Data and knowledge-driven intelligent investment cognitive reasoning model

The modeling and analysis of information flow from various sources (e.g., analyst reports, news, and social media), and their impact on assets and investment decision- making, have drawn lots of attention. In this paper, we propose a new knowledge inference design framework that provides concrete prescriptions for developing systems capable of supporting knowledge-based investment decision-making. Our framework design incorporates the advantages of both knowledge graphs and symbolic reasoning engines through the concept of a dual system. On the other hand, it overcomes the weaknesses of traditional expert systems, saving time in the knowledge input process, reducing the introduction of errors, and achieving more comprehensive knowledge coverage to obtain better predictive performance. Moreover, our proposed design artifacts are of significant importance in addressing the issues of causality and interpretability in the literature

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

ChatGPT Informed Graph Neural Network for Stock Movement Prediction

ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored frontier. In this research, we introduce a novel framework that leverages ChatGPT's graph inference capabilities to enhance Graph Neural Networks (GNN). Our framework adeptly extracts evolving network structures from textual data, and incorporates these networks into graph neural networks for subsequent predictive tasks. The experimental results from stock movement forecasting indicate our model has consistently outperformed the state-of-the-art Deep Learning-based benchmarks. Furthermore, the portfolios constructed based on our model's outputs demonstrate higher annualized cumulative returns, alongside reduced volatility and maximum drawdown. This superior performance highlights the potential of ChatGPT for text-based network inferences and underscores its promising implications for the financial sector.Comment: Under Review. 10 pages, 2 figure

Technology Assessment and High-Speed Trains: facing the challenge of emergent digital society

The present PhD dissertation addresses the extension of selective environments of new technologies within the high-speed train technological system from business and regulations to the wider society. And, it argues the recognition of society as an actor in that system. Motivating it is the observed ever increase exposure of high-speed trains to public acceptance, caused by empowered society from fast ICT advancements. They refer to digitalization - the rise of social media and big data, combined with the widespread use of mobile technology - changing if not revolutionizing our understanding of product and service selection. Unprecedented societal demands, opening a new market segment, require new technologies to integrate with the emergent digital system. Moreover, societal actors became themselves innovators. Inevitable they have to become part of the value chain widening the collective of stakeholders. However, such raises the dilemma of promotion and control and adds complexity and uncertainty to the industry in deciding which technology to select. Statistical evidence shows that businesses are figuring out ways to embed societal actors in their value creation. In this dissertation, I demonstrate to the high-speed train industry how is it falling short in addressing societal embedding in their product creation and argue why requires improvement. Technology Assessment provides the approach for the orchestration of the necessary dialogue with societal actors for better anticipating potential development in the full system and for embedding the resulting technology options within. By exploiting it to the high-speed train industry innovation strategic management, the aim of my dissertation is, borrowing the words of Douglas K. R. Robinson, to “arrive to a better informed designs of future working worlds, which are structured by theory while empirically well grounded, so they are usable by decision makers”. With this work, I expect to contribute to the new governance structure for research and development set buy the railway industry SHIFT2RAIL (Joint Undertaking for Rail Research and Innovation)

On the Evolution of Knowledge Graphs: A Survey and Perspective

Knowledge graphs (KGs) are structured representations of diversified knowledge. They are widely used in various intelligent applications. In this article, we provide a comprehensive survey on the evolution of various types of knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs) and techniques for knowledge extraction and reasoning. Furthermore, we introduce the practical applications of different types of KGs, including a case study in financial analysis. Finally, we propose our perspective on the future directions of knowledge engineering, including the potential of combining the power of knowledge graphs and large language models (LLMs), and the evolution of knowledge extraction, reasoning, and representation

Cumulative causation in the formation of a technological innovation system: The case of biofuels in the Netherlands

Despite its worldwide success, the innovation systems approach is often criticized for being theoretically underdeveloped. This article aims to contribute to the conceptual and methodical basis of the (technological) innovation systems approach. We propose an alteration that improves the analysis of dynamics, especially with respect to emerging innovation systems. We do this by expanding on the technological innovation systems and system functions literature, and by employing the method of 'event history analysis'. By mapping events, the interactions between system functions and their development over time can be analysed. Based on this it becomes possible to identify forms of positive feedback, i.e. cumulative causation. As an illustration of the approach, we assess the biofuels innovation system in The Netherlands as it evolved from 1990 to 2005.

From Ideas to Practice, Pilots to Strategy: Practical Solutions and Actionable Insights on How to Do Impact Investing

This report is the second publication in the World Economic Forum's Mainstreaming Impact Investing Initiative. The report takes a deeper look at why and how asset owners began to include impact investing in their portfolios and continue to do so today, and how they overcame operational and cultural constraints affecting capital flow. Given that impact investing expertise is spread among dozens if not hundreds of practitioners and academics, the report is a curation of some -- but certainly not all -- of those leading voices. The 15 articles are meant to provide investors, intermediaries and policy-makers with actionable insights on how to incorporate impact investing into their work.The report's goals are to show how mainstream investors and intermediaries have overcome the challenges in the impact investment sector, and to democratize the insights and expertise for anyone and everyone interested in the field. Divided into four main sections, the report contains lessons learned from practitioner's experience, and showcases best practices, organizational structures and innovative instruments that asset owners, asset managers, financial institutions and impact investors have successfully implemented