Search results

The nature and extent of our knowledge of stock market efficiency are examined. The development of “efficiency”, as a way of thinking about stock markets, is traced from Roberts (1959) and Fama (1965) onward. The early work successfully introduced competitive economic theory to the study of stock markets and paved the way for a flood of empirical research on the relation between information and stock prices. This literature irreversibly altered our views on stock market behavior. The theory and evidence of seemingly‐rational use of information lay in sharp contrast to prior beliefs. It was associated with a widespread increase in respect for stock markets, financial markets, and markets in general, at the time. Researchers began developing and using a variety of formal models of security prices. Nevertheless, “efficiency” has its limitations, both theoretically (as a way of characterizing markets) and empirically (by stretching the quality of the data, the estimation techniques used, and our knowledge of price behavior in competitive markets). Extensive evidence of anomalies suggests either that the market systematically misprices securities or that the theoretical or empirical limitations are binding, or both. The less interesting research question now is whether markets are efficient, and the more interesting question is how we can learn more about price and transactions behavior in competitive stock markets. The concept of an “efficient stock market” has stimulated both insight and controversy since Fama (1965) introduced it to the financial economics literature. As a construct, “efficiency” models the stock market in terms of the reaction of prices to the flow of information. Like all theory choices, modelling the market in this fashion involved tradeoffs. The benefits included opening the literature to an abundance of high‐quality researchable data, covering a variety of information, and the resulting insights obtained on the role of information in setting prices. The opportunity costs included temporarily closing the literature to alternative ways of viewing stock markets, for example by modelling public information as a homogenous good and thus ignoring factors such as differences in beliefs among investors, differences in information processing costs, and the “animal spirits” that might drive group behavior. The costs also included reliance on particular asset‐pricing models of how an “efficient” market would set prices. Not surprisingly, the ensuing deluge of research has produced some startling evidence, for and against the proposition that financial markets are “efficient”. Strongly‐conflicting views and puzzling anomalies remain. The early evidence seemed unexpectedly consistent with the theory. The theory, and its implications, also seemed clear at the time. After a period that seems short in retrospect, the growing body of evidence in favor of the efficient market hypothesis emerged as one of the most influential empirical areas of economics. Fama's (1970) review described a flourishing, coherent and confident literature. This research had an irreversible effect on our knowledge of and attitude toward stock markets, and financial markets generally. It coincided with an emergence of interest in, and respect for, all markets among economists and politicians, and influenced the worldwide trend toward “liberalizing” financial and other markets. The research consistently appeared to show an unbiased reaction of stock prices to public information. The property of “unbiased reaction” to public information, which formed the basis of the early definitions of “efficiency”, was seen to be an implication of rational, maximizing investor behavior in competitive securities markets (Fama 1965, p.4). Reduced to a basic level, the reasoning was that any systematicallybiased reaction to public information is costlessly publicly observable, and thus provides pure profit opportunities to be competed away. Characterizing the market in terms of its reaction to information is only one of many feasible ways of modelling stock price behavior, but it introduced economic theoryto the empirical studyof stock prices, which had received little serious attention from economists prior to that point. Despite the subsequent spate of anomalies, the early efficiency literature not only adapted standard economic theoryto provide the first formal economic insights into how stock prices behave, but it helped pave the way for an outporing of theoretical and empirical work on stock markets and capital markets in general. Subsequent empirical research was not as consistent with the theory. Evidence of “anomalous” return behavior now is widespread and well‐known. It generallytakes the form of variables (for example, size, day‐of‐the‐week, P/E ratio, market/book value ratio, rank of scaled earnings change, dividend yield) that are significantly but inexplicablyrelated to subsequent abnormal stock returns. Much of this evidence has defied rational economic explanation to date and appears to have caused many researchers to strongly qualify their views on market efficiency. Disagreement has not been not confined to the evidence. The literature has produced a variety of research designs, ranging from the “market model” of Fama, Fisher, Jensen and Roll (FFJR, 1969) to Shiller's (1981a,b) variance‐bounds tests. The very term “efficiency” has engendered controversy: there is a modest literature on precisely what efficiency means, on the role of transaction costs, and on whether efficient markets are logically feasible. Making sense of this literature requires careful definition of “efficiency” in this context and careful analysis of the type of evidence that has been offered in relation to it. This involves an assessment of the strengths and weaknesses of both the theory of efficient markets, as a way of characterizing stock markets, and of the data and research designs used in testing it. Not surprisingly, a mixed conclusion emerges. While the concept of efficient markets was an audacious departure from the comparative ignorance and suspicion among economists of stock markets that preceded it, and provides valuable insights into their behavior, the concept has its limitations, in terms of both its internal logical coherence and its fit with the data. Section 1 ofthis survey sketches the development of the efficient market theory, reviewing the principal contributions in terms of their usefulness in guiding and evaluating empirical research. Section 2 addresses the limitations inherent in what is knowable about stock market efficiency, given the present state of theory about how security prices might behave in an “efficient” market. It argues that there are binding limitations in the theoryof asset pricing, some of which are known and others of which are unknown or even unknowable. These limitations must be borne in mind when choosing whether to interpret the data as evidence of: (1) market efficiency, under the maintained hypothesis that a specific research design, including a specific model of asset pricing used to benchmark price behavior, correctly describes pricing in an efficient market; or (2) the ability of our models and research designs to encapsulate how prices behave in an efficient market, under the maintained hypothesis of efficiency. Against this background, section 3 then provides an assessment of the accomplishments of the theory of stock market efficiency, including an interpretation of the evidence. It focuses on the nature and influence of the evidence and does not attempt to provide a comprehensive literature taxonomy. The final section offers conclusions. The principal conclusion is that the theory of efficient markets has irreversibly enhanced our knowledge of and respect for stock markets (and perhaps for all financial market or even for markets in general) but that, like all theories, it is fundamentally flawed.

Examines the impact of genetic testing and therapy on health care in light of the development of the system in the USA since the turn of the twentieth century. Genetic testing and therapy have the potential to create a great advance in health care but also to become a business of multi‐billion dollar proportions. If present trends of investment and long‐range plans mature, health care will be adversely affected in terms of its distribution, access and economy. Developed and sold as commodities in a free‐market economy, genetic advances will economically stress health care and fail to meet the ethical and legal standards demanded by voluntary informed consent and counseling. Without abandonment of the marketplace approach to health care and thorough reform, many will find themselves excluded from the benefits and vulnerable to discrimination.

In February this year the sequence of the human genome was published, opening a new chapter in medicine. Soon genetic testing will be at the heart of diagnosis, epidemiology, drug development and even regenerative medicine. Before we are born there will be new opportunities to remedy genetic defects, and afterwards to make almost lifelong prognoses. The debate will intensify on the use of human embryos in medical research, while the prospect of human cloning will fascinate some scientists and horrify others. Europe needs to be in the vanguard of this new industrial revolution, but a host of ethical concerns must first be addressed – because genomics is as much about privacy as Petri dishes.

The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.

This data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.

The proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.

The research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.

The emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.

In this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

This study aims to evaluate the health literacy level of the parents of children diagnosed with Down syndrome (DS) within one institution in Turkey.

A cross-sectional survey measuring demographics, information of the child and the parent and health literacy was administered to participants. The health literacy levels in the study were measured with the European Health Literacy Scale (EHLS), which consists of 47 questions.

Of the 65 participants who completed the questionnaire, 56.9% were mothers, 68.1% were diagnosed in the neonatal outpatient clinic examination after birth, and 58.5% stayed in the neonatal intensive care unit after birth. The mean score of the IHLS scale was 25.06 ± 6.59. Of the parents, 63.1% were found to be inadequate, 18.5% problematic-limited, and 18.5% adequate health literate. Any parent with excellent health literacy level was identified. High education level (p < 0.001), high income level (p < 0.001), living in the city center (p < 0.05), planned pregnancy (p < 0.05) and being a health worker (p < 0.001) were found to be statistically significant with a high EHSL score.

The presence of Down syndrome (DS) in a child also necessitates ongoing monitoring for a range of conditions, including eye diseases and heart disease. Some surgical procedures, such as heart or gastrointestinal surgeries, may also be required. Additionally, the child may require the administration of various medications. Finally, due to the potential lifelong need for assistance, the child may require the support of an adult throughout their lifetime. This is because of the child's inability to live independently due to their mental state. Therefore, parent education is the most important issue in the follow-up of the disease.

To the best of the authors’ knowledge, this is the first study to determine that parents of children diagnosed with DS have very limited knowledge of the disease and health literacy. Explanation of current diseases, treatments and training of parents should also be included in genetic counseling.

DS is a chromosomal disease that requires multidisciplinary care. Parents have to know the course of the disease and its complications.

The findings of this study indicate that parents of children with Down syndrome exhibit a profound lack of knowledge regarding the nature of their child's condition and the available healthcare options. It is therefore imperative that genetic counseling incorporates an explanation of the diagnosed diseases, treatments, and educational resources for parents.

Depressive disorders are a focus of growing social and economic concern. While antidepressant medications are widely accepted, they are ineffective for nearly 40% of users, and cause numerous adverse drug reactions. The pharmacogenomics of depression attempts to better understand the role of genetic variation in antidepressant metabolism in the hope of improving drug efficacy and tolerability. However, the development and delivery of genome‐based antidepressants face many hurdles. In this paper we provide an overview of the potential impact of the pharmacogenomics of depression on public mental health care by focusing on the social and ethical issues at stake. These include questions about genetic testing, informed consent, drug access, and market fragmentation. We end the paper with a brief discussion of the wider context and how the pharmacogenomics of depression relates to broader trends in psychiatry and biomedicine.

The study of birth defects has increased in importance in recent years because the rate of infant mortality due to other causes (such as infection and nutritional disease) has decreased more quickly than has the rate of deaths due to birth defects. Today, abnormalities are detected in approximately 3 percent of newborn humans, and twice as many prenatally acquired defects are found in children after infancy as are discovered at birth. In addition, many of the more than 500,000 miscarriages and stillbirths that occur each year in the United States are due to abnormal fetal development.

The purpose of this paper is to provide a comprehensive overview of the current knowledge regarding chronic hepatitis C (CHC) infection, antiviral therapy, depression, and gender.

CHC and its treatment options were reviewed examining their relationship with depression and gender.

CHC is a high prevalent chronic infection worldwide, being similar in men and women. However, the infection shows many gender differences in terms of innate response, genetic variability (i.e. IL-28B), route of transmission (i.e. intravenous drug use), disease progression (i.e. fibrosis), lifetime period (i.e. pregnancy), and risk factors (i.e. HIV). Both the hepatitis C infection and antiviral treatment (especially when using the pro-inflammatory cytokine interferon α), are highly associated with depression, where female gender constitutes a risk factor. It seems that the new direct-acting antiviral combinations produce fewer neuropsychiatric side effects. In fact, the presence of depression at baseline is no longer a limitation for the initiation of antiviral treatment. Antidepressant drugs have been recommended as current depression and prophylactic treatment in risk subgroups. However, caution should be exercised due to the risk of drug-drug interactions with some antiviral drugs. Women should be counselled prenatal, during and after pregnancy, taking into account the clinical situation, and the available evidence of the risks and benefits of antiviral and antidepressant treatments. Multidisciplinary approach shows cost-efficacy results.

The paper clarifies the complex management of CHC therapy and the importance of individualizing treatment. The results also underline the need for an integrated multidisciplinary approach.

Genetic engineering is an ultra‐modern form of biotechnology. It is a broad term used to describe a multitude of closely related processes such as crossbreeding, plant hybridization and fermentation—all of which promise to transform, for the better, many important areas of concern in the world. Genetic scientists working in the medical field, for instance, are close to finding cures for diseases such as cystic fibrosis by experimenting with the transfer of genes between microbes, plants and animals. There are currently about 1,000 genetic diseases that can be diagnosed by DNA methodologies. In the process, however, genetic scientists are altering life forms and even creating new ones.