Countries from the majority of the developed world face one challenge: Their population is steadily aging; the average age of individuals has been rising over recent periods. The United States is not different in this sense. Whenever there’s a never-inevitable reaching of higher ages, people reconsider their choices and often cut on riskier ones. So, is there a potential link between demographic changes associated with aging and aggregate financial market outcomes?

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?

The theme of rebalancing in longer-term investing is neglected but important as it influences the overall portfolio’s performance and risk. Unfortunately, many investors are inconsistent in choosing dates for their rebalances of portfolios, resulting in hardly predictable results (whether positively or negatively affecting it), and not contributing to handling risk management properly. The following article presents our analysis of the impact of rebalancing on portfolio returns. It also serves as an introduction to the methodology for an upcoming Quantpedia Pro report that our users would be able to use to quickly assess the impact of the rebalancing period on any selected combination of trading strategies, custom equity curves, and ETFs.

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.

Today, we analyze how all the factors we use in our Multi-Factor Regression Model performed during various Market Cycles (in sample), including the Bull/ Bear market, the High/ Low inflation, and the Rising/ Falling interest rates. Further, we also examine the performance of a Balanced Portfolio ETF – AOR, over past 100 years. This is done by creating the Factor AOR, which we constructed using our Multi-Factor Regression Model from AOR ETF. In addition to a chart comparison of equity curves, we also compare the performance of factor AOR to that of all the factors by means of risk/return tables, i.e. quantitatively. All the tables are sorted based on the Sharpe ratio from the best (at the top) to the worst (at the bottom).