English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper is a comprehensive investigation of the January Effect evolution in the US stock market over the period 1791–2015. It employs various statistical techniques (average analysis, Student’s t-test, ANOVA, Mann-Whitney test) and a trading simulation approach to analyze the evolution of this anomaly. The results suggest that January effect during the XVIII–XXI century passed the way from rise to fall. The rise of the January Effect starts in the end of the XIX century and this anomaly mostly disappeared in middle of the XX century. Nowadays the January Effect is not present in the US stock market, but even today January stays one of the best months for purchases in the US stock market. Alex Plastun (Ukraine), Viacheslav Plastun (Ukraine) BUSINESS PERSPECTIVES LLC “СPС “Business Perspectives” Hryhorii Skovoroda lane, 10, Sumy, 40022, Ukraine www.businessperspectives.org January effect: 200 years of evolution in the us stock market Received on: 2nd of November, 2018 Accepted on: 12th of December, 2018 INTRODUCTION According to the Efficient Market Hypothesis (EMH) it is impossible to predict prices in the stock market and to “beat” the market by making abnormal profits (Fama, 1965). Still, stock market data provide a lot of evidences against this. The most influencing examples are presence of market anomalies, persistence in stock prices, fat tails in price distributions, and other of market inefficiency. According to the EMH prices in the stock markets should be random, but there are many empirical evidences of abnormal behavior of prices (Cross, 1973; De Bondt & Thaler, 1985; Ariel, 1987; Caporale & Plastun, 2018). These evidences are usually called market anomalies. The most commonly recognized market anomalies are momentum effect and size effects, price bubbles, value investing, and calendar anomalies. Among calendar anomalies one of the most well-known is the January effect – January exhibits higher returns comparing with the other months of the year. Still empirical evidences about the January effect are mixed. This can be explained by the differences in data sets and data periods, data frequency and methodology, as well as differences in choice of markets, financial assets and countries as objects of analysis. One more possible reason is evolution of the markets (Lo, 2004). Based on evolutionary concept in this paper we will try to show that market anomalies (caw of January effect) are not market myths and they evolve in time. Using monthly data from the US stock market over the period 1791– 2015 we will analyze the evolution of the January effect. To do this we employ various statistical techniques (average analysis, Student’s t-test, ANOVA, the Mann-Whitney test) and a trading simulation approach. © Alex Plastun, Viacheslav Plastun, 2018 Alex Plastun, Doctor of Economics, Professor, Department of Foreign Economic Relations, Sumy State University, Ukraine. Viacheslav Plastun, Doctor of Economics, Associate Professor, Kyiv School of Economics, Ukraine. This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International license, which permits re-use, distribution, and reproduction, provided the materials aren’t used for commercial purposes and the original work is properly cited. calendar anomaly, January effect, stock market, Dow Jones Index