A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

The expression of anger amongst Turkish taxi drivers

The present study tested the four factor structure of the DAX on a sample of Turkish taxi drivers and the relationship these factors had with a number of other variables. Confirmatory Factor Analysis found that the data broadly fit the four factor solution of the DAX. These factors included three aggressive expressions: Verbal Aggressive Expression; Personal Physical Aggressive Expression; Use of a Vehicle to Express anger, and one Adaptive/Constructive factor. Driving experience was negatively related to the three types of aggressive expression. The Total Aggressive Expression was positively related to annual mileage and preferred driving speed, but negatively related to age and experience. The present research also found that the three aggressive types of anger expression were significantly related to potentially crash related conditions, such as losing control of the vehicle, loss of concentration and near-misses. However, none of the DAX factors was significantly related to either minor or major crashes.     

基于MIDAS的DAX网络发布技术在油田WebGIS中的应用研究

提出了基于MIDAS的DAX网络发布技术在WebGIS中的设计方法。该方法是一个共享的中间层上封装了商业规则，集中的实现了应用服务器所要实现的功能，使客户程序将重点放在显示数据和与用户交互上，将这项技术应用于油田中的地质图绘制方面，解决了油田中多层分布构数据库的访问，管理和地质图件的动态生成问题，研制开发的“基于WebGIS地质图绘制软件包”在大庆油田推广使用。取得了良好的效果。     

Simulating and modelling the DAX index and the USO Etf financial time series by using a simple agent-based learning architecture

This work presents an extensive case study on modelling the DAX (Deutscher Aktienindex) index and United States Oil Fund (USO) exchange-traded fund (Etf) time series with the financial agent-based system learning financial agent-based simulator (L-FABS) that exploits simulated annealing as a learning method. The USO Etf time series is highly correlated with oil price behaviour, and the DAX index is based on the weighted and accumulated behaviour of the share prices of some of the largest companies traded on the Frankfurt Stock Exchange. These two time series are driven by completely different economic factors and thus provide two diverse empirical settings to evaluate the effectiveness of our methodology. Our experimentation shows that a relatively simple computational representation of real financial markets is effective in capturing the overall behaviour of the time series with varying approximation levels while the prediction target is moved into the future. The reported experimental investigation of L-FABS shows that it is robust notwithstanding the learning method used and the data sets exploited. L-FABS indeed produced a relatively low approximation error in several settings even when evaluated with respect to other modelling approaches, for example, 0.88% and 1.61% errors on average for 1 day ahead experiments in, respectively, DAX index and USO Etf.     

Development of a short form of the driving anger expression inventory

The present study developed a revised version of the driving anger expression inventory (25-items) and a short (15-item) version using data from 551 drivers. Split half factor analyses on both versions confirmed the original four factors; personal physical aggressive expression, use of a vehicle to express anger, verbal aggressive expression and adaptive/constructive expression. The two DAX versions were strongly correlated, demonstrating the suitability of both forms of the scale and the aggressive forms of expression were higher for drivers who reported initiating road rage interactions. Total aggressive expression was also higher for drivers who reported recent crash-related conditions, such as: loss of concentration, losing control of their vehicle, moving violations, near-misses and major crashes. The revised DAX and DAX-short provide shorter versions of the 49-item DAX that can more easily be combined with other questionnaires and require smaller sample sizes to analyse. Further research is required to validate these tools among different samples and populations.     

A Simple and Rapid Evaluation of Explosive Performance – The Disc Acceleration Experiment

It is crucial in the development of a new explosive to obtain an evaluation of performance early in the process when the availability of material is limited. Evaluation requires dynamic measurements of detonation velocity, pressure, and expansion energy – typically in separate experiments that require large amounts of material, time, and expense. There is also a need for evaluation of the total available thermodynamic energy. The dynamic evaluations, in particular, have been a major hindrance to development of new explosives. The new experimental testing method to be described here requires small charges and obtains accurate measurement of all three of the detonation performance characteristics in a single test. The design, a Disc Acceleration eXperiment (DAX), provides an initial condition of steady detonation and a charge‐geometry amenable to 2D hydrodynamic simulations. The velocity history of a metal disk attached to the end of the explosive charge is measured with Photonic Doppler Velocimetry (PDV). This disc velocity data is analyzed to give both CJ pressure and expansion energy. The detonation velocity is obtained with probes along the charge length. The experiments and subsequent analyses are concentrated on LX‐16, a known PETN based explosive, for the purpose of establishing the accuracy of the method and to provide a standard for comparison with other explosives. We present details of the experimental design and also detonation velocity and PDV results from a number of experiments. The total available internal energy for the explosive was obtained from published detonation calorimetry measurements by Ornellas [1], and from thermodynamic equilibrium calculations. An equation‐of‐state (EOS) for LX‐16 was derived from hydrodynamic simulations of thin plate‐push velocity‐time data. We will show a successful comparison with a previously published Jones‐Wilkins‐Lee (JWL) EOS for PETN by Green and Lee [2–4].     

Highly dealuminated Y zeolite as efficient adsorbent for the hydrophobic fraction from wastewater treatment plants effluents

In this work we report that highly dealuminated zeolite Y is a hydrophobic material that is able to remove selectively fatty acids and hydrocarbon compounds from the effluent of an urban wastewater treatment plant (UWTP). This adsorbent capability of zeolite Y could lead to an improved quality of UWTP effluents. Typical domestic wastewaters contain detergents, soaps and surfactants that are only partially removed in conventional UWTP. In the present work using an effluent from a UWTP located at Ribarroja del Turia (Valencia, Spain) containing 10 ppm of total organic carbon, we have been able to retain by adsorption on the dealuminated Y zeolite up to 16 and 60% of the organic matter of the effluent at pH values 7.2 and 4, respectively. Characterization of the adsorbed organic matter by Fourier transformed infrared (FT-IR), ¹H NMR and GC–MS after derivatization has shown that the zeolite adsorbs selectively the hydrophobic compounds of the effluent.