Passive index funds are believed to offer low fees, nearly limitless liquidity, very low trading costs and (most of the time) they beat most active managers. Although not all of the above are accurate, there are still many arguments in favour of passive indexing. However, what is often left forgotten are avoidable travails linked to index funds. In general, after an index rebalances, traditional cap-weighted index funds buy high and sell low. Their tendency to add recent highfliers and drop unloved value stocks is what causes investors to lose. Arnott et al. (2022) target the stock selection problem around index rebalancing and propose several ideas on how to adjust index strategies in order to earn above-market returns. They present simple ways to construct an index, thanks to which it is possible to reduce both negative effects of buy-high/sell-low dynamic and the turnover costs of cap-weighted indices.

At the moment, there is a lot of attention surrounding overnight anomalies in various types of financial markets. While such effects have been well documented in research, especially in US equities and derivatives, there are other asset classes that are not as well addressed. A recent (2022) paper from Jiang, Luo, and Ye contributed appealing evidence in favor of validating these phenomena in the Chinese market. We highlight the finding that the market MKT factor beta premium is earned exclusively overnight and tend to reverse intraday (and in smaller potency also value HML and profitability RMW), which is the same finding as for the US equities. In contrast, the size SMB factor exhibit significantly opposite pattern: positive intraday premium and negative overnight premium (and the same for investment CMA factor).

Finding high-quality data with a long history can be challenging. We have already examined How To Extend Historical Daily Bond Data To 100 years, How To Extend Daily Commodities Data To 100 years, and How To Build a Multi-Asset Trend-Following Strategy With a 100-year Daily History. Following the theme of our previous articles, we decided to extend historical data of a new factor, the Dollar Factor. This article explains how to combine multiple data sources to create a 100-year daily data history for the Dollar Factor (the value of the United States Dollar relative to its most important trading partners’ currencies), introduces data sources, and explains the methodology.

Choosing the correct portfolio-construction techniques is very important. The new paper that is written by Amar Soebhag, Bart van Vliet, and Patrick Verwijmeren explores the various ways in which different design choices in portfolio construction can, either intentionally or unintentionally, influence and distort the statistical results of a market factor’s research. Their takeaway is that seemingly small differences in design can significantly impact the resultant portfolio’s performance.

The size effect assumes a negative relationship between average stock returns and firm size. In other words, it states that low capitalization stocks outperform stocks with large capitalization. Although generally accepted, the size effect keeps being challenged. Researchers have been asking how important the firm size characteristic actually is, and whether it is possible to replace the traditional size factor of Fama and French asset pricing model (1993) with more accurate factor. Recently, one potential challenger has emerged – so-called takeover factor, employed by Easterwood et al. (2022). In their study, they work on the assumption that small firms are often targets of takeovers, which gives us a different perspective on merger and acquisition news in regards to size effect. Their results show that M&A component of average returns explains the size premium entirely.