Who Is the Next “Wolf of Wall Street”? Detection of Financial Intermediary Misconduct

Financial intermediaries are essential for investors’ participation in financial markets. Because of their position within the financial system, intermediaries who commit misconduct not only harm investors but also undermine trust in the financial system, which ultimately has a significant negative impact on the economy as a whole. Building upon information manipulation theory and warranting theory and making use of self-disclosed data with different levels of external verification, we propose different classifiers to automatically detect financial intermediary misconduct. In particular, we focus on self-disclosed information by financial intermediaries on the business network LinkedIn. We match user profiles with regulator-disclosed information and use these data for classifier training and evaluation. We find that self-disclosed information provides valuable input for detecting financial intermediary misconduct. In terms of external verification, our classifiers achieve the best predictive performance when also taking regulator-confirmed information into account. These results are supported by an economic evaluation. Our findings are highly relevant for both investors and regulators seeking to identify financial intermediary misconduct and thus contribute to the societal challenge of building and ensuring trust in the financial system

A Taxonomy of Financial Market Manipulations: Establishing Trust and Market Integrity in the Financialized Economy through Automated Fraud Detection

Financial market manipulations represent a major threat to trust and market integrity in capital markets. Manipulations contribute to mispricing, market imperfections and an increase in transaction costs for market participants and in costs of capital for issuers. Manipulations are facilitated by increased transaction velocity, speculative trading and abusive usage of new trading technologies, i.e., they are directly linked to financial sector changes that drive financialization. Research at the intersection of financialization and IS might support regulatory authorities and market operators in improving market surveillance and helping to detect fraudulent activities. However, confusing terminology is prevalent on financial markets with respect to different manipulation techniques and their characteristics, which hampers efficient fraud detection. Furthermore, recognizing manipulations is challenging given the large number of information sources and the vast number of trades occurring not least because of high-frequency traders. Therefore, automated market surveillance tools require a comprehensive taxonomy of financial market manipulations as a basis for appropriate configuration. Based on a cluster analysis of SEC litigation releases, a review of the latest market abuse regulation and academic studies, we develop a taxonomy of manipulations that structures and details existing manipulation techniques and reveals how these techniques differ along several dimensions. In a case study, we show how the taxonomy can be utilized to guide the development of appropriate decision support systems for fraud detection

A Taxonomy of Violations in Digital Asset Markets

Numerous frauds, market manipulations and other violations have recently shaken investor confidence in digital asset markets and digital assets themselves. Yet, investor confidence and market integrity are key requirements for the continued success of crypto and other digital assets. In order to facilitate the integrity of digital asset markets and avoid integrity incidents in the future, a systematic overview of violations and their main characteristics is needed to develop appropriate countermeasures. Therefore, we develop a taxonomy of violations in digital asset markets and evaluate the taxonomy based on real-world cases. Our results show that many types of market manipulation in traditional financial markets can also be observed in digital asset markets. However, there are new and additional violations in digital asset markets. We also find that many violations depend on specific capabilities of the violator, certain trading conditions, and asset-specific characteristics

Development of a Wet Suit for Children with Down’s Syndrome

Individuals with Down syndrome have body types that make it difficult to fit for a standard wet suit. In general, their body composition includes an increase in central body adiposity and an endomorphic somatotypic body type in which the trunk is large while the limbs are shorter. Because of these physical characteristics, the participation by individuals with Down syndrome in aquatics exercise programs in which wet suits are needed can be very limiting. We observed that it was challenging to get these aquatic exercise participants into and out of the wet suits that had a standard wet suit design. This article describes a modified wet suit that is more accommodating for individuals with Down syndrome and allows them to wear the suit with less assistance. Although designed with the Down syndrome individual in mind, this modification may be useful for others with movement restrictions or who have an increase in central body fat

Posterior Collapse in Variational Gradient Origin Networks

Posterior collapse is a phenomenon that occurs when the posterior distribution degenerates to the prior, leading to a decline in the quality of latent encodings and generative models. While it is known to occur in Variational autoencoders (VAEs), it is unknown whether it occurs in Variational Gradient Origin Networks (VGONs). The goal of this paper is to compare the posterior collapse of Variational Gradient Origin Networks and Variational Autoencoders. By checking the latent encodings of VGONs against the key posterior collapse metrics, our experiments reveal that VGONs do exhibit posterior collapse both in the decline of the Kullback-Leibler divergence (KLD) and the collapse of individual variables. Furthermore, the results show that VGONs and VAEs have a similar polarized regime, suggesting that the cause of posterior collapse is not specific to the architecture of the model used to find an encoding. These findings support the claim made in previous research that posterior collapse is a general issue that affects a wide range of latent variable models

Safe system demonstration project in a remote Aboriginal and Torres Strait Islander community

This paper reports on key findings and recommendations of the first known application of a comprehensive Safe System audit in a remote Aboriginal and Torres Strait Islander community; commissioned by the Indigenous Road Safety Working Group with funding from Austroads. The audit was conducted in Bidyadanga WA in collaboration with the Bidyadanga Community Council during June-August 2010, including: review of policy, management and police records; physical observation of roads, speeds and vehicles; and interviews with community members and local stakeholders including regarding road user issues and vehicle access. Bidyadanga was found to have high quality roads and safe speeds within residential areas, with limited need for upgrades and new work; however, several issues were identified on roads to access the nearest town, including a high crash “blackspot” location. Access to safe vehicles was limited. Unlicensed driving, lack of child restraints, drink driving and fatigue were key road user concerns. Needs for across-government improvements in policy and management were identified. Cost effective actions were identified. This project demonstrated that application of the Safe System was feasible in a remote Aboriginal community, while lessons learned can be adapted and applied nationally to improve Aboriginal road safety

Regeneration and poverty: evidence and policy review. Final report

First paragraph: This review assesses the impact of regeneration on poverty. It is one of a series of evidence reviews produced for the Joseph Rowntree Foundation (JRF) as part of a programme of work to develop an anti-poverty strategy for the UK

Comparison of standard exponential and linear techniques to amplify small cDNA samples for microarrays

BACKGROUND: The need to perform microarray experiments with small amounts of tissue has led to the development of several protocols for amplifying the target transcripts. The use of different amplification protocols could affect the comparability of microarray experiments. RESULTS: Here we compare expression data from Pinus taeda cDNA microarrays using transcripts amplified either exponentially by PCR or linearly by T7 transcription. The amplified transcripts vary significantly in estimated length, GC content and expression depending on amplification technique. Amplification by T7 RNA polymerase gives transcripts with a greater range of lengths, greater estimated mean length, and greater variation of expression levels, but lower average GC content, than those from PCR amplification. For genes with significantly higher expression after T7 transcription than after PCR, the transcripts were 27% longer and had about 2 percentage units lower GC content. The correlation of expression intensities between technical repeats was high for both methods (R(2 )= 0.98) whereas the correlation of expression intensities using the different methods was considerably lower (R(2 )= 0.52). Correlation of expression intensities between amplified and unamplified transcripts were intermediate (R(2 )= 0.68–0.77). CONCLUSION: Amplification with T7 transcription better reflects the variation of the unamplified transcriptome than PCR based methods owing to the better representation of long transcripts. If transcripts of particular interest are known to have high GC content and are of limited length, however, PCR-based methods may be preferable