@misc{HerdtSchulteMattler, author = {Herdt, Manfred and Schulte-Mattler, Hermann}, title = {On the predictability of long-term stock market returns: Design configuration of deep neural networks}, series = {Journal of AI, Robotics \& Workplace Automation}, volume = {2}, journal = {Journal of AI, Robotics \& Workplace Automation}, number = {1}, issn = {2633-562X}, pages = {70 -- 93}, abstract = {In 1998, Robert J. Shiller and John Y. Campbell proposed that long-term stock market returns are not random walks and can be predicted by a valuation measure called the cyclically adjusted price-to-earnings (CAPE) ratio. This paper is set to identify the predictive power of long-term stock market returns with deep neural networks and trace the impact of different architectural components of deep neural networks. We present three network types — recurrent neural network (RNN), long short-term memory (LSTM) neural network, and gated recurrent units (GRU) neural network — to ascertain what impact the different networks have on predicting long-term stock market returns and whether a parsimonious neural network model (PNNM) can be identified for practical application. The networks above have different design features that allow returns to be predicted and the effects of the various elements of the networks to be understood. For our study, we use monthly CAPE ratios and real ten-year annualised excess returns of the S\&P 500 from 1881-01 to 2012-06, with data from 1876-06 (real earnings) to 2022-06 (real total return price) needed to determine the two datasets. Our results show improved forecasting accuracy over linear regression for all analysed neural networks. Only the complex trial-and-error procedure leads to the network design with the optimal result of minimising the root-mean-squared error (RMSE). This approach is usually associated with a considerable time and cost factor. Therefore, for time series studies of the present type, we propose a parsimonious GRU architecture with low complexity and comparatively low out-of-sample error, which we call 'GRU-101010'.}, language = {en} } @misc{HerdtSchulteMattler, author = {Herdt, Manfred and Schulte-Mattler, Hermann}, title = {Operationalization of the construct "Business model of a Bank" : clustering analyses with deep neural networks}, series = {Journal of banking regulation}, volume = {26}, journal = {Journal of banking regulation}, number = {3}, publisher = {Springer Science and Business Media LLC}, address = {London}, issn = {1745-6452}, doi = {10.1057/s41261-025-00269-y}, pages = {392 -- 407}, abstract = {This paper presents a framework to operationalize the multidimensional construct of a bank's business model (BBM). We conceptualize the construct from a structural perspective, defining it as its balance sheet's strategic composition and structure, encompassing asset allocation and funding sources. In contrast to prior research, our study describes the strategic decisions made by bank management as the starting point for the analysis, excluding the results of entrepreneurial activity from the construct's conceptualization. We analyze balance sheet data for 163 European SSM banks and their subsidiaries—which we call SSM institutions—from 2014 to 2023, sourced from the S\&P MI platform's SNL Financial Institutions database. The study focuses on six balance sheet positions—three from the asset and three from the liability side—expressed as ratios to total assets. We apply a deep autoencoder-based clustering (DAC) model to operationalize the construct and compare the results with the k-means and k-medoids approach. Our empirical analyses identify four BBMs: diversified retail, non-diversified retail, wholesale, and investment-oriented banking. The DAC model leverages the nonlinear capabilities of deep learning, outperforming traditional clustering methods. This paper contributes to the literature on BBMs on a theoretical and technical level. Theoretically, a methodology for operationalizing the construct of a BBM is presented, which can be used to conduct cause-effect analyses. Technically, advanced clustering techniques, including deep learning models, are used to improve classification accuracy and provide new insights into the diversity of banks. The approach presented in this study offers valuable applications for both academics and practitioners in analyzing the impact of BBMs on other constructs, such as performance. Policymakers can leverage this framework to evaluate and guide the development of resilient business models.}, language = {en} }