Time-Varying Equity Premia with a High-VIX Threshold

What does one of the most popular and well-known metrics, VIX, tell us about future returns? Academic research (Bansal and Stivers, July 2023) shows that a common, intuitive 20/80 thumb rule can be applied as time-variation in the returns earned from equity-market exposure can be explained well by a simple 2-term risk-return specification, which predicts (1) much higher returns 20% of the time following after VIX exceeds a high threshold at around its 80th percentile and (2) lower excess returns following a high market sentiment. They argue that VIX and market sentiment tend to measure complementary aspects of risk: the level of risk (VIX) and the price of risk or risk appetite (sentiment), and that, thus, both terms should be accounted for when evaluating time variation in the equity market’s risk premium.

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Analysis of Price-Based Quantitative Strategies for Country Valuation

The motivation for this study comes from the idea of simplifying the concept of relative valuation among the countries. There exist several ideas for relative value approaches that compare the “visible price” (or market capitalization) of the stock market to some unseen “intrinsic value” of the market. The ideas of what we can use to measure the unseen “intrinsic value” of each individual country/market are numerous – it may be a number derived from GDP (like in a Buffet Indicator), total earnings of listed companies in the selected country (Shiller’s CAPE ratio), or ratios derived from yields, demographic, etc., etc. We asked ourselves – can we create a relative valuation model and use just the price data?

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Language Analysis of Federal Open Market Committee Minutes

If there were a Superbowl of Finance for equities, it’d definitely be FOMC (Federal Open Market Committee) meetings. Investors and traders from around the world gather and make their decisions on the brink of releasing a statement and following the press conference. Shah, Paturi, and Chava (May 2023) contribute with a new cleaned, tokenized, and labeled open-source dataset for FOMC text analysis of various data categories (meeting minutes, speeches, and press conferences). They also propose a new sequence classification task to classify sentences into different monetary policy stances (hawkish, dovish, and neutral) and show the application of this task by generating a hawkish-dovish classification measure from the trained model that they later use in an interesting trading strategy.

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Quantpedia in August 2023

Hello all,

What have we accomplished in the last month?

– A new Component Analysis report for Quantpedia Pro subscribers
– 11 new Quantpedia Premium strategies have been added to our database
– 11 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 7 new backtests written in QuantConnect code
– 5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month
– we present an independent test of Quantpedia’s strategy by AllocateSmartly
– and finally, we would like to invite you to “A systematic approach to ESG investing” webinar we co-organize

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Performance of Factor Strategies in India

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.

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Dissecting the Performance of Low Volatility Investing

Low volatility investing is an appealing approach to compound wealth in the stock market for the long term. This particular factor investing style exploits the popular naive notion that lower (higher) risk must always equal lower (higher) overall returns. But in fact, this naive assumption is not true, as low-volatility investments often yield more than their high-volatility counterparts. While low-volatility investing has many advantages, it also results in some disadvantages. How to overcome them? Bernhard Breloer, Martin Kolrep, Thorsten Paarmann, and Viorel Roscovan, in their study Dissecting the Performance of Low Volatility Investing, propose a solution.

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