibility interests. The interviewed employers applied flexible non-standard employment arrangements mainly due to rapidly varying labor needs established in the industries. It was important for them to strengthen the non-standard core employees' sense of belonging to the work community. However, employees with work ability challenges were in risk to end up in peripheral positions at the labor market.

Previous research on multiple jobholding has not combined employers’ perspectives of MJH to employees’ experiences of career paths.

The purpose of this study aims to analyze the dynamic behavior of the relationship between atmospheric carbon emissions and the Organisation for Economic Co-operation and Development (OECD) industrial production index (IPI) in the short and long term by applying multifractal techniques.

Multifractal de-trended cross-correlation technique is used for this analysis based on the relevant literature. In addition, it is the most widely used approach to estimate multifractality because it generates robust empirical results against non-stationarities in the time series.

It is revealed that industrial production causes long and short term environmental costs. The OECD IPI and atmospheric carbon emissions were found to have a strong correlation between the time domain. However, this relationship does not mostly take into account the frequency-based correlations with the tail effects caused by shocks that are effective on the economy. In this study, the long-term dependence of the relationship between the OECD IPI and atmospheric carbon emissions differs from the correlation obtained by linear methods, as the analysis is based on the frequency. The major finding is that the Hurst coefficient is in the range 0.40-0.75 indicating.

In this study, the local singular behavior of the time-series is analyzed to test for the multifractality characteristics of the series. In this context, the scaling exponents and the singularity spectrum are obtained to determine the origins of this multifractality. The multifractal time series are defined as the set of points with a given singularity exponent a where this exponent a is illustrated as a fractal with fractal dimension f(α). Therefore, the multifractality term indicates the existence of fluctuations, which are non-uniform and more importantly, their relative frequencies are also scale-dependent.

The results provide information based on the fluctuation in IPI, which determines the main conjuncture of the economy. An optimal strategy for shaping the consequences of climate change resulting from industrial production activities will not only need to be quite comprehensive and global in scale but also policies will need to be applicable to the national and local conditions of the given nation and adaptable to the needs of the country.

The results provide information for the analysis of the environmental cost of climate change depending on the magnitude of the impact on the total supply. In addition to environmental problems, climate change leads to economic problems, and hence, policy instruments are introduced to fight against the adverse effects of it.

This study may be of practical and technical importance in regional climate change forecasting, extreme carbon emission regulations and industrial production resource management in the world economy. Hence, the major contribution of this study is to introduce an approach to sustainability for the analysis of the environmental cost of growth in the supply side economy.

The purpose of this paper is to explore the impact of recent developments in corporate reporting, specifically from the carbon disclosure project (CDP) environment, in the evolution of European post-crisis financial markets.

Theoretical and instrumental advancements from nonlinear dynamics have been applied to the analysis of market behaviour and the online presence or reputation of major European listed banks.

The application of a nonlinear statistical methodology (i.e. the autoregressive fractionally integrated moving average [ARFIMA] estimation model) demonstrates the presence of a long history of collected data, thus indicating a certain degree of predictability in the time series. Also, this study confirms the existence of structural breakpoints, specifically the impact of the CDP reporting in both stock prices and online search trends of the sampled companies for certain periods.

This study introduces new methodological perspectives in corporate reporting studies, as the application of nonlinear techniques can be more effective in capturing corporate transparency issues. A limitation to overcome is to explore whether the impact of reporting is different due to the specific reporting behaviour each company adopts.

The “breakpoint” concept should enlighten the importance to firms of providing more information in specific moments, which can impact on both traditional (i.e. stock prices) and modern (i.e. online popularity) performance metrics. Additionally, it should be taken into account by stakeholders, when analysing the accountability of firms to improve their decision-making processes and policymakers, for monitoring and contrasting speculative and insider trading activities.

Online search trends represent a new public attitude to how society “measures” the effectiveness of firms’ disclosure behaviours.

Combining ARFIMA with structural break techniques can be regarded as a relevant and complementary addition to classic “market reaction” or “value relevance” techniques.

Empirical findings on interest rate dynamics imply that short rates show some long memories and non-Markovian. It is well-known that fractional Brownian motion (IBm) is a proper candidate for modelling this empirical phenomena. IBm. however. is not a semimartingale process. For this reason. it is very hard to apply such processes for asset price modelling.

Without using Ito formula, we investigate the IBm interest rate theory‘ We obtain a pure discount bond price. and Greeks by using Malllavin calculus.

This paper aims at developing a behavioral agent-based model for interacting financial markets. Additionally, the effect of imposing Tobin taxes on market dynamics is explored.

The agent-based approach is followed to capture the highly complex, dynamic nature of financial markets. The model represents the interaction between two different financial markets located in two countries. The artificial markets are populated with heterogeneous, boundedly rational agents. There are two types of agents populating the markets; market makers and traders. Each time step, traders decide on which market to participate in and which trading strategy to follow. Traders can follow technical trading strategy, fundamental trading strategy or abstain from trading. The time-varying weight of each trading strategy depends on the current and past performance of this strategy. However, technical traders are loss-averse, where losses are perceived twice the equivalent gains. Market makers settle asset prices according to the net submitted orders.

The proposed framework can replicate important stylized facts observed empirically such as bubbles and crashes, excess volatility, clustered volatility, power-law tails, persistent autocorrelation in absolute returns and fractal structure.

Artificial models linking micro to macro behavior facilitate exploring the effect of different fiscal and monetary policies. The results of imposing Tobin taxes indicate that a small levy may raise government revenues without causing market distortion or instability.

This paper proposes a novel approach to explore the effect of loss aversion on the decision-making process in interacting financial markets framework.

This work aims to explore the behavior of stock market prices according to the autoregressive fractional differencing integrated moving average model. This behavior will be compared with a measure of online presence, search engine results as measured by Google Trends.

The study sample is comprised by the companies listed at the STOXX® Global 3000 Travel and Leisure. Google Finance and Yahoo Finance, along with Google Trends, were used, respectively, to obtain the data of stock prices and search results, for a period of five years (October 2012 to October 2017). To guarantee certain comparability between the two data sets, weekly observations were collected, with a total figure of 118 firms, two time series each (price and search results), around 61,000 observations.

Relationships between the two data sets are explored, with theoretical implications for the fields of economics, finance and management. Tourist corporations were analyzed owing to their growing economic impact. The estimations are initially consistent with long memory; so, they suggest that both stock market prices and online search trends deserve further exploration for modeling and forecasting. Significant differences owing to country and sector effects are also shown.

This research contributes in two different ways: it demonstrate the potential of a new tool for the analysis of relevant time series to monitor the behavior of firms and markets, and it suggests several theoretical pathways for further research in the specific topics of asymmetry of information and corporate transparency, proposing pertinent bridges between the two fields.

Research on grey systems is becoming more sophisticated, and grey relational and prediction analyses are receiving close review worldwide. Particularly, the application of grey systems in marine economics is gaining importance. The purpose of this paper is to summarize and review literature on grey models, providing new directions in their application in the marine economy.

This paper organized seminal studies on grey systems published by Chinese core journal database – CNKI, Web of Science and Elsevier from 1982 to 2018. After searching the aforementioned database for the said duration, the authors used the CiteSpace visualization tools to analyze them.

The authors sorted the studies according to their countries/regions, institutions, keywords and categories using the CiteSpace tool; analyzed current research characteristics on grey models; and discussed their possible applications in marine businesses, economy, scientific research and education, marine environment and disasters. Finally, the authors pointed out the development trend of grey models.

Although researches are combining grey theory with fractals, neural networks, fuzzy theory and other methods, the applications, in terms of scope, have still not met the demand. With the increasingly in-depth research in marine economics and management, international marine economic research has entered a new period of development. Grey theory will certainly attract scholars’ attention, and its role in marine economy and management will gain considerable significance.

This study contributes to a better understanding of the important actor-specific, micro-level legitimacy dimensions in dynamic entrepreneurial ecosystems (EEs).

Using an embedded case study approach and rich longitudinal data collected over 16 years within a French EE, the study analyzes the legitimacy gaining process of two actors coming from opposite founding conditions.

Three dimensions of legitimacy (3L) are necessary to be accepted as functional actors within EEs: institutional legitimacy (IL) refers to the EE's acceptance of an actor as an institution active in the field of entrepreneurship; cultural legitimacy (CL) means that the actor is recognized as possessing and promoting values considered appropriate by the entrepreneurial community; relational legitimacy (RL) relates to the willingness of the entrepreneurial community to interact with the actor. These are complementary dimensions that members of EEs need to possess to acquire full legitimacy. Different paths are possible to achieve this full legitimacy.

Replicating the study with a comparative approach including more actors could represent an interesting avenue for research.

This research provides insights into the underlying dimensions of legitimacy in EEs, how various actors gain legitimacy in such contexts and how this influences the dynamics of EEs.

The results provide novel insights into the issue of legitimacy in EEs and legitimacy theory in general.

La presente investigación examina la existencia de memoria de largo plazo por medio del cálculo del coeficiente de Hurst y Hurst ajustado, y del análisis de características de estructuras caóticas en la serie del mercado bursátil de Chile, específicamente a través del Índice de Precios Selectivo de Acciones.

Se desarrolló un breve análisis del mercado, según la metodología de Box y Jenkings. La validez de los resultados se realizó por medio de la prueba propuesta por Brock, Dechert y Scheinkman. En segundo lugar, se procedió a analizar la dinámica y patrones del índice y de su rendimiento, para observar si existía evidencia de memoria de largo plazo.

Los resultados demuestran la presencia de esta memoria en el mercado bursátil chileno, determinado a través del índice accionario en dos escalas, diaria y trimestral, lo que además corrobora resultados obtenidos por otros autores, confirmando el uso de la metodología de Rango Re-escaldo para la identificación y determinación de memoria de largo plazo en una serie temporal.

Este estudio permitirá a futuros investigadores realizar análisis similares en otros mercados, aportando un nuevo enfoque al analizar la memoria de la largo plazo y los factores que inciden en ella.

Exponente de Hurst, Índice bursátil, Mercados eficientes, Mercados fractales

Artículo de investigación

This research examined the existence of long-term memory by calculating the coefficient of Hurst and Hurst set, and the analysis of characteristics of chaotic structures in the series of stock market of Chile, specifically through the Selective Price Index Shares.

A brief analysis of the market was developed, according to Box and Jenkins methodology. The validity of the results was performed by means of the test proposed by Brock, Dechert and Scheinkman. Secondly, we proceeded to analyze the dynamics and patterns of the index and its performance, to see if there was evidence of long-term memory.

The results demonstrate the presence of long-term memory in the Chilean stock market, determined by stock index in two scales, daily and quarterly, which also corroborates results obtained by other authors, confirming the use of the methodology Range Re-scalded for the identification and determination of long-term memory in a time series.

This study will allow future researchers to perform similar analyzes in other markets, providing a new approach when analyzing the long-term memory and the factors that affect it.