| 2017 |
University of Zurich |
Claudio Ambühl |
Master Thesis |
Assessing the individual risk tolerance of retail investors – risk profiling in theory and praxis |
| 2018 |
LMU Munich |
David Milewski |
Master Thesis |
Forecasting Financial Time Series with Deep Learning |
| 2018 |
LMU Munich |
Karem El-Oraby + Raphael Rehms |
Industry Project |
Forecasting Financial Markets with Text Data |
| 2020 |
LMU Munich |
Cem Öztürk |
Master Thesis |
A comprehensive analysis of the use of deep learning models for forecasting the cross-section of stock returns |
| 2020 |
UC Berkeley |
Zacharie Bouhnik + William Lambert |
Industry Project |
Business News Topic Modeling |
| 2020 |
UC Berkeley |
Alex Bietrix + Paul-Noël Digard |
Industry Project |
Analysis of stock return predictors using profitability metrics |
| 2020 |
UC Berkeley |
Arthur Ji + Shimai Zhang |
Industry Project |
LSTM for Stock Return Prediction and Portfolio Construction |
| 2020 |
UC Berkeley |
Jiayin Guo + Yvonne Zhu |
Industry Project |
Interpretable Models for Portfolio Management |
| 2021 |
UC Berkeley |
Taige Hong |
Industry Project |
Investment Strategy with Long History Data and Feature Engineering |
| 2021 |
UC Berkeley |
Ayush Gupta |
Industry Project |
Forecasting stock market returns – Focus: AutoML |
| 2021 |
UC Berkeley |
Kunal Chakraborty |
Industry Project |
Forecasting stock market returns – Focus: Ensemble |
| 2021 |
UC Berkeley |
Alec Madayan |
Industry Project |
Forecasting stock market returns – Focus: Deep Learning |
| 2021 |
UC Berkeley |
Philip Spalding |
Industry Project |
Ensemble Sentiment Dictionaries for Financial Applications |
| 2021 |
UC Berkeley |
Neil Gulati |
Industry Project |
Textual Analysis on Financial Statements to Forecast Single Stock Returns with ESG Verbiage |
| 2021 |
UC Berkeley |
Yifei Tong |
Industry Project |
Studying the Relationship between Amazon Customer Reviews and Stock Returns |
| 2022 |
TU Munich |
Dominik Eichhorn |
Master Thesis |
Empirical Asset Pricing via Machine Learning – An after trading costs perspective |
| 2022 |
UC Berkeley |
Divya Singh |
Industry Project |
Evaluating Performance of Volatility Managed Portfolios with Machine Learning and macroeconomic data |
| 2022 |
UC Berkeley |
Andrew Lazzeri |
Industry Project |
Using Machine Learning to Develop Equity Style Rotation Strategies |
| 2022 |
UC Berkeley |
Peter Masforroll |
Industry Project |
Volatility Managed Factor Portfolios Leveraging Machine Learning and Macroeconomic Data |
| 2022 |
UC Berkeley |
Nathan Sheng |
Industry Project |
Stock Selection with Deep Learning |
| 2022 |
TU München |
Antonio Di Giovanni |
Master Thesis |
Automatically tuned deep neural networks applied in forecasting the cross-section of stock returns |
| 2023-2024 |
LMU Munich |
Dennis Mao |
PhD Thesis |
DCC-GARCH-COPULA Models |
| 2023 |
Frankfurt School |
Jan-Luca Frick |
Internship |
Multi-Period Portfolio Optimization |
| 2023 |
UC Berkeley |
Elisa Piaraly |
Industry Project |
Dynamic Portfolio Allocation in Goal-Based Wealth Management for Multiple Time Periods |
| 2023 |
UC Berkeley |
Mélanie Fabiani |
Industry Project |
Deep understanding of the Python Package „riskfolio-lib“ and empirical application |
| 2023 |
UC Berkeley |
Juliette Ould-Aklouche |
Industry Project |
Cash Flow Models for Alternative Investments |
| 2023 |
University of Cologne |
> 20 Students |
Industry Project |
Machine Learning for Economists |
| 2024 |
University of Cologne |
Janina Dierk |
Master Thesis |
Forecasting Net Values and Cashflows of Private Assets with Machine Learning |
| 2024 |
UC Berkeley |
Angel Chen, Jan-Luca Frick |
Industry Project |
Modeling the Global Economy, Public Asset Classes and Private Asset Classes for Scenario Analysis |