Liedtke, Gerrit
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Liedtke, Gerrit
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Liedtke, Gerrit
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Item type:Publication, A Trend Factor for the Cross Section of Cryptocurrency Returns(Cambridge University Press, 2025-07); ; ; ; We propose CTREND, a new trend factor for cryptocurrency returns, which aggregates price and volume information across different time horizons. Using data on more than 3,000 coins, we employ machine learning methods to exploit information from various technical indicators. The resulting signal reliably predicts cryptocurrency returns. The effect cannot be subsumed by known factors and remains robust across different subperiods, market states, and alternative research designs. Moreover, it survives the impact of transaction costs and persists in big and liquid coins. Finally, an asset pricing model that incorporates CTREND outperforms competing factor models, providing a superior explanation of cryptocurrency returns.journal articleBand:60Heft:751 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cryptocurrency factor momentumIs there a momentum effect in cryptocurrency anomalies? To answer this, we analyze data from over 3900 coins spanning the years 2014 to 2022 and replicate 34 anomalies in the cross-section of cryptocurrency returns. We document a discernible pattern in factor premia: past winners consistently outperform losers. The effect persists across subperiods, withstands various methodological approaches, and its magnitude parallels that of its stock market counterpart. However, the autocorrelation in factor returns is not widespread and primarily stems from size and volatility anomalies. Additionally, unlike in stocks, cryptocurrency factor momentum originates from price momentum, which subsequently transfers to the factor level.journal articleBand:23Heft:12134 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cryptocurrency anomalies and economic constraintsThe asset pricing literature documents a growing list of predictable patterns in the cross-section of cryptocurrency returns. But can they be forged into viable trading profits? We answer this question by examining the interplay between economic restrictions and return predictability in cryptocurrency markets. We find that size and volume anomalies originate from micro-cap coins of negligible economic importance. Conversely, the momentum effect prevails in larger cryptocurrencies but incurs substantial trading costs and extracts alphas largely from short positions. Most abnormal returns occur primarily in bull markets and fade over time. Therefore, protocols for identifying tradable cryptocurrency anomalies should focus on long positions, account for transaction costs, consider hard-to-trade coins, and emphasize performance in recent years.journal articleHeft:9469 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Recurrent double-conditional factor model(Springer Science and Business Media LLC, 2025); ; In economic applications, the behavior of objects (e.g., individuals, firms, or households) is often modeled as a function of microeconomic and/or macroeconomic conditions. While macroeconomic conditions are common to all objects and change only over time, microeconomic conditions are object-specific and thus vary both among objects and through time. The simultaneous modeling of microeconomic and macroeconomic conditions has proven to be extremely difficult for these applications due to the mismatch of dimensions, potential interactions, and the high number of parameters to estimate. By marrying recurrent neural networks with conditional factor models, we propose a new white-box machine learning method, the recurrent double-conditional factor model (RDCFM), which allows for the modeling of the simultaneous and combined influence of micro- and macroeconomic conditions while being parsimoniously parameterized. Due to the low degree of parameterization, the RDCFM generalizes well and estimation remains feasible even if the time-series and the cross-section are large. We demonstrate the suitability of our method using an application from the financial economics literature.journal articleBand:476 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A factor model for the cross-section of country equity risk premiaWe employ instrumented principal component analysis (IPCA) to provide a new factor model for the cross-section of country equity risk premia. Using data from 71 equity markets, we identify latent factors and condition betas on a comprehensive set of accounting and market characteristics from the finance literature. A four-factor conditional asset pricing model best captures the variation in country returns, beating prominent factor models. IPCA’s superior performance stems primarily from its enhanced ability to predict emerging market returns while also generalizing well to developed markets. Among the global “signal zoo”, size, momentum, volatility, political risk, and valuation are the most important predictors of return differences.journal articleBand:1718 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Recurrent double-conditional factor modelIn economic applications, the behavior of objects (e.g., individuals, firms, or households) is often modeled as a function of microeconomic and/or macroeconomic conditions. While macroeconomic conditions are common to all objects and change only over time, microeconomic conditions are object-specific and thus vary both among objects and through time. The simultaneous modeling of microeconomic and macroeconomic conditions has proven to be extremely difficult for these applications due to the mismatch of dimensions, potential interactions, and the high number of parameters to estimate. By marrying recurrent neural networks with conditional factor models, we propose a new white-box machine learning method, the recurrent double-conditional factor model (RDCFM), which allows for the modeling of the simultaneous and combined influence of micro- and macroeconomic conditions while being parsimoniously parameterized. Due to the low degree of parameterization, the RDCFM generalizes well and estimation remains feasible even if the time-series and the cross-section are large. We demonstrate the suitability of our method using an application from the financial economics literature.journal article37