ROBUST DECISION SUPPORT SYSTEMS WITH MATRIX FORECASTS AND SHARED LAYER PERCEPTRONS FOR FINANCE AND OTHER APPLICATIONS

The recent financial crisis showed the need for more robust decision support systems. In this paper, we introduce a novel type of recurrent artificial neural network, the shared layer perceptron, which allows forecasts that are robust by design. This is achieved by not over-fitting to a specific variable. An entire market is forecast. By training not one, but many networks, we obtain a distribution of outcomes. Further, multi-step forecasts are possible. Our system uses hidden states to model internal dynamics. This allows the network to build a memory and hardens it against external shocks. Using a single shared weight matrix offers the possibility of interpreting system output. An often cited disadvantage of neural networks, the black box character, is not an issue with our approach. We focus on two case studies: determining value at risk and transaction decision support. We also present other applications, including load forecast in electricity networks

Decision Support for the Automotive Industry: Forecasting Residual Values using Artificial Neural Networks

The leasing business is one of the most important distribution channels for the automotive industry. This implies that forecasting accurate residual values for the vehicles is a major factor for determining monthly leasing rates: Either a systematic overestimation or underestimation of future residual values can incur large potential losses in resale value or, respectively, competitive disadvantages. In this paper, an operative DSS with the purpose of facilitating residual value related management decisions is introduced, with a focus on its forecasting capabilities. Practical implications are discussed, a multi-variate linear model and an artificial neural network approach are benchmarked and further, the effects of price trends and seasonal influences are investigated. The analysis is based on more than 150,000 data sets from a major German car manufacturer. We show that artificial neural network ensembles with only a few input variables are capable of achieving a significant improvement in forecasting accuracy

Advanced neural networks : finance, forecast, and other applications

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Applications in computational finance with a focus on approximation of financial time series by neurocomputing

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Decision Support for the Automotive Industry - Forecasting Residual Values Using Artificial Neural Networks

In the automotive industry, it is very common for new vehicles to be leased rather than sold. This implies forecasting an accurate residual value for the vehicles, which is a major factor for determining monthly leasing rates. Either a systematic overestimation or underestima- tion of future residual values can incur large potential losses in resale value or, respectively, competitive disad- vantages. For the purpose of facilitating residual value related management decisions, an operative decision sup- port system is introduced with emphasis on its forecasting capabilities. In the paper, the use of artificial neural net- works for this application is demonstrated in a case study based on more than 250,000 data sets of leasing contracts from a major German car manufacturer, completed between 2011 and 2017. The importance of determining price factors and the effect of different time horizons on forecasting accuracy are investigated and practical impli- cations are discussed. In addition, the authors neither found a significant explanatory nor predictive power of external economic factors, which underlines the importance of collecting and taking advantage of vehicle-specific data or, in more general terms, the exclusive data of corporations, which is often only available internally

Neural forecasting: Introduction and literature overview

Neural network based forecasting methods have become ubiquitous in large-scale industrial forecasting applications over the last years. As the prevalence of neural network based solutions among the best entries in the recent M4 competition shows, the recent popularity of neural forecasting methods is not limited to industry and has also reached academia. This article aims at providing an introduction and an overview of some of the advances that have permitted the resurgence of neural networks in machine learning. Building on these foundations, the article then gives an overview of the recent literature on neural networks for forecasting and applications.Comment: 66 pages, 5 figure

On Explainable Deep Learning for Macroeconomic Forecasting and Finance

Deep Learning (DL) has gained momentum in recent years due to its incredible generalisation performance achieved across many learning tasks. Nevertheless, practitioners and academics have sometime been reluctant to apply these models because perceived as black boxes. This is particularly problematic in Economics and Finance. The objective of this thesis is to develop interpretable DL models and explainable DL tools with a focus on macroeconomic and financial applications. In doing so we highlight connections between such models and the standard economic ones. The first part of this work introduces a new class of interpretable models called Deep Dynamic Factor Models. The study merges the DL literature on autoencoders with that of the Econometrics on Dynamic Factor Models. Empirical validations of the approach are carried out both on synthetic and on real-time macroeconomic data. Part two of the work analyses feature attribution methods and Shapley values among explainability tools that are used to additively decompose model predictions. One of their limitations is highlighted, given that it is necessary to define a baseline that represents the missingness of a feature. A solution to the problem is proposed and compared against the ones currently in use both on simulated data and in the financial context of credit card default. We show that the proposed baseline is the only one that accounts for the specific use of the model. The final part of the work discusses the use of DL techniques for dynamic asset allocation. Using US market data, a comparison in recursive out-of-sample among different machine learning, economic-financial and hybrid models, including the one introduced in the first part of the work, is performed. Finally, a nonlinear factor-based portfolio performance attribution via the use of Shapley values and the baseline proposed in part two of the work is presented

Exploring Machine Learning Approaches to Precipitation Prediction: Post Processing of Daily Accumulated North American forecasts

This thesis presents recent work on exploring machine learning (ML) and deep learning (DL) models to improve the accuracy of 24 hour precipitation forecasts. Leveraging a comprehensive North American dataset of precipitation values from Numerical Weather Prediction (NWP) models and secondary meteorological features, the research showcases the need of ML techniques in post-processing NWP precipitation predictions. The evaluation reveals remarkable performance improvements over baseline model, with certain ML models achieving a 15% reduction in Mean Absolute Error (MAE), a 5% decrease in Root Mean Squared Error (RMSE), a 45% reduction in Median Absolute Error (MdAE), and a 50% decrease in Relative Bias (RB). Convolutional Neural Networks (CNN) and Gradient Boosting Regressor (GBR) emerged as top performers, demonstrating their proficiency in accurately predicting daily precipitation.Natural Sciences and Engineering Research Council of Canada Alliance Grant with Weatherlogics Inc.Master of Science in Applied Computer Scienc