Stock Market Prediction via Deep Learning Techniques: A Survey

The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction

Analysing Regional Sustainability Through a Systemic Approach: The Lombardy Case Study

The intrinsic complexity of the sustainability concept challenges research towards more sophisticated ways to model and assess the dimensions underlying it. However, currently adopted modelling techniques and indicators frameworks are not able to give an integrated assessment through the different components of sustainability, providing incomplete visuals of the reality that they aim to catch. This paper tries to assess how the INSURE methodology can provide a contribution in the analysis of sustainability through indicator frameworks, describing its application to the Lombardy region (Italy). Developed on the course of a 6th European Framework Program – financed project to measure sustainability in the European regions, the methodology provides two distinct sustainability representations, based on a quantitative “top-down” System Dynamics model and on a qualitative “bottom-up” System Thinking approach. The models are then linked to a hierarchical indicator framework setting policy priorities. The overall objective is thus to create a set of regional indicators, adapting the models of regional sustainability to different policy agendas. The purpose of the paper is twofold: defining a new approach to sustainability appraisal, and assessing how the Region is holistically behaving towards sustainable development. Starting from a basis analysis of the main shortcomings highlighted by the use of most adopted methodologies, the paper will verify the contribution given by the INSURE methodology to research in the fields of modelling and indicators approaches, providing insights over methodological adjustments and the results obtained from the application to Lombardy. The conclusions will show how the methodology has tried to overcome identified constraints in current models, like the strong dependence on existing datasets of the obtained representations, the under-coverage of “immaterial factors” role and the scarce integration between sustainability dimensions.ustainable Development, Regional Economics, Econometric and Input Output Models, Development Planning and Policy, Regional Analyses

A new perspective on the competitiveness of nations

The capability of firms to survive and to have a competitive advantage in global markets depends on, amongst other things, the efficiency of public institutions, the excellence of educational, health and communications infrastructures, as well as on the political and economic stability of their home country. The measurement of competitiveness and strategy development is thus an important issue for policy-makers. Despite many attempts to provide objectivity in the development of measures of national competitiveness, there are inherently subjective judgments that involve, for example, how data sets are aggregated and importance weights are applied. Generally, either equal weighting is assumed in calculating a final index, or subjective weights are specified. The same problem also occurs in the subjective assignment of countries to different clusters. Developed as such, the value of these type indices may be questioned by users. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices. For this purpose, a methodology composed of three steps is proposed. To start, a hierarchical clustering analysis is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient for purposes of country clustering. In the proposed methodology, 178 criteria are used for this purpose. Next, relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in our third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one

Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms