Analyzing the Long Term Cohesive Effect of Sector Specific Driving Forces

Abstract

Financial markets are partially composed of sectors dominated by external driving forces, such as commodity prices, infrastructure and other indices. We characterize the statistical properties of such sectors and present a novel model for the coupling of the stock prices and their dominating driving forces, inspired by mean reverting stochastic processes. Using the model we were able to explain the market sectors’ long term behavior and estimate the coupling strength between stocks in financial markets and the sector specific driving forces. Notably, the analysis was successfully applied to the shipping market, in which the Baltic dry index (BDI), an assessment of the price of transporting the major raw materials by sea, influences the shipping financial market. We also present the analysis of other sectors—the gold mining market and the food production market, for which the model was also successfully applied. The model can serve as a general tool for characterizing the coupling between external forces and affected financial variables and therefore for estimating the risk in sectors and their vulnerability to external stress.

Analytical study of index-coupled herd behavior in financial markets

“…-λ was considered as fixed, while in practice, stocks may be coupled differently to the index. For example, stocks of oil companies may be strongly coupled to the oil price and less strongly to the index price [28], while others, like bank stocks are likely to be strongly coupled to the stock market index. The effect of varied coupling strengths will make large deviations in the correlations between different stocks and between specific stocks and the index, and it will not be possible to obtain expressions as simple as eq.…”

“…Following the above, we should note that there is a qualitative difference between the effect of coupling in stock returns and in stock prices. These two coupling mechanisms may result in very different long-term behaviors and may be independent of one another [28]. For example, the stock price of two oil companies traded in two different stock markets may be similar in the long run due to the global effect of the oil price.…”

“…The model described in this paper corresponds to the shorter time scales, in which the overall trend reflected by the index return affects the behavior of single stocks. Sectorial external forces, such as commodity prices, are known to have a substantial effect on longer time scales on stock prices [28].…”

“…This is left for future work. Establishing the above, an important direction in which this work can be continued is its integration with the long-term coupling model of stock prices and sectorspecific driving forces [28].…”