I am affiliated with Montpellier Business School (France) and Poznan University of Economics and Business (Poland) as an associate professor of finance. 

My research interests encompass empirical asset pricing, with a particular focus on the cross-section of stock returns. I also work on cryptocurrencies, machine learning, and long-run historical data.

Montpellier Business School

2300 Avenue des Moulins

34080 Montpellier, France

Poznań University of Economics and Business

al. Niepodległości 10

61-875 Poznań, Poland 

E-mail: a.zaremba@montpellier-bs.com, adam.zaremba@ue.poznan.pl

Research: Google Scholar, SSRN, Scopus, Web of Science, ORCiD

Socials: LinkedIn, Twitter

My curriculum vitae.


Journal: I assumed the role of Editor-in-Chief at Modern Finance, a new open access journal in finance. 

Conference: The paper "Predicting Returns with Machine Learning Across Horizons, Firms Size, and Time," which is a joint work with Nusret Cakici (FU), Christian Fieberg (CUAS), and Daniel Metko (UB), will be presented at the 2023 KAFE-SKKU International Conference on Finance and Economics in Seoul, Korea.


Working Papers

Joint work with Nusret Cakici (FU), Syed Jawad Hussain Shahzad (MBS), and Barbara Będowska-Sójka (PUEB). 

Abstract: We employ a repertoire of machine learning models to investigate the cross-sectional re-turn predictability in cryptocurrency markets. While all methods generate substantial economic gains—unlike in other asset classes—the benefits from model complexity are limited. Return predictability derives mainly from a handful of simple characteristics, such as market price, past alpha, illiquidity, and momentum. Contrary to the stock market, abnormal returns in cryptocurrencies originate from the long leg of the trade and persist over time. Furthermore, despite high portfolio turnover, most machine learning strategies remain profitable after trading costs. However, alphas are concentrated in hard-to-trade assets and critically depend on harvesting extreme returns on small, illiquid, and volatile coins.

Presentations: FFEA Ghent 2023, 43rd EBES, John von Neumann University, Prince Sultan University, 

Online application visualizes the performance of the ML strategies tested in the paper.

Joint work with Nusret Cakici (FU), Christian Fieberg (CUAS), and Daniel Metko (UB).

Abstract: Does factor momentum drive the stock price momentum? Inspired by the recent findings from the United States, we revisit this relationship across 51 markets. The factor momentum effect remains strong—both within and across countries—regardless of typical drivers of return predictability. However, its ability to capture the stock momentum profits depends fundamentally on methodological and dataset choices. Consequently, the factor momentum cannot robustly subsume the stock momentum in global markets. On the contrary, the latter explains the former better than vice versa. Our conclusions challenge the view that momentum only times other factors rather than constituting a distinct anomaly.

Presentations: FMA European Conference 2023.

Journal Publications

Published in  Review of Finance (2023, in press).

Joint work with Nusret Cakici (FU), Christian Fieberg (CUAS), and Daniel Metko (UB).

Abstract: Using new data from U.S. and global markets, we revisit market risk premium predictability by equity anomalies. We apply a repertoire of machine learning methods to 42 countries to reach a simple conclusion: anomalies, as such, cannot predict aggregate market returns. Any ostensible evidence from the U.S. 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.

Published in  Journal of Economic Dynamics and Control (2023, 148, 104618).

Joint work with Nusret Cakici (FU), Christian Fieberg (CUAS), and Daniel Metko (UB).

Abstract: We 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.

Presentations: Technical University of Munich, Robeco, Ono Academic College, American University of Sharjah, American University in Dubai.

Published in Journal of Economic Dynamics and Control (2023, 148, 104618).

Joint work with Mardy Chiah (NBS), Huaigang Long (ZUFE), and Zaghum Umar (ZU).

Abstract: Using the change in the real effective exchange rate (REER) to reflect trade competitiveness, we examine its role in the cross-section of global equity returns. The changes in REER negatively affect stock market returns. The REER effect is robust after controlling for known risk factors and market characteristics. Furthermore, it remains pervasive across different periods and subsamples. Our findings support the conventional wisdom that appreciating currency harms trade values, consequently dampening a firm's stock market performance.

Published in Journal of Banking and Finance  (2023, 149, 106760).

Joint work with Nusret Cakici (FU).

Abstract: We hypothesize that local economic discomfort influences investors’ risk aversion, leading to cross-sectional variation in risk premia in segmented equity markets. To test this assertion, we employ the misery index (MI)—which aggregates both unemployment and inflation rates—as a gauge of macroeconomic welfare. Using six decades of data from 69 markets, we demonstrate that economic discomfort reliably predicts cross-sectional stock returns. A quartile of countries with the highest MI outperforms those with the lowest by 0.74% per month. The effect prevails in markets where prices are set locally and gradually declines over time as markets become more integrated.

Published in Journal of Economic Dynamics and Control  (2023, 147, 104596).

Joint work with Nusret Cakici (FU), Robert J. Bianchi (GU), and Huaigang Long (ZUFE).

Abstract: Interest rate changes typically affect equity values. However, if investors react slowly, the repricing may stretch over time. Using a century of data from sixty countries, we demonstrate that past interest rate changes predict the cross-section of equity returns worldwide. The quintile of stock markets with the highest change in government bond yields underperforms the countries with the lowest change by 0.76% per month. The phenomenon is distinctly robust and cannot be explained by known risk factors. Furthermore, the low correlation with other return patterns paves the way for effective country allocation strategies.

Published in Journal of Financial Economics (2022, 146(2), 689-725).

Joint work with Nusret Cakici (FU).

Abstract: Motivated by existing evidence of the salience theory (ST) effect in the United States, we investigate its importance in 49 countries over the past three decades. Initial results suggest a negative relationship between the ST measure and future returns. The underperformance of low ST stocks is the strongest in countries with high idiosyncratic risk. However, the salience effect has three vital limitations. First, a substantial part of the anomaly can be attributed to the short-term return reversal. Second, it is priced primarily among microcaps. Third, the premium is realized predominantly following severe down markets and volatility spikes. Outside of microcaps and extreme market conditions, the salience effect does not exist.

Published in Journal of Financial Markets (2022, 61, 100736).

Joint work with Huaigang Long (ZUFE), Wenyu Zhou (ZU), and Elie Bouri (LEA).

Abstract: Leading economic indicators assist in forecasting future business conditions. Can they also predict aggregate stock returns? To answer this question, we examine six decades of data from 39 countries. Short-term changes in the composite leading indicator (CLI) positively correlate with future stock returns in the cross-section. The quintile of markets with the highest CLI increase outperforms the quintile with the lowest CLI change by 1.43% per month. The predictive power of the CLI survives multiple robustness checks and cannot be absorbed by established risk factors. Our findings imply an exploitable investment strategy that can be pursued with exchange-traded funds.

Published in Journal of Financial Stability (2022, 58, 100964).

Joint work with Nusret Cakici (FU), Ender Demir (RU), and Huaigang Long (ZUFE).

Abstract: Using a news-based gauge of geopolitical risk, we study its role in asset pricing in global emerging markets. We find that changes in risk positively predict future stock returns. The countries with the highest increase in geopolitical uncertainty outperform their counterparts with the lowest change by up to 1% per month. The anomaly is not explained by other established asset pricing effects and remains robust to many considerations. We link the observed phenomenon with investor overreaction to geopolitical news driven by the availability bias.

Published in Journal of Banking and Finance (2021, 133, 106238).

Joint work with Robert J. Bianchi (GU) and Mateusz Mikutowski (PUEB).

Abstract: We perform the longest study of long-run reversal in commodity returns. Using a unique dataset of 52 agricultural, industrial, and energy commodities, we examine the price behavior for the years 1265 to 2017. The findings reveal a strong and robust long-run reversal effect. The returns of the past one to three years negatively predict subsequent performance in the cross-section of returns. The effect is robust to extensive subsample and subperiod analysis and is not driven by statistical biases, extreme events, or macroeconomic risks. Our findings support the explanation that the long-term reversal originates from supply and demand adjustments following price changes. Finally, the phenomenon is elevated in more volatile commodities and in periods of high return dispersion.

Published in Tourism Manaeement (2021, 84, 104281).

Joint work with Tomasz Kaczmarek (PUEB), Katarzyna Perez (PUEB), and Ender Demir (RU),

Abstract: What protects travel and leisure companies from a global pandemic, such as COVID-19? To answer this question, we investigate data on over 1200 travel and leisure companies in 52 countries. We consider 80 characteristics, such as company financial ratios, macroeconomic variables, and government policy responses. Using regressions and machine learning tools, we demonstrate that firms with low valuations, limited leverage, and high investments have been more immune to the pandemic-induced crash. We also find a beneficial effect of stringent containment and closure policies. Finally, our results indicate that countries with less individualism may be better positioned to cope with the pandemic. Our findings have implications for regulatory bodies, managers, and investors concerning future pandemic outbreaks.

Published in Journal of Banking and Finance (2020, 121, 105966).

Joint work with Mehmet Umutlu (ENU) and Alina Maydybura (RIT).

Abstract: We are the first to demonstrate the decline in the cross-sectional predictability of country and industry returns in recent years. We examine 53 anomalies in country and industry indices from 64 markets for the years 1973–2018. The profitability of the strategies has significantly decreased recently, driven particularly by the disappearance of value and reversal effects. The phenomenon is strongest in large developed markets. Neither changes in country- and industry-specific risks, nor investor learning from the academic literature can explain the effect. Our findings support the view that the fall in return predictability is caused by the overall improvement in market efficiency.

Published in Journal of Empirical Finance (2020, 55, 177-199).

Joint work with Renatas Kizys (US) Muhammad Wajid Raza (RIT).

Abstract: We perform the most comprehensive test of long-term reversal in national equity indices ever done. Having examined data from 71 countries for the years 1830 through 2019, we demonstrate a strong reversal pattern: the past long-term return negatively predicts future performance. The phenomenon is not subsumed by other established cross-sectional return patterns, including the value effect. The long-term reversal is robust to many considerations but highly unstable through time. Finally, our findings support the overreaction explanation of this anomaly.

Published in Journal of Banking and Finance (2019, 98, 80-94).

Abstract: We are the first to document the cross-sectional return seasonality effect in international government bonds. Using a variety of tests, we examine fixed-income securities from 22 countries for the years 1980–2018. The bonds with high (low) returns in the same-calendar month in the past continue to overperform (underperform) in the future. The effect is robust to many considerations, including controlling for established predictors of bond returns. Our results support the behavioural story of the anomaly, demonstrating its highest profitability in periods of elevated investor sentiment and in the market segments wotjstrong limits to arbitrage. Nonetheless, investment application of bond seasonality might be challenging due to high trading costs and the required short holding periods.