TACD: a transportable ant colony discrimination model for corporate bankruptcy prediction

This paper presents a transportable ant colony discrimination strategy (TACD) to predict corporate bankruptcy, a topic of vital importance that is attracting increasing interest in the field of economics. The proposed algorithm uses financial ratios to build a binary prediction model for companies with the two statuses of bankrupt and non-bankrupt. The algorithm takes advantage of an improved version of continuous ant colony optimisation (CACO) at the core, which is used to create an accurate, simple and understandable linear model for discrimination. This also enables the algorithm to work with continuous values, leading to more efficient learning and adaption by avoiding data discretisation. We conduct a comprehensive performance evaluation on three real-world data sets under a stratified cross-validation strategy. In three different scenarios, TACD is compared with 11 other bankruptcy prediction strategies. We also discuss the efficiency of the attribute selection methods used in the experiments. In addition to its simplicity and understandability, statistical significance tests prove the efficiency of TACD against the other prediction algorithms in both measures of AUC and accuracy.     

A final price prediction model for english auctions: a neuro-fuzzy approach

Online auction has now been a popular mechanism in setting prices for internet users. However, auction price prediction, involving the modeling of uncertainty regarding the bidding process, is a challenging task primarily due to the variety of factors changing in auction settings. Even if all the factors were accounted for, there still exist uncertainties in human behavior when bidding in auctions. In this paper, three models, regression, neural networks and neuro-fuzzy, are constructed to predict the final prices of English auctions, using real-world online auction data collected from Yahoo-Kimo Auction. The empirical results show that the neuro fuzzy system can catch the complicated relationship among the variables accurately much better than the others, which is of great help for the buyers to avoid overpricing and for the sellers to facilitate the auction. Besides, the knowledge base obtained from neuro fuzzy provides the elaborative relationship among the variables, which can be further tested for theory building.     

Individual adoption of HR analytics: a fine grained view of the early stages leading to adoption

Abstract

High performing organizations are using analytics for evidence-based decision-making. However, the human resource (HR) function in many organizations has been slow to adopt this innovation. This study applies innovation theory, informed by the Theory of Planned Behavior (TPB), to examine the individual’s decision to adopt HR Analytics in an effort to identify why the adoption rate is lagging. We examined early stages of the individual decision process beginning from Stage 1 (knowledge) and leading to Stage 3, (the decision) to adopt or not to adopt the innovation. We found several points in the process that can act as barriers or facilitators. Organizations and champions of this innovation wishing to facilitate HR analytics adoption can take action to remove as many of these barriers to the individual’s decision as possible. Further research should focus on the best way to remove these barriers.     

Barriers to capital accumulation in a model of technology adoption and schooling

Standard growth models require large differences in barriers to capital accumulation to reproduce the observed disparities in the wealth of nations. I introduce technology adoption and schooling decisions into a standard growth model and show that the required differences in barriers implied by this model are much smaller. In particular, a calibrated version of the model implies per capita income differences 3 times larger than a standard model. Per capita income differences are amplified by two reinforcing factors: schooling capital differences and aggregate total factor productivity differences. The results suggest caution in the role of factor inputs derived from standard development accounting exercises. A development policy that subsidizes education is not optimal in the presence of barriers to capital accumulation, removing barriers can replicate educational outcomes and generate higher income levels by several orders of magnitude.     

Understanding discontinuance: Towards a more realistic model of technological innovation and industrial adoption in Britain

The paper outlines the ideas and theory behind a recently begun research project. The project consists of an investigation of the complexities, and particularly the problems, inherent in introducing technological innovation into production systems in industry. The practical and theoretical need for work of this kind is examined and the character and focus of the proposed investigation sketched. The concept of “discontinuance” is analysed and seen to apply both to the cessation of innovation production by a supplier and the termination of innovation usage by an adoptor. Rogers and Shoemaker's (1971) division of adoptor discontinuance into “replacement” and “disenchantment” discontinuance is considered and reformulated using the terms “replacement adoption”, “negative changeover effects” and “regressive discontinuance”. Finally, a number of models both of innovation development and innovation adoption are considered, synthesized and revised. An initial framework for the comprehension of innovation development and adoption is sketched and some of the potentially important issues therein are highlighted.     

The adoption of blockchain technology in the financial sector during the era of fourth industrial revolution: a moderated mediated model

Quality & Quantity - Blockchain technology is considered a core technology in the financial sector with promising prospects like economic development, financial innovation, and internet...     

FFORMPP: Feature-based forecast model performance prediction

This paper introduces a novel meta-learning algorithm for time series forecast model performance prediction. We model the forecast error as a function of time series features calculated from historical time series with an efficient Bayesian multivariate surface regression approach. The minimum predicted forecast error is then used to identify an individual model or a combination of models to produce the final forecasts. It is well known that the performance of most meta-learning models depends on the representativeness of the reference dataset used for training. In such circumstances, we augment the reference dataset with a feature-based time series simulation approach, namely GRATIS, to generate a rich and representative time series collection. The proposed framework is tested using the M4 competition data and is compared against commonly used forecasting approaches. Our approach provides comparable performance to other model selection and combination approaches but at a lower computational cost and a higher degree of interpretability, which is important for supporting decisions. We also provide useful insights regarding which forecasting models are expected to work better for particular types of time series, the intrinsic mechanisms of the meta-learners, and how the forecasting performance is affected by various factors.     

Criminal incident prediction using a point-pattern-based density model

Law enforcement agencies need crime forecasts to support their tactical operations; namely, predicted crime locations for next week based on data from the previous week. Current practice simply assumes that spatial clusters of crimes or “hot spots” observed in the previous week will persist to the next week. This paper introduces a multivariate prediction model for hot spots that relates the features in an area to the predicted occurrence of crimes through the preference structure of criminals. We use a point-pattern-based transition density model for space–time event prediction that relies on criminal preference discovery as observed in the features chosen for past crimes. The resultant model outperforms the current practices, as demonstrated statistically by an application to breaking and entering incidents in Richmond, VA.     

A two-stage segment and prediction model for mortgage prepayment prediction and management

Predicting the risk of mortgage prepayments has been the focus of many studies over the past three decades. Most of these works have used single prediction models, such as logistic regressions and survival models, to seek the key influencing factors. From the point of view of customer relationship management (CRM), a two-stage model (i.e., the segment and prediction model) is proposed for analyzing the risk of mortgage prepayment in this research. In the first stage, random forests are used to segment mortgagors into different groups; then, a proportional hazard model is constructed to predict the prepayment time of the mortgagors in the second stage. The results indicate that the two-stage model predicts mortgage prepayment more accurately than the single-stage model (non-segmentation model).     

液固比智能集成预测模型

文章以中国铝业公司河南分公司氧化铝生产过程为背景,着重研究了液固比的智能集成预测模型的建立和应用,以原矿浆配料过程为研究对象,应用神经网络和灰色理论建立了液固比预测模型。现场运行结果表明该模型的有效性和精确性,很好地实现液固比的在线预测。     

Developing people: a strategy for competitive advantage

This article will provide vital insights, acquired in a recent study with eight manufacturing companies, to guide the development of a human resource development strategy. This study was a pilot for a larger study, planned for the fall of 1995 with 50 manufacturing companies randomly chosen from the Directory of Massachusetts High Technology Companies.     

A new error measure for forecasts of household-level,high resolution electrical energy consumption

As low carbon technologies become more pervasive, distribution network operators are looking to support the expected changes in the demands on the low voltage networks through the smarter control of storage devices. Accurate forecasts of demand at the individual household-level, or of small aggregations of households, can improve the peak demand reduction brought about through such devices by helping to plan the most appropriate charging and discharging cycles. However, before such methods can be developed, validation measures which can assess the accuracy and usefulness of forecasts of the volatile and noisy household-level demand are required. In this paper we introduce a new forecast verification error measure that reduces the so-called “double penalty” effect, incurred by forecasts whose features are displaced in space or time, compared to traditional point-wise metrics, such as the Mean Absolute Error, and p$p$-norms in general. The measure that we propose is based on finding a restricted permutation of the original forecast that minimises the point-wise error, according to a given metric. We illustrate the advantages of our error measure using half-hourly domestic household electrical energy usage data recorded by smart meters, and discuss the effect of the permutation restriction.     

安徽省流通业发展思考

现代流通业已经成为国民经济中的先导产业。文中论述安徽流通业发展现状,分析了流通业在安徽经济发展中的地位,指出了安徽流通业发展存在的问题,提出了促进安徽流通业成为先导产业的政策建议。     

Searching for big data: How incumbents explore a possible adoption of big data technologies

Big data is often described as a new frontier of IT-enabled competitive advantage. A limited number of exemplary firms have been used recurrently in the big data debate to serve as successful illustrations of what big data technologies can offer. These firms are well-known, data-driven organizations that often, but not always, are born digital companies. Comparatively little attention has been paid to the challenges that many incumbent organizations face when they try to explore a possible adoption of such technologies. This study investigates how incumbents handle such an exploration and what challenges they face. Drawing on a four-year qualitative field study of four large Scandinavian firms, we are able to develop a typology of how incumbents handle the exploration of and resistance to adopting big data technologies. Directly affecting the incumbents’ exploration are two aspects that separate the adoption of big data technologies from that of other technologies. First, being an elusive concept, big data technologies can mean different things to different organizations. This makes the technologies difficult to explain before an investing body, while it simultaneously opens up possibilities for creative definitions. Second, big data technologies have a transformative effect on the organization of work in firms. This transformative capability will make managers wary as it might threaten their position in the firm, and it will create ripple effects, transforming other systems besides those directly connected to the technology.     

Relative prices and electronic substitution: Changes in household-level demand for postal delivery services from 1986 to 2004

This paper quantifies the extent to which the shift in the aggregate household-level demand for postal delivery services can be attributed to the appearance of alternative modes of communication versus the concomitant rise in postal prices. We find that both recent postal price increases and the penetration of personal computer technology among US households led to similar reductions in postal expenditure. We further find that a 5% postal price increase, such as the one introduced in January 2006 reduces revenue collected from US households by $215 million and imposes an aggregate welfare loss on US households of $333 million.     

Multi-player dynamic game model for Bitcoin transaction bidding prediction

With the rapid rise of cryptocurrencies, it has become an urgent problem to realize the flat use of digital currency, with making it really put into use, and giving full play to its utility in the current economic market. This paper innovatively takes the maximization of user benefit as the key point to predict transaction bidding price combining dynamic game theory. The bidding price of user transaction not only refers to historical transactions, but also considers the impact on future subsequences, and the result describes the interaction between transactions in detail. Also this paper proposes a method to express user satisfaction and establishes a user benefit model accordingly, so as to ensure the transaction is packaged successfully to the greatest extent within the acceptable range of transaction pricing. Finally this paper compares the proposed model with conventional machine learning prediction algorithms, finding that when user does not participate in the trading for the first time, the prediction effect of this proposal is better than that of machine learning over small data sets, moreover superior to machine learning methods in prediction accuracy and sensitivity, with a lower time complexity.     

Developing grey prediction with Fourier series using genetic algorithms for tourism demand forecasting

Quality & Quantity - Predicting the number of foreign tourists is significant for governments in devising development policies for tourism demand. Time series related to tourism often feature...     

Financial distress and bankruptcy prediction: An appropriate model for listed firms in Vietnam

This paper aims to develop a comprehensive model, the first of its kind in Vietnam, for the purpose of predicting financial distress and bankruptcy at Vietnamese listed firms. The period 2003–2016 is used to study the likelihood of financial distress in different scenarios. Various factors are utilized, including (1) accounting factors in the emerging market score model; (2) market factors in the distance-to-default model; and (3) macroeconomic indicators. The area under the receiver operating characteristics (AUC) curve is used to compare the usefulness of various models that predict financial distress and bankruptcy. Empirical findings from this study show that accounting and market factors, together with macroeconomic fundamental factors, both affect financial distress when they are considered in isolation. However, in a comprehensive model, the effects from accounting factors appear to be more significant than those from market-based factors. The default prediction model, which includes accounting factors with macroeconomic indicators, appears to perform much better than the model comprising market-based factors with macroeconomic fundamentals.     

Developing a Reputation for Reticence

A sender who has disclosable information with probability less than one may partially conceal bad news by choosing to withhold information and pooling with uninformed types. The success of this strategy depends on receivers' beliefs about the probability that the sender has disclosable news. In a dynamic context, informed senders try to cultivate a reputation for reticence either by concealing good news along with the bad, or by concealing some good news and disclosing some bad news. A reputation for reticence is valuable because it makes receivers less skeptical of past or future nondisclosures. The model provides insight into the choice by firms such as Google not to disclose quarterly earnings guidance to analysts, as well as Tony Blair's reticence over his son's vaccine record during the measles–mumps–rubella scare in the United Kingdom.