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.

In this article we are going to continue the discussion on portfolio insurance strategies. An exhaustive description of this methodology was already presented in the article Introduction to CPPI. This article will focus on an extension of the original model introduced by Estep and. Kritzman (1988), namely Time Invariant Portfolio Protection. Constant Proportion Portfolio Insurance (CPPI) and Time-Invariant Portfolio Protection (TIPP) are two of the most famous portfolio insurance strategies that play an important role in the realm of investment management and risk mitigation. These strategies are designed to address the fundamental challenge of balancing the pursuit of financial growth with the imperative of capital protection against market downturns. Ideally, the guaranteed protection is achieved at the lowest possible premium for the investors.

Hello all,

What have we accomplished in the last month?

– A new technical upgrade of the Portfolio Manager, users can now create/store/manage multiple model portfolios and benchmarks
– 12 new Quantpedia Premium strategies have been added to our database
– 12 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 8 new backtests written in QuantConnect code
– 6 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month
– and finally, we would like to announce that the video recording of our “A systematic approach to ESG investing” webinar is now available on YouTube

Six new strategies have been added. Seven new related research papers have been included into existing strategy reviews and three short free blog posts have been published during last few weeks. Plus, five trading strategies have been backtested in QuantConnect in the previous two weeks.

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.

Following the recent release of the popular large language model ChatGPT, the topic of machine learning and AI seems to have skyrocketed in popularity. The concept of machine learning is, however, a much older one and has been the topic of various research and technology projects over the last decade and even longer. In this article, we would like to discuss what machine learning is, how it can be used in quantitative trading, and how has the popularity of ML strategies increased over the years.