David Blitz's scientific contributions
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Publications (28)
The authors examine the effects of incorporating a potential tax on carbon emissions into a value investment strategy. They show that in a portfolio optimization problem, a carbon tax at the stock level is mathematically equivalent to a carbon constraint at the portfolio level. Using this insight, the authors derive a value–carbon efficient frontie...
Traditional business cycle indicators do not capture much of the large cyclical variation in factor returns. Major turning points of factors seem to be caused by abrupt changes in investor sentiment instead. The author infers a quant cycle directly from factor returns, which consists of a normal stage that is interrupted by occasional drawdowns of...
The performance characteristics of recently introduced thematic indexes are examined using standard asset pricing theory. The main finding is that thematic indexes generally exhibit strong negative exposures to the profitability and value factors, indicating that they hold growth stocks that invest now for future profitability. As such, investors i...
This article examines the performance of equity factor portfolios during the quant crisis of 2018-2020. The author finds that there was basically only one way to outperform during this period, namely by investing in the largest and most expensive growth stocks. Other factors were only effective to the extent that they provided implicit exposure to...
Stocks with low return volatility have high risk-adjusted returns, which might be driven by low media attention for such stocks. Using news coverage data we formally test whether the ‘attention-grabbing’ hypothesis can explain the volatility effect for a sample of international stocks over the period 2001 to 2018. A low-volatility effect is still p...
Citations
... In recent years, machine learning techniques have gained popularity in the financial industry (Blitz et al. 2023;Ning, Lin, and Jaimungal 2021;Moallemi and Wang 2022). We demonstrate how reinforcement learning, a sub-field of machine learning, can be used to optimally distribute orders between brokers using an algo wheel. ...
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... In this article, we provide an overview of machine learning in asset management, discussing both the challenges and the wide range of applications it offers. More detailed issues regarding practical implementation of machine learning for quantitative asset management are discussed by Blitz et al. (2023). As financial institutions continue to adopt machine learning at an unprecedented pace, it is crucial for practitioners to understand the underlying concepts and applications of these models. ...
... Machine learning can uncover nonlinear and interaction effects (Blitz, 2023) and offer performance improvements compared to traditional methods using the same dataset (Gu et al., 2020). Leung et al. (2021) also emphasized the challenge of excessive turnover associated with ML models due to their reliance on short-period forecasts, highlighting a key difference with traditional linear models less constrained by such limitations, such as gradient boosting machines. ...
Reference: Smart Beta Investing
... Various papers research the asset pricing implications of emissions and the accompanying risks in various market settings. Bushnell et al. (2013) and Oestreich and Tsiakas (2015) study the EU ETS in the EU as total and Germany, Bolton and Kacperczyk (2021) and Hsu et al. (2022) research the U.S. equity market, Wen et al. (2020) study the Shenzhen pilot ETS, the pilot of the Chinese ETS and Blitz and Hoogteijling (2022) research a worldwide theoretical setting. Some of this research focuses on the effects of carbon pricing and the number of firms' emissions on equity markets to research the presence of a 'carbon risk premium'. ...
... The adoption of cleaner energy sources has the potential to hinder the growth of traditional energy markets, causing shifts in the assessment of fossil fuel corporations and their associated assets. Moreover, the growing incorporation of clean energy technology across diverse sectors has the ability to provide new investment opportunities and reshape market dynamics [4][5][6]. ...
... Negative exposure to quality factors not explained by sector tilts is another example of a style exposure that needs to be taken into account when forecasting thematic returns. In this respect, Blitz (2021) found that thematic indices proposed by S&P and MSCI, two index providers, invested mainly in high volatility, low quality and high valuation stocks. Such style biases, if not just caused by sector biases, are likely to create a drag on returns that need to be counter-balanced with sufficiently high thematic alpha. ...
Reference: Allocating to Thematic Investments
... overvaluation is not corrected in form of subsequent negative, abnormal returns. 21 At least for the period between 2018 and 2020 not yet analyzed in previous studies, this finding is in line with Blitz (2021) who shows that only the largest and most expensive growth stocks outperformed the market return in developed countries (incl. the U.S.). ...
... The notion of SRI has received a lot of interest from investors and has even been referenced in academic literature (Derwall et al., 2005). Integrating sustainability concepts into the investment process is one of the most visible developments in the financial world (Blitz and de Groot, 2019) that has escalated swiftly in the past few years (Orlitzky, 2013;Bauer et al., 2005;Galema et al., 2008). By incorporating environmental and social impact along with financial aspects into investment decisions, responsible investors strive to optimize economic and social value (Palma-Ruiz et al., 2020;Puaschunder, 2019;Renneboog et al., 2007;Livesey, 2002;Matten and Crane, 2005;Schueth, 2003). ...
... First, marketization improvement, especially the factor market integration, improves the pricing mechanism of the factor market, which helps to improve the inter-regional production factor liquidity, adjusting the supply structure of regional production factors and narrowing the inter-regional factor income gap so that the factor price can reflect its marginal output value [45,46]. Therefore, in the factor market, the digital economy can achieve a high degree of industry agglomeration under the platform economy, overcome the space limitations, realize regional integration of factor supply and demand, and expand the factor allocation spatial network. ...
... Simply, when the performance of the value strategy worsens, the growth strategy based on the profitability factor improves, and vice versa. From 2010 to 2019, except for the high excess returns of the market premium, the aggregate returns of the other four factors approached zero, similar to 1990-1999 (Blitz 2020). The average return of the value factor was particularly poor in this period. ...
