The issue of the January Effect has attracted a lot of interest by both practitioners and researchers. The idea that stock returns in January are statistically bigger than in other months was first presented several decades ago. This study analyzes the issue of the January effect in a systematic and global way of studying the performance of 106 indexes in 86 countries and jurisdictions. It was observed that while this effect can still be appreciated in some markets it would appear that it is decreasing globally over time. It was also found that there appears to be an Inverted January Effect in several markets with the returns in January being lower than the returns in some other months. This analysis was performed with nonparametric tests. The hypothesis that the returns of the indexes do not follow in general a normal distribution was also confirmed with several tests.
The Monday effect is a well know effect in some countries around the world. The Monday effect is the observation that stock returns on Monday are statically significantly lower than for the rest of the days of the week. There is no obvious fundamental reason behind this occurrence and if it actually exists it might be due to human behavioral patterns. This Monday effect observation originated in the U.S. several decades ago and it has since being observed in several other countries. In this article the occurrence of the Monday effect is analyzed in the mainland China equity market. It was found that for the period from 2011 to 2016 there was no statistically significant Monday effect but interestingly there are indications of a possible Thursday effect. This concept was tested with several market indexes covering the two major mainland China stock exchanges (Shanghai and Shenzhen). These indexes covered also a broad spectrum of company sizes. The ChiNext index, which is a Nasdaq like type of index for the Chinese market, was also included. In this article it was also tested and confirmed that the returns on Chinese equities, as expected, do not follow a normal distribution.
Clear epigenetic signatures were found in hypertensive and pre-hypertensive patients using DNA methylation data and neural networks in a classification algorithm. It is shown how by selecting an appropriate subset of CpGs it is possible to achieve a mean accuracy classification of 86% for distinguishing control and hypertensive (and pre-hypertensive) patients using only 2239 CpGs. Furthermore, it is also possible to obtain a statistically comparable model achieving an 83% mean accuracy using only 22 CpGs. Both of these approaches represent a substantial improvement over using the entire amount of available CpGs, which resulted in the neural network not generating accurate classifications. An optimization approach is followed to select the CpGs to be used as the base for a model distinguishing between hypertensive and pre-hypertensive individuals. It is shown that it is possible to find methylation signatures using machine learning techniques, which can be applied to distinguish between control (healthy) individuals, pre-hypertensive individuals and hypertensive individuals, illustrating an associated epigenetic impact. Identifying epigenetic signatures might lead to more targeted treatments for patients in the future.
A nonlinear approach to identifying combinations of CpGs DNA methylation data, as biomarkers for Alzheimer (AD) disease, is presented in this paper. It will be shown that the presented algorithm can substantially reduce the amount of CpGs used while generating forecasts that are more accurate than using all the CpGs available. It is assumed that the process, in principle, can be non-linear; hence, a non-linear approach might be more appropriate. The proposed algorithm selects which CpGs to use as input data in a classification problem that tries to distinguish between patients suffering from AD and healthy control individuals. This type of classification problem is suitable for techniques, such as support vector machines. The algorithm was used both at a single dataset level, as well as using multiple datasets. Developing robust algorithms for multi-datasets is challenging, due to the impact that small differences in laboratory procedures have in the obtained data. The approach that was followed in the paper can be expanded to multiple datasets, allowing for a gradual more granular understanding of the underlying process. A 92% successful classification rate was obtained, using the proposed method, which is a higher value than the result obtained using all the CpGs available. This is likely due to the reduction in the dimensionality of the data obtained by the algorithm that, in turn, helps to reduce the risk of reaching a local minima.
Value investment and growth investment have attracted a large amount of research in recent decades, but most of this research focuses on the U.S. and Europe. This article covers the Thai stock market which has very different characteristics compared to western markets and even South East Asian countries such as Indonesia or Malaysia. Among South East Asian countries, Thailand has one of the most dynamic capital markets. In order to see if some well-known trends in other markets exist in Thailand the performance of value and growth stocks in the Thai market were analyzed for a period of 17 years using existing style indexes (MSCI) as well as creating portfolios using individual stocks. For this entire period, when using the indexes, returns are statistically significant superior for value stocks compared to growth stocks. However, when analyzing the performance of the market in any given calendar year from 1999 to 2016, the results are much more mixed with in fact growth stocks outperforming in several of those years. Interestingly, when building portfolios using criteria such as low P/E or low P/B the results are not statistically different. Suggesting perhaps that the classification into value or growth stocks is more complex than it would appear. One of the common assumptions of value investing is that those stocks outperform over long periods of time. It might well be that in the Thai case one year is not a long enough period for value stocks to outperform. While there have been some clear efforts over recent years to modernize the stock market of Thailand, it remains relatively underdeveloped, particularly when compared to markets such as the U.S. Hence, its behavior regarding value versus growth investment might be rather different.
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