Decision Support Systems for Financial Market Surveillance

Entscheidungsunterstützungssysteme in der Finanzwirtschaft sind nicht nur für die Wis-senschaft, sondern auch für die Praxis von großem Interesse. Um die Finanzmarktüber-wachung zu gewährleisten, sehen sich die Finanzaufsichtsbehörden auf der einen Seite, mit der steigenden Anzahl von onlineverfügbaren Informationen, wie z.B. den Finanz-Blogs und -Nachrichten konfrontiert. Auf der anderen Seite stellen schnell aufkommen-de Trends, wie z.B. die stetig wachsende Menge an online verfügbaren Daten sowie die Entwicklung von Data-Mining-Methoden, Herausforderungen für die Wissenschaft dar. Entscheidungsunterstützungssysteme in der Finanzwirtschaft bieten die Möglichkeit rechtzeitig relevante Informationen für Finanzaufsichtsbehörden und Compliance-Beauftragte von Finanzinstituten zur Verfügung zu stellen. In dieser Arbeit werden IT-Artefakte vorgestellt, welche die Entscheidungsfindung der Finanzmarktüberwachung unterstützen. Darüber hinaus wird eine erklärende Designtheorie vorgestellt, welche die Anforderungen der Regulierungsbehörden und der Compliance-Beauftragten in Finan-zinstituten aufgreift

Supporting Financial Market Surveillance: An IT Artifact Evaluation

In this paper, an IT artifact instantiation (i.e. software prototype) to support decision making in the field of financial market surveillance, is presented and evaluated. This artifact utilizes a qualitative multi-attribute model to identify situations in which prices of single stocks are affected by fraudsters who aggressively advertise the stock. A quantitative evaluation of the instantiated IT artifact, based on voluminous and heterogeneous data including data from social media, is provided. The empirical results indicate that the developed IT artifact instantiation can provide support for identifying such malicious situations. Given this evidence, it can be shown that the developed solution is able to utilize massive and heterogeneous data, including user-generated content from financial blogs and news platforms, to provide practical decision support in the field of market surveillance

Generative Adversarial Networks in finance: an overview

Modelling in finance is a challenging task: the data often has complex statistical properties and its inner workings are largely unknown. Deep learning algorithms are making progress in the field of data-driven modelling, but the lack of sufficient data to train these models is currently holding back several new applications. Generative Adversarial Networks (GANs) are a neural network architecture family that has achieved good results in image generation and is being successfully applied to generate time series and other types of financial data. The purpose of this study is to present an overview of how these GANs work, their capabilities and limitations in the current state of research with financial data, and present some practical applications in the industry. As a proof of concept, three known GAN architectures were tested on financial time series, and the generated data was evaluated on its statistical properties, yielding solid results. Finally, it was shown that GANs have made considerable progress in their finance applications and can be a solid additional tool for data scientists in this field

From Minority Games to $-Games

G02 Behavioral Finance: Underlying Principles Chapter in book to Appear in "Hanbook on Computational Economics and Finance" In press 2014In this chapter we will first argue for the use of game theory/agent-based modeling, to go beyond the standard methods used in traditional approach of Finance. First will be introduced some very general thoughts of elements needed in a new framework for Finance. Then some few concrete examples of heterogeneous agent-based models will be introduced and several of their main results will be discussed. Finally applications and methods to real market data will be introduced, notably the idea of "decoupling" to explain the short-lasting synchronization of investors

HOW DO LARGE STAKES INFLUENCE BITCOIN PERFORMANCE? EVIDENCE FROM THE MT.GOX LIQUIDATION CASE

Bitcoin as the first and still most important decentralized cryptocurrency has gained wide popu-larity due to the steep rise of its price during the second half of 2017. Because of its digital na-ture, Bitcoin cannot be valuated exclusively with fundamental approaches, which is why factors such as investor sentiment have become a common alternative to capture its performance. In this work, we studied whether and how the sale of Bitcoins from the insolvency assets of Mt.Gox, which represent about 1.1% of the current global total, relates to Bitcoin price movements. We used social media sentiment analysis of Twitter data to examine how investors are influenced in their decision to buy or sell Bitcoin when confronted with the trade actions of Nobuaki Koba-yashi, the trustee in charge of the Mt.Gox case. We built a vector error correction model to ana-lyze the long-run relationship between cointegrated variables. Our analysis confirms the posi-tive association of Bitcoin performance with positive Twitter sentiment and tweet volume and the negative association with negative sentiment. We further found empirical evidence that Mt.Gox selloff events have a lasting negative impact on the Bitcoin price and that we can measure this effect by Twitter sentiment and tweet volume

Contributions to the study and modeling of knowledge development systems

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A CONCEPTUAL MODEL TO PROTECT BRAND REPUTATION FACING ” FAKE NEWS”

openThis study aims to explore the nature and impact of Fake News on brands and their customers, identify different categories of Fake News, and propose a conceptual model for companies to protect their brand reputation and mitigate the effects of Fake News. The research focuses on the managerial approaches and methodologies that brands can adopt to counter Fake News and safeguard their reputation in the digital era. A comprehensive literature review examines existing studies on Fake News, brand management, and the impact of Fake News on brands and consumers, highlighting the gaps in the literature. The review defines and categorizes Fake News, explores techniques for detecting and mitigating it, and investigates the relationship between Fake News and brand management. The findings reveal a lack of research on the managerial strategies for brands to tackle Fake News effectively. The study emphasizes the importance of developing proactive measures to detect and counter Fake News, as well as building resilience against Fake News attacks. By addressing these gaps, the study aims to contribute to the development of effective strategies for brands to navigate the challenges posed by Fake News in the digital media landscape.This study aims to explore the nature and impact of Fake News on brands and their customers, identify different categories of Fake News, and propose a conceptual model for companies to protect their brand reputation and mitigate the effects of Fake News. The research focuses on the managerial approaches and methodologies that brands can adopt to counter Fake News and safeguard their reputation in the digital era. A comprehensive literature review examines existing studies on Fake News, brand management, and the impact of Fake News on brands and consumers, highlighting the gaps in the literature. The review defines and categorizes Fake News, explores techniques for detecting and mitigating it, and investigates the relationship between Fake News and brand management. The findings reveal a lack of research on the managerial strategies for brands to tackle Fake News effectively. The study emphasizes the importance of developing proactive measures to detect and counter Fake News, as well as building resilience against Fake News attacks. By addressing these gaps, the study aims to contribute to the development of effective strategies for brands to navigate the challenges posed by Fake News in the digital media landscape

Agent-based Modeling And Market Microstructure

In most modern financial markets, traders express their preferences for assets by making orders. These orders are either executed if a counterparty is willing to match them or collected in a priority queue, called a limit order book. Such markets are said to adopt an order-driven trading mechanism. A key question in this domain is to model and analyze the strategic behavior of market participants, in response to different definitions of the trading mechanism (e.g., the priority queue changed from the continuous double auctions to the frequent call market). The objective is to design financial markets where pernicious behavior is minimized.The complex dynamics of market activities are typically studied via agent-based modeling (ABM) methods, enriched by Empirical Game-Theoretic Analysis (EGTA) to compute equilibria amongst market players and highlight the market behavior (also known as market microstructure) at equilibrium. This thesis contributes to this research area by evaluating the robustness of this approach and providing results to compare existing trading mechanisms and propose more advanced designs.In Chapter 4, we investigate the equilibrium strategy profiles, including their induced market performance, and their robustness to different simulation parameters. For two mainstream trading mechanisms, continuous double auctions (CDAs) and frequent call markets (FCMs), we find that EGTA is needed for solving the game as pure strategies are not a good approximation of the equilibrium. Moreover, EGTA gives generally sound and robust solutions regarding different market and model setups, with the notable exception of agents’ risk attitudes. We also consider heterogeneous EGTA, a more realistic generalization of EGTA whereby traders can modify their strategies during the simulation, and show that fixed strategies lead to sufficiently good analyses, especially taking the computation cost into consideration.After verifying the reliability of the ABM and EGTA methods, we follow this research methodology to study the impact of two widely adopted and potentially malicious trading strategies: spoofing and submission of iceberg orders. In Chapter 5, we study the effects of spoofing attacks on CDA and FCM markets. We let one spoofer (agent playing the spoofing strategy) play with other strategic agents and demonstrate that while spoofing may be profitable in both market models, it has less impact on FCMs than on CDAs. We also explore several FCM mechanism designs to help curb this type of market manipulation even further. In Chapter 6, we study the impact of iceberg orders on the price and order flow dynamics in financial markets. We find that the volume of submitted orders significantly affects the strategy choice of the other agents and the market performance. In general, when agents observe a large volume order, they tend to speculate instead of providing liquidity. In terms of market performance, both efficiency and liquidity will be harmed. We show that while playing the iceberg-order strategy can alleviate the problem caused by the high-volume orders, submitting a large enough order and attracting speculators is better than taking the risk of having fewer trades executed with iceberg orders.We conclude from Chapters 5 and 6 that FCMs have some exciting features when compared with CDAs and focus on the design of trading mechanisms in Chapter 7. We verify that CDAs constitute fertile soil for predatory behavior and toxic order flows and that FCMs address the latency arbitrage opportunities built in those markets. This chapter studies the extent to which adaptive rules to define the length of the clearing intervals — that might move in sync with the market fundamentals — affect the performance of frequent call markets. We show that matching orders in accordance with these rules can increase efficiency and selfish traders’ surplus in a variety of market conditions. In so doing, our work paves the way for a deeper understanding of the flexibility granted by adaptive call markets