Stock Market Analysis Research Articles

Research on application of fractional calculus in signal analysis and processing of stock market

Abstract The linkage effect of stock returns is the basis for investors to conduct industry asset allocation and portfolio risk control. Traditionally, time-domain correlation-based stock signal analysis does not consider the dependence of the rate of return on frequency. However, the recent study of linkage in the spectrum analysis framework ignores the correlation of the rate of return over time on different frequency scales. The change. Wavelet analysis theory evolved from the Fourier analysis of fractional calculus. Using wavelet analysis fractional integration to analyze continuous wavelet transform, multi-scale wavelet transforms and s-for wavelet transform is an effective way to study stock market signals. algorithm. In order to overcome the limitations of the above-mentioned methods for industry linkage analysis, this paper uses wavelet coherence and wavelet phase difference based on continuous wavelet transform to quantitatively analyze the linkage effect of stock signal returns in time-frequency domain.

Relation Analysis of Taiwan and Japan Stock Markets with the Factors of U.S. and U.K. Stock Markets

Relation Analysis of Taiwan and Japan Stock Markets with the Factors of U.S. and U.K. Stock Markets

Present State-of-The-ART of Dynamic Association Rule Mining Algorithms

Association Rule Mining (ARM) is a data mining approach for discovering rules that reveal latent associations among persisted entity sets. ARM has many significant applications in the real world such as finding interesting incidents, analyzing stock market data and discovering hidden relationships in healthcare data to mention few. Many algorithms that are efficient to mine association rules are found in the existing literature, apriori-based and Pattern-Growth. Comprehensive understanding of them helps data mining community and its stakeholders to make expert decisions. Dynamic update of association rules that have been discovered already is very challenging due to the fact that the changes are arbitrary and heterogeneous in the kind of operations. When new instances are added to existing dataset that has been subjected to ARM, only those instances are to be used in order to go for incremental mining of rules instead of considering the whole dataset again. Recently some algorithms were developed by researchers especially to achieve incremental ARM. They are broadly grouped into Apriori-based and Pattern-Growth. This paper provides review of Apriori-based and Pattern-Growth techniques that support incremental ARM.

Detecting bursty terms in computer science research

Research topics rise and fall in popularity over time, some more swiftly than others. The fastest rising topics are typically called bursts; for example “deep learning”, “internet of things” and “big data”. Being able to automatically detect and track bursty terms in the literature could give insight into how scientific thought evolves over time. In this paper, we take a trend detection algorithm from stock market analysis and apply it to over 30 years of computer science research abstracts, treating the prevalence of each term in the dataset like the price of a stock. Unlike previous work in this domain, we use the free text of abstracts and titles, resulting in a finer-grained analysis. We report a list of bursty terms, and then use historical data to build a classifier to predict whether they will rise or fall in popularity in the future, obtaining accuracy in the region of 80%. The proposed methodology can be applied to any time-ordered collection of text to yield past and present bursty terms and predict their probable fate.

An Effective Stock Classification Method Via MDS Based on Modified Mutual Information Distance

This paper proposes a multidimensional scaling (MDS) method based on modified mutual information distance (M-MDS) to analyze stock market data. To better describe the relativity of financial data, it is worthwhile to point out that the commonly used proximity matrix in MDS is replaced with modified mutual information distance (M-MI-D) matrix. Refer to M-MI-D, a higher dissimilarity leads to a larger distance. In order to demonstrate the stability and accuracy of M-MDS, logistic time series are used in simulation experiments. In addition, a comparison of this new M-MDS method with classical MDS is given using the stock market data. It is noted that the new M-MDS method shows better stability than that of classical MDS method. Moreover, not only the stocks in the same US stock block, but also the stocks in different blocks have been discussed to illustrate the efficiency of M-MDS method.

Revisiting stock market integration in Central and Eastern European stock markets with a dynamic analysis

ABSTRACT Considering the importance of continuously analysing stock market integration, and based on an earlier study, this paper adopts a sliding windows approach, jointly with the Detrended Cross-Correlation Analysis correlation coefficient, in order to assess the evolution of integration in Central and Eastern European stock markets. With this approach, we are able to analyse stock market integration in a dynamic way. Our results show that the stock markets of the Czech Republic, Hungary, Croatia, Poland and Romania are most integrated, while those of Bosnia, Montenegro, Serbia and Slovakia are less so. Moreover, we found that during crises, levels of integration increased, while the Brexit referendum seems to have had the contrary effect on markets. This is important information for investors, for example, when wanting to build portfolios, but also for authorities, for whom the information about correlations could be important in detecting potential price crashes.

Islamic Finance and Herding Behavior Theory: A Sectoral Analysis for Gulf Islamic Stock Market

This study examines herding behavior in four sectors of the Gulf Islamic stock markets. Based on the methodology of Chiang and Zheng (2010), results showed evidence of herding among investors in major sectors for the Gulf Cooperation Council (hereinafter GCC) Islamic stock market during falling periods. In addition, we found that conventional return dispersions have a dominant influence during both falling and rising market periods. We also found evidence of herding around the conventional sectors during down market periods only in banking, hotel and restaurant sectors. There is evidence of herding around the conventional sectors during up market periods for insurance and industrial sectors.

Stock Market Reactions and CSR Disclosure in the Context of Negative CSR Events

This paper analyses stock market reactions after the occurrence of major negative corporate social responsibility (CSR) events and the possibility of mitigating these effects through the upfront provision of CSR information in firms’ annual reports. For this purpose, we follow a three-step procedure. First, we analyse the major concerns gathered from REPRisk® data via event study analysis. Herein, we cover a window of 5 to 20 days. Second, we analyse all annual reports of the firms mentioned in the covered period over the entire time horizon and conduct a textual analysis to examine firms’ disclosure of CSR information. Finally, we draw conclusions from the two approaches and show that firms with more upfront CSR information suffer from stronger negative market reactions after the occurrence of a negative CSR event. Herein, we show that if the occurrence of a negative CSR event conflicts with investors’ expectations, then it leads to an important update of investors’ beliefs about firms’ prospects. Our results also confirm that such an event leads to an adjustment of the subsequent year’s CSR disclosure in the annual reports.

Whither the Regulatory 'War on Coal'? Scapegoats, Saviors, and Stock Market Reactions

Complaints about excessive economic burdens associated with regulation abound in contemporary political and legal rhetoric. In recent years, perhaps nowhere have these complaints been heard as loudly as in the context of U.S. regulations targeting the use of coal to supply power to the nation’s electricity system, as production levels in the coal industry dropped by nearly half between 2008 and 2016. The coal industry and its political supporters, including the president of the United States, have argued that a suite of air pollution regulations imposed by the U.S. Environmental Protection Agency during the Obama administration seriously undermined coal companies’ bottom lines, presenting an existential threat to the industry. Under the Trump administration, industry players have lobbied hard for (and sometimes received) financial subsidies and regulatory changes, with the president seemingly all too happy to play the role of the industry’s savior. Stepping back, we consider the extent to which regulations have really led to the decline in demand for coal and how much the coal industry can actually expect to gain from the deregulatory policies of the current administration. To illuminate these questions, we statistically analyze stock market reactions to important events in what critics called the regulatory “war on coal” during the Obama administration. Using an event-study framework that measures abnormal market activity in the immediate wake of these events, we are able to isolate any potential impact of regulatory developments above and beyond market factors, such as secular trends in natural gas prices and market performance as a whole. Surprisingly, we find no systemic evidence consistent with a “war on coal” based on investor assessments of the industry’s financial prospects in the wake of new regulatory developments, even though our methods do find evidence of stock market reactions to other events, such as bankruptcies of other companies. Coal firms’ investors—the very actors with financial stakes in understanding the impact of regulation on the industry—appear to have behaved as if they never actually bought into the regulatory “war on coal” narrative. Our findings are consistent both with broader evidence about the effects of regulation and with an underlying political economy of regulatory scapegoating, according to which actors in a declining industry prefer to blame regulation rather than competitive factors for their businesses’ decline. By calling attention to the pervasive incentives for scapegoating and cheap talk by politicians seeking to be saviors, we offer an account that can explain the mismatch between our findings and the rhetoric of the “war on coal.” Along the way, our account reinforces how important it is for courts, elected officials, and the public to demand that government agencies base their regulatory decisions on evidence instead of relying on political rhetoric.

Economic Forces and Firm Stock Returns Volatility: Role of Firm Features

Stock market volatility is always been a major concern for investors, regulators, policy makers and academicians. Unfortunately, firm level volatility has not been given the due attention. The studies dealing with the firm level volatility are scarce. Moreover, a common assumption of homogenous nature of firms is used in the aggregate stock market analysis, sectoral level analysis and even in a firm level analysis. This homogenous assumption was objected by several researchers and suggested that firms are heterogeneous even in a narrowly defined sector. Furthermore, firms are different from each other because of possessing different characteristics. Based on that firm’s response to macroeconomic changes would not be the same. Hence, the hypothesis testing ignoring this fact could be spurious. This study proposes five categories in which firms can be classified, such as firm age, firm size, firm nature of business, firm trading nature and the sectoral location of the firm. This study proposes to examine the linkages between the macro economic factors and the firm level stock returns volatility considering the given firm features. It is expected from the empirical testing that the macroeconomic factors effect firm stock returns volatility belonging to different firm features differently, both in terms of magnitude and sign.

Cointegration Analysis of Major African Stock Markets

Globalization, technological advancements and financial liberalization have made it possible for stock markets in different countries to interact and affect and/or influence each other both in the short-run and in the long-run. This study uses the Dickey-Fuller, Engle-Granger method and the Johansen method to test for cointegration using a pair wise analysis between the stock markets in five African countries. These countries are Nigeria, Egypt, South Africa, Morocco, and Mauritius. The Granger causality test is equally used to understand the short-run linkages between the stock markets. All statistical tests are carried out using the R statistical software. Weekly stock indices from January 2010 to December 2015 are employed, with each stock price expressed in local currency. Correlation between the stock markets is very low, with the strongest correlation coefficient being just 31% between the stock Exchange of Mauritius and the Johannesburg Stock Exchange. Cointegration analyses reveal long-run associationship between twelve, out of the twenty-one pairs of stock markets. Granger causality tests reveal bidirectional causality between the South African and Mauritian stock markets and the South African and Nigerian stock markets. Unidirectional causality was also found from the Nigerian stock market to the Moroccan stock market and from the Nigerian stock market to the Mauritian stock market. This research will help investors to invest wisely in the stock markets examined. It will equally aid economic experts and policy makers in these countries to understand the impact of a shock on economic variables on the economy.

Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak

A Markov chain is commonly used in stock market analysis, manpower planning, and in many other areas because of its efficiency in predicting long run behavior. However, the Air Quality Index (AQI) suffers from not using a Markov chain in its forecasting approach. Therefore, this paper proposes a simple forecasting tool to predict the future air quality with a Markov chain model. The proposed method introduces the Markov chain as an operator to evaluate the distribution of the pollution level in the long term. Initial state vector and state transition probability were used in forecasting the behavior of Air Pollution Index (API) that has been obtained from the observed frequency for one state shift to another. The study explores that regardless of the present status of API, in the long run, the index shows a probability of 0.9231 for a good state, and a moderate and unhealthy state with a probability of 0.0722 and 0.0037, while for very unhealthy and hazardous states a probability of 0.0001 and 0.0009. The outcome of this study reveals that the model development could be used as a forecasting method that able to help government to project a prevention action plan during hazy weather.

ЕКОНОМІЧНИЙ ІНДЕКС ПЕРЕКУПЛЕНОСТІ ТА ПЕРЕПРОДАНОСТІ ФОНДОВОГО РИНКУ

Introduction. Our stock market and stock exchange infrastructure are far behind European or US counterparts. Borrowing from them the experience of creating and using the developments in the domestic market will allow to develop the economy. Foreign indicators, research and sources, most of which are in the open, can give impetus to young and prospective scientists to improve the economic system in general and the development of the stock market in particular. The imperfection of the domestic legislation and the lack of transparency in the economic system do not allow to develop the stock market fully and effectively. It is not possible to find the perfect metric for every market, for every generation and for every asset, but it is possible and necessary to look for ways of analyzing and exploring data to be able to extrapolate them to current economic and geopolitical conditions. The purpose of the study is to analyze one of the known in the West indexes, but not common in the domestic expanses in the world of finance and quotations on Wall Street. Objectives of the article: increase the financial literacy of the population; to encourage prospective specialists to develop the stock market; to analyze known world sources and economic indicators; to convey the opportunity and feasibility of analyzing the economy based on stock market analysis. Conclusions. An important advantage of this indicator is its consolidated structure and the presence of several evaluation criteria. An easy and understandable form of presentation and prognostic character are also additional positive features. Skeptics argue that this index is more a tool for researching the current market condition than a predictive indicator, also pointing to its volatile nature and not always accurate signals. It is definitely worth using this indicator in your own analytical system, but it needs to be analyzed in parallel with other macroeconomic indicators. Keywords: stock market, economy, stock market, trading, volatility, stocks, bonds, options, futures.

Information-driven stock return comovements across countries

This article analyses stock market comovements at a global level for 37 advanced and emerging countries in the last two decades. The article reports that international stock return comovements were greater in advanced countries than in emerging ones, but increased more rapidly in emerging countries than in advanced ones. The driving forces behind these comovements were country-specific fixed effects and time-varying factors over the period 2007–2015. These factors include not only the openness of international trade and finance but also institutional factors representing the development of information and communication technologies, the protection of property rights, and the transparency of information disclosure. These institutional factors worked in line with an information-driven comovement theory.

Combining a gravitational search algorithm, particle swarm optimization, and fuzzy rules to improve the classification performance of a feed-forward neural network

Background and objectiveA feed-forward neural network (FNN) is a type of artificial neural network that has been widely used in medical diagnosis, data mining, stock market analysis, and other fields. Many studies have used FNN to develop medical decision-making systems to assist doctors in clinical diagnosis. The aim of the learning process in FNN is to find the best combination of connection weights and biases to achieve the minimum error. However, in many cases, FNNs converge to the local optimum but not the global optimum. Using open disease datasets, the purpose of this study was to optimize the connection weights and biases of the FNN to minimize the error and improve the accuracy of disease diagnosis. MethodIn this study, the chronic kidney disease (CKD) and mesothelioma (MES) disease datasets from the University of California Irvine (UCI) machine learning repository were used as research objects. This study applied the FNN to learn the features of each datum and used particle swarm optimization (PSO) and a gravitational search algorithm (GSA) to optimize the weights and biases of the FNN classifiers based on the algorithms inspired by the observation of natural phenomena. Moreover, fuzzy rules were used to optimize the parameters of the GSA to improve the performance of the algorithm in the classifier. ResultsWhen applied to the CKD dataset, the accuracies of PSO and GSA were 99%. By using fuzzy rules to optimize the GSA parameter, the accuracy of fuzzy–GSA was 99.25%. The accuracies of the combined algorithms PSO–GSA and fuzzy–PSO–GSA reached 100%. In the MES disease dataset, all methods exhibited good performance with 100% accuracy. ConclusionsThis study used PSO, GSA, fuzzy–GSA, PSO–GSA, and fuzzy–PSO–GSA on CKD and MES disease datasets to identify the disease, and the performance of different algorithms was explored. Compared with other methods in the literature, our proposed method achieved higher accuracy

The “day-of-the-week” effects in the exchange rate of Latin American currencies

This paper examines the “day-of-the-week” anomaly in the foreign exchange market of six major Latin American countries’ currencies: (Argentina, Brazil, Chile, Colombia, Mexico, and Peru), all with respect to the United States’ dollar. The returns of daily exchange rates are stationary, so we use linear regressions combined with GARCH, TARCH and EGARCH models to explore the presence of the “day-of-the-week” anomaly. The results confirm the presence of “abnormal” effects in some of the currencies and in some days of the week, particularly on Fridays and Mondays. Moreover, volatility in exchange rates shows clustering behavior, as well as leverage effects, which are carefully modelled in our analysis. This paper contributes to the literature by studying the “day-of-the-week” effects in currency exchange rate markets, a clear innovation with respect to the typical stock market analysis. The results reported are useful for foreign exchange market traders, currency exposure management decision makers, monetary authorities, and financial policy designers in the countries included in the study. Indeed, the results suggest the presence of a typical behavior of the exchange rate of all the currencies included in the sample.

Applying of a Company’s Stock Price Prediction using Data Mining

Stock market analysis is a common economic activity that has been an attractive topic to research and used in different forms of day-to-day life in order to predict the stock prices. Techniques like major analysis, Statistical investigation, Time arrangement analysis and so on are reliably worthy forecast device. In this paper, Data mining, Machine learning (ML) and Sentiment analysis are techniques used for analyzing public emotions in order predict the future stock prices. The goal of a project is to review totally different techniques to predict stock worth movement victimization the sentiment analysis from social media, data processing. Sentiment classifiers are designed for social media text like product reviews, blog posts, and email corpus messages. In the company’s communication network, information mining calculation is utilized as to mine email correspondence records and verifiable stock costs. Implementing various Machine learning and Classification models such as Deep Neural network, Random forests, Support Vector Machine, the company can successfully implemented a company-specific model capable of predicting stock price movement with efficient accuracy.

Evaluation of Unsupervised Anomaly Detection Methods in Sentiment Mining

Anomaly detection has vital role in data preprocessing and also in the mining of outstanding points for marketing, network sensors, fraud detection, intrusion detection, stock market analysis. Recent studies have been found to concentrate more on out

Short Term Firm-Specific Stock Forecasting with BDI Framework

In today’s information age, a comprehensive stock trading decision support system which aids a stock investor in decision making without relying on random guesses and reading financial news from various sources is the need of the hour. This paper investigates the predictive power of technical, sentiment and stock market analysis coupled with various machine learning and classification tools in predicting stock trends over the short term for a specific company. Large dataset stretching over a duration of ten years has been used to train, test and validate our system. The efficacy of supervised non-shallow and prototyping learning architectures are illustrated by comparison of results obtained through myriad optimization, classification and clustering algorithms. The results obtained from our system reveals a significant improvement over the efficient market hypothesis for specific companies and thus strongly challenges it. Technical parameters and algorithms used have shown a significant impact on the predictive power of the system. The predictive accuracy obtained is as high as 70–75% using linear vector quantization. It has been found that sentiment analysis has strong correlation with the future market trends. The proposed system provides a comprehensive decision support system which aids in decision making for stock trading. We also present a novel application of the BDI framework to systematically apply the learning and prediction phases.

When does crowdsourcing benefit firm stock market performance?

Crowdsourcing is a particular form of open innovation (OI) that aims to boost idea-generation in innovation processes. The underlying rationale is that the collective intelligence of a large number of contributors outside the firm’s boundaries increases the likelihood of achieving ‘extreme outcomes’, i.e., high quality ideas with exceptional business potential. Due to the idiosyncrasies that differentiate crowdsourcing from other forms of OI, the findings from prior research on the performance implications of OI cannot be directly extended to crowdsourcing. Similarly, the findings on the effect of internal R&D on firm performance cannot be directly applied to crowdsourcing due to the greater uncertainty in dealing with a crowd of unknown individuals outside the organization whose ideas have to be evaluated and ultimately processed internally. Thus, while crowdsourcing research has recently burgeoned, it is ambiguous as to whether and when crowdsourcing is beneficial for firms. In fact, the overall effect of crowdsourcing on a firm’s future profits has not been thoroughly investigated. To fill this gap, we conducted an event study analyzing stock market reactions to crowdsourcing announcements, a forward-looking market-based measure able to isolate the effect of crowdsourcing on a firm’s future profits, which we refer to as firm stock market performance. Drawing on the resource-based view, we argue that an external crowd can become a valuable resource if the firm is able to extract value from it. Our findings show that two key contingency factors, i.e., brand value and investment opportunities, determine the boundary conditions that enable firms to extract value from the crowd, resulting in a positive stock market reaction to the announcement of a crowdsourcing campaign. In addition to advancing scholarly knowledge on crowdsourcing, our results provide practitioners with relevant indications for profitable crowdsourcing campaigns.