Trends in financial markets: uncovering the distribution of intensity and duration

In financial investment, market trends are ubiquitous. Put simply, trending markets are characterized by changes in price that are persistent in time. In this research, we are interested in understanding the global properties of trending markets ex-post, as there is a shortage of research in this direction. The primary goal of our study is to provide a reliable approach for categorizing financial market trends by defining their strength and persistence. However, the noisy characteristics of financial data and the hidden character of a true market trend make this endeavor nontrivial. Towards this end, we use resampling techniques and establish empirical labeling algorithms in parallel with Hidden Markov models and Bayesian smoothing filtering to estimate the underlying structure and dynamics of market trends. From our results, we can comment on the market trend intensity and duration across various financial markets and asset classes. Here, we focus on labeling trends, as opposed to identifying them in real-time, as this can provide valuable diagnostic information ex-post about how the macroeconomic conditions of the market influences the dynamics and characteristics of trends