We investigate the second-by-second impact of Fed Chair Jerome Powell's facial and vocal emotional expressions during FOMC press conferences on the market microstructure of SPY and TLT ETFs. Using unique high-frequency multimodal data and robust wild cluster bootstrap inference, we find complex and often counterintuitive effects. Contrary to standard hypotheses, negative emotional cues (facial or vocal) predict significantly lower subsequent volatility, while positive facial expressions predict higher SPY volatility. Positive vocal cues improve TLT liquidity (narrower spreads), but negative vocal cues also predict narrower spreads at longer horizons. Significant interactions between nonverbal cues and textual sentiment reveal further complexity, such as congruent positive signals predicting lower TLT depth. The Treasury market appears more sensitive than the equity market. Our results challenge simple valence-based theories for high-frequency nonverbal cues, highlighting the importance of modality, asset class, microstructure dimension, and interactions with text in shaping immediate market dynamics.

Exploiting the adoption of mandatory central clearing by U.S. regulators, we explore the effects of this regulatory reform on banks’ derivative offsetting. Using a triple-difference testing procedure, we find that derivative offsetting increases (decreases) for banks with higher (lower) capital ratios after the adoption of mandatory central clearing, in comparison to the control group. The results are economically significant and robust to a variety of alternative measurements and tests. Our findings suggest banks with different target capital ratios respond to central clearing reform differently.

Cryptocurrencies exhibit high volatile behavior and observable anomaly effects, yet systematic anomaly detection within these markets remains underexplored. This study introduces a tailored Business Intelligence (BI) system for cryptocurrency anomaly detection. Leveraging advanced analytics and visualization, our novel BI system provides a comprehensive and real-time overview of market behaviors, enabling swift identification of cryptocurrency price anomalies. Our unique system empowers users to navigate the dynamic cryptocurrency trading, enhancing anomaly detection informed decision-making and risk management.

GNU Taler is a payment system that offers privacy-preserving payments for buyers while ensuring income-transparency for sellers. Our payment system does not use a blockchain and can process tens of thousands of payments on a single computer with negligible costs per transaction. Modern cryptography is used to implement digital cash held decentralized in self-custody.

Because spending digital cash does not require authentication, GNU Taler offers superior usability compared to other payment solutions. Building on ideas from Oral Information Management (OIM), we created a version of the Taler wallet that does not require users to be able to read, and have conducted usability studies with illiterate and innumerate people that were quickly able to correctly use the GNU Taler wallet for payments despite being unable to read 2-digit numbers.

As Free/Libre Open Source software we are able to develop the solution in collaboration with a growing group of academics, volunteers, non-profits and small businesses. This allows our small team to drive an outsized number of innovations in a field where proprietary systems have long stifled innovation. As a result, secure payments to offline merchants, programmable money, business contracts using secure multiparty computation, subscriptions, coupons, privacy-preserving tax deductable donations and even post-quantum cryptography are other present or upcoming features.

The payment system is operational today in Swizerland (eCHF), and is expected to be operated by GLS for Euros starting in Q3'2025.

We offer a new metric, "net-zero alignment strength" (NZAS), which can be used by socially responsible investors for portfolio selection. NZAS is based on the current corporate GHG emission intensity (EI) and its reduction rate (ERR) and, hence, can be used for choosing among the companies with high EI and high ERR versus the companies with low EI and low ERR. We have incorporated NZAS into the mean-variance portfolio (MVP) framework, which yields simultaneous optimization upon high returns and NZAS, and low price volatility. The NZAS contribution to the MVP minimization function is controlled by the net-zero commitment parameter, which is investor’s choice. An example for a portfolio with 29 major constituents of the Energy sector illustrates an interplay between the company Sharpe ratios and NZAS, which determines the effects of the net-zero commitment on the major portfolio holdings. We suggest to use this framework for finding the best-in-class companies within the chosen equity sectors.

Despite voluminous empirical research and numerous theoretical contributions, the ``dividend puzzle'' persists. The lack of data on firms ' widely hypothesized dividend payout targets is an impediment to understanding why firms pay dividends and how much to pay. Researchers have relied on limited survey data collected at a point in time, over a short period, or by using realized dividend payout ratios. This research builds a novel database of voluntary numerical medium-term forward-looking dividend payout target disclosures by Japanese firms in their Annual Securities Reports. Text analysis methods are employed to extract a panel of data on firms' targets. This data is unique as medium-term targets are not disclosed systematically elsewhere. I examine the characteristics of the target disclosures in the time series and cross-section, the financial characteristics of the firms that voluntarily disclose targets, and use the targets to examine dividend smoothing and the speed of dividend adjustment.