Search Results (4)

This study establishes a combination method-based prediction model for the CSI 300 stock index price embedded with options market information. Firstly, utilizing options and spot market information, a BP neural network is employed to predict the CSI 300 stock index price. Secondly, a logical framework based on a combination method is constructed to further optimize the CSI 300 stock index price prediction through decomposition–clustering, error adjustment, and weighted integration approaches. The results demonstrate the following: (1) Compared to price predictions based solely on spot market information, the introduction of options market information significantly enhances the forecasting performance for the CSI 300 index price. (2) From the perspective of options moneyness classification, after incorporating options information, different types of options contracts exhibit varying impacts on the CSI 300 index price prediction. Prior to optimization, predictions incorporating in-the-money call options with maximum trading volume yield the optimal performance based on the MSE metric. (3) Under the logical framework of the combination method, the prediction effect for the CSI 300 stock index price is gradually improved after introducing the decomposition–clustering method, the error adjustment method, and the price-weighted integration method, which shows that it is appropriate to use the combination method to optimize the price prediction. Overall, this study proposes a combination method for price forecasting incorporating options market information across diverse contract types. It allows for weighted integration of prediction results derived from various options information, offering a novel research angle for spot market price prediction. The study also underscores the importance of implicit information mining and multi-market information fusion for price prediction, which is expected to become a key research focus in this field. Full article

Mandarin fruits with good physicochemical quality are desired for the fresh fruit market. The ease of crosses among citrus taxa has been used to expand genetic variability and select new varieties for different fruit markets. With the aim of selecting new commercial varieties, eleven hybrids obtained from the cross between Murcott tangor and Ponkan mandarin were grafted on Rangpur lime rootstock and evaluated for genetic and phenotypical characteristics. It was observed that there are significant differences in the fruit shape, content, and organoleptic characteristics in juice and differential responses to Alternaria alternate, the causal agent of the Alternaria brown spot (ABS) disease. The phenotypic and molecular characterization showed two groups of hybrids, those that resemble Ponkan and others with characteristics closer to the parental Murcott. There are significant differences in the period of fruit maturation among the hybrids and their parents. Considering the parents stand out as the main commercial varieties in the Brazilian mandarin fresh fruit market, these new hybrids have great potential for commercial acceptance and could be an excellent option to extend the period of supply of mandarins. Genetic and phenotypical information will be fundamental for the registration and/or protection of new varieties, which are essential steps that precede the commercial release to the producer. Full article

While policies targeting education in schools, marketing campaigns, and taxation strategies are of great importance to tackle our population’s malnutrition, there is growing concern about enhancing the supermarket environment to promote healthier food consumption. Public and private policies targeting the food retail sector can contribute to achieving this goal. Supermarkets play a significant role in presenting food options. As gatekeepers of the food system’s impact on consumers’ health, they have the power to help people make better food choices. Examples include making healthy checkouts and nudging consumption of vegetables through infographics. Currently, few policies specify how supermarkets’ environments could be set up to improve healthy purchases (e.g., the “Partnership for a healthy diet” in Norway and “hange4Life Convenience Stores” in the UK). Furthermore, we see recent shifts in the policy agendas of countries like Germany and Argentina, with new nutrition behavioural policies being legislated and implemented. However, we lack general guidelines that unify the available information on this topic. Addressing this gap has the potential to guide future supermarket policy recommendations. We will perform a review and delve deeper to search for governmental policies and policies of major supermarket brands in selected countries (i.e., Germany and Argentina) to taxonomize the policy initiatives, examining their projects and campaigns designed to improve supermarket environments and encourage consumers to choose healthy options. We will harvest sources published since the release of the SDGs (2015), from ScienceDirect, the FAO policy search engine Informas, and the NOURISHING platform. We will identify the grey literature by searching related websites and databases as well as national government pages or private supermarket policy documents. We will extract the information relevant to policymaking to improve environments in terms of product reformulation, labelling strategies, placement, product processes, and sweet beverage representation. While spotting incentive, procurement, and regulative policies, we will group them in terms of interventions and further taxonomize the different approaches/initiatives. Our objective is to provide unified policy recommendations by leveraging existing experiences and policies and designing applicable recommendations to improve the food environment in supermarkets. Furthermore, we aim to promote the creation of science-based policies that consider these recommendations. Full article

This study analyses a series of live cattle spot and futures prices from the Boi Gordo Index (BGI) in Brazil. The objective is to develop a model that best portrays this commodity’s behaviour to estimate futures prices more accurately. The database created contains 2010 daily entries in which trade in futures contracts occurs, as well as BGI spot sales in the market, from 1 December 2006 to 30 April 2015. One of the most important reasons why this type of risk needs to be measured is to set loss limits. To identify patterns in price behaviour in order to improve future transaction results, investors must analyse fluctuations in asset values for longer periods. Bibliographic research reveals that no other study has conducted a comprehensive analysis of this commodity using this approach. Cattle ranching is big business in Brazil given that in 2021, this sector moved BRL 913.14 billion (USD 169.29 billion). In that year, agribusiness contributed 26.6% of Brazil’s total gross domestic product. Using the proposed risk modelling technique, economic agents can make the best decision about which options within these investors’ reach produce more effective risk management. The methodology is based on Holt–Winters exponential smoothing algorithm, autoregressive integrated moving-average (ARIMA), ARIMA with exogenous inputs, generalised autoregressive conditionally heteroskedastic and generalised autoregressive moving-average (GARMA) models. More specifically, five different methods are applied that allow a comparison of 12 different models as ways to portray and predict the BGI commodity behaviours. The results show that GARMA with order c(2,1) and without intercept is the best model. Investors equipped with such precise modelling insights stand at an advantageous position in the market, promoting informed investment decisions and optimising returns. Full article