Where have the profits gone? Market efficiency and the disappearing equity anomalies in country and industry returns

Zaremba, Adam; Umutlu, Mehmet; Maydybura, Alina

doi:10.1016/j.jbankfin.2020.105966

Cited by 33 publications

(7 citation statements)

References 72 publications

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“…Evidence for the case is inconclusive, and it may be that we will never honestly know if ESG investing is profitable, given that financial markets are increasingly more efficient, agents adapt to perceived or fundamental changes to their portfolios. Market anomalies tend to disappear after some time (Zaremba et al, 2020), which matters because if the incorporation of ESG information does not generate expected excess financial returns, there must be other forces that speed up ESG diffusion. One such force is investors' preferences.…”

Section: Methodology and Resultsmentioning

confidence: 99%

“…given that financial markets are increasingly more efficient, agents adapt to perceived or fundamental changes to their portfolios. Market anomalies tend to disappear after some time (Zaremba et al, 2020) Of course, group members may be myopic about their reality. The social investment industry is growing exponentially and can be at the cusp of a tipping point (Riding, 2020), with their funds' days of non-ESG investing being numbered.…”

Section: Fiduciary Dutymentioning

confidence: 99%

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Why don't asset managers accelerate ESG investing? A sentiment analysis based on 13,000 messages from finance professionals

Zeidan

2022

Bus Strat Env

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Mitigating climate change effects requires investors (and their proxies, fund managers) to shift their business‐as‐usual strategies. This article analyzes Environmental, Social, and Governance (ESG) investing behavior through more than 13,000 messages exchanged by finance professionals from 2017 to 2020. There is a consensus that low and high ESG firms' discrimination is a necessary but not sufficient condition for ESG investing. Moreover, it is one thing for fund managers to claim that they are integrating ESG factors in their portfolios and another to do it properly. The restriction of the strategy space, internal and external transaction costs, and data quality are still viewed as overwhelming obstacles for seamlessly integrating ESG into financial portfolios. Sentiment analysis indicates that asset managers hold a negative view of ESG investing, surprising as ethical investing is becoming increasingly common. Unlocking the potential positive externalities from ESG investing may require regulators and investors' actions to improve the quality of information disclosure and pressure fund managers into incorporating nonfinancial criteria in their investment models.

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Section: Methodology and Resultsmentioning

confidence: 99%

Section: Fiduciary Dutymentioning

confidence: 99%

Why don't asset managers accelerate ESG investing? A sentiment analysis based on 13,000 messages from finance professionals

Zeidan

2022

Bus Strat Env

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“…Investors investing in stocks must consider the risks that will occur. According to Boussaidi (2017) and Zaremba et al (2020), investing in loser stocks tends to be riskier than a portfolio of winner stocks. This study's results are consistent with De findings regarding market overreaction caused by the emergence of investor overreaction to information.…”

Section: Discussionmentioning

confidence: 99%

“…The financial behavior of investors on stock market anomalies reflects the rationality that investors have limited and leads to irrationality. With such limitations, investor decisions cannot be easily arbitrage d. Limited rationality presents inefficient market conditions (Woo et al, 2020;Zaremba, Umutlu, & Maydybura, 2020).…”

Section: Literature Review and Hypothesismentioning

confidence: 99%

Investigation of winner-loser portfolio anomalies and size effect anomalies in LQ45 index, Indonesia stock exchange

Miswanto,

Putri

2023

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This study investigates the anomalies of winner-loser portfolios and anomalies of size effect on LQ45 stocks in the Indonesia Stock Exchange (IDX), period 2015-2020. The paired sample t-test (parametric) and Wilcoxon test (non-parametric) are used to test samples. Sample selection is carried out using the non-probability and purposive sampling methods. The shares of companies that met the sample criteria were 26. The primary data used cumulative abnormal returns and market capitalization as proxies for a company's stock size. The anomaly of the winner-loser effect phenomenon is observed by dividing the data into two periods: the formation period and the testing period. The periods are both short-term and long-term. The short-term period is six months, and the long-term is 12 months. The first finding shows reversals in average cumulative abnormal returns (CARs) on the winner-loser stock portfolio. In the testing period, the average CARs were turned down, and many average CARs became negative. Portfolio loser in the testing period, the average CARs reversed to positive. The second is a reversal in average CARs in winner-loser stock portfolios caused by the size effect. The performance of small winner-loser portfolios is proven to outperform big winners' portfolios. The reversal occurs in both the short-term and long-term. This finding suggests that behavioral finance can explain this market anomaly, and it is helpful for investors in coming up with strategies to get abnormal returns.

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“…Inspired by a number of studies suggesting that returns may differ across market states (e.g., Cooper et al 2004;Wang and Xu 2015), we evaluate the performance of the models separately for bull and bear markets. Following (Zaremba et al 2020), we define these states as subperiods of positive (bull market) and negative (bear market) total excess return on the market portfolio during the last 12 months. The results for the predictive accuracy and economic gains from the individual models are reported in Tables 7 and 8, respectively.…”

Section: Performance Within Subperiodsmentioning

confidence: 99%

Machine learning techniques for cross-sectional equity returns’ prediction

et al. 2022

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We compare the performance of the linear regression model, which is the current standard in science and practice for cross-sectional stock return forecasting, with that of machine learning methods, i.e., penalized linear models, support vector regression, random forests, gradient boosted trees and neural networks. Our analysis is based on monthly data on nearly 12,000 individual stocks from 16 European economies over almost 30 years from 1990 to 2019. We find that the prediction of stock returns can be decisively improved through machine learning methods. The outperformance of individual (combined) machine learning models over the benchmark model is approximately 0.6% (0.7%) per month for the full cross-section of stocks. Furthermore, we find no model breakdowns, which suggests that investors do not incur additional risk from using machine learning methods compared to the traditional benchmark approach. Additionally, the superior performance of machine learning models is not due to substantially higher portfolio turnover. Further analyses suggest that machine learning models generate their added value particularly in bear markets when the average investor tends to lose money. Our results indicate that future research and practice should make more intensive use of machine learning techniques with respect to stock return prediction.

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Where have the profits gone? Market efficiency and the disappearing equity anomalies in country and industry returns

Cited by 33 publications

References 72 publications

Why don't asset managers accelerate ESG investing? A sentiment analysis based on 13,000 messages from finance professionals

Why don't asset managers accelerate ESG investing? A sentiment analysis based on 13,000 messages from finance professionals

Investigation of winner-loser portfolio anomalies and size effect anomalies in LQ45 index, Indonesia stock exchange

Machine learning techniques for cross-sectional equity returns’ prediction

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