Stock Market Information Research Articles

Empirical Analysis on Unexpected Information Effect of Monetary Policy and Stock Price Fluctuation: Taking Military and Defense Enterprises as an Example

The existing studies have shown that the unexpected information effect of monetary policy (IEOMP) would affect the fluctuation of stock prices. This paper tests the effect of unexpected IEOMP on stock prices in China. Taking Chinese listed military and defense enterprises, for example, the author further explored how the influence of unexpected IEOMP over stock price varies between industries. The empirical analysis reveals that, despite the industrial heterogeneity of the said influence in China, the stock price of Chinese listed military and defense enterprises is still affected by unexpected IEOMP. Our analysis enables Chinese military and defense enterprises to build a more robust market mechanism for the military and defense market, open more effective channels for the industrial chain, capital chain, and private capital, and drive the national economy with the sci‐tech results of defense. In addition, policymakers are recommended to keep an eye on the price fluctuation in the stock market and important economic information from stock price fluctuation in order to effectively identify the potential risks in the future economy and to improve the predictability of the feedback mechanism of monetary policy.

An Empirical Study on Stock Price Forecasting Based on ARIMA Model

The time series data in the financial market contains historical information, which can reveal the operation law of the system. This paper analyzes and predicts the stock price of China Merchants Bank based on the ARIMA model, selects 243 sample data of the daily stock closing price of China Merchants Bank from July 1, 2021 to July 1, 2022 as the research object, establishes the ARIMA model with Eviews, and uses Python for drawing. Finally, the closing price of the next 3 working days is predicted based on the model. The empirical results show that the ARIMA model has a good effect on the short-term prediction of stock prices, but in the long-term prediction, it can be considered to combine with other forecasting methods, and pay more attention to the stock market information and national macro policies, so as to improve the accuracy of the model prediction.

Changes and trends in the investment environment before and after the COVID-19 outbreak

The COVID-19 outbreak in 2020 has had a huge impact on the global economy and stock markets, and investors have changed their direction. In order to find some laws in the drastic stock market changes, we selected the stock market information of ten different industries, using the Markowitz model and Index model, and came up with some important data such as minimum variance, effective frontier and so on. Based on our research and the data available, we believe that investors should pay more attention to buying shares of high-tech companies and, where appropriate, sell shares of traditional energy companies and maintain continued buying of stocks of commodity companies in order to maximize returns with minimal risk.

Listed public–private enterprises: stock market information, agency costs and productive efficiency outcomes

PurposeThis paper addresses the social value of commercial enterprises that are jointly owned by a government and private sector investors and where the shares are listed on a stock exchange: thus, “listed public–private enterprises” (LPPEs). The theoretical part of the paper addresses how differences in ownership patterns influence the behavior and performance of LPPEs.Design/methodology/approachWe develop a conceptual taxonomy, drawing on the empirical evidence on the behavior and performance of public–private hybrid enterprises and on the application of agency theory to that evidence. The taxonomy discussion predicts how different ownership patterns affect enterprise productive efficiency and the ability of governments to achieve social goals through LPPEs. We review the empirical literature on government enterprise ownership and on the concentration of private share ownership to deduce how these matter for owner and managerial behavior and productive efficiency. We review the literature that considers the informational content that listing of an enterprise's shares on a stock exchange can provide to enterprise owners, managers and other domestic audiences with a policy interest. We employ a social welfare perspective to derive policy implications as to when the LPPE governance structure is most appropriate.FindingsWe show how the monitoring and performance weaknesses of state ownership are offset by some private ownership, particularly when combined with listing on a stock exchange. We demonstrate the effects of different governance structures on enterprise productive efficiency. We find that the LPPE structure is particularly appropriate as an alternative to nationalization or to full privatization and regulation of natural monopoly public utilities, and as an alternative to full private ownership and taxation of non-renewable natural resource extractive enterprises.Originality/valueThis paper explicitly addresses the question of why and how the combination of government ownership, private investor ownership and listing on an exchange is socially valuable in providing information on productive efficiency to governments.

Theory and applications of financial chaos index

We develop a new stock market index that captures the chaos existing in the market by measuring the mutual changes of asset prices. This new index relies on a tensor-based embedding of the stock market information, which in turn frees it from the restrictive value- or capitalization-weighting assumptions that commonly underlie other various popular indexes. We show that our index is a robust estimator of the market volatility which enables us to characterize the market by performing the task of segmentation with a high degree of reliability. In addition, we analyze the dynamics and kinematics of the realized market volatility as compared to the implied volatility by introducing a time-dependent dynamical system model. Our computational results which pertain to the time period from January 1990 to December 2019 imply that there exists a two-way causal relation between the processes underlying the realized and implied volatility of the stock market within the given time period. Specifically, we show that the latter has a stronger causal effect on the former as compared to the opposite. This result connotes that the implied volatility of the market plays a key role in characterization of the market’s realized volatility.

Detecting inorganic financial campaigns on Twitter

Online financial content is widespread on social media, especially on Twitter. The possibility to access open, real-time data about stock market information and firms’ public reputation can bring competitive advantages to industry insiders. However, as many studies extensively demonstrated before, manipulative campaigns by social bots do not spare the financial sector either.In this work, we show that the more viral a stock is on Twitter, the more that virality is artificially caused by social bots. This result is also confirmed when considering accounts suspended by Twitter instead of bots. Starting from this finding, we then propose two methods for detecting the presence and the extent of financial disinformation on Twitter, via classification and regression. Our systems exploit hundreds of features to encode the characteristics of viral discussions, including features about: participating users, textual content of shared posts, temporal patterns of diffusion, and financial information about stocks. We experiment with different combinations of algorithms and features, achieving excellent results for the detection of financial disinformation (F1=0.97) and promising results for the challenging task of estimating the extent of inorganic activity within financial discussions (R2=0.81, MAE=4.9%). Our compelling results pave the way for the deployment of novel systems for protecting against financial disinformation.

Private Information Dissemination in the Secondary Loan Market: The Effect on Stock Bid-ask Spreads

We consider loans being marked to market to constitute new information that is only immediately available to large institutional traders, so-called qualified institutional buyers (QIBs). Smaller investors (non-QIBs) do not have instant access to such information. Investigating the effects of privileged information releases on bid-ask spreads of borrowing firms’ equity, we find a significant elevation in the level of information asymmetry that affects those spreads. Our paper reveals that private information dissemination in the secondary loan market affects the stock market information environment and yields benefits to large insiders with priority access to important information about the borrowing firms’ quality.

A novel graph convolutional feature based convolutional neural network for stock trend prediction

Stock trend prediction is one of the most widely investigated and challenging problems for investors and researchers. Since the convolutional neural network (CNN) was introduced to analyze financial data, many researchers have dedicated to predicting stock trend by transforming stock market data into images. However, most of the existing studies just focused on individual stock information, and ignored stock market information, such as the existing correlations between stocks. In fact, the price volatility of a stock may be affected by those of other stocks, thus, taking the stock market information into the stock trend prediction can further improve the prediction performance. In this paper, we propose a novel method for stock trend prediction using graph convolutional feature based convolutional neural network (GC–CNN) model, in which both stock market information and individual stock information are considered. Specifically, an improved graph convolutional network (IGCN) and a Dual-CNN are designed to construct GC–CNN, which can simultaneously capture stock market features and individual stock features. Six randomly selected Chinese stocks are used to demonstrate the superior performance of the proposed GC–CNN based method. The experimental analysis demonstrates that the proposed GC–CNN based method outperforms several stock trend prediction methods and stock trading strategies.

Stock Market Information and Innovative Investment in the Supply Chain

Abstract We investigate whether suppliers adjust innovative supply chain investment following stock market signals about customers’ economic prospects. We show that suppliers increase R&D and investment in customer-related patents after positive market reactions to customers’ new product announcements. A battery of falsification tests suggest that spurious factors are unlikely to explain our results. Market signals about customers appear more important when information asymmetry is greater, when suppliers face greater competitive threats, and when suppliers are financially unconstrained. Our evidence suggests that the stock market can be a viable source of information to mitigate supply chain frictions. (JEL G14, G30, L14)

OCVID-19 Lockdown and Market Reaction to Earnings News

We examine the influence of mobility on the stock market reaction around earnings announcements during the COVID-19 pandemic. The pandemic has led to abrupt implementation of stay-at-home mandatory restrictions and social distancing requirements all around the world, resulting in the collapse of face-to-face interactions on a large scale. Using Google’s Community Mobility Reports to obtain daily, county-level mobility of U.S. residents, we find that firms located in counties with higher mobility experience stronger immediate price responses during earnings announcements and weaker post-announcement drift, suggesting that COVID-19 restrictions dampen price discovery in financial markets. We also show that investor attention is a potential mechanism by which mobility affects the market reaction. Together, our study finds empirical support for the importance of face-to-face interactions in facilitating stock market information.

The Impact of COVID-19 Pandemic on the Financial Contagion among Turkey, US, and China Stock Markets

Purpose – The aim of this study is to empirically examine the impact of COVID-19 on the dynamic correlation among the stock markets of Turkey, the United States, and China, and demonstrate the effects of search-based investor attention and newspaper-based infectious disease equity market volatility on the correlation among these markets. Design/methodology/approach – In this study, VAR(1)-DCC-GARCH(1,1) methodology is employed to examine the changes in the variances and dynamic correlations among markets after the outbreak of Covid-19. Then, least square regression analyses are done to examine the influences of search-based sentiment and newspaper-based infectious disease equity market volatility on the correlations obtained from VAR(1)-DCC-GARCH(1,1) model. Findings – Findings of this study demonstrate that the integration of the China stock market with Turkey and the US markets diminishes after the outbreak of a pandemic, while the dynamic correlation between the US and Turkey stock markets does not change significantly after Covid-19. Moreover, we present that increase in the media coverage of the Covid-19 related equity market volatility and search-based sentiment have explanatory power on the correlation between Turkey and US markets, especially after the Covid-19 is pronounced as a pandemic. Likewise, individual attention to Covid-19 negatively influences the correlation between Turkey and China. Discussion – This study presents that stock market integration is highly related to human health. Therefore, the results of this study offer inputs to investors and policymakers that can be used during infectious disease periods. Moreover, as public attention has a significant impact on the international stock market correlation, media coverages and information releases during low frequency, high severity events should be managed wisely.

Marginal Benefits of the Core Securities Laws

To every thing there is a season. In the area of securities regulation, it is the season for expansion. In this Article, I show why this anticipated expansion should not be pursued in the name of investor protection. More specifically, the Article demonstrates that the reductions to stock market information asymmetry provided by the core securities laws provide, at best, extremely limited benefits on the margin for passive investors today. Importantly, this conclusion holds true even if one disagrees with the dominant law and economics thinking about these disclosure, fraud, and insider trading laws (i.e., that there is little to no relevant investor-protection problem in the first place). Thus, I conclude that the investor-protection benefits of the core securities laws that regulate public companies and the market in which their stock is traded are marginal in another sense of the word as well.

Emotion Detection for Afaan Oromo Using Deep Learning

Emotion detection in text has become more popular due to its various useful applications in a different area, such as tracking product perception, public opinion detection about political tendencies, stock market monitoring, text summarization, information extraction, recommendation system, question answering and etc. However, manually identifying emotion of a million of people and aggregating them towards a rapid and efficient decision is quite a challenging task due to the rapid growth of social media user. This study aimed to develop Afaan Oromo emotion detection model in order to tackle this challenge. This study adopts artificial neural network approach. We used python tools with Keras library. We conduct our experiments on five emotion class (anger(arii), love(jaalala), joy(gamachu), disgust(jibba), and sadness(gadda)) by collecting a total of 1005 emotional sentence of Afaan Oromo language that have been manually annotated. The sentence has been scraped from different official Facebook page such as Oromia Broadcasting Network (OBN) pages, Fana Broadcasting Corporation (FBC) Afaan Oromo page, and British Broadcasting Corporation (BBC) Afaan Oromo pages using Facepager tools by creating Facebook API id. After collecting these data all preprocessing steps like tokenization, stop word removal and normalization have been undertaken. We used word embedding’s for feature extraction of preprocessed data. Subsequently, we have applied three artificial neural network algorithms such as Feed forward neural network, long short-term Memory and Bidirectional long short-term memory for classification purpose of the vectorized sentence into their emotion class. We compared the three artificial neural network algorithms and found out that Bidirectional long short-term memory achieved the best performance. We have achieved an average accuracy of 66%, 78%, 83% using Feed Forward Neural Network, Long Short-Term Memory and Bidirectional Long Short-Term Memory respectively. Based on experimental result, the researcher concluded that increasing amount of dataset, tuning hyper parameters properly and trying by different algorithms can, in some case, improve the performance of the model. Keywords : Emotion Identification, Afaan Oromo, Artificial Neural Network, Social Media DOI: 10.7176/NMMC/92-01 Publication date: August 31 st 2020

The effect of insider trading laws and enforcement on stock market transaction cost

Theoretical arguments suggest that as countries enact insider trading laws and complement them with enforcement, stock market information risk reduces and investor participation increases, and this will therefore have a negative effect on liquidity trading cost. Consistent with this expectation, based on panel data comprised of 32 countries for the period 2001–2015, we find that stringent insider trading laws and enforcement reduce stock market transaction cost. However, in countries where investor protection is poor, our results show that stringent insider trading laws have no effect on liquidity trading cost. We further find that stringent insider trading laws interact with institutional quality to reduce liquidity trading cost. Our findings are robust to difference-in-differences based on the 2008 global financial crises. The overall evidence implies that market participants will experience lower liquidity trading cost if insider trading laws are enforced.

Stock markets and the COVID-19 fractal contagion effects

This article investigates the fractal contagion effect of the COVID-19 pandemic on the stock markets. The stock market information of the top 32 coronavirus affected economies (as of 31st March 2020) was sampled for ex-ante and ex-post COVID-19 outbreak analysis using the Detrended Moving Cross-Correlation Analysis (DMCA) and Detrended Cross-Correlation Analysis (DCCA) techniques. The results confirm a fractal contagion effect of the COVID-19 pandemic on the stock markets. Furthermore, this fractal contagion effect fizzles out over time (in the middle and long run) for both the stock markets return and volatility. Therefore, this article provides pieces of evidence for the COVID-19 fractal contagion effect on the stock markets.

Private Information Dissemination in the Secondary Loan Market

We consider loans being marked to market to constitute information about borrowing firms’ profitability and risk only immediately available to large institutional traders, so-called qualified institutional buyers (QIBs). Smaller investors, so-called non-QIBs, do not have immediate access to such information because they are not qualified to enter the secondary loan market. We investigate the effect of privileged information releases first on stock bid-ask spreads, then on the abnormal returns QIBs earn by trading borrowing firms’ stock. We show QIBs to actively trade on information in the equity market, elevating the level of information asymmetry while earning significant returns. Our paper reveals private information dissemination in the secondary loan market to affect the stock market information environment and yield benefits to large insiders with priority access to important information about the quality of borrowing firms.

Gender differences in financial literacy and investment in SPSS

Financial literacy refers to a person's know-how of primary financial concepts. I start by documenting buyers' de mographic traits and monetary literacy the usage of a incredibly huge pattern of contributors recruited from Amazon's Me chanical Turk under varying levels of investment enjoy. I replicate on a sample of 3 accounting research experiments that vary inside the type and intensity of the underlying accounting problem. Across the 3 experiments, the information displays two important outcomes: First, investment enjoy strengthens the have an impact on of economic accounting disclosures on participants' in-vestment-associated judgments. Second, financial literacy further strengthens the have an effect on of eco nomic accounting disclosures on investors' (however now not non-investors') judgments. The results reveal the existence of various behavioral biases among Indian Investors Including overconfidence and self-attribution, default impact, anchoring bias, delegation, men-tal accounting, and emotional bias and herding. Thus, the findings help the view that person traders do not continually act ra-tionally. The results confirmed that economic literacy turned into undoubtedly correlated with mental accounting bias, but not appreciably correlated with overconfidence and emotional bias. Age, career, and investment enjoy are the most crucial demo-graphic variables associated with the behavioral biases of man or woman buyers within the sam ple. Regarding gender, men are extra confident than females about stock market information. SPSS statistics is a data man agement, advanced analytics, multivariate analytics, business intelligence, and criminal investigation developed by IBM for a statistical software package. A long time, spa inc. was created by, IBM purchased it in 2009. The logo name for the most recent variations is IBM SPSS facts. Demographics, Financial Literacy, Financial Knowledge, Risk Perception, Investment Decision. The Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is 1.275 which indi cates 42.5 % reliability. From the lit-erature review, the above 30.3 % Cronbach's Alpha value model can be considered for analysis.

Testing the Weak-Form Efficiency of the Nairobi Securities Exchange Market

The study aimed to test whether the Nairobi Securities Exchange Market is efficient in the weak form, specifically if stock prices movements are random or non-random. This study employed explanatory survey design on 20 firms sampled out of 68 listed firms. The parametric auto-correlation test and the non-parametric runs test were employed to test for serial independence in the daily prices. The data didn’t follow random walk model which postulates a zero mean. The results, Durbin-Watson Coefficient = 0.169 indicates non-independent observations. The study rejected the null hypothesis and concluded that NSE firms’ daily prices were non-random. It is possible that stock market prices are not informative and the market is inefficient in terms of resource allocation. The results is informative to investors and technical analysts to make use of historical data as they predict future prices. The market regulatory body should revise the markets information services and come up with innovative ways to increase free fair and equal dissemination of stock market information. Innovative and superior modeling of past daily prices needed to earn superior profits. Evaluation of factors that make the NSE weak-form inefficient is required implement policies to tackle the causes of inefficiency. Keywords: Weak-Form Efficiency, Stock Prices, Nairobi Securities Exchange Market DOI : 10.7176/RJFA/10-24-09 Publication date: December 31 st 2019

LARGE SHAREHOLDERS AND INFORMATION ASYMMETRY IN A TRANSITION ECONOMY – EVIDENCE FROM VIETNAM

A growing volume of studies indicate that the information asymmetry problem is a serious issue which significantly hinders stock market development. This problem is more pronounced in emerging markets with weak institutions. The domination of large shareholders in a firm might be a cause of information asymmetry because they are commonly believed to have access to private and value-relevant information. The current paper offers insight into the relationship between multiple large shareholder ownership and stock market information asymmetry in the context of Vietnam, an important emerging market. Employing fixed effects and GMM estimators for a panel data sample of firms listed on the Ho Chi Minh City stock exchange covering the period 2007–2015, the results suggest that the concentration of large shareholder ownership is positively and significantly associated with information asymmetry. This finding has strong implications for policy making process in promoting stock market development.

Stock Market Price and the Performance of the Residential Property Market in Kenya

The purpose of the study was to assess the effect of stock market price on the performance of the residential property market in Kenya. The surge of the residential property prices in Kenya has ignited concerns about the affordability of residential property houses in Kenya. The escalation of residential property prices raises question as to whether it is in tandem with other markets in the Country especially the stock market. Researchers have inconclusive findings on the direction of causation or the strength of the relationship between stock market and performance of residential property market in Kenya. This study adopted a positivist philosophical attitude using causal research design. The study used quarterly secondary data from 2005 to 2018. The study employed vector error correction residual serial correlation langrange multiplier test and vector error residual heteroskedasticity test as the diagnostic tests. Vector error correction model and auto-regressive distributed lag model were employed to test the hypothesis in the short run and long run respectively. The study found that stock market price had a negative effect on performance of residential property market in Kenya in the short term suggesting substitution effect while in the long run stock market price had a positive effect on the performance of residential property market in line with the wealth effect. The study concludes that stock market information spills over and affects residential property market performance in Kenya both in the long run and short run. The study has also contributed to the confounding empirical and theoretical literature and narrowed the research gap especially on the conflicting substitution effect and wealth effect of stock market and residential property market performance in Kenya. Keywords: Stock Market Price, Performance, Residential Property Market DOI : 10.7176/RJFA/10-18-15 Publication date :September 30 th 2019