In a game of poker, it is usually said that when you do not know who the patsy is, you’re the patsy. The world of finance is not different. It is good to know who your counterparties are and which investors/traders drive the return of anomalies you focus on. We discussed that a few months ago in a short blog article called “Which Investors Drive Factor Returns?“. Different sets of investors and their approaches drive different anomalies, and we have one more paper that helps uncover the motivation of investors and traders for trading and their impact on anomaly returns.

India is a big emerging market, actually the second biggest after China. We primarily look at developed markets, mostly the U.S. and Europe, and from Emerging Markets, China at most, and we are aware that we neglect this prospective country. We would like to correct this notion and give attention to a country that is (along with China) being cited as a new potential rising superpower and already looking to take the lead of Emerging Markets (EM) countries. Today, we would like to review the paper that analyzes the performance of main equity factors (with an emphasis on the Quality factor) and is a good starting point to understand the specifics of factor investing strategies in India.

Bearish trends or deep corrections in international equity markets starting in 2022 and rising interest rates worldwide brought investors’ attention back to not only once-proclaimed dead factor investing. From long-run and short run, during different market cycles, different factors behave differently. What’s fortunate is that it is pretty predictable to some extent. Andrew Ang, Head of Factor Investing Strategies at BlackRock, in his Trends and Cycles of Style Factors in the 20th and 21st Centuries (2022), used Hodrick-Prescott (HP) filter and spectral analysis to investigate different models to draw some general conclusions on most-widely used factors. We will take a look at a few of quite the most interesting ones of them.

Nobody would argue that nowadays, we live in an information-rich society – the amount of available information (data) is constantly rising, and news is becoming more accessible and frequent. It is indisputable that this evolvement has also affected financial markets. Machine learning algorithms can chew up big chunks of data. We can analyze the sentiment (which is frequently related to the news). Big data does not seem to be a problem anymore, and high-frequent trading algorithms can react almost instantly. But how important is the news? Kerssenfischer and Schmeling (2021) provide several answers by studying the impact of scheduled and unscheduled news (frequently omitted in other news-related studies) in connection with high-frequency changes in bond yields and stock prices in the EU and US as well. The research points out that the effect is tremendous and significant.