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The stock market forecast is an important and challenging issue. Its distribution forecast of returns can provide information that is more complete, compared to point forecast and interval forecast. As intraday high-frequency information is available, we incorporate intraday returns into the predictive modelling of daily return distribution in two ways: realized volatility and scale calibration. Three parametric models, EGARCH, EGARCH-X, and realGARCH, and two nonparametric models, NP and realNP, are used. Our improved NP model, the realNP model, is based on intraday returns calibration. The results show that intraday information improves goodness-of-fit and forecasting effect, and the realGARCH model is relatively the best. According to the realNP model results, the intraday returns can only contribute about a 30% description of the daily distribution and less than 1% information for a one-step-ahead distribution forecast. Furthermore, three combinations are considered, and the log-score and CRPS combinations are found to have direction predictability and excess profitability statistically. The non-short-selling situation consistently has more excess profits than the short-selling situation, which implies that the non-short-selling rule protects investors. This study reveals the importance of incorporating intraday information and model combinations for stock market forecast modelling. [ABSTRACT FROM AUTHOR]

Many alternative approaches for selecting mortality models and forecasting mortality have been proposed. The usual practice is to base forecasts on a single mortality model selected using in-sample goodness-of-fit measures. However, cross-validation measures are increasingly being used in model selection, and model combination methods are becoming a common alternative to using a single mortality model. We propose and assess a stacked regression ensemble that optimally combines different mortality models to reduce out-of-sample mean squared errors and mitigate model selection risk. Stacked regression uses a meta-learner to approximate horizon-specific weights by minimizing a cross-validation criterion for each forecasting horizon. The horizon-specific weights determine a mortality model combination customized to each horizon. We use 44 populations from the Human Mortality Database to compare the stacked regression ensemble with alternative methods. We show that, using one-year-ahead to 15-year-ahead out-of-sample mean squared errors, the stacked regression ensemble improves mortality forecast accuracy by 13% - 49% for males and 19% - 90% for females over individual mortality models. The stacked regression ensembles also have better predictive accuracy than other model combination methods, including Simple Model Averaging, Bayesian Model Averaging, and Model Confidence Set. We provide an R package, CoMoMo, that combines forecasts for Generalized-Age-Period-Cohort models. [ABSTRACT FROM AUTHOR]

Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environment, an analyst needs to appropriately fine-tune their strategy, or discover how to combine weak signals in novel alpha creating manners. Both aspects, namely fine-tuning and combination, have been extensively researched using several methods, but emerging techniques such as Generative Adversarial Networks can have an impact on such aspects. Therefore, our work proposes the use of Conditional Generative Adversarial Networks (cGANs) for trading strategy calibration and aggregation. To this end, we provide a full methodology on: (i) the training and selection of a cGAN for time series data; (ii) how each sample is used for strategy calibration; and (iii) how all generated samples can be used for ensemble modelling. To provide evidence that our approach is well grounded, we have designed an experiment with multiple trading strategies, encompassing 579 assets. We compared cGAN with an ensemble scheme and model validation methods, both suited for time series. Our results suggest that cGANs are a suitable alternative for strategy calibration and combination, providing outperformance when the traditional techniques fail to generate any alpha. [ABSTRACT FROM AUTHOR]

Model averaging for forecasting and mixed estimation is a recognized improved statistical approach. This paper is a first report on: (1). aggregate information from different conditional quantiles within a given model and, (2). model averaging with quantile averaging. Based on a subset of possible methods, we show that aggregating information over different quantiles, with and without combining information across different models, can produce superior forecasts, outperforming forecasts based on conditional mean regressions. We observe a variety of quantile aggregation schemes within a model can significantly improve over forecasts obtained from model combination alone. We provide simulation and empirical evidence. In addition economic value of our proposals is demonstrated within an optimal portfolio decision setting. Higher values of average utility are observed with no exception when an investor employs forecasts which aggregate both within and across model information. [ABSTRACT FROM AUTHOR]

A procedural modelling method based on model combination and spatial semantic constraint is proposed to realize the automatic modelling of high-speed railway scenes for management decisions and scientific experiments at different stages. The construction and description of basic-element models were first discussed in detail according to the fixed characteristics of the geometric appearances of and the spatial relationships between the components of high-speed railways. Then, spatial semantic constraint rules and scene mapping and instantiation methods were designed to accurately and rapidly integrate various basic-element models for automatically generating high-fidelity three-dimensional high-speed railway scenes. Finally, a prototype modelling system was developed to implement preliminary experiments. The experimental results show that the method proposed in this article is suitable for the automatic generation of virtual high-speed railway scenes by combining sophisticated basic-element models in a seamless manner. Modelling operations and professional knowledge are decoupled to reduce the complexity and difficulty of multidisciplinary collaborative modelling, which improves the modelling efficiency of virtual high-speed railway scenes. [ABSTRACT FROM AUTHOR]

Statistical Learning refers to statistical aspects of automated extraction of regularities (structure) in datasets. It is a broad area which includes neural networks, regression-trees, nonparametric statistics and sieve approximation, boosting, mixtures of models, computational complexity, computational statistics, and nonlinear models in general. Although Statistical Learning Theory and Econometrics are closely related, much of the development in each of the areas is seemingly proceeding independently. This special issue brings together these two areas, and is intended to stimulate new applications and appreciation in economics, finance, and marketing. This special volume contains ten innovative articles covering a broad range of relevant topics. [ABSTRACT FROM AUTHOR]

We describe a method for evaluating an ensemble of predictive models given a sample of observations comprising the model predictions and the outcome event measured with error. Our formulation allows us to simultaneously estimate measurement error parameters, true outcome--the "gold standard"--and a relative weighting of the predictive scores. We describe conditions necessary to estimate the gold standard and to calibrate these estimates and detail how our approach is related to, but distinct from, standard model combination techniques. We apply our approach to data from a study to evaluate a collection of BRCA1/BRCA2 gene mutation prediction scores. In this example, genotype is measured with error by one or more genetic assays. We estimate true genotype for each individual in the data set, operating characteristics of the commonly used genotyping procedures, and a relative weighting of the scores. Finally, we compare the scores against the gold standard genotype and find that Mendelian scores are, on average, the more refined and better calibrated of those considered and that the comparison is sensitive to measurement error in the gold standard. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) models such as Artificial Neural Networks (ANNs), Decision Trees and Dempster–Shafer's Theory of Evidence have long claimed to be more error-tolerant than conventional statistical models, but the way error is propagated through these models is unclear. Two sources of error have been identified in this study: sampling error and attribute error. The results show that these errors propagate differently through the three AI models. The Decision Tree was the most affected by error, the Artificial Neural Network was less affected by error, and the Theory of Evidence model was not affected by the errors at all. The study indicates that AI models have very different modes of handling errors. In this case, the machine-learning models, including ANNs and Decision Trees, are more sensitive to input errors. Dempster–Shafer's Theory of Evidence has demonstrated better potential in dealing with input errors when multisource data sets are involved. The study suggests a strategy of combining AI models to improve classification accuracy. Several combination approaches have been applied, based on a ‘majority voting system’, a simple average, Dempster–Shafer's Theory of Evidence, and fuzzy-set theory. These approaches all increased classification accuracy to some extent. Two of them also demonstrated good performance in handling input errors. Second-stage combination approaches which use statistical evaluation of the initial combinations are able to further improve classification results. One of these second-stage combination approaches increased the overall classification accuracy on forest types to 54% from the original 46.5% of the Decision Tree model, and its visual appearance is also much closer to the ground data. By combining models, it becomes possible to calculate quantitative confidence measurements for the classification results, which can then serve as a better error representation. Final classification products include not only the predicted hard classes for individual cells, but also estimates of the probability and the confidence measurements of the prediction. [ABSTRACT FROM AUTHOR]

The paper examines the medium-term forecasting ability of several alternative models of currency volatility. The data period covers more than eight years of daily observations, January 1991 to March 1999, for the spot exchange rate, 1- and 3-month volatility of the DEM/JPY, GBP/DEM, GBP/USD, USD/CHF, USD/DEM and USD/JPY. Comparing with the results of 'pure' time series models, the reported work investigates whether market implied volatility data can add value in terms of medium-term forecasting accuracy. This is done using data directly available from the marketplace in order to avoid the potential biases arising from 'backing out' volatility from a specific option pricing model. On the basis of the over 34 000 out-of-sample forecasts produced, evidence tends to indicate that, although no single volatility model emerges as an overall winner in terms of forecasting accuracy, the 'mixed' models incorporating market data for currency volatility perform best most of the time. [ABSTRACT FROM AUTHOR]

We consider the problem of how to combine a collection of general regression fit vectors to obtain a better predictive model. The individual fits may be from subset linear regression, ridge regression, or something more complex like a neural network. We develop a general framework for this problem and examine a cross-validation--based proposal called "model mix" or "stacking" in this context. We also derive combination methods based on the bootstrap and analytic methods and compare them in examples. Finally, we apply these ideas to classification problems where the estimated combination weights can yield insight into the structure of the problem. [ABSTRACT FROM AUTHOR]