arXiv subjects

Georgios Sermpinis

Publications and source records attributed to Georgios Sermpinis.

5 recordsLinked to original sources

arXiv2023

Voter Coalitions and democracy in Decentralized Finance: Evidence from MakerDAO

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.

cs.SI↗

arXiv2023

Liquidity Risks in Lending Protocols: Evidence from Aave Protocol

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.

q-fin.RM↗

arXiv2022

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

stat.AP↗

arXiv2021

A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection

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.

q-fin.CP↗

arXiv2019

Technical Analysis and Discrete False Discovery Rate: Evidence from MSCI Indices

We investigate the performance of dynamic portfolios constructed using more than 21,000 technical trading rules on 12 categorical and country-specific markets over the 2004-2015 study period, on rolling forward structures of different lengths. We also introduce a discrete false discovery rate (DFRD+/-) method for controlling data snooping bias. Compared to the existing methods, DFRD+/- is adaptive and more powerful, and accommodates for discrete p-values. The profitability, persistence and robustness of the technical rules are examined. Technical analysis still has short-term value in advanced, emerging and frontier markets. Financial stress, the economic environment and market development seem to affect the performance of trading rules. A cross-validation exercise highlights the importance of frequent rebalancing and the variability of profitability in trading with technical analysis.

q-fin.ST↗