Characteristics of the order flow through an electronic open limit order book

We estimate and examine certain characteristics of the order flow through an electronic open limit order book, using order (not trade) data. In doing this, we bring out new evidence on order flow from a market with microstructure different from that of the NYSE. We find that the proportion of informed orders is less than 10%, lower than previous estimates. Informed traders choose smaller orders than uninformed traders, but do not materially differ in their choice of limit or market orders. The proportion of informed investors is similar between good and bad news days. Finally, there are U-shaped intraday patterns in order arrival, and the information content of the order flow appears to follow this pattern across the day.     

Modelling non-linearity in economic classification with neural networks

In this paper results are presented of a study on economic classification with neural networks. Comparison is made between neural networks and linear modelling techniques and, in particular, comments are made on the problem of overfitting and the estimation of prediction errors in cases where the available data sets are relatively small. It is shown that selecting network parameters by k-fold cross-validation and weight decay training are effective remedies for these phenomena. The conclusions are illustrated in two cases: predicting the volume of the mortgage market in the Netherlands and the classification of bond ratings. © 1997 John Wiley & Sons, Ltd.     

A two‐step system for direct bank telemarketing outcome classification

A two‐step system is presented to improve prediction of telemarketing outcomes and to help the marketing management team effectively manage customer relationships in the banking industry. In the first step, several neural networks are trained with different categories of information to make initial predictions. In the second step, all initial predictions are combined by a single neural network to make a final prediction. Particle swarm optimization is employed to optimize the initial weights of each neural network in the ensemble system. Empirical results indicate that the two‐step system presented performs better than all its individual components. In addition, the two‐step system outperforms a baseline one where all categories of marketing information are used to train a single neural network. As a neural networks ensemble model, the proposed two‐step system is robust to noisy and nonlinear data, easy to interpret, suitable for large and heterogeneous marketing databases, fast and easy to implement.     

商业银行跨境人民币结算业务发展策略探讨

本文分析了我国商业银行在跨境人民币结算推广中遇到的困难和问题,包括企业需求尚未充分挖掘出来,区域和产品结构发展不平衡,业务发展受到一些政策制约等,探讨了我国商业银行发展跨境人民币结算的策略,即商业银行必须将跨境人民币结算提升为战略高度,扩大宣传,大力开发组合产品,发展跨境人民币结算贸易融资、资金运用、结算渠道,抓住离岸市场人民币结算机会,加强政策研究和流程管理,切实防范风险。     

美国201钢铁贸易保护争端对我国的影响与对策

美国在国内经济增长衰退，钢铁产业不断衰落的背景下，根据国内法律《l974年贸易法》第201条款出台了钢铁保障措施，实行以损害他国经济利益为条件而谋求本国利益最大化的保护主义，从而导致全球性的贸易争端。中国作为世界钢铁生产和消费大国，受到美国201钢铁保障措施及其引发的贸易保护主义的直接损害。中国在遵守WTO有关规则和不断调适外贸政策经济目标的同时，采取相应的对策措施，以维护正当贸易利益和保护本国钢铁产业的健康发展。     

金融支持服务贸易的理论分析与对策研究

服务贸易作为经济发展的动力和引擎,对于转变我国经济发展和对外贸易增长方式具有重大战略意义,但由于缺乏足够的金融支持,我国服务贸易整体水平还处于比较落后的状态。本文将从产业结构调整理论和国家竞争优势理论的视角分析对服务贸易进行金融支持的必要性,并提出构建我国服务贸易多元化金融支持体系的对策建议。     

对金融支持边境贸易发展的思考——以吉林省延边州为例

边境贸易是边疆地区经济增长的重要组成部分。要大力发展边境贸易对于促进边疆地区经济发展,建设"富庶、开放、生态、和谐、幸福"的边疆,具有重要意义。但要发展边境贸易离不开金融支持。     

Focal points for improving communications about electromagnetic fields and health: a mental models approach

Central to the precautionary policy is the provision of information about electromagnetic fields (EMF) technology, exposures, potential health risks and exposure management actions to the public. To meet this need at the broadest level, beyond the specific technology foci of previous research, a research project was commissioned as part of Dutch Electromagnetic Fields and Health Research Programme. This study provides an assessment of Dutch EMF information needs from an ensemble of sources by addressing people’s existing ideas and beliefs, using a mental models approach. A summary expert model of influences on and consequences of exposure derived from search of the relevant literature is informed by interviews with 15 scientists and professionals with diverse expertise. Although the professionals characterize the physical characteristics and psychological aspects of exposure to EMF in daily life similarly, there is no consensus regarding potential health effects. Interviews with 12 lay people followed by a confirmatory survey of the general Dutch public (n = 403) reveal not only wide variation in beliefs regarding potential health effects of EMF, but also overestimation of the amount of radiation from public sources relative to personal sources of EMF. People do not feel adequately informed by the government about EMF, and knowledge of government policies on EMF is limited. Together, the evidence suggests three focal points for improving EMF risk communications: providing more clarity regarding the uncertainty of evidence for health effects, illuminating personal EMF exposures in daily life and providing more accessible and transparent information on governmental policies.     

论金融支持西部内贸发展和扩大消费

西部大开发十年来,西部地区金融机构通过不断创新信贷产品、改善服务形式,促进了西部地区贸易的快速发展、扩大了消费需求、推动了相关产业的发展,是西部地区扩大内需的有效途径之一。本文从西部地区内贸和消费十年成就出发,分析金融在其中发挥的作用及存在问题,对未来更好地发挥金融支持西部内贸发展和扩大消费的作用提出切实可行的政策建议。     

Neural network copula portfolio optimization for exchange traded funds

This paper attempts to investigate if adopting accurate forecasts from Neural Network (NN) models can lead to statistical and economically significant benefits in portfolio management decisions. In order to achieve that, three NNs, namely the Multi-Layer Perceptron, Recurrent Neural Network and the Psi Sigma Network (PSN), are applied to the task of forecasting the daily returns of three Exchange Traded Funds (ETFs). The statistical and trading performance of the NNs is benchmarked with the traditional Autoregressive Moving Average models. Next, a novel dynamic asymmetric copula model (NNC) is introduced in order to capture the dependence structure across ETF returns. Based on the above, weekly re-balanced portfolios are obtained and compared using the traditional mean–variance and the mean–CVaR portfolio optimization approach. In terms of the results, PSN outperforms all models in statistical and trading terms. Additionally, the asymmetric skewed t copula statistically outperforms symmetric copulas when it comes to modelling ETF returns dependence. The proposed NNC model leads to significant improvements in the portfolio optimization process, while forecasting covariance accounting for asymmetric dependence between the ETFs also improves the performance of obtained portfolios.     

A continuous time Bayesian network classifier for intraday FX prediction

Abstract

Prediction of foreign exchange (FX) rates is addressed as a binary classification problem in which a continuous time Bayesian network classifier (CTBNC) is developed and used to solve it. An exact algorithm for inference on CTBNC is introduced. The performance of an instance of these classifiers is analysed and compared to that of dynamic Bayesian network by using real tick by tick FX rates. Performance analysis and comparison, based on different metrics such as accuracy, precision, recall and Brier score, evince a predictive power of these models for FX rates at high frequencies. The achieved results also show that the proposed CTBNC is more effective and more efficient than dynamic Bayesian network classifier. In particular, it allows to perform high frequency prediction of FX rates in cases where dynamic Bayesian networks-based models are computationally intractable.     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

A comparison of statistical tests for the adequacy of a neural network regression model

An integral part of econometric practice is to test the adequacy of model specifications. If a model is adequately specified, it should not leave interesting features of the data-generating process in the errors. Despite the common tradition, the importance of diagnostic checking as a safeguard against mis-specification has only recently been recognized by neural network (NN) practitioners, possibly because this type of semi-parametric methodology was not originally designed for economic and financial applications. The purpose of this paper is to compare a number of analytical statistical testing procedures suitable to diagnostic checking on a neural network regression model. We present the standard Lagrange multiplier (LM) testing framework designed under the assumption of identically distributed disturbances and also examine two modifications that are robust to heteroskedasticity in errors. One modification also gives the researcher an opportunity to incorporate information concerning the volatility structure of the data-generating process in the testing procedure. By means of a Monte Carlo simulation, we investigate the performance of these tests under GARCH-type heteroskedasticity in errors and various distributional assumptions. The results show that although the primary concern of the researcher may be to design a regression model that accurately captures relations in the mean of the conditional distribution, developing a good approximation of the underlying volatility structure generally increases the efficiency of tests in detecting non-adequacy of a NN model.     

A study of the internal control structure for electronic data interchange systems using the analytic hierarchy process

Electronic data interchange (EDI) systems involve the direct exchange of structured business data between trading partner computer systems. A reliable internal control structure is the primary means of providing assurance of information integrity in EDI systems. This paper reports the results of a study that examined information system (IS) managers' and computerised information system (CIS) auditors' judgements of the relative importance of elements of the internal control structure for EDI systems, using the analytic hierarchy process (AHP). It then assessed the degree of consensus in their judgements. Generally consensus was found to be high. However, the areas where there was lack of consensus may indicate potential areas of control weakness in EDI systems.     

湖南与中国周边国家和地区经贸合作的对策--基于“一带一路”战略的思考

“一带一路”战略为各省市开展与周边国家和地区经贸合作提供了新的机遇。湖南作为中部省份,一直以来外贸依存度都比较低,新阶段亟需充分利用“一带一路”战略为湖南带来的新机遇,加强与中国周边国家和地区开展经贸合作,争取在经贸合作主体建设、经贸合作平台利用、经贸合作方式深化、经贸合作政策支持方面取得新的突破,为湖南打造成中部地区开放型经济的新高地提供支撑。     

The multi-layer network nature of systemic risk and its implications for the costs of financial crises

The inability to see and quantify systemic financial risk comes at an immense social cost. Systemic risk in the financial system arises to a large extent as a consequence of the interconnectedness of its institutions, which are linked through networks of different types of financial contracts, such as credit, derivatives, foreign exchange, and securities. The interplay of the various exposure networks can be represented as layers in a financial multi-layer network. In this work we quantify the daily contributions to systemic risk from four layers of the Mexican banking system from 2007 to 2013. We show that focusing on a single layer underestimates the total systemic risk by up to 90%. By assigning systemic risk levels to individual banks we study the systemic risk profile of the Mexican banking system on all market layers. This profile can be used to quantify systemic risk on a national level in terms of nation-wide expected systemic losses. We show that market-based systemic risk indicators systematically underestimate expected systemic losses. We find that expected systemic losses are up to a factor of four higher now than before the financial crisis of 2007–2008. We find that systemic risk contributions of individual transactions can be up to a factor of one thousand higher than the corresponding credit risk, which creates huge risks for the public. We find an intriguing non-linear effect whereby the sum of systemic risk of all layers underestimates the total risk. The method presented here is the first objective data-driven quantification of systemic risk on national scales that reveal its true levels.     

A predictive system integrating intrinsic mode functions,artificial neural networks,and genetic algorithms for forecasting S&P500 intra‐day data

There is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal‐processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra‐day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra‐day price data. For comparison purposes, the performance of the EMD‐GA‐ANN presented is compared with that of a GA‐ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA‐general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root‐mean‐squared errors show evidence of the superiority of EMD‐GA‐ANN over WT‐GA‐ANN and GA‐GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time‐consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.