Research Assistant | PhD Candidate
Brian von Knoblauch
Research
Financial Reserach
I am a research assistant at the institut for banking and finance at the Leibniz University Hannover. My research areas of interest are:
Deep Learning in Asset Pricing
Machine learning algorithms offer many new opportunities to improve the explainability and predictability of companies’ performances. If implemented correctly, flexible neural network models enable insights into how characteristics and asset pricing pheomena are related to each other. This may enable academics to improve economic theory. It may also increase market efficiency in real world financial markets and will be of high interest for investing financial instutitions.
Valuation and Corporate Finance
Analysis of the methods used to value companies. Most models are based on discounted cash flow methods. These methods also play an important role in strategic decisions about whether or not to undertake a project or a transaction. A very important aspect and area of research is the use of the correct, risk-adequate discount rates.
(Behavioral) Decision Science
I analyze the process of decision making (under uncertainty). Normative theory tells us how rational decision should be made and is largely based on expected utility theory (EUT). However, many decision anomalies have been documented in descriptive decision research. Alternative decision models, such as cumulative prospect theory (CPT), have been introduced to explain the perhaps irrational decision process. Understanding how we make decisions is critical to better understand financial markets and how investors make decisions.
Teaching
Courses and Supervision
I am supervising courses and student projects on bachelor and master level.
Corporate Finance
Seminar that deals with the valuation for firms. It teaches discounted cash flow methods and advanced concepts for assessing risk-adjusted cost of capital used for discounting. The seminar is held in coorperation with industry experts and an annualy changing partner firm that is to be evaluated.
Decision Analysis
This class deals with the normative and prescriptive analyses of decisions under either certainty or uncertainty. It is designed for final year bachelor students. Key aspects are the introduction to the expected utility theore (EUT) as well as the prospect theory (CPT) of Kahneman & Tversky.
Bachelor and Master Theses
Supervision of final theses at bachelor and master level. Topics offered are in the related fields of research outlined above.
Details
Our Research Topics.
Financial EconomEtrics
Behavioral Finance
Asset Pricing
Valuation
Corporate Finance
Forecasting
Decision Analysis
Financial Research
Machine Learning Methods
Powerful common and newly developed machine learning algorithms have the ability to give new insights into the financial research if applied correctly. Neural networks (NN) are highly flexible models allowing researchers to identify important patterns that are related to companies’ performances.
FeedForward NN
Plain vanilla NN that provides a powerful and flexible framework
Autoencoder
Are related to traditional multi-factor models but are more flexible.
Recurrent NN
Are used for sequential data modeling to identify time dependencies.
GAN
Generative Adversarial Networks are used to generate and classify data
Contact
Students from the Leibniz University are advised to use the official email adress of the university.
