investment patterns
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The cost of reproduction hypothesis suggests that allocation to current reproduction constrains future reproduction. How organisms accrue reproductive costs and allocate energy across their lifetime may differ among species adapted to different resource types. We test this by comparing lifetime reproductive output, patterns of reproductive allocation, and senescence between two species of burying beetles, Nicrophorus marginatus and N. guttula, that differ in body size, across a range of carcass sizes. These two species of burying beetles maximized lifetime reproductive output on somewhat different–sized resources. The larger N. marginatus did better on large and medium carcasses while the smaller N. guttula did best on small and medium carcasses. For both species, reproduction is costly and reproduction on larger carcasses reduced lifespan more than reproduction on smaller carcasses. Carcass size also affected lifetime reproductive strategies. Each species’ parental investment patterns were consistent with terminal investment on carcasses on which they performed best (optimal carcass sizes). However, they exhibited reproductive restraint on carcass sizes on which they did not perform as well. Reproductive senescence occurred largely in response to carcass size. For both species, reproduction on larger carcasses resulted in more rapid senescence. These data suggest that whether organisms exhibit terminal investment or reproductive restraint may depend on type and amount of resources for reproduction.

AbstractIdentifying investment patterns as part of customer segmentation is one of the most important tasks in retail banking. Clustering customers effectively is an important element of improving marketing policy and strategic planning. There are several methods for identifying similar groups of customers and describing their characteristics to offer them appropriate products. However, using machine learning methods is rare, and the application is limited for certain types of data. The aim of this study is to investigate the benefits of using a two-stage clustering method using neural-network-based Kohonen self-organizing maps followed by hierarchical clustering for identifying the investment patterns of potential retail banking customers. The unique benefit of this method is the ability to use both categorical and numerical variables at the same time. This research examined 1,542 responses received for an online investment survey, focusing on the questions that are related to the respondents’ investment preferences and their current financial assets. The research utilizes descriptive statistics and multiple correspondence analysis (MCA) to understand the variables and Kohonen self-organizing maps (SOMs), in combination with hierarchical clustering, to identify customer groups and describe the characteristics of these clusters. The analysis was able to identify clusters of potential customers with similar preferences and gained insights into their investment patterns related to their investment portfolio and investment behavior, including their savings profile, attitude to risk-taking, and preferences for investment advice. These findings were supported by additional insights through the application of multiple correspondence analysis (MCA) describing patterns of financial instruments and portfolios. The main contribution of the research is the combined application of the machine learning methods Kohonen SOM, hierarchical clustering, and MCA for investment pattern analysis in the retail banking business.

The Regional Comprehensive Economic Partnership (RCEP) presents strong potential to mold regional trade and investment patterns well into the future and to influence the direction of global economic cooperation at a challenging time. This paper evaluates the RCEP’s impact on global and regional incomes, trade, economic structure, factor returns, and employment using a computable general equilibrium model. The results suggest that the RCEP agreement could generate sizable global income gains. Together with the Comprehensive and Progressive Agreement on Trans-Pacific Partnership, the RCEP will also strengthen the region’s manufacturing supply chains, raising productivity and increasing wages and employment.

This article assesses the usefulness of the growth models perspective for understanding contemporary British capitalism in the context of its ongoing ‘productivity puzzle’ and stagnating economic growth. The analysis of British capitalism supports our argument that growth models perspective analyses currently have limited capacity to understand the developmental trajectory of growth models, the instabilities and dysfunctionalities of these models, and how growth comes to be distributed differently across models. Through analysis of capital investment patterns and labour market characteristics, it reveals the importance of the ‘politics of productivity’, embedded in state institutions, which shapes the nature and distribution of economic growth. The article outlines a new framework for growth models analysis that ‘brings the supply-side back in’ for a more holistic approach to the political economy of capitalist growth (and non-growth). It argues this is critical for understanding patterns of political economic development in the British model of capitalism and beyond.

The European audiovisual market has unique contextual characteristics that constrain the sustainability and development of audiovisual content. Among other shifts, the rise of global subscription video-on-demand players like Netflix have been reshaping this market. Although Netflix has been investing in Europe, little is known about their actual investment strategies. This study’s goal is to analyse Netflix original investment in European scripted series and examine their implications for the European market. Based on a mapping of all European Netflix Originals, we identify four investment patterns. The analysis shows a significant uptake of Netflix investment, yet concurrently these reinforce existing discrepancies between large and small states in Europe.

The authors previously proposed an economic and mathematical model of investing in personnel and also the algorithm for determining of risk-free and the size of non-market risks. The goals of the article are to develop the threat rates and risk sensitive search methods during engineering of investment portfolio through arbitrage pricing theory for investment in personnel to return on investment. Questions of risk evaluation when choosing assets to invest in human capital of a company are fully provided. The covariance structure of allocation of funds of risk-free part of investment portfoliois addressed, that helps to calculate covariance between return on assets and threat rates of investment patterns that is necessary to find factor betas of portfolio. Also, the matrix of budget sharpening is offered that shows the detail investment budgets risk-free pieces split for all assets having regard to risks. The elements got standard interpretation. Then there is the balance condition that is to implement the proof of calculation. Finally, the method of how to calculate and estimate them is given.

It is interesting to have a look at how the previous crisis periods can teach us to predict the outcomes of the COVID-19 pandemic on the mining industry and the prospects for recovery from the COVID crisis. The mining industry is known to be cyclical, which leads to volatility in the prices of raw materials and shares of mining companies, as well as a change in investment patterns. As the COVID-19 crisis affects the medium-term pricing outlook for many commodities and puts pressure on planned investments, mining companies have a unique opportunity and need to rethink their capital expansion strategies. The unprecedented coronavirus pandemic crisis is also forcing companies to consider transforming their supply chains in the future. Improving the efficiency of the end-to-end value chain from production to market can be a major source of value creation in perspectives.