The problem of missing financial data is widespread yet often overlooked. An interesting insight into the structure of missing financial data provides a novel research paper by authors Bryzgalova et al. (2022). Firstly, examining the dataset of the 45 most popular characteristics in asset pricing, the authors found that missing data is frequent among almost any characteristic and affects all kinds of firms – small, large, young, mature, profitable, or in financial distress. The requirement of multiple characteristics simultaneously makes the problem even worse. Moreover, the data is not missing randomly; missing values clusters both cross-sectionally and over time. This may lead to a selection bias, making most famous ad-hoc approaches like the median invalid. Considering the abovementioned findings, the authors propose a novel imputation method based on Principal Component Analysis (PCA).

We have brought attention to the importance of evaluating factors models in different market regimes, and now, we will take a closer look at the size factor. Size [SMB (small minus big)] factor is a popular investment choice for asset investigation by many portfolio managers worldwide. The Size earned prominence in Fama and French’s three and five-factor models, and enjoy the continued discussion about its place in today’s portfolio construction. But it’s crucially important for investors seeking to capture the Size premium to realize that it is dependent on the monetary policy being pursued by the Federal Reserve, as the monetary easing seems to induce a Size premium.

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).

What’s the performance of a balanced portfolio during rising rates? How does it behave when inflation is high? What about a combination of these market states? And how do trend-following strategies fare in such an environment? These and even more questions we will attempt to resolve in our today’s article. We will be looking at different market cycles and how a balanced portfolio and a typical trend-following strategy perform over these different market states.

Which assets perform best when rates are rising, and inflation is high? And what happens if rates are still rising but inflation is already falling? And what’s the impact of the business cycle? These are the questions that everyone is currently trying to answer. Today, we will start a longer series of articles with the goal of giving an exact quantitative answer to all questions related to cycles in inflation, interest rates, and economic growth. This series of articles can also serve as an introduction to the methodology that we will use in the upcoming Quantpedia Pro report.