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Moderne Industrie ist datengetrieben. Neueste Technologien, Produkte und Dienstleistungen können nur so gut sein, wie die zugrundeliegenden Informationen. Dies gilt für nahezu jede Branche. In Finanzsektor, Telekommunikation, Gesundheitswesen oder Logistik bilden Daten die Grundlage für alle wichtigen Geschäftsentscheidungen.
Verminderte Wettbewerbsfähigkeit, Rückgang der Kundenzufriedenheit oder erhöhtes Risiko von behördlichen Strafen sind unweigerliche Konsequenzen schlechter Datenqualität. Zahlreiche Softwarelösungen wurden in den letzten Jahren entwickelt, um dem Problem entgegenzuwirken.
Diese Arbeit untersucht die Datenqualitätsmanagement-Software, die im Microsoft SQL Server 2016 verankert ist und sich aus drei ergänzenden Teilkomponenten zusammensetzt: Data Quality Services (DQS), SQL Server Integration Services (SSIS) und Master Data Services (MDS). Es werden zunächst theoretische Aspekte der Datenqualität sowie die auf dem Markt vorhandenen Typen von Softwarelösungen betrachtet und daraus Anforderungen an eine Data Quality Suite abgeleitet. Im Anschluss wird der Funktionsumfang mithilfe von Testdaten im Hinblick auf die Erfüllung der Anforderungskriterien analysiert und bewertet. Insbesondere sollen Möglichkeiten des Data Profilings und Data Cleansings mit speziellem Fokus auf Duplikaterkennung und Konsolidierung untersucht werden. Ein großer Wert wird dabei auf die praktische Anwendung der getesteten Software gelegt. Alle Funktionen von SQL Server 2016 werden anschließend auf Basis der definierten Evaluationskriterien bewertet.
A Deep Reinforcement Learning Agent Using Multiple Assets Financial Signals for Portfolio Management
(2019)
In this study, we investigated possible applications of reinforcement learning in the area of portfolio management. Aa specific topology of reinforcement learning is choosen to study the feasibility, Deep Deterministic Policy Gradient (DDPG), to train neural networks to perform trade in an environment simulated real world trading. The results show that the DDPG agent is able to learn price pattern and perform profitable trades. In single stock backtest, the DDPG is able to generate an annual return of 7%. While in multiple stocks backtest, DDPG agent can generate an annual return of 12%.
In this study, we investigate the usage of generative adversarial neural networks(GAN) for a sequence labeling task. We applied sequence generative adversarial neural network (SeqGAN) to extract cause-effect and moderator-mediator relations from hypotheses from scientific papers. This research focuses on the structure of SeqGAN and the problems that come with it. However, there are two main problems with GAN’s. Firstly, a vanilla GAN (Goodfellow et. Al. 2014) is designed for generating real-valued, continuous data but we want to label discrete words into tokens. The second problem is that a GAN can only provide a loss for a complete sequence (Yu, Zhang, Wang, & Yu, 2017, p. 1). To address these problems two things are changed. First, the sequence generation process is realized as a decisionmaking process. The sequences are going to be evaluated by the discriminator model. To solve the problem with the discrete data we follow Yu et. Al. and regard the generative model as a policy gradient. To approximate the sequence value a Monte Carlo Search is employed in the generative model. We show the performance of different parameter settings and which tricks improve the results. The python and r scripts are appended in this document. They can also be found in GitHub (https://github.com/clamkewitz/GANCause).
This research paper explores the application of the Universal Language Model Fine-tuning (ULMFiT) technique, a novel deep transfer learning approach in the Natural Language Processing (NLP) field, to the financial statements fraud detection task. Additionally, the artifact investigated a simpler model, represented by a one-dimensional Convolutional Neural Network (CNN). Both methods have been assessed with respect to the training time and predefined evaluation metrics. Overall, ULMFiT turned out to be considerably more computationally expensive to train and achieved an accuracy of 77% with F-measure of 13%, if used with a decision boundary of 0.5, and accuracy of 59% with F-measure of 42%, if calculated with a threshold of 0.2. In contrast, CNN model trained significantly faster and obtained the following metrics: accuracy of 82% with F-measure of 42% for threshold 0.5, and accuracy of 83% with F-measure of 58% for threshold 0.2. As a result, ULMFiT has been outperformed by one-dimensional CNN on all examined metrics. The results are reported by using two different values for decision boundary due to the precision-recall trade-off, depending on the use case. In addition, this thesis investigated the impact of data preprocessing. The findings have shown that removing all numbers and special symbols with supplementary text truncation, limiting the sequence length, had a positive effect on both models, mentioned above.
This thesis examines the effectiveness of the latest Transfer Learning techniques for Natural Language Processing applied to the classification of research methods used in scientific journals in the domain of Information Systems. The task of automated knowledge extraction from academic articles has seen ongoing progress in recent years. However, the combination of transfer and Deep Learning in order to assign research methods to scientific papers has not been addressed in the literature yet. The main contribution of this thesis is, therefore, an artifact that applies cutting-edge Transfer Learning techniques to a Deep Learning model by conducting several experiments and comparing their effectiveness. The prototype considers various ways of fine-tuning that are crucial to retain the knowledge transferred from pretrained models and avoid catastrophic forgetting. Additionally, this work discusses the literature with regard to the task-specific Theory Ontology Learning and the method-specific state of the art in Transfer Learning for Natural Language Processing. As a result, the artifact surpassed the performance of previously developed models for research method extraction, presented in the literature, without applying any custom feature engineering and only using around a thousand of labeled observations.
This thesis analyzes whether financial news articles can predict the stock prices of companies and the S&P 500 Index. The selected companies for the analysis are Facebook Inc., Apple Inc., Microsoft Corp., Google (Alphabet Inc.), and Amazon.com Inc. This thesis evaluates the predictive power of financial news by comparing the sentiment of financial news articles published on a business day to
the corresponding closing value of stock prices and the S&P 500 Index. For the analysis, financial news from various resources is used for a time period between January 2018 and May 2018. The sentiment of the news articles is analyzed using a lexicon-based approach. Then, regression models are used to predict the stock market. The models used for forecasting the stock prices and the S&P 500 Index
are Auto-Regressive Integrated Moving Average (ARIMA), Support Vector
Regression (SVR), and Linear Regression (LR). The results of the research in overall indicate that ARIMA performs better for predicting the stock prices and the S&P 500 Index.