Habilitation, 2024
University of Liechtenstein
PhD Economics, 2015
University of Innsbruck
MSc Business Admin., 2007
University of Innsbruck
MSc Techn. Mathematics, 2007
University of Innsbruck
| Logo | Description |
|---|---|
 | my crypto2-package to retrieve
survivorship bias free cryptocurrency data from
coinmarketcap.com and
coingecko.com, with a crosswalk between the two |
 | my ffdownload-package to retrieve
research data from
Kenneth French's famous website |
R-packages | my rafe-package to judge return forecasts by the
economic damage their errors cause rather than by their size, accompanying our paper
Lost in Translation? |
 | my eventclock-package to measure
information time around scheduled events from traded event probabilities |
 | my databentoR-package, an
R client for the
Databento historical and reference market data API |
 | my rqmoms-package to calculate
option-implied moments according to
Grigory Vilkov's python package |
Sorting on a characteristic buys an industry bet nobody asked for: 18 percent of a factor’s variance, 0.3 percent a year. Ranking within industries removes it without estimating a hedge, and lifts the factor zoo’s maximum Sharpe ratio in every twenty-year window since 1930.
Each dimension of a return forecast an investor acts on costs 1/T, whatever signal it carries. Setting the number of rank groups from the forecast’s information coefficient, and holding 1/N when the forecast cannot rank, earns an annualized Sharpe ratio of 1.2 on a hundred large CRSP stocks, against 0.6 for 1/N and 1.0 for the decile sort.
We price the shrinkage of the mean when the covariance matrix it relies on is itself estimated. The best shrinkage intensity follows from the number of assets and the length of the sample alone, and the corrected mean dominates the sample mean whenever the sample is longer than the number of assets.
How much of the price variation ahead of a scheduled event reflects genuine learning rather than valuation shocks? Three prediction-market venues identify the learning-spanned share without a loading normalization, and in the 2024 U.S. election most of it arrives only in the final days.
Large language models are used to score populist rhetoric and four dimensions of liberalism across party manifestos, showing that right-wing populist anti-liberalism is largely indistinguishable from its left-wing counterpart.
Options spanning a scheduled event are priced from observed event-clock inputs, with sequential overidentification restrictions tested on the Brexit referendum and on 2024 U.S. presidential-election option data.
This paper proposes a multivariate risk-adjusted error measure for economic forecast performance and analyzes the limitations of traditional MSE-based measures in capturing the economic value of return forecasts.
We examine the effect of populism on financial markets around national elections. We find that the electoral success of populist parties has a direct impact on volatility in major domestic market indexes with different signs depending on the political ideology of the populist parties.
This paper investigates herding behavior in the crypto market. We consider the full, survivorship-bias free cross-section of cryptocurrencies, and document - against existing evidence - significant herding in this dataset. The effect is stronger, when using bitcoin as a transfer currency.
We use data from betting markets to analyze the sensitivity of stock returns to potential outcomes of political events such as elections. By classifying stocks into expected conditional winners and losers prior to such an event, we form portfolios that generate large positive returns after the event date, conditional on correctly anticipating the outcome. We illustrate this using data from the 2016 US presidential election and the 2016 Brexit referendum.
This paper introduces a novel measure of global macro-financial uncertainty and examines the state-dependent transmission of uncertainty to economic activity.
Bias-corrected asset pricing, market structure, and banking contagion in cryptocurrency markets — plus the open-source data infrastructure that makes the work possible.
Methods for selecting, timing, and combining factors when the zoo is crowded, the cross-section is noisy, and estimation error dominates.
Using market prices to measure how political events and populist electoral shifts are priced into equities, volatility, and cross-country asset returns.
Closing the gap between statistical forecast accuracy and the Sharpe ratio investors actually care about.
Measuring, pricing, and hedging the fact that investors do not know the true distribution of returns.
Erasmus+ project (2022–2025) delivering digital finance education through an online investment simulation — the Cesim Invest platform — for schools, universities, and professional training across Europe.
Developed an R-package and public Shiny app to optimize pension decisions for individuals in Liechtenstein's pension system, using large-scale optimization and machine learning.
Erasmus+ project (2019–2022) developing free online courses and interactive tools for life-cycle investments and personal financial planning — for the general public and higher-education students across Europe.
Erasmus+ project (2016–2019) developing free online courses and interactive tools for pension finance and personal pension planning — for the general public and higher-education students across Europe.
Reach out via email or directly book an appointment.
Fürst-Franz-Josef-Strasse
Vaduz, 9490
Liechtenstein
Monday 11:00 to 12:00
Otherwise book an appointment