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!

In recent years, the diversification potential of commodities has come under scrutiny. While the majority of studies examining the role of commodities in a portfolio typically focus on U.S. investors or those dealing primarily with U.S. dollar-denominated assets, Dequiedt et al. (2023) offer a unique perspective by considering the viewpoint of domestic investors in a sample of 38 developed and emerging countries. The study explores the relationship between diversification benefits of commodities for local investors and country’s level of commodity risk exposure. Findings reveal that incorporating commodities tends to enhance the Sharpe ratio of the optimal domestic asset portfolios in most countries with low commodity dependence but doesn’t benefit highly commodity-dependent ones.

Even though gold is generally a volatile asset, it is often considered a key diversifier, hedging against inflation or protecting during economic uncertainties. According to the authors (Pim van Vliet and Harald Lohre), in times of extreme macroeconomic events, including war, hyperinflation, or major economic recessions, gold investing is widely regarded as a safe haven. However, using gold as a hedge comes at the cost of lower returns. The authors explored the importance of gold in investment portfolios and its ability to reduce the risk of losses combined with bonds and stocks. Compared to many existing studies, they also consider a longer timeframe and the impact of inflation.

It’s always fun to push the boundaries of technology and see what it can do. The AI chatbots are the hot topic of current discussion in the quant blogosphere. So we have decided to test OpenAI’s ChatGPT abilities. Will we persuade it to become a data analyst for us? While we may not be there yet, it’s clear that AI language models like ChatGPT can soon revolutionize how we approach to finance and data analysis.

If you’re a regular reader of our blogs (and we hope you are!), you would not miss that we like to touch macro-economic subjects. One of that never-fading topics is the role of gold as a crisis hedge. The probably most known commodity is a popular choice for a portion of the total portfolio, from small investors to central banks, for various reasons (be it diversification or hedging). So let’s not further delay it, and today we ask: Is gold really a safe haven?

We have already published a few articles about how the different market cycles affect the performance of your portfolio and performance of market factors. So far, these states of the market were identified in-sample, with the benefit of hindsight. The full methodology of how we defined bull/ bear market, low/ high inflation, and rising/ falling interest rates is described in this article.

Today, we are going to define the same market states out-of-sample. We will describe our methodology and the thinking behind it all in this article. Both in sample and out of sample market cycle analysis may be useful for making investment decisions. It’s crucial to understand the differences and how to use this kind of analysis to your benefit.