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Sustainable Fisheries: Models and Management
Published in Brian D. Fath, Sven E. Jørgensen, Megan Cole, Managing Biological and Ecological Systems, 2020
Fabian Zimmermann, Katja Enberg
Population dynamics models are a key component of fisheries sciences to describe the changes in populations over time and their responses to fishing. The need for modeling approaches originates from the difficulties to observe fish directly. Together with the large socioeconomic relevance of fisheries, this has put fisheries models at the forefront of modeling biological systems. The focus has traditionally been on describing changes in population biomass through the growth and decay of a population’s biomass. Historically, models have been divided into biomass models that lump entire populations into one biomass and models that are structured by age or size, allowing for more specific dynamics such as growth of body size, maturation, reproduction, recruitment and mortality. In this section, we will contrast biomass models with their structured counterparts, present models of growth, stock-recruitment and mortality, and outline current applications in a fisheries context.
Evaluation and Selection of Glamour Stocks by a Hybrid MADM Model
Published in Gwo-Hshiung Tzeng, Kao-Yi Shen, New Concepts and Trends of Hybrid Multiple Criteria Decision Making, 2017
According to Lakonishok et al. (1994), while investors get overly excited about stocks that have performed very well and shown growth tendency, enthusiastic buy-in forces might cause these stocks to become overpriced; these stocks are named growth stocks or glamour stocks. If investors focus merely on examining their fundamentals, such as return on equity (ROE) or free cash flow (FCF), they might overlook a critical aspect that causes the high prices of glamour stocks: psychological bias or concern. As a result, a theoretical framework that considers both the financial and psychological aspects is required. In this context, Mohanram (2005) developed the G-score model, covering three dimensions of growth stock investing: (1) Earnings and cash flow profitability (D1), (2) Naïve extrapolation (D2), and (3) Accounting conservatism (D3). The first and the third dimensions measure the financial and accounting aspects, respectively, and the second addresses the psychological or behavioral bias of investors. The three dimensions and their criteria are briefly defined in Table 13.1 and discussed in the following paragraphs.
Airline Capital Structure and Cost of Capital
Published in Bijan Vasigh, Ken Fleming, Liam Mackay, Foundations of Airline Finance, 2018
Bijan Vasigh, Ken Fleming, Liam Mackay
Constant dividend growth A constant dividend growth stock is one where the company is expected to grow over time. As a result, the corresponding dividend payments are expected to grow at a constant rate. The dividend growth rate can be found by comparing the current dividend payment versus the prior dividend payment and assuming that such growth will continue into perpetuity.
Joint reserve stock and just-in-time inventory under regular preventive maintenance and random disruptions
Published in International Journal of Production Research, 2022
Lama Moussawi-Haidar, Hoda Daou, Khalil Khalil
PM is not the only choice of remedy in the presence of machine breakdowns. A manufacturing organisation may maintain safety/reserve stocks of finished goods to hedge against random disruptions caused by both natural disasters and intentional and unintentional human actions (Snyder et al. 2016). Supply interruptions resulting from unpredictable events can produce adverse effects on the production/inventory system (Li, Xu, and Hayya 2004). The economic impact of disruptions can be enormous. In a series of empirical studies, Hendricks and Singhal (2003, 2005a, 2005b) find that companies that experienced even minor disruptions faced significant decline in sales growth, stock returns, and shareholder wealth, and these effects tend to linger for at least two years after the disruption. Thus it is extremely important to use mechanisms to hedge against disruptions. Research on random disruptions proposed safety stock policies as a way to hedge against random disruptions (Groenevelt, Pintelon, and Seidmann 1992; Moinzadeh and Aggarwal 1997, nov; Das and Sarkar 1999; Akella and Kumar 1986; Gharbi, Kenne, and Beit 2007; Cheung and Hausman 1997; Parlar 1997; Dohi, Okamura, and Osaki 2001).