Georgios Sermpinis

Empirical M&A research offers a large, heterogeneous set of predictors of acquisition performance, with little agreement on which controls to include. Using double machine learning on a large sample of U.S. domestic and cross-border acquisitions over four decades, we identify determinants that are statistically significant and stable across specifications. Only a small subset consistently explains performance. The drivers of announcement returns differ systematically from those of operating performance: the former are consistent with signalling and information characteristics (the non-NPV content of the deal), the latter with its fundamental aspects. This divergence persists across alternative control sets, and the resulting factor sets suggest a parsimonious benchmark for future M&A research.

1. Few areas of empirical economics have been transformed as rapidly as finance by the twin forces of machine learning (ML) and big data. Financial markets generate data of extraordinary volume, ve...

This study examines systemic risks in decentralized lending protocols stemming from “mega-whales”—large users with transaction volumes exceeding $100 million—who dominate liquidity dynamics. Using high-frequency transaction-level data from major protocols ( Aave v2/v3 , Compound v2/v3 , Morpho Blue , Flux , and SparkLend ) spanning August 2024 to August 2025, we identify 550 mega-whales and analyze their impacts across market size, structural risks, and user activity. Key findings reveal the following: (1) The net growth in both mega-whale deposits and borrowing is negatively correlated with the market-size dynamics of lending protocols, while arbitrage-driven behaviors (such as token diversification and platform switching) further fragment the growth of Total Value Locked (TVL); (2) Although whale borrowing serves to temporarily stabilize leverage ratios, deposit activities may adversely impact systemic stability; however, an increase in mega-whale deposits is found to positively stimulate the growth and participation of unique depositors; (3) Utilizing event-window controls around DeFi shocks, we confirm that these effects are strongly associated with mega-whale activities rather than market-wide events. These results point to participation patterns that may be inconsistent with DeFi’s egalitarian ethos, suggesting that economic concentration could influence market dynamics.

Transaction cost model predicts opportunity costs should negatively affect money demand. Examining the effect of cost of carry (CC) on cash holdings at the firm‐level, rather the average effect for entire population, we find that such a pervasive negative relation does not hold in times of low interest rate with about a 10% chance of observing positive effects. Firm size emerges as the primary driver of this heterogeneity, demonstrating a hump‐shaped effect on the cash‐CC link. Our findings suggest that policymakers should track the distributional impacts of opportunity cost of money demand over time to better evaluation of monetary policy.

Decentralized Autonomous Organization (DAO) provides a decentralized governance solution through blockchain, where decision-making process relies on on-chain voting and follows majority rule. This paper focuses on MakerDAO, and we find three voter coalitions after applying clustering algorithm to voting history. The emergence of a dominant voter coalition is a signal of governance centralization in DAO, and voter coalitions have complicated influence on Maker protocol, which is governed by MakerDAO. This paper presents empirical evidence of multicoalition democracy in DAO and further contributes to the contemporary debate on whether decentralized governance is possible.

Lending Protocols (LPs), as blockchain-based lending systems, allow any agents to borrow and lend cryptocurrencies. However, liquidity risks could occur, especially when salient loans are initiated by a particular group of borrowers. This paper proposes measurements of liquidity risks, focusing on both available liquidity and market concentration in LPs. By using Aave as a case study, we find that liquidity risks are highly volatile and show complex effects on Aave, and liquidity in Aave may affect across on-chain lending market. Compared to new users, regular users that repeatedly borrow cryptocurrencies may negatively affect Aave protocol, implying that user loyalty is a double-edged sword for LPs.

Decentralized Autonomous Organization (DAO) is very popular in Decentralized Finance (DeFi) applications as it provides a decentralized governance solution through blockchain. We analyze the governance characteristics in the Maker protocol, its stablecoin DAI and governance token Maker (MKR). To achieve that, we establish several measurements of centralized governance. Our empirical analysis investigates the effect of centralized governance over a series of factors related to MKR and DAI, such as financial, transaction, network and twitter sentiment indicators. Our results show that governance centralization influences both the Maker protocol, and the distribution of voting power matters. The main implication of this study is that centralized governance in MakerDAO very much exists, while DeFi investors face a trade-off between decentralization and performance of a DeFi protocol. This further contributes to the contemporary debate on whether DeFi can be truly decentralized. centralized governance in MakerDAO very much exists, while DeFi investors face a trade-off between efficiency and decentralization. This further contributes to the contemporary debate on whether DeFi can be truly decentralized.

The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, our results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field.

Ever since the start of the coronavirus pandemic, lockdowns to curb the spread of the virus have resulted in an increased interest of retail investors in the stock market, due to more free time, capital, and commission-free trading brokerages. This interest culminated in the January 2021 short squeeze wave, caused in no small part due to the coordinated trading moves of the r/WallStreetBets subreddit, which has rapidly grown in user base since the event. In this paper, we attempt to discover if coordinated trading by retail investors can make them a market moving force and attempt to identify proactive signals of such movements in the post activity of the forum, to be used as a part of a trading strategy. Data about the most mentioned stocks is collected, aggregated, combined with price data for the respective stock and analysed. Additionally, we utilise predictive modelling to be able to better classify trading signals. It is discovered that despite the considerable capital that retail investors can direct by coordinating their trading moves, additional factors, such as very high short interest, need to be present to achieve the volatility seen in the short squeeze wave. Furthermore, we find that autoregressive models are better suited to identifying signals correctly, with best results achieved by a Random Forest classifier. However, it became apparent that even the best performing model in our experimentation cannot make accurate predictions in extreme volatility, evidenced by the negative returns shown by conducted back-tests.