ERN: Neural Networks & Related Topics (Topic)最新文献

{"title":"Macroeconomic Indicator Forecasting with Deep Neural Networks","authors":"Aaron Smalter Hall, T. Cook","doi":"10.2139/SSRN.3046657","DOIUrl":"https://doi.org/10.2139/SSRN.3046657","url":null,"abstract":"Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models {{p}} that exhibit model dependence and have high data demands. {{p}} We explore deep neural networks as an {{p}} opportunity to improve upon forecast accuracy with limited data and while remaining agnostic as to {{p}} functional form. We focus on predicting civilian unemployment using models based on four different neural network architectures. Each of these models outperforms bench- mark models at short time horizons. One model, based on an Encoder Decoder architecture outperforms benchmark models at every forecast horizon (up to four quarters).","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133979581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fair or Unfair Algorithmic Differentiation? Luck Egalitarianism As a Lens for Evaluating Algorithmic Decision-Making.","authors":"Laurens Naudts","doi":"10.2139/ssrn.3043707","DOIUrl":"https://doi.org/10.2139/ssrn.3043707","url":null,"abstract":"Differentiation is often intrinsic to the functioning of algorithms. Within large data sets, ‘differentiating grounds’, such as correlations or patterns, are found, which in turn, can be applied by decision-makers to distinguish between individuals or groups of individuals. As the use of algorithms becomes more wide-spread, the chance that algorithmic forms of differentiation result in unfair outcomes increases. Intuitively, certain (random) algorithmic, classification acts, and the decisions that are based on them, seem to run counter to the fundamental notion of equality. It nevertheless remains difficult to articulate why exactly we find certain forms of algorithmic differentiation fair or unfair, vis-a-vis the general principle of equality. Concentrating on Dworkin’s notions brute and option luck, this discussion paper presents a luck egalitarian perspective as a potential approach for making this evaluation possible. The paper then considers whether this perspective can also inform us with regard to the interpretation of EU data protection legislation, and the General Data Protection Regulation in particular. Considering data protection’s direct focus on the data processes underlying algorithms, the GDPR might, when informed by egalitarian notions, form a more practically feasible way of governing algorithmic inequalities.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"296 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115868506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Robust Machine Availability Problem","authors":"Guopeng Song, D. Kowalczyk, R. Leus","doi":"10.2139/ssrn.3052283","DOIUrl":"https://doi.org/10.2139/ssrn.3052283","url":null,"abstract":"We define and solve the robust machine availability problem in a parallel machine environment, which aims to minimize the number of identical machines required while completing all the jobs before a given deadline. Our formulation preserves a user-defined robustness level regarding possible deviations in the job durations. For better computational performance, a branch-andprice procedure is proposed based on a set covering reformulation. We use zero-suppressed binary decision diagrams (ZDDs) for solving the pricing problem, which enable us to manage the difficulty entailed by the robustness considerations as well as by extra constraints imposed by branching decisions. Computational results are reported that show the effectiveness of a pricing solver with ZDDs compared with a MIP solver.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126190639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating the Gains from New Rail Transit Investment: A Machine Learning Tree Approach","authors":"Seungwoo Chin, Matthew E. Kahn, H. Moon","doi":"10.1111/1540-6229.12249","DOIUrl":"https://doi.org/10.1111/1540-6229.12249","url":null,"abstract":"Urban rail transit investments are expensive and irreversible. Since people differ with respect to their demand for trips, their value of time, and the types of real estate they live in, such projects are likely to offer heterogeneous benefits to residents of a city. Using the opening of a major new subway in Seoul, we contrast hedonic estimates based on multivariate hedonic methods with a machine learning approach that allows us to estimate these heterogeneous effects. While a majority of the \"treated\" apartment types appreciate in value, other types decline in value. We explore potential mechanisms. We also cross-validate our estimates by studying what types of new housing units developers build in the treated areas close to the new train lines.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124795214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting the Market Risk Premium with Artificial Neural Networks","authors":"Leoni Eleni Oikonomikou","doi":"10.2139/ssrn.2743374","DOIUrl":"https://doi.org/10.2139/ssrn.2743374","url":null,"abstract":"This paper aims to forecast the Market Risk premium (MRP) in the US stock market by applying machine learning techniques, namely the Multilayer Perceptron Network (MLP), the Elman Network (EN) and the Higher Order Neural Network (HONN). Furthermore, Univariate ARMA and Exponential Smoothing models are also tested. The Market Risk Premium is defined as the historical differential between the return of the benchmark stock index over a short-term interest rate. Data are taken in daily frequency from January 2007 through December 2014. All these models outperform a Naive benchmark model. The Elman network outperforms all the other models during the insample period, whereas the MLP network provides superior results in the out-of-sample period. The contribution of this paper to the existing literature is twofold. First, it is the first study that attempts to forecast the Market Risk Premium in a daily basis using Artificial Neural Networks (ANNs). Second, it is not based on a theoretical model but is mainly data driven. The chosen calculation approach fits quite well with the characteristics of ANNs. The forecasting model is tested with data from the US stock market. The proposed model-based forecasting method aims to capture patterns in the data that improve the forecasting accuracy of the Market Risk Premium in the tested market and indicates potential key metrics for investment and trading purposes for short time horizons.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115712478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regional Forecasting with Support Vector Regressions: The Case of Spain","authors":"Oscar Claveria, E. Monte, Salvador Torra","doi":"10.2139/ssrn.2945533","DOIUrl":"https://doi.org/10.2139/ssrn.2945533","url":null,"abstract":"This study attempts to assess the forecasting accuracy of Support Vector Regression (SVR) with regard to other Artificial Intelligence techniques based on statistical learning. We use two different neural networks and three SVR models that differ by the type of kernel used. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian kernel shows the best forecasting performance. The best predictions are obtained for longer forecast horizons, which suggest the suitability of machine learning techniques for medium and long term forecasting.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131445533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short-Term Financial Forecasting Using ANN Adaptive Predictors in Cascade","authors":"E. Dobrescu, D. Năstac, E. Pelinescu","doi":"10.1504/IJPMB.2014.065519","DOIUrl":"https://doi.org/10.1504/IJPMB.2014.065519","url":null,"abstract":"Our purpose is to verify the predictive performances of the artificial neural networks (ANNs) under volatile statistics and possibly incomplete information. Daily forecasts of exchange rate using exclusively primary available information for an emergent economy (such as the Romanian one) could be a proper experimental ground with such a goal. The present paper extends the previous authors’ research (Dobrescu et al., 2006; Nastac et al., 2007) on the same issue to improve the accuracy of exchange rate forecasting by using a set of neural predictors in cascade, instead of a single one. The results show that the presented model, despite its proved advantages, could be further improved in order to avoid the translation into residuals of the high serial correlation present in the primary database.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114662774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social Networks in Accounting Research","authors":"A. Andrikopoulos, Konstantinos Kostaris","doi":"10.2139/ssrn.2349727","DOIUrl":"https://doi.org/10.2139/ssrn.2349727","url":null,"abstract":"This study explores collaborative research that is published in four accounting journals: the Accounting Review, the Journal of Accounting Research, the Journal of Accounting and Economics and Accounting, Organizations and Society. We employ social network analysis in order to discover the relational structure of co-authorship in accounting research. We find that that the network of authoring accounting academics has become increasingly integrated over time. The co-authorship network exhibits \"small-world\" properties: a giant component that covers the biggest part of the network of collaborating authors and collaborating institutions, small average distance within the giant component and a high clustering coefficient.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125985275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Would Credit Scoring Work for Islamic Finance? A Neural Network Approach","authors":"Hussein A. Abdou, Shaair T. Alam, James Mulkeen","doi":"10.1108/IMEFM-03-2013-0038","DOIUrl":"https://doi.org/10.1108/IMEFM-03-2013-0038","url":null,"abstract":"Purpose - – This paper aims to distinguish whether the decision-making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit, and highlight significant variables that are crucial in terms of accepting and rejecting applicants, which can further aid the decision-making process. Design/methodology/approach - – A real data set of 487 applicants is used consisting of 336 accepted credit applications and 151 rejected credit applications made to an Islamic finance house in the UK. To build the proposed scoring models, the data set is divided into training and hold-out subsets. The training subset is used to build the scoring models, and the hold-out subset is used to test the predictive capabilities of the scoring models. Seventy per cent of the overall applicants will be used for the training subset, and 30 per cent will be used for the testing subset. Three statistical modeling techniques, namely, discriminant analysis, logistic regression (LR) and multilayer perceptron (MP) neural network, are used to build the proposed scoring models. Findings - – The findings reveal that the LR model has the highest correct classification (CC) rate in the training subset, whereas MP outperforms other techniques and has the highest CC rate in the hold-out subset. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest misclassification cost above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision-making process. Originality/value - – This contribution is the first to apply credit scoring modeling techniques in Islamic finance. Also in building a scoring model, the authors' application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122244525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Support Vector Machine GARCH and Neural Network GARCH Models in Modeling Conditional Volatility: An Application to Turkish Financial Markets","authors":"M. Bildirici, Ozgur Omer Ersin","doi":"10.2139/ssrn.2227747","DOIUrl":"https://doi.org/10.2139/ssrn.2227747","url":null,"abstract":"The Turkish version of this paper can be found at: http://ssrn.com/abstract=2222071 The study aims to investigate linear GARCH, fractionally integrated FI-GARCH and Asymmetric Power APGARCH models and their nonlinear counterparts based on Support Vector Regression (SVR) and Neural Network (NN) models. GARCH family models are extended to NN-GARCH architecture of Donaldson and Kamstra (1997) to various NN-GARCH family models (Bildirici and Ersin, 2009) such as NN-APGARCH model. The study aims to introduce a class of extended NN-GARCH and SVR-GARCH family of models with nonlinear augmentations in modeling both the conditional mean and variance. The SVR-GARCH, SVR-APGARCH and SVR-FIAPGARCH and their Multi-Layer Perceptron architecture based counterparts, MLP-GARCH, MLP-APGARCH and MLP-FIAPGARCH are evaluated. An application to daily returns in Istanbul ISE100 stock index is provided. Results suggest that volatility clustering, asymmetry and nonlinearity characteristics are modeled more efficiently with the models possessing neural network architectures.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"559 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133155065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}