Empirical Finance

A New Traders’ Game? – Empirical Analysis of Response Functions in a Historical Perspective ArXiv ID: 2503.01629 “View on arXiv” Authors: Unknown Abstract Traders on financial markets generate non-Markovian effects in various ways, particularly through their competition with one another which can be interpreted as a game between different (types of) traders. To quantify the market mechanisms, we empirically analyze self-response functions for pairs of different stocks and the corresponding trade sign correlators. While the non-Markovian dynamics in the self-responses is liquidity-driven, it is expectation-driven in the cross-responses which is related to the emergence of correlations. We empirically study the non-stationarity of these responses over time. In our previous data analysis, we only investigated the crisis year 2008. We now considerably extend this by also analyzing the years 2007, 2014 and 2021. To improve statistics, we also work out averaged response functions for the different years. We find significant variations over time revealing changes in the traders’ game. ...

Revisiting Cont’s Stylized Facts for Modern Stock Markets ArXiv ID: 2311.07738 “View on arXiv” Authors: Unknown Abstract In 2001, Rama Cont introduced a now-widely used set of ‘stylized facts’ to synthesize empirical studies of financial price changes (returns), resulting in 11 statistical properties common to a large set of assets and markets. These properties are viewed as constraints a model should be able to reproduce in order to accurately represent returns in a market. It has not been established whether the characteristics Cont noted in 2001 still hold for modern markets following significant regulatory shifts and technological advances. It is also not clear whether a given time series of financial returns for an asset will express all 11 stylized facts. We test both of these propositions by attempting to replicate each of Cont’s 11 stylized facts for intraday returns of the individual stocks in the Dow 30, using the same authoritative data as that used by the U.S. regulator from October 2018 - March 2019. We find conclusive evidence for eight of Cont’s original facts and no support for the remaining three. Our study represents the first test of Cont’s 11 stylized facts against a consistent set of stocks, therefore providing insight into how these stylized facts should be viewed in the context of modern stock markets. ...

The Link between Fama-French Time-Series Tests and Fama-Macbeth Cross-Sectional Tests ArXiv ID: ssrn-1271935 “View on arXiv” Authors: Unknown Abstract Many papers in the empirical finance literature implement tests of asset pricing models either via Fama-French time-series regressions or via Fama-Macbeth cros Keywords: Asset Pricing Models, Fama-French Regressions, Fama-MacBeth Regressions, Empirical Finance, Cross-Sectional Returns, Equity Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper’s mathematical framework relies on established econometric and asset pricing models, which are advanced but not unusually dense; however, it heavily emphasizes empirical implementation, using real financial data and detailed testing methodologies. flowchart TD A["Research Goal:<br>Test Asset Pricing Models"] --> B{"Choose Methodology"} B --> C["Fama-French Time-Series<br>Regressions"] B --> D["Fama-MacBeth Cross-Sectional<br>Regressions"] C --> E["Input: Time-Series Data<br>Portfolio Returns & Factors"] E --> F["Compute: Regression<br>R_it - R_ft = α_i + β_i<br>Factor_t + ε_it"] D --> G["Input: Cross-Sectional Data<br>Cross-Section of Returns<br>at Each Time t"] G --> H["Compute: Regress Returns<br>on Risk Factors<br>Across Assets at Each t"] F --> I["Key Finding:<br>Link & Equivalence<br>Under Null Hypothesis"] H --> I style A fill:#e1f5fe style I fill:#f3e5f5