Factor allocation

Investing truly is an intense intellectual undertaking. For a Portfolio Manager (PM) to execute an investment, they must first convince themselves, then others, that the rationale behind the investment is sound. The variables they utilize in developing their rationale are of the upmost importance; These variables inevitably serve as a foundation in the evaluation of a given Asset, and therefore possess the power to influence a PM’s level of confidence in the investment. If a variable is weak, it can lead to a poor diagnosis of the asset in question, which can lead to unfavorable results on a given investment. If a variable is strong, then it will indeed provide insight into asset and therefore help paint a clear picture into the future of the asset. To be on the right side of this sword, it is imperative that portfolio managers correctly implement quantitative reasoning if not within their decision-making process, then definitely around it. This article introduces the theory of mutual information as a tool for asset managers to gauge the predictive efficiency of their selected variables.

More and more, a few mega-cap companies dominate the US stock market performance. Financial journals come up with different names for those stocks every few years. They are now called the “Magnificent Seven”, but we all remember FAANG, right? Naturally, several questions arise – Is the current status quo, when the stock market capitalization is highly concentrated among the few extremely large companies, an exception or rule over history? And what’s the impact of this concentration on the performance of the one particular factor – the Size premium? We present the research paper written by Emery and Koëter that tries to answer those questions. 

Two weeks ago, we promised you a surprise, and now it’s finally time to unveil what we have prepared for you :).

Our Quantpedia Awards 2024 aims to be the premier competition for all quantitative trading researchers. If you have an idea in your head about systematic/quantitative trading or investment strategy, and you would like to gain visibility on the professional scene, then submit your research paper, and you can compete for an attractive list of prizes. All info about the prizes, submission process, expert committee, and our partners are described in detail on our dedicated subpage: Quantpedia Awards 2024. But we will also give you a quick overview in this blog post.

The period of high inflation in the 1970s prompted researchers to carefully examine the relationship between inflation and stock returns and to look for ways to avoid unexpected inflation. The year 2022 brought back inflationary pressures to the U.S. economy not seen in more than 40 years, and this has spurred new efforts to answer long-standing questions about inflation and asset prices. Authors from the Board of Governors of the Federal Reserve System (2023) bring a fresh perspective on this topic, and their paper allows us to get a FED insider’s view on the ageless question of how inflation affects equity returns.

In the ever-evolving world of finance, the quest for stable returns and risk mitigation remains paramount. Traditional asset classes, such as stocks and bonds, have long been the cornerstone of investment portfolios, but their inherent volatilities and susceptibilities to market fluctuations necessitate a more diversified approach. Enter the domain of alternative risk premia (ARP) – strategies designed to capture returns from diverse sources of risk, often orthogonal to traditional market risks. Our exploration in this blog post delves deep into this subject, shedding light on which ARP strategies can truly serve as robust diversifiers in the complex financial tapestry.

You may remember our blog post from the end of March, where we tested the current state-of-the-art LLM chatbot. Time flies fast. More than six months have passed since our last article, and half a year in a fast-developing field like Artificial intelligence feels like ten times more. So, we are here to revisit our article and try some new hacks! Has the OpenAI chatbot made any significant improvement? Can ChatGPT be used as a backtesting engine? We retake our risk parity asset allocation and test the limits of current AI development again!