26617 research outputs found
Sort by
Distributional prediction of insurance claims using deep transformation models
No full textDISTRIBUTIONAL PREDICTION OF INSURANCE CLAIMS USING DEEP TRANSFORMATION MODELS
Distributional prediction of insurance claims using deep transformation models (1)
List of Figures (iii)
List of Tables (iii)
List of Abbreviations (iv)
1 Introduction (1)
1.1 Motivation (1)
1.2 Related Work (2)
1.3 Research Questions and Contributions (4)
2 Theoretical Background (6)
2.1 Benchmark Models (6)
2.1.1 Tree-based Methods (6)
2.1.2 Ensembles (7)
2.1.3 Random Forest (7)
2.1.4 Gradient Boosting (8)
2.1.5 Extreme Gradient Boosting (8)
2.2 (Deep) Neural Networks (9)
2.2.1 (Deep) Feedforward Networks (9)
2.2.2 Back-Propagation and (Stochastic) Gradient Descent (10)
2.2.3 Other Types of Neural Networks (11)
2.3 Distributional Regression using (Deep) Transformation Models (12)
2.3.1 (Conditional) Transformation Models (12)
2.3.2 Shift(-only) Transformation Models (14)
2.3.3 (Deep) Transformation Models (14)
3 Data and Setup (17)
3.1 Data (17)
3.2 Specification of Models (18)
3.2.1 (Deep) Transformation Models (18)
3.2.2 Benchmark Models (22)
3.3 Training Procedure (22)
3.3.1 (Deep) Transformation Models (22)
3.3.2 Benchmark Models (23)
3.4 Evaluation Metrics (23)
3.4.1 Negative Log-Likelihood (23)
3.4.2 Quantile Loss (23)
3.4.3 Root Mean Square Error and Mean Absolute Error (25)
3.4.4 Additional Assumptions and Transformations (25)
3.4.4.1 (Deep) Transformation Models (25)
3.4.4.2 Benchmark Models (26)
4 Results and Discussion (27)
4.1 Results on Evaluation Metrics (27)
4.2 Analysis and Interpretation of the Predictors (30)
4.3 Example Distributional Predictions (34)
4.4 Comparison with the Other Participants of the Competition (36)
5 Conclusions (39)
References (41)
Appendix (46)
A Software (46)
B Dataset and Pre-processing Procedure (46)
C Extrapolation of Transformation Functions (52)
D Top 20 Tabular Variables according to the (D)TMs (53
Stock market returns and presidential elections
No full textSTOCK MARKET RETURNS AND PRESIDENTIAL ELECTIONS
Stock market returns and presidential elections (1
Predicting the direction of stock prices with machine learning using technical indicators and macroeconomic variables
No full textPREDICTING THE DIRECTION OF STOCK PRICES WITH MACHINE LEARNING USING TECHNICAL INDICATORS AND MACROECONOMIC VARIABLES
Predicting the direction of stock prices with machine learning using technical indicators and macroeconomic variables (1)
Motivation (1)
Related Literature (1)
Research Question & Research Design (4)
Research Question (4)
Research Gap & Contribution (4)
Research Design (5)
Methodology (6)
Data (7)
3-Class Classification (9)
Models (11)
Benchmark Models (11)
Random Forests (13)
Extreme Gradient Boosting (XGB) (13)
Assessment of Model Performance (15)
Trading Strategy (16)
Exploratory Analysis (18)
Results (19)
Random Forests (19)
Replication of Basher & Sadorsky (2022) (19)
Stocks (22)
Extreme Gradient Boosting (30)
Benchmark Models (31)
Logistic Regression (31)
ARIMA (33)
Performance across Models (35)
3-Class Classification Approach (36)
Rolling Window Analysis (37)
Trading Strategy (39)
Binary Classification Approach (40)
3-Class Classification Approach (42)
Probabilistic Approach (43)
Comparison (44)
Statistical Analysis (45)
Discussion (46)
Key Findings (46)
Limitations (47)
Future Research (48)
Conclusion (49
Bond tokenization in practice
No full textBOND TOKENIZATION IN PRACTICE
Bond tokenization in practice (2)
1. Introduction (6)
1.1. Research topic (6)
1.2. Research problem (7)
1.3. Research objectives and questions (8)
1.4. Scope and significance of the research (9)
2. Literature review (10)
2.1. Bond issuance, settlement and stakeholders (10)
2.1.1. The bond issuance process (10)
2.1.2. The bond settlement process (11)
2.1.3. Key stakeholders in bond issuance and settlement process (11)
2.2. Distributed ledger technology (DLT), blockchain & smart contract fundamentals (14)
2.3. State of asset tokenization (15)
3. Research methodology (16)
3.1. Research approach (16)
3.2. Research strategy (16)
3.3. Data collection & Analysis (17)
3.4. Research phase (18)
3.5. Research process (20)
4. Case study presentation (22)
4.1. Case selection criteria (22)
4.2. Case details (23)
4.3. Relevance of the case in bond tokenization (24)
5. Results (25)
5.1. Issuance process (25)
5.2. Settlement process (26)
5.3. Regulatory and compliance aspects (28)
5.4. Observed challenges (29)
5.5. Comparative overview of the initiatives (30)
5.6. Addressing the research questions (31)
6. Discussion (33)
6.1. Intro to the discussion (33)
6.2. Efficiency (33)
6.3. Transparency (34)
6.4. Programmability (34)
6.5. Limitations (34)
6.6. Synthesis (35)
7. Implications, limitations, and future research recommendations (38)
7.1. Implications (38)
7.2. Limitations (39)
7.3. Future research recommendations (40)
8. Conclusion (42)
9. References (44
Der Einfluss der Zinsschranke auf die Kapitalstruktur potenziell betroffener österreichischer Unternehmen
No full textDER EINFLUSS DER ZINSSCHRANKE AUF DIE KAPITALSTRUKTUR POTENZIELL BETROFFENER ÖSTERREICHISCHER UNTERNEHMEN
Der Einfluss der Zinsschranke auf die Kapitalstruktur potenziell betroffener österreichischer Unternehmen (1
Organisationale Ambidextrie in sozialen NPOs
No full textORGANISATIONALE AMBIDEXTRIE IN SOZIALEN NPOS
Organisationale Ambidextrie in sozialen NPOs (1
Reddit comments sentiment and stock movement
No full textREDDIT COMMENTS SENTIMENT AND STOCK MOVEMENT
Reddit comments sentiment and stock movement (1)
Introduction (6)
Literature Review (8)
Data Collection and Pre-processing (9)
Reddit comments download (9)
BERT and RoBERTa model classification (devlin2019bert) (11)
Pre-training and fine-tuning BERT (12)
RoBERTa model (liu2019roberta) (13)
Comment labeling (13)
Sentiment classification (13)
Coordination classification (15)
Outcomes from classification models (16)
Stock market data (18)
Empirical analysis and results (20)
Comment volume and absolute returns (20)
Bullishness, coordination and stock returns (22)
Different sentiment and coordination combination and stock returns (27)
Limitations and Future Work (31)
Conclusion (32)
Appendix (33)
References (34
Identification of discrepancies in ESG ratings
No full textIDENTIFICATION OF DISCREPANCIES IN ESG RATINGS
Identification of discrepancies in ESG ratings (1)
Introduction to the academic research (2)
Literature Review (4)
Methodology (7)
Pre-processing Workflow (7)
Exploratory Data Analysis (9)
Machine Learning models selection (10)
Limitations (16)
Data Review (17)
Results and insights (18)
Pillar-Level Results and Insights (19)
Pillar-and-Sector-Level Results and Insights (23)
Pillar-and-Year-Level Results and Insights (27)
SHAP interpretation and Insights (30)
Conclusion (32)
Appendices (32