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Diana TunaruView profile

Lecturer

• Empirical Finance
• Interest Rate Modeling
• Dynamic Estimation and Forecasting
• +4 more

Dr. Diana Tunaru is a Lecturer in Finance at Kent Business School , University of Kent, with expertise in empirical finance and quantitative methods. Her research focuses on dynamic financial modeling and forecasting, particularly in interest rate structures and volatility spillover effects. Education: Diploma in Mathematics (University of Craiova), Master in Banking and Finance (Middlesex University), PhD in Finance (University of Westminster) Research Interests include multi-factor continuous-time models, Gaussian estimation techniques, and the analysis of financial variables like interest rates, equities, and derivatives during economic distress. She applies her industry experience in investment banking to bridge theoretical and practical finance. In publications , Diana has explored topics ranging from agricultural commodity forecasting to green finance, with recent work emphasizing sustainability-driven portfolio performance and market interconnectedness. Her methodological focus on statistical accuracy tests and spillover effects reflects her commitment to advancing financial modeling. Teaching: Quantitative Methods for Finance, Introduction to Econometrics, Financial Investments, Economics of Financial Markets Diana serves as Senior Tutor at Kent Business School (Medway campus) and contributes to postgraduate education through her supervision of undergraduate dissertations on empirical finance and financial time series analysis.

Guofu ZhouView profile

Professor

• Empirical Asset Pricing
• Machine Learning in Finance
• Financial Economics
• +10 more

Guofu Zhou , the Frederick Bierman & James E. Spears Professor of Finance at Washington University's Olin Business School , has been a faculty member since 1990. His academic career includes multiple Reid Teaching Awards (2020, 2019, 2018, 2014, 2010) Best Paper Awards (Institute for Quantitative Investment Research 2019, Chinese Finance Association 2010, Inquire UK/Europe 2019 & 2024) and affiliations with journals like the Journal of Financial Economics and Management Science . Education: PhD, Duke University (1990) MA, Duke University (1987) MS, Academia Sinica (1985) BS, Chengdu University of Technology (1982) His research bridges empirical asset pricing and applied AI/machine learning , with a focus on market efficiency anomaly exploitation Bayesian inference option pricing Chinese financial markets behavioral finance He has contributed to understanding equity risk premium predictability, technical analysis, and portfolio optimization techniques. Key trends in his Journal of Financial Economics , Journal of Finance , and Review of Financial Studies publications include machine learning applications in asset pricing , anomaly-market linkages , and fear sentiment in Treasury markets . Recent work with ChatGPT explores textual analysis of earnings calls. Scientific Awards: Best Paper Award, Institute for Quantitative Investment Research (2019) Finalist for Crowell Memorial Prize (2024) Led multiple Best Paper Awards at conferences like FMA and SIF Contact: zhou@wustl.edu | Office: Simon Hall Room 207

Yusuf HartaviView profile

Assistant Professor

• Finance
• Financial Markets
• Quantitative Finance
• +7 more

Yusuf Hartavi is an Assistant Professor of Finance at the Birmingham Business School, University of Birmingham Dubai, located in Dubai International Academic City. He is also the Head of External Engagement at the Dubai campus, contributing to academic-industry partnerships and outreach initiatives. Education: PhD in Finance, University of Reading MSc in International Securities, Investment and Banking MSc Business Administration (MBA) BSc in Business Administration BSc in Industrial Engineering His research focuses on data-intensive empirical analysis of financial markets, particularly the impact of high-frequency and algorithmic trading on market quality indicators such as liquidity and volatility. He has conducted influential research on the risk profile of the London Stock Exchange’s Alternative Investment Market (AIM), challenging conventional views about its inherent riskiness. His work combines quantitative methods with real-world financial data to inform market structure policies and investment strategies. His recent publications include a key 2015 book that presents compelling empirical evidence on trading dynamics and risk in AIM, co-authored with leading scholars. The research demonstrates trends in market microstructure and contributes to broader discussions on equity market efficiency and regulation. Scientific Awards: No awards listed in the provided text. Yusuf Hartavi has extensive teaching experience across undergraduate and postgraduate programs in Financial Markets and Quantitative Finance. He previously served as a sessional lecturer at Henley Business School, where he designed and delivered Programming for Finance workshops. He has taught modules including Financial Modelling Techniques, Corporate Finance, and International Treasury Management. There is no mention of research grants in the provided text. He is affiliated with the Department of Finance at Birmingham Business School and contributes to research and academic engagement at the University of Birmingham Dubai campus. His work bridges academic research and practical applications in financial services and policy.

Chris MonicoView profile

Associate Professor

• Cryptology
• Computational Algebra
• Number Theory
• +6 more

Chris Monico is an Associate Professor in the Department of Mathematics & Statistics at Texas Tech University . He has been a faculty member there since 2003, following post-doctoral research at the University of Notre Dame. Education B.S. in Mathematics – Monmouth University M.S. in Mathematics – University of Notre Dame Ph.D. in Mathematics – University of Notre Dame Research Focus Monico’s scholarship centers on the intersection of cryptology , computational algebra , and number theory . A significant recent thrust has been the application of machine-learning techniques to mathematical finance , evidenced by work on random-forest models for option pricing and high-frequency trading risk metrics. Parallel lines of inquiry include post-quantum cryptographic schemes built on tropical algebra and semigroup actions, as well as classical problems in Ramsey theory and combinatorial semigroups . Publication Trends Between 2015 and 2025 Monico has published prolifically, with a clear shift around 2020 toward mathematical finance and machine-learning applications , alongside continued output in algebraic cryptanalysis and combinatorics . His 2024–2025 articles emphasize data-driven models in trading, whereas 2020–2021 works concentrate on cryptanalyses of tropical and group-based key-exchange systems. Earlier contributions focus on computational number theory and semigroup-based cryptography. Contact Information Email: c.monico@ttu.edu Phone: 806-834-4144 Office: Department of Mathematics & Statistics, Texas Tech University, 1108 Memorial Circle, Lubbock, TX 79409-1042 Advising & Grants No specific doctoral or master’s students, funded grants, or named awards are detailed in the provided text. Laboratory or Research Group The text does not mention any dedicated laboratory or research group.

Nikolaus HautschView profile

Professor

• Econometrics
• Finance
• Market Microstructure
• +3 more

Nikolaus Hautsch is a full Professor at the Faculty of Economics, Institute of Statistics and Operations Research. His work focuses on econometrics, finance, and high-frequency data analysis. Research Interests : Market microstructure, volatility modeling, transaction costs, systemic risk, and machine learning applications in finance. Publication Trends (2025–2018): 2025: High-dimensional portfolio optimization, dynamic systemic risk 2024: Blockchain asset arbitrage, DeFi, polarization metrics, jump detection 2023–2022: Microstructural noise, volatility forecasting, neural networks Scientific Awards : Fellow of the Society for Financial Econometrics (2014) Projects : Artificial Intelligence in Rowing (2022–2025) Vienna Graduate School of Finance (2018–2022) Risk management of CCPs

Hailiang ChenView profile

Professor

• Artificial Intelligence
• FinTech
• Social Media
• +8 more

Hailiang Chen serves as Professor in Innovation and Information Management, Assistant Dean (Taught Postgraduate), and Director of the Artificial Intelligence Research Institute at HKU Business School, The University of Hong Kong. His academic journey includes a PhD and MS from Purdue University and a BM from Tsinghua University. Doctoral Degree: Management Information Systems, Purdue University Master Degree: Economics, Purdue University Bachelor Degree: Information Management and Information Systems, Tsinghua University Professor Chen's research spans artificial intelligence, FinTech, social media analytics, and platform economics, with significant contributions to understanding how digital interactions shape financial markets and consumer behavior. His work frequently examines the intersection of technology adoption and economic outcomes, particularly in cryptocurrency markets, live-stream commerce, and venture capital decision-making. His research methodology combines large-scale data analysis with experimental designs to uncover causal relationships in digital ecosystems. His publications in elite journals like Journal of Financial Economics and Management Science demonstrate consistent impact, with multiple ESI Highly Cited Papers. Current projects include Gov-RAG for e-government services and comparative studies of AI search tools. His research has received continuous funding from Hong Kong's Research Grants Council for five consecutive years (2019-2023). Faculty Outstanding Researcher Award, HKU Business School (2022-23) INFORMS ISS Sandra A. Slaughter Early Career Award (2022) Association for Information Systems Early Career Award (2019) Three ESI Highly Cited Papers (Top 1% in field) Professor Chen actively contributes to academic service as Associate Editor for Journal of Management Information Systems and MIS Quarterly , and serves as Program Chair for the International Conference on Smart Finance. His industry collaborations include Alibaba, HSBC, and China Construction Bank, bridging academic research with real-world business applications in AI implementation and digital transformation.

Wing Wah ThamView profile

Associate Professor

• Econometrics
• Market Microstructure
• Asset Pricing
• +3 more

Associate Professor Wing Wah Tham holds a position at the School of Banking and Finance within the University of New South Wales (UNSW) Business School . He also maintains affiliations as a Marie Skłodowska-Curie Fellow and a Tinbergen Institute Fellow . Previously, he served as an Associate Professor of Financial Econometrics at the Econometric Institute, Erasmus School of Economics (2014–2016) and as an Assistant Professor there (2009–2014). Education : PhD, University of Warwick MSc, Imperial College, UK BEng, Nanyang Technological University of Singapore Research Interests : Professor Tham specializes in econometrics , market microstructure , asset pricing , and financial innovation . His work bridges quantitative finance with applications in artificial intelligence and sustainable development goals (SDG) , focusing on reproducibility, liquidity dynamics, and policy impacts. Recent Publications span high-impact journals like Journal of Finance , Management Science , and Review of Financial Studies , addressing topics such as nonstandard errors , AI for SDG mapping , and toxic arbitrage . These works are frequently presented at American Finance Association and Econometric Society meetings. Scientific Honors : PanAngora Crowell Second Prize Midwest Finance Association Best Paper Award Literati Network Awards for Excellence ARC Discovery Projects Grant Additional Activities : He has served as a visiting scholar at the Haas Business School, UC Berkeley and National University of Singapore , with interests in triathlons and ocean swimming.

• Corporate Finance
• Corporate Governance
• Financial Technology (Fintech)
• +9 more

Gilles Chemla is a Professor of Finance at Imperial College Business School and Co-Director of the Centre for Financial Technology. He holds affiliations with the Centre National de la Recherche Scientifique (CNRS), the Centre for Economic Policy Research (CEPR), and the Rimini Centre for Economic Analysis (RCEA). His academic journey includes a PhD in Economics from the London School of Economics, an MSc from Paris School of Economics, and an engineering degree from Ponts Paristech. Chemla’s research focuses on corporate finance, fintech innovation, causal inference methodologies, and corporate governance. His work bridges theoretical economics with practical applications in finance, energy markets, and policy. Recent articles explore AI adoption in finance, crowdfunding dynamics, and executive compensation dynamics. He has contributed to top journals like Journal of Financial Economics and Journal of Empirical Finance . Education: PhD in Economics, London School of Economics (1996) MSc in Economics, Paris School of Economics (1992) Engineering Degree in Mathematics and Economics, École des Ponts ParisTech (1992) Awards: Multiple teaching prizes (unspecified). Professional Roles: Non-executive director roles in corporations and financial institutions Associate Editor, Journal of Empirical Finance Chemla’s research also addresses interdisciplinary topics such as medical trial biases and policy-relevant experiments. He advises on fintech innovation, financial regulation, and energy market dynamics, leveraging mathematical modeling and data science expertise.

Mawuli Kouami SegnonView profile

Researcher

• Econometrics
• Time Series Analysis
• Financial Forecasting
• +5 more

Dr. Mawuli Kouami Segnon is a researcher at the Chair of Empirical Economics, School of Business and Economics, University of Münster. His work focuses on econometric modeling, financial time series analysis, and volatility forecasting across various domains including cryptocurrencies, energy markets, and macroeconomic indicators. Research interests include: Development of advanced volatility models (GARCH, multifractal, regime-switching) Applications to financial markets, energy economics, and macroeconomic policy High-frequency data analysis and mixed-frequency forecasting Count data modeling with conditional heteroscedasticity Portfolio risk management using copula and multifractal approaches Recent publications demonstrate expertise in: Geopolitical risk impacts on stock volatility Comparative analysis of realized variance measures Inflation uncertainty modeling in G7 countries Electricity price volatility in Australian markets Bitcoin market forecasting Historical economic data analysis Current projects (since 2020) involve: Innovative economic/financial time series forecasting Financial market volatility modeling Applications of multifractal structures in econometrics

• Household Finance
• Asset Pricing
• Financial Frictions
• +7 more

Alex Michaelides is a Professor of Finance at Imperial College Business School since 2013 and a Research Fellow at CEPR (International Macroeconomics and Financial Economics Programmes). He holds affiliations with Imperial's Energy Futures Lab and Language and Communication Intelligence initiatives. Previously, he served at the London School of Economics (LSE) as Lecturer (2001-2006) and Associate Professor (2006-2010), and at the University of Cyprus as Professor of Finance (2010-2014). He earned his PhD in Economics from Princeton University (1997) and BA in Economics from Harvard (1993). His research focuses on household finance (e.g., life-cycle portfolio choice), asset pricing with heterogeneous agents, housing markets, and macro-finance intersections. Notable work includes analyses of pension systems, sovereign debt crises (e.g., Cyprus), and banking system dynamics. He has held visiting roles at the Federal Reserve Bank of New York, ECB, and served on the Central Bank of Cyprus' board (2013-2014). Leadership roles include Head of Finance at Imperial (2014-2021) and Director of Postgraduate Studies (since 2023). His work bridges theoretical models with policy-relevant insights, addressing fiscal procyclicality, central bank independence, and climate regulatory impacts.

• Banking and Financial Intermediation
• Monetary Policy
• Corporate Finance
• +6 more

Prof. Farzad Saidi is a Professor in the Department of Economics at the University of Bonn. He is affiliated with the Institute for Macroeconomics and Econometrics, ECONtribute, and the CRC TR 224 EPoS research centers. His work focuses on financial intermediation, monetary policy, corporate finance, and labor markets. He has contributed extensively to understanding bank behavior under negative interest rates, the effects of financial regulation, and the interplay between social preferences and economic outcomes during crises. Research highlights include analyzing strategic communication among banks, the impact of regulatory changes on insurance industries, and the role of social preferences in pandemic responses. His awards include the Jensen Prize for outstanding corporate finance research. Current projects explore credit supply dynamics, data-driven business cycles, and the macroeconomic implications of financial sanctions. Prof. Saidi's work spans theoretical and empirical domains, addressing topics such as banking structure, liquidity management, and the transmission of monetary policy. His affiliations with interdisciplinary centers reflect a commitment to collaborative, policy-relevant research.

• Financial Economics
• High-Frequency Trading
• Blockchain Technology
• +3 more

Konstantin Sokolov is a Professor at the University of Memphis, affiliated with the Fogelman College of Business and Economics. His research bridges finance, technology, and market design, focusing on liquidity dynamics, high-frequency trading, and blockchain systems. University of Memphis - Fogelman College of Business and Economics Collaborations with institutions across the U.S. and globally His work explores market microstructure , cryptocurrency , and cybersecurity , with recent studies on ransomware impacts, gamification in finance, and blockchain governance. Articles show a trend toward integrating machine learning and network theory into financial systems. Key themes include: Liquidity and volatility in algorithmic trading environments Blockchain congestion and cybersecurity externalities Regulatory implications of fast trading technologies Behavioral responses to gamified financial platforms

• Management Science
• Operations Management
• Mathematical & Quantitative Methods
• +8 more

Professor Lilian M. de Menezes is a Professor of Decision Sciences at Bayes Business School within the Faculty of Management at City St George's, University of London. Her expertise spans Management Science, Operations Management, Statistics, and Energy Markets. She holds a BSc and MSc from Pontifícia Universidade Católica in Brazil and a PhD from London Business School. Lilian's research focuses on forecasting methodologies, energy market integration, quality management, and flexible work arrangements. She has contributed to studies on healthcare performance measurement, workforce optimization, and sustainability in business excellence models. Her work bridges academic research with practical applications in industries like energy and healthcare. She has received notable awards, including the 2015 EEX Excellence Award for Melanie Houllier's thesis on European electricity markets and the 2014 Best Paper Award for analyzing job satisfaction and quality management links. Her affiliations include the European Operations Management Association and the Royal Statistical Society. Lilian's research also explores flexible working policies and their impact on employee performance and well-being. Her recent work addresses challenges in energy market forecasting and sustainable organizational practices. She maintains a visiting appointment at ESCP Europe’s Research Centre for Energy Management.

Hyejin KuView profile

Professor

• Mathematical Finance
• Machine Learning
• Risk Management
• +4 more

Hyejin Ku is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her research focuses on the intersection of Mathematical Finance and Machine Learning, addressing challenges in risk measurement, portfolio optimization, and quantitative finance. She develops advanced mathematical models to enhance decision-making through reinforcement learning and data analytics. Notable projects include novel algorithms for credit rating prediction using neural networks and sequence-based clustering for credit risk assessment. Her work integrates applied mathematics with real-world financial applications, such as systemic risk reduction in multi-layer networks and option pricing under liquidity constraints. She holds a prominent position in mathematical finance, contributing to both theoretical advancements and practical solutions for financial markets. Her research trends emphasize interdisciplinary approaches, combining machine learning techniques with financial modeling to solve complex problems in risk management and asset valuation. Her publications span over two decades, showcasing contributions to portfolio optimization, derivatives pricing, and computational finance. Dr. Ku is affiliated with York University’s Department of Mathematics and Statistics, where she contributes to academic leadership and research mentorship. Her office is located in DB 2025, and she can be reached at hku@yorku.ca.

Georgy ChabakauriView profile

Associate Professor

• Asset Pricing
• Portfolio Choice
• Risk Management
• +2 more

Dr. Georgy Chabakauri is an Associate Professor in the Department of Finance at the London School of Economics and Political Science. With a PhD in Finance from London Business School and a PhD in Mathematics from Moscow State University, his research focuses on asset pricing under frictions, portfolio choice, and risk management. His primary research interests include: Dynamic equilibrium models with heterogeneous investors Effects of portfolio constraints on asset prices Collateral requirements in financial markets Information asymmetry in multi-asset environments Mean-variance optimization techniques Chabakauri's publications demonstrate consistent focus on theoretical asset pricing, with recent work exploring informational efficiency, index investing effects, and cross-sectional return drivers. His articles frequently incorporate equilibrium modeling and empirical validation across different market conditions. Awards and honors: European Finance Association Best Conference Paper (2016) SFS Finance Cavalcade Best Paper in Asset Pricing JEDC Outstanding Referee Award Russian Academy of Sciences Medal for Best Student Paper (2001) He teaches core finance courses including Corporate Finance, Investments, and Asset Markets at both undergraduate and graduate levels, and serves as reviewer for leading finance journals.