A Robust Data‐Mining Approach to Bankruptcy Prediction

In this study, new variants of genetic programming (GP), namely gene expression programming (GEP) and multi‐expression programming (MEP), are utilized to build models for bankruptcy prediction. Generalized relationships are obtained to classify samples of 136 bankrupt and non‐bankrupt Iranian corporations based on their financial ratios. An important contribution of this paper is to identify the effective predictive financial ratios on the basis of an extensive bankruptcy prediction literature review and upon a sequential feature selection analysis. The predictive performance of the GEP and MEP forecasting methods is compared with the performance of traditional statistical methods and a generalized regression neural network. The proposed GEP and MEP models are effectively capable of classifying bankrupt and non‐bankrupt firms and outperform the models developed using other methods. Copyright © 2011 John Wiley & Sons, Ltd.     

A measure of time series' predictability using genetic programming applied to stock returns

Based on the standard genetic programming (GP) paradigm, we introduce a new probability measure of time series' predictability. It is computed as a ratio of two fitness values (SSE) from GP runs. One value belongs to a subject series, while the other belongs to the same series after it is randomly shuffled. Theoretically, the boundaries of the measure are between zero and 100, where zero characterizes stochastic processes while 100 typifies predictable ones. To evaluate its performance, we first apply it to experimental data. It is then applied to eight Dow Jones stock returns. This measure may reduce model search space and produce more reliable forecast models. Copyright © 1999 John Wiley & Sons, Ltd.     

The evolution of BioBike: Community adaptation of a biocomputing platform

Programming languages are, at the same time, instruments and communicative artifacts that evolve rapidly through use. In this paper I describe an online computing platform called BioBike. BioBike is a trading zone where biologists and programmers collaborate in the development of an extended vocabulary and functionality for computational genomics. In the course of this work they develop interactional expertise with one another’s domains. The extended BioBike vocabulary operates on two planes: as a working programming language, and as a pidgin in the conversation between the biologists and engineers. The flexibility that permits this community to dynamically extend BioBike’s working vocabulary—to form new pidgins—makes BioBike unique among computational tools, which usually are not themselves adapted through the collaborations that they facilitate. Thus BioBike is itself a crucial feature—which it is tempting to refer to as a participant—in the developing interaction.     

Forecasting carbon price using a multi-objective least squares support vector machine with mixture kernels

For improving forecasting accuracy and trading performance, this paper proposes a new multi-objective least squares support vector machine with mixture kernels to forecast asset prices. First, a mixture kernel function is introduced into taking full use of global and local kernel functions, which is adaptively determined following a data-driven procedure. Second, a multi-objective fitness function is proposed by incorporating level forecasting and trading performance, and particle swarm optimization is used to synchronously search the optimal model selections of least squares support vector machine with mixture kernels. Taking CO₂ assets as examples, the results obtained show that compared with the popular models, the proposed model can achieve higher forecasting accuracy and higher trading performance. The advantages of the mixture kernel function and the multi-objective fitness function can improve the forecasting ability of the asset price. The findings also show that the models with a high-level forecasting accuracy cannot always have a high trading performance of asset price forecasting. In contrast, high directional forecasting usually means a high trading performance.     

Predicting opening spot prices using extended futures trading

Previous studies found that extended futures trading contains useful information in explaining subsequent overnight spot returns. This study therefore compares the performance of using the extended trading of the TAIFEX (Taiwan Futures Exchange) index futures and single‐stock futures to predict their opening underlying spot prices. Furthermore, according to the efficient market hypothesis, the share price fully reflects all the information available and should adjust to new information instantaneously. However, several studies have demonstrated that short‐sales restrictions delay the speed of price adjustment to negative information. The relevant question is whether short‐selling restrictions also slow down the speed at which the opening spot price adjusts to the new information revealed through extended futures trading, and thus reducing the price prediction function of extended futures trading. The empirical results find that using the opening futures price and the prediction method proposed in this study can more accurately predict the opening spot price on the same day. Furthermore, the performance of using the extended trading of index futures to predict the opening spot index price is superior to that of using the extended trading of single‐stock futures to predict the opening stock price. Finally, as found in previous studies, short‐selling restrictions also slow down the speed of stock price adjustment to the new information revealed through extended futures trading. Thus both the up‐tick rule and the short‐selling bans (especially the latter) negatively affect the price forecasting performance of extended futures trading.     

High‐frequency Markov switching models in the foreign exchange market

This paper estimates two‐state Markov models for three daily exchange rate series, and investigates the profitability of following the generated forecasts using the performance of simple chartist trading rules as benchmarks. It is shown that (1) the data are well approximated by Markov models, (2) the performance of previously profitable trading rules has dramatically declined in the 1990s, and (3) the Markov models are unstable and not suitable for forecasting in their current form. Copyright © 2000 John Wiley & Sons, Ltd.     

Adaptive Evolutionary Neural Networks for Forecasting and Trading without a Data‐Snooping Bias

In this paper, we present two neural‐network‐based techniques: an adaptive evolutionary multilayer perceptron (aDEMLP) and an adaptive evolutionary wavelet neural network (aDEWNN). The two models are applied to the task of forecasting and trading the SPDR Dow Jones Industrial Average (DIA), the iShares NYSE Composite Index Fund (NYC) and the SPDR S&P 500 (SPY) exchange‐traded funds (ETFs). We benchmark their performance against two traditional MLP and WNN architectures, a smooth transition autoregressive model (STAR), a moving average convergence/divergence model (MACD) and a random walk model. We show that the proposed architectures present superior forecasting and trading performance compared to the benchmarks and are free from the limitations of the traditional neural networks such as the data‐snooping bias and the time‐consuming and biased processes involved in optimizing their parameters. Copyright © 2015 John Wiley & Sons, Ltd.     

Modelling and Trading the English and German Stock Markets with Novelty Optimization Techniques

The motivation for this paper was the introduction of novel short‐term models to trade the FTSE 100 and DAX 30 exchange‐traded funds (ETF) indices. There are major contributions in this paper which include the introduction of an input selection criterion when utilizing an expansive universe of inputs, a hybrid combination of partial swarm optimizer (PSO) with radial basis function (RBF) neural networks, the application of a PSO algorithm to a traditional autoregressive moving model (ARMA), the application of a PSO algorithm to a higher‐order neural network and, finally, the introduction of a multi‐objective algorithm to optimize statistical and trading performance when trading an index. All the machine learning‐based methodologies and the conventional models are adapted and optimized to model the index. A PSO algorithm is used to optimize the weights in a traditional RBF neural network, in a higher‐order neural network (HONN) and the AR and MA terms of an ARMA model. In terms of checking the statistical and empirical accuracy of the novel models, we benchmark them with a traditional HONN, with an ARMA, with a moving average convergence/divergence model (MACD) and with a naïve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the FTSE 100 and DAX 30 ETF time series over the period January 2004 to December 2015 using the last 3 years for out‐of‐sample testing. Finally, the empirical and statistical results indicate that the PSO‐RBF model outperforms all other examined models in terms of trading accuracy and profitability, even with mixed inputs and with only autoregressive inputs. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting and trading currency volatility: an application of recurrent neural regression and model combination

In this paper, we examine the use of non‐parametric Neural Network Regression (NNR) and Recurrent Neural Network (RNN) regression models for forecasting and trading currency volatility, with an application to the GBP/USD and USD/JPY exchange rates. Both the results of the NNR and RNN models are benchmarked against the simpler GARCH alternative and implied volatility. Two simple model combinations are also analysed. The intuitively appealing idea of developing a nonlinear nonparametric approach to forecast FX volatility, identify mispriced options and subsequently develop a trading strategy based upon this process is implemented for the first time on a comprehensive basis. Using daily data from December 1993 through April 1999, we develop alternative FX volatility forecasting models. These models are then tested out‐of‐sample over the period April 1999–May 2000, not only in terms of forecasting accuracy, but also in terms of trading efficiency: in order to do so, we apply a realistic volatility trading strategy using FX option straddles once mispriced options have been identified. Allowing for transaction costs, most trading strategies retained produce positive returns. RNN models appear as the best single modelling approach yet, somewhat surprisingly, model combination which has the best overall performance in terms of forecasting accuracy, fails to improve the RNN‐based volatility trading results. Another conclusion from our results is that, for the period and currencies considered, the currency option market was inefficient and/or the pricing formulae applied by market participants were inadequate. Copyright © 2002 John Wiley & Sons, Ltd.     

A Comparison of the Forecasting Ability of Immediate Price Impact Models

As a consequence of recent technological advances and the proliferation of algorithmic and high‐frequency trading, the cost of trading in financial markets has irrevocably changed. One important change, known as price impact, relates to how trading affects prices. Price impact represents the largest cost associated with trading. Forecasting price impact is very important as it can provide estimates of trading profits after costs and also suggest optimal execution strategies. Although several models have recently been developed which may forecast the immediate price impact of individual trades, limited work has been done to compare their relative performance. We provide a comprehensive performance evaluation of these models and test for statistically significant outperformance amongst candidate models using out‐of‐sample forecasts. We find that normalizing price impact by its average value significantly enhances the performance of traditional non‐normalized models as the normalization factor captures some of the dynamics of price impact. Copyright © 2016 John Wiley & Sons, Ltd.     

神经树在煤与瓦斯突出预测中应用研究

由于煤与瓦斯突出影响因素之间存在着复杂的非线性关系,为准确预测煤与瓦斯突出的危险性,本文提出了基于柔性神经树的煤与瓦斯突出预潮模型,其中利用多表达式编程和粒子群优化算法分别优化了自身的结构及相关参数,使得神经树具有强大的预测和分类能力,与传统神经网络相比具有更加灵活的自动优化能力.通过采用实测数据对算法进行了验证. 结果 表明与常规预测方法相比较,该模型的预测准确性高,具有良好的适应性和有效性.     

Forecasting and trading strategies based on a price trend model

In this paper, we consider the price trend model in which it is assumed that the time series of a security's prices contain a stochastic trend component which remains constant on each of a sequence of time intervals, with each interval having random duration. A quasi‐maximum likelihood method is used to estimate the model parameters. Optimal one‐step‐ahead forecasts of returns are derived. The trading rule based on these forecasts is constructed and is found to bear similarity to a popular trading rule based on moving averages. When applying the methods to forecast the returns of the Hang Seng Index Futures in Hong Kong, we find that the performance of the newly developed trading rule is satisfactory. Copyright © 2000 John Wiley & Sons, Ltd.     

Complementarity and the selection of nature reserves: algorithms and the origins of conservation planning, 1980–1995

This paper reconstructs the history of the introduction and use of iterative algorithms in conservation biology in the 1980s and early 1990s in order to prioritize areas for protection as nature reserves. The importance of these algorithms was that they led to greater economy in spatial extent (“efficiency”) in the selection of areas to represent biological features adequately (that is, to a specified level) compared to older methods of scoring and ranking areas using criteria such as biotic “richness” (the number of features of interest). The development of these algorithms was critical to producing a research program for conservation biology that was distinct from ecology and eventually led to what came to be called systematic conservation planning. Very similar algorithmic approaches were introduced independently in the 1980–1990 period in Australia, South Africa, and (arguably) the United Kingdom. The key rules in these algorithms were the use of rarity and what came to be called complementarity (the number of new or under-represented features in an area relative to those that had already been selected). Because these algorithms were heuristic, they were not guaranteed to produce optimal (most “efficient”) solutions. However, complementarity came to be seen as a principle rather than a rule in an algorithm and its use was also advocated for the former reason. Optimal solutions could be produced by reformulating the reserve selection problem in a mathematical programming formalism and using exact algorithms developed in that context. A dispute over the relevance of full optimality arose and was never resolved. Moreover, exact algorithms could not easily incorporate criteria determining the spatial configuration of networks of selected areas, in contrast to heuristic algorithms. Meanwhile metaheuristic algorithms emerged in the 1990s and came to be seen as a credible more effective alternative to the heuristic algorithms. Ultimately what was important about these developments was that the reserve selection problem came to be viewed a complex optimal decision problem under uncertainty, resource, and other constraints. It was a type of problem that had no antecedent in traditional ecology.     

ConEntity：一种基于进程的自主并发实体编程模型及其实现

摘要本文在虚拟计算环境之上，研究支持具有自主能力、高并发的新型互联网应用开发方法，在已有的基于进程、面向并发的编程模型中引入实体建模机制，扩展出一种兼具进程和自主并发实体的程序设计模型ConEntity，并给出了形式化定义和描述．ConEntity模型具有表达性、并发性和可伸缩性的特点，能对虚拟计算环境资源高效、透明访问．通过扩展Erlang／OTP将其实现为Erlang语言设施UniAgent．本文的模型为在虚拟计算环境上快速直接构建具有自主、高并发能力实体的新型互联网应用提供了模型和语言上的支持．     

机器人离线编程系统

机器人离线编程系统ROPS(Robt Off—1ine Programming System)是当前机器人研究领域最活跃最前沿的研究方向，因而引起了众多研究人员的注意。本文介绍了国内外机器人离线编程系统的发展现状，离线编程系统的构成及今后的发展趋势。     

The detection of earnings manipulation: the three‐phase cutting plane algorithm using mathematical programming

The primary goal of this study was to propose an algorithm using mathematical programming to detect earnings management practices. In order to evaluate the ability of this proposed algorithm, the traditional statistical models are used as a benchmark vis‐à‐vis their time series counterparts. As emerging techniques in the area of mathematical programming yield better results, application of suitable models is expected to result in highly performed forecasts. The motivation behind this paper is to develop an algorithm which will succeed in detecting companies that appeal to financial manipulation. The methodology is based on cutting plane formulation using mathematical programming. A sample of 126 Turkish manufacturing firms described over 10 financial ratios and indexes are used for detecting factors associated with false financial statements. The results indicate that the proposed three‐phase cutting plane algorithm outperforms the traditional statistical techniques which are widely used for false financial statement detections. Furthermore, the results indicate that the investigation of financial information can be helpful towards the identification of false financial statements and highlight the importance of financial ratios/indexes such as Days' Sales in Receivables Index (DSRI), Gross Margin Index (GMI), Working Capital Accruals to Total Assets (TATA) and Days to Inventory Index (DINV). Copyright © 2009 John Wiley & Sons, Ltd.     

Trading zones and interactional expertise

The phrase ‘trading zone’ is often used to denote any kind of interdisciplinary partnership in which two or more perspectives are combined and a new, shared language develops. In this paper we distinguish between different types of trading zone by asking whether the collaboration is co-operative or coerced and whether the end-state is a heterogeneous or homogeneous culture. In so doing, we find that the voluntary development of a new language community—what we call an inter-language trading zone—represents only one of four possible configurations. In developing this argument we show how different modes of collaboration result in different kinds of trading zone, how different kinds of trading zone may be ‘nested’ inside each other and discuss how a single collaboration might move between different kinds of trading zone over time. One implication of our analysis is that interactional expertise is a central component of at least one class of trading zone.     

Probability distributions,trading strategies and leverage: an application of Gaussian mixture models

The purpose of this paper is twofold. Firstly, to assess the merit of estimating probability density functions rather than level or classification estimations on a one‐day‐ahead forecasting task of the EUR/USD time series. This is implemented using a Gaussian mixture model neural network, benchmarking the results against standard forecasting models, namely a naïve model, a moving average convergence divergence technical model (MACD), an autoregressive moving average model (ARMA), a logistic regression model (LOGIT) and a multi‐layer perceptron network (MLP). Secondly, to examine the possibilities of improving the trading performance of those models with confirmation filters and leverage. While the benchmark models perform best without confirmation filters and leverage, the Gaussian mixture model outperforms all of the benchmarks when taking advantage of the possibilities offered by a combination of more sophisticated trading strategies and leverage. This might be due to the ability of the Gaussian mixture model to identify successfully trades with a high Sharpe ratio. Copyright © 2004 John Wiley & Sons, Ltd.     

International equity flows and the predictability of US stock returns

We examined the link between international equity flows and US stock returns. Based on the results of tests of in‐sample and out‐of‐sample predictability of stock returns, we found evidence of a strong positive (negative) link between international equity flows and contemporaneous (one‐month‐ahead) stock returns. Our results also indicate that an investor, in real time, could have used information on the link between international equity flows and one‐month‐ahead stock returns to improve the performance of simple trading rules. Copyright © 2007 John Wiley & Sons, Ltd.