Prevalence of the metabolic syndrome in Chinese adolescents

Since national figures on the occurrence of metabolic syndrome among Chinese adolescents are lacking, this study aims to estimate its prevalence and distribution among Chinese youngsters. The 2002 China National Nutrition and Health Survey is a nationally representative cross-sectional study. Applying the criteria for US adolescents, we estimated the prevalence of metabolic syndrome among 2761 adolescents aged 15 to 19 years. The prevalence of metabolic syndrome among Chinese adolescents overall was 3·7% (10% in US adolescents). It was 35·2 %, 23·4% and 2·3% among adolescents who were overweight (BMI $95th percentile), at risk of overweight (BMI between 85th and 95th percentile) and normal weight (BMI below the 85th percentile), respectively. Urban boys had the highest rate (5·8$85th percentile and one or two parent(s) with metabolic syndrome, the prevalence was 46·4 %. A total of 96% of overweight adolescents had at least one and 74·1% overweight adolescents had at least two abnormalities of metabolic syndrome. Based on these figures, it is estimated that more than three million Chinese adolescents have metabolic syndrome. Both overweight and metabolic syndrome prevalence among adolescents are still relatively low in China, but the prevalence of metabolic syndrome among Chinese overweight adolescents is similar to those living in the USA

Data analytics enhanced component volatility model

Volatility modelling and forecasting have attracted many attentions in both finance and computation areas. Recent advances in machine learning allow us to construct complex models on volatility forecasting. However, the machine learning algorithms have been used merely as additional tools to the existing econometrics models. The hybrid models that specifically capture the characteristics of the volatility data have not been developed yet. We propose a new hybrid model, which is constructed by a low-pass filter, the autoregressive neural network and an autoregressive model. The volatility data is decomposed by the low-pass filter into long and short term components, which are then modelled by the autoregressive neural network and an autoregressive model respectively. The total forecasting result is aggregated by the outputs of two models. The experimental evaluations using one-hour and one-day realized volatility across four major foreign exchanges showed that the proposed model significantly outperforms the component GARCH, EGARCH and neural network only models in all forecasting horizons

Forecasting Water Demand and Supply of China for 2025

Water resource is the most critical resource; it is the basic condition of human survival, production and maintaining a good ecological environment. Water is a kind of renewable resources, but China is faced with a serious shortage of freshwater because of its huge population. In this paper, two models are built to make a forecast of water demand and supply of China in 2025. These two forecasting models for water demand and supply based on the algorithm of Double Exponential Smoothing. Based on the results of these models, the water amount of the nation and every province is obvious and two advices are proposed for China government makes any water strategy to meet the water demand of China in 2025, such as building desalination plants in the coastal provinces which are lack of water, and meanwhile, more water diversion projects should be constructed between different provinces in the middle of China.Key words: Double Exponential Smoothing, Demand forecast, Math models, Water Strateg

Computational intelligent hybrid model for detecting disruptive trading activity

The term “disruptive trading behaviour” was first proposed by the U.S. Commodity Futures Trading Commission and is now widely used by US and EU regulation (MiFID II) to describe activities that create a misleading appearance of market liquidity or depth or an artificial price movement upward or downward according to their own purposes. Such activities, identified as a new form of financial fraud in EU regulations, damage the proper functioning and integrity of capital markets and are hence extremely harmful. While existing studies have explored this issue, they have, in most cases, either focused on empirical analysis of such cases or proposed detection models based on certain assumptions of the market. Effective methods that can analyse and detect such disruptive activities based on direct studies of trading behaviours have not been studied to date. There exists, accordingly, a knowledge gap in the literature. This paper seeks to address that gap and provides a hybrid model composed of two data-mining-based detection modules that effectively identify disruptive trading behaviours. The hybrid model is designed to work in an on-line scheme. The limit order stream is transformed, calculated and extracted as a feature stream. One detection module, “Single Order Detection,” detects disruptive behaviours by identifying abnormal patterns of every single trading order. Another module, “Order Sequence Detection,” approaches the problem by examining the contextual relationships of a sequence of trading orders using an extended hidden Markov model, which identifies whether sequential changes from the extracted features are manipulative activities (or not). Both models were evaluated using huge volumes of real tick data from the NASDAQ, which demonstrated that both are able to identify a range of disruptive trading behaviours and, furthermore, that they outperform the selected traditional benchmark models. Thus, this hybrid model is shown to make a substantial contribution to the literature on financial market surveillance and to offer a practical and effective approach for the identification of disruptive trading behaviour

Coarse and fine identification of collusive clique in financial market

Collusive transactions refer to the activity whereby traders use carefully-designed trade to illegally manipulate the market. They do this by increasing specific trading volumes, thus creating a false impression that a market is more active than it actually is. The traders involved in the collusive transactions are termed as collusive clique. The collusive clique and its activities can cause substantial damage to the market's integrity and attract much attention of the regulators around the world in recent years. Much of the current research focused on the detection based on a number of assumptions of how a normal market behaves. There is, clearly, a lack of effective decision-support tools with which to identify potential collusive clique in a real-life setting. The study in this paper examined the structures of the traders in all transactions, and proposed two approaches to detect potential collusive clique with their activities. The first approach targeted on the overall collusive trend of the traders. This is particularly useful when regulators seek a general overview of how traders gather together for their transactions. The second approach accurately detected the parcel-passing style collusive transactions on the market through analyzing the relations of the traders and transacted volumes. The proposed two approaches, on one hand, provided a complete cover for collusive transaction identifications, which can fulfill the different types of requirements of the regulation, i.e. MiFID II, on the other hand, showed a novel application of well known computational algorithms on solving real and complex financial problem. The proposed two approaches are evaluated using real financial data drawn from the NYSE and CME group. Experimental results suggested that those approaches successfully identified all primary collusive clique scenarios in all selected datasets and thus showed the effectiveness and stableness of the novel application

A paired neural network model for tourist arrival forecasting

Tourist arrival and tourist demand forecasting are a crucial issue in tourism economy and the community economic development as well. Tourist demand forecasting has attracted much attention from tourism academics as well as industries. In recent year, it attracts increasing attention in the computational literature as advances in machine learning method allow us to construct models that significantly improve the precision of tourism prediction. In this paper, we draw upon both strands of the literature and propose a novel paired neural network model. The tourist arrival data is decomposed by two low-pass filters into long-term trend and short-term seasonal components, which are then modelled by a pair of autoregressive neural network models as a parallel structure. The proposed model is evaluated by the tourist arrival data to United States from twelve source markets. The empirical studies show that our proposed paired neural network model outperforming the selected benchmark model across all error measures and over different horizons