Time Series Momentum in the US Stock Market: Empirical Evidence and Theoretical Implications

2020 ◽  
Author(s):  
Valeriy Zakamulin ◽  
Javier Giner
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Empirical Evidence ◽  
The Us

scholarly journals Does Eco-labeling of Services Matter? Evidence from Higher Education

2016 ◽  
Vol 16 (4) ◽  
Author(s):  
Daniel C. Hickman ◽  
Andrew G. Meyer
Keyword(s):  
Higher Education ◽  
Time Series ◽  
Empirical Evidence ◽  
University Presidents ◽  
Interrupted Time Series ◽  
Department Of Education ◽  
Considerable Heterogeneity ◽  
College And University ◽  
The Us ◽  
The Impact

Abstract: Eco-labeling of services has become increasingly common, yet little empirical evidence exists concerning its effectiveness. We address this gap in the literature by analyzing a highly visible eco-label, the American College and University Presidents’ Climate Commitment (ACUPCC), in the sector of higher education. We match information about the ACUPCC to the US Department of Education IPEDS database to examine the impact of signing on student applications, admissions, and enrollment. We mainly utilize a difference-in-difference approach to identify the effects of interest but confirm results with an interrupted time series model. We find that signing the ACUPCC increases applications and admitted students by 2.5–3.5 %. However, the evidence regarding enrollment is weaker with only some specifications finding increases of around 1–2 %. Overall, there is considerable heterogeneity across sectors and selectivity of the institutions. These results show that, at the minimum, voluntary and information-based approaches (VIBAs) for services can be effective in generating visibility and influencing less-costly consumer behavior.

scholarly journals Analysis of the January Effect in Time Series of Mexican Stock Market Indexes

2019 ◽  
pp. 43-62
Author(s):  
Mariana Garay Alvarado ◽  
Michael Demmler
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Empirical Evidence ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
January Effect ◽  
Efficient Market ◽  
Significant Evidence ◽  
Current Article ◽  
Market Indexes

The current article has the research objective to search for empirical evidence of the January effect within the time series of the IPC and the sector indexes of the Mexican stock market using econometric GARCH analysis. The dataset is formed by the log returns of the daily closing prices corresponding to the IPC as well as the sector indexes covering the period from 01/01/2010 to 12/31/2018. The main results of the article are as follows: Based on the January effect the Efficient Market Hypothesis in its weak form sense cannot be rejected for the Mexican stock market as the results do not provide significant evidence of the existence of the respective calendar anomaly within the analyzed time series of the IPC and the different sector indexes.

Design of data mining algorithm based on rough entropy for us stock market abnormality

2020 ◽  
Vol 39 (4) ◽  
pp. 5213-5221 ◽  
Author(s):  
Guangtong Wang ◽  
Jianchun Miao
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Stock Market ◽  
Global Economy ◽  
Data Mining Algorithm ◽  
Market Anomalies ◽  
Mining Algorithm ◽  
The Us ◽  
Stock Market Anomalies ◽  
Market Anomaly

The economic interaction between the countries of the world is gradually strengthening. Among them, the US stock market is a “barometer” of the global economy, which has a huge impact on the global economy. Therefore, it is of great significance to study the data in the US stock market, especially the data mining algorithm of abnormal data. At present, although data mining technology has achieved many research results in the financial field, it has not formed a good research system for time series data in stock market anomalies. According to the actual performance and data characteristics of the stock market anomaly, this paper uses data mining techniques to find the abnormal data in the stock market data, and uses the isolated point detection method based on density and distance to analyze the obtained abnormal data to obtain its implicit useful information. However, due to the defects of traditional data mining algorithms in dealing with stock market anomalies containing uncertain factors, that is, the errors caused by other human factors, this paper introduces the roughening entropy of the uncertainty data and applies its theory to the field of data mining, a data mining algorithm based on rough entropy in the US stock market anomaly is designed. Finally, the empirical analysis of the algorithm is carried out. The experimental results show that the data mining algorithm based on rough entropy proposed in this paper can effectively detect the abnormal fluctuation of time series in the stock market.

The Empirical Evidence of GARCH Effects on Return Series: Vietnam Stock Market 2000-2003

2019 ◽  
Author(s):  
Quan-Hoang Vuong
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Empirical Evidence ◽  
Statistical Methods ◽  
Serial Correlation ◽  
Empirical Models ◽  
English Abstract ◽  
Regional Markets ◽  
National Economic

The Vietnamese Stock Market was officially born on July 20, 2000, and considered an experiment, in the sense that it would likely accept adjustment and constraints to reflect the contemporaneous national economic settings. This paper is one of the first applied econometric studies investigating an evidence of GARCH effects on return series of 10 individual assets and the VNI, an index devised as the market general price indicator. The results are encouraging: Firstly, we found evidence that the time series exhibit many similar properties as those for other regional markets, such as autoregressive and serial correlation; Secondly, using rather sophisticated empirical models for a newborn market, we succeed in achieving some nontrivial remarks with respect to the use of policy matters. This paper demonstrates the importance of the application of statistical methods, a topic still not received much attention from the economic researchers in Vietnam. (Downloadable paper in Vietnamese, with English abstract.)

scholarly journals Approaching Stock Market Trading with Echo State Networks

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Dev Patel ◽  
Krish Patel ◽  
Charles Dela Cuesta
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Prediction Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Modern Society ◽  
Series Data ◽  
Echo State Networks ◽  
The Us

The US stock market is an integral part of modern society. Nearly 55% of Americans  own corporate shares in the US stock market (What Percentage of Americans Own Stock?, 2019), and as of June 30th, 2020, the total value of the US stock market was over 35 trillion USD (Total Market Value of U.S. Stock Market, 2020). The stock market is also extremely volatile, and many people have gone bankrupt from poor investments. To minimize the risk and capitalize off the massive amounts of data on corporations and share prices present in the world, algorithmic trading began to rise. Trading algorithms have the potential for huge returns, and while many algorithms employ strategies like Long-Short Equity, very few attempt to use machine learning due to the unpredictable nature of the stock market. Many time series prediction models like autoregressive integrated moving average (ARIMA), and even neural networks like long short term memory (LSTMs) often fail when predicting stock market data, because unlike other time series data, the stock market is almost never univariate, or follows seasonal trends. However, where other models come short, echo state networks (ESNs) excel, due to their reservoir like computing model, which allows them to perform better on messy, non traditional time series data. Using a combination of ESNs to predict prices, and clustering we created an algorithm model that can predict trends with over 95% confidence, but had mixed results accurately predicting returns.

scholarly journals A Comprehensive Framework for Uncovering Non-Linearity and Chaos in Financial Markets: Empirical Evidence for Four Major Stock Market Indices

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1435
Author(s):  
Lucia Inglada-Perez
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Financial Markets ◽  
Empirical Evidence ◽  
Wide Range ◽  
Dow Jones Industrial Average ◽  
Noisy Time Series ◽  
The Subject ◽  
Chaotic Nature ◽  
Comprehensive Framework

The presence of chaos in the financial markets has been the subject of a great number of studies, but the results have been contradictory and inconclusive. This research tests for the existence of nonlinear patterns and chaotic nature in four major stock market indices: namely Dow Jones Industrial Average, Ibex 35, Nasdaq-100 and Nikkei 225. To this end, a comprehensive framework has been adopted encompassing a wide range of techniques and the most suitable methods for the analysis of noisy time series. By using daily closing values from January 1992 to July 2013, this study employs twelve techniques and tools of which five are specific to detecting chaos. The findings show no clear evidence of chaos, suggesting that the behavior of financial markets is nonlinear and stochastic.

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