Zaremba, Adam
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Zaremba, Adam
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Zaremba, Adam
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Item-typ:Veröffentlichung, Factor momentum versus price momentum: Insights from international marketsDoes factor momentum drive stock price momentum? We examine this relationship across 51 countries. Factor momentum proves strong across many markets and international portfolios, independent of typical return predictability drivers. However, its ability to capture stock momentum profits depends on methodological and dataset choices. Empirical factor momentum cannot entirely subsume stock or industry momentum globally. Conversely, price momentum often better explains its factor counterpart than vice versa. Notably, factor momentum based on principal components is more robust, capturing a major share of price momentum gains in developed and emerging markets. Our findings challenge the view that momentum merely times other factors rather than constituting a distinct anomaly.Wissenschaftlicher ArtikelHeft:170102 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Do Anomalies Really Predict Market Returns? New Data and New Evidence(Oxford University Press (OUP), 2024); ; ; Using new data from US and global markets, we revisit market risk premium predictability by equity anomalies. We apply a repertoire of machine-learning methods to forty-two countries to reach a simple conclusion: anomalies, as such, cannot predict aggregate market returns. Any ostensible evidence from the USA lacks external validity in two ways: it cannot be extended internationally and does not hold for alternative anomaly sets—regardless of the selection and design of factor strategies. The predictability—if any—originates from a handful of specific anomalies and depends heavily on seemingly minor methodological choices. Overall, our results challenge the view that anomalies as a group contain helpful information for forecasting market risk premia.Wissenschaftlicher Artikel105 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, 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.Wissenschaftlicher ArtikelBand:1718 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Predicting Returns with Machine Learning across Horizons, Firm Size, and TimeResearchers and practitioners hope that machine learning strategies will deliver better performance than traditional methods. But do they? This study documents that stock return predictability with machine learning depends critically on three dimensions: forecast horizon, firm size, and time. It works well for short-term returns, small firms, and early historical data; however, it disappoints in opposite cases. Consequently, annual return forecasts have failed to produce substantial economic gains within most of the US market in the past two decades. These findings challenge the practical utility of predicting returns with machine learning models.Wissenschaftlicher ArtikelBand:5Heft:4206 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Non-standard errors in the cryptocurrency worldMotivated by recent findings from the equity market, we investigate non-standard errors in cryptocurrency research. We examine ten prevalent decisions related to data sources, sample preparation, and portfolio construction, generating 20,736 research designs for 43 sorting variables. Our findings reveal remarkable variation in portfolio performance tied to seemingly trivial choices. The non-standard errors in cryptocurrency studies not only surpass those in the stock market but also clearly exceed standard errors—though varying considerably across coin characteristics. Notwithstanding the above, the most prominent cryptocurrency factors, such as size and momentum, remain consistently robust across numerous specifications. Lastly, we find that reducing the influence of the smallest coins effectively decreases the non-standard errors.Wissenschaftlicher ArtikelHeft:9235 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Pockets of Predictability: A Replication(Wiley, 2025-08); ; Farmer, Schmidt, and Timmermann (FST) document time-variation in market return predictability, identifying “pockets” of significant predictability through kernel regressions. However, our analysis reveals a critical discrepancy between the method outlined by FST and the code actually implemented. Instead of using a one-sided kernel, which guarantees out-of-sample forecasts, they perform in-sample estimation with a two-sided kernel. As a result, future information leaks into the forecasting model, undermining its reliability. Rectifying this error qualitatively alters the findings, invalidating most conclusions of the FST study. Thus, attempts to exploit such “pockets”—should they exist—offer little help in forecasting market returns.Wissenschaftlicher ArtikelBand:80Heft:619 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Machine learning goes global: Cross-sectional return predictability in international stock marketsWe examine return predictability with machine learning in 46 stock markets around the world. We calculate 148 firm characteristics and use them to feed a repertoire of different models. The algorithms extract predictability mainly from simple yet popular factor types—such as momentum, reversal, value, and size. All individual models generate substantial economic gains; however, combining them proves particularly effective. Despite the overall robustness, the machine learning performance depends heavily on firm size and availability of recent information. Furthermore, it varies internationally along two critical dimensions: the number of listed firms in the market and the average idiosyncratic risk limiting arbitrage.Wissenschaftlicher ArtikelHeft:15579 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Cross-country factor momentumWe study a new class of the momentum effect: cross-country factor momentum. We document a persistent international pattern: factors in winning countries consistently outperform those in losing countries. The effect holds across most anomalies and is robust to many considerations.Wissenschaftlicher ArtikelHeft:23532 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Factor momentum versus price momentum: Insights from international marketsDoes factor momentum drive stock price momentum? We examine this relationship across 51 countries. Factor momentum proves strong across many markets and international portfolios, independent of typical return predictability drivers. However, its ability to capture stock momentum profits depends on methodological and dataset choices. Empirical factor momentum cannot entirely subsume stock or industry momentum globally. Conversely, price momentum often better explains its factor counterpart than vice versa. Notably, factor momentum based on principal components is more robust, capturing a major share of price momentum gains in developed and emerging markets. Our findings challenge the view that momentum merely times other factors rather than constituting a distinct.Wissenschaftlicher ArtikelBand:17012 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns(With Intelligence, 2025-04); ; ; The article examines the cross-sectional predictability of factor returns. Using machine learning techniques, the authors extract information from a comprehensive set of factor characteristics measuring their past returns, risks, and spreads. Applying this method to a repertoire of US stock market anomalies, they find robust predictability in their performance. The decile of factors with the highest expected return outperforms those with the worst outlook by up to 1.39% per month. The alphas are robust to many considerations but gradually decline over time. Predictability is mainly driven by factor momentum, which captures most of the cross-sectional variation in anomaly returns.Wissenschaftlicher ArtikelBand:51Heft:678