Abstract
The study of how financial markets behave continues to be interesting. The existence of more and more data and the development of statistical techniques are some reasons for the increase in research in finance. However, the difficulty in understanding some markets’ behavior is a continuous challenge. In this context, a new research area called Econophysics has emerged, which is constantly increasing in size. We propose in this work to use methodologies related to Econophysics to analyze one stock index composed of firms producing clean energy (S&P Global Clean Energy Index) and compare it with the New York Stock Exchange (NYSE) as a stock market benchmark and with the price of crude oil. In a context where environmental issues are on the agenda, this is an important area of research, because it could help investors to make their decisions. Our results show that the clean energy index seems to have higher time serial dependence than the others, and is less exposed to oil price than the NYSE.
Highlights
The study of how financial markets behave is not new in the financial literature
Later in that century, [2,3] revolutionized the study of finance by identifying the efficient market hypothesis (EMH), which in its weak form states that asset prices are expected to follow a random walk, meaning return rates should not suffer from any kind of serial dependence
Identifying three different forms of market efficiency, what is relevant for the analysis proposed in this paper is the weak form, which states that no-one can obtain abnormal profits using past information on the prices of a given asset
Summary
The study of how financial markets behave is not new in the financial literature. At least since the seminal work of [1], researchers have tried to explain how these markets evolve. Identifying three different forms of market efficiency (weak, semi-strong and strong), what is relevant for the analysis proposed in this paper is the weak form, which states that no-one can obtain abnormal profits using past information on the prices of a given asset. Despite contradictory results concerning verification of the EMH, it is crucial to note that even identifying that data could be generated by non-random processes, no work has been able to prove that the existence of dependence of any type (linear, non-linear, serial or other) implies market participants could gain abnormal returns in financial markets, and so rejecting the EMH. We present evidence of the previous literature
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