Open Access

We examine the impact of increasing competition among the fastest traders by analyzing a new low-latency microwave network connecting exchanges trading the same stocks. Using a difference-in-differences approach comparing German stocks with similar French stocks, we find improved market integration, faster incorporation of stock-specific information, and an increased contribution to price discovery by the smaller exchange. Liquidity worsens for large caps due to increased sniping but improves for mid caps due to fast liquidity provision. Trading volume on the smaller exchange declines across all stocks. We thus uncover nuanced effects of fast trader participation that depend on their prior involvement.

We show that exposure to anti-capitalist ideology can exert a lasting influence on attitudes towards capital markets and stock-market participation. Utilizing novel survey, bank, and broker data, we document that, decades after Germany's reunification, East Germans invest significantly less in stocks and hold more negative views on capital markets. Effects vary by personal experience under communism. Results are strongest for individuals remembering life in the German Democratic Republic positively, e. g., because of local Olympic champions or living in a "showcase city". Results reverse for those with negative experiences like religious oppression, environmental pollution, or lack of Western TV entertainment.

Proposed reform of the Pan-European Pension Product and an initiative opinion on the proposal for a regulation establishing a framework for Financial Data Access

This study compares the performance of various machine learning methods in predicting the outcomes of mergers and acquisitions (M&A), with application in merger arbitrage. Merger arbitrage capitalizes on price inefficiencies around merger announcements, empirically offering consistent, near-market-neutral returns with Sharpe ratios around 1.20 and a beta of 0.14. Leveraging logistic regression, random forest, gradient boosting machine, and neural network, I analyse 21,020 M&A deals with up to 522 predictors from 1999 to 2023. I examine two datasets: one with all features available prior to deal resolution, serving as an upper bound for predictability, and another with only features available on the announcement. Among the applied methods, XGBoost outperforms in predicting deal closure probabilities, with pseudo-out-of-sample receiver operating characteristic area under the curve (ROC-AUC) scores of 0.99 and 0.81 for the full-feature and announcement-date-only sets, respectively. I apply these predictions to cash-only merger arbitrage from 2021 to 2023, using a classification method and testing a promising fair value investment criterion. I find that equal-weighted portfolios perform best, driven by diversification and small-size premia, achieving annualized alphas of 10 to 20% against the Fama-French five-factor model. XGBoost’s superior predictive power translates into the best merger arbitrage performance, delivering Sharpe ratios of up to 1.57 for long-only portfolios and 0.60 for zero-net-investment long-short strategies, with the latter maintaining market neutrality. I confirm these results during a second trading period from 2018 to 2020, revealing different market dynamics and similar or better model performance, with Sharpe ratios as high as 2.15. These findings establish new benchmarks for M&A deal closure prediction, highlight the value of machine learning-driven strategies in enhancing merger arbitrage performance, and offer valuable insights for both researchers and practitioners.