The association between stock price volatility and financial news – a sentiment analysis approach

Kybernetes ◽  
2017 ◽  
Vol 46 (8) ◽  
pp. 1341-1365 ◽  
Author(s):  
Jia-Lang Seng ◽  
Hsiao-Fang Yang
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Stock Returns ◽  
Price Volatility ◽  
Model Verification ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Content Type ◽  
Financial News ◽  
Depth Analysis

Purpose The purpose of this study is to develop the dictionary with grammar and multiword structure has to be used in conjunction with sentiment analysis to investigate the relationship between financial news and stock market volatility. Design/methodology/approach An algorithm has been developed for calculating the sentiment orientation and score of data with added information, and the results of calculation have been integrated to construct an empirical model for calculating stock market volatility. Findings The experimental results reveal a statistically significant relationship between financial news and stock market volatility. Moreover, positive (negative) news is found to be positively (negatively) correlated with positive stock returns, and the score of added information of the news is positively correlated with stock returns. Model verification and stock market volatility predictions are verified over four time periods (monthly, quarterly, semiannually and annually). The results show that the prediction accuracy of the models approaches 66% and stock market volatility with a particular trend-predicting effect in specific periods by using moving window evaluation. Research limitations/implications Only one news source is used and the research period is only two years; thus, future studies should incorporate several data sources and use a longer period to conduct a more in-depth analysis. Practical implications Understanding trends in stock market volatility can decrease risk and increase profit from investment. Therefore, individuals or businesses can feasibly engage in investment activities for profit by understanding volatility trends in capital markets. Originality/value The ability to exploit textual information could potentially increase the quality of the data. Few scholars have applied sentiment analysis in investigating interdisciplinary topics that cover information management technology, accounting and finance. Furthermore, few studies have provided support for structured and unstructured data. In this paper, the efficiency of providing the algorithm, the model and the trend in stock market volatility has been demonstrated.

Volatility and the Regulation of Stock Markets

2016 ◽  
pp. 134-145
Author(s):  
Filiz Eryilmaz
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Volume ◽  
Price Volatility ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Causality Analysis ◽  
Financial Trading ◽  
Trading Halts ◽  
The Relationship

In recent years, one of the most important topics related to stock market volatility which is attracting attention, is stock returns, or in other words, the relationship between stock price volatility and trading volume. The aim of this study was to obtain information about the financial market structure of Bangladesh, India, Pakistan by applying Granger Causality Analysis to the relationship of trading volume and stock returns volatility in the period 1980–2012. The study then examines some of the stock market regulations that have been proposed in South Asia to attenuate stock market volatility, which have usually included proposals to limit volatility by imposing temporary trading halts, limiting the legal leverage available to investors in financial assets, altering exchange trading practices to accommodate volume, and by raising the transactions costs of financial trading.

scholarly journals Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach

Risks ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 89
Author(s):  
Muhammad Sheraz ◽  
Imran Nasir
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Exchange ◽  
Approximate Entropy ◽  
Market Volatility ◽  
Comparative Approach ◽  
Stock Market Volatility ◽  
Information Theoretic ◽  
Information Theoretic Measures ◽  
Garch Modeling

The volatility analysis of stock returns data is paramount in financial studies. We investigate the dynamics of volatility and randomness of the Pakistan Stock Exchange (PSX-100) and obtain insights into the behavior of investors during and before the coronavirus disease (COVID-19 pandemic). The paper aims to present the volatility estimations and quantification of the randomness of PSX-100. The methodology includes two approaches: (i) the implementation of EGARCH, GJR-GARCH, and TGARCH models to estimate the volatilities; and (ii) analysis of randomness in volatilities series, return series, and PSX-100 closing prices for pre-pandemic and pandemic period by using Shannon’s, Tsallis, approximate and sample entropies. Volatility modeling suggests the existence of the leverage effect in both the underlying periods of study. The results obtained using GARCH modeling reveal that the stock market volatility has increased during the pandemic period. However, information-theoretic results based on Shannon and Tsallis entropies do not suggest notable variation in the estimated volatilities series and closing prices. We have examined regularity and randomness based on the approximate entropy and sample entropy. We have noticed both entropies are extremely sensitive to choices of the parameters.

The dependence structure in volatility between Shanghai and Shenzhen stock market in China

2016 ◽  
Vol 6 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Mingyuan Guo ◽  
Xu Wang
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Estimation Method ◽  
Garch Model ◽  
Price Volatility ◽  
Market Volatility ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Content Type

Purpose – The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data. Design/methodology/approach – Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China. Findings – This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view. Originality/value – Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

scholarly journals Margin requirements and Stock Market Volatility in Bangladesh

2012 ◽  
Vol 3 (2) ◽  
pp. 29
Author(s):  
A. F. M. Mainul Ahsan ◽  
Mohammad Osman Gani ◽  
Md. Bokhtiar Hasan
Keyword(s):  
Stock Returns ◽  
Stock Price ◽  
Stock Exchange ◽  
Price Volatility ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Margin Requirements ◽  
Margin Requirement ◽  
Daily Stock Returns ◽  
The Impact

Officially margin requirements in bourses in Bangladesh were initiated on April 28, 1999, to limit the amount of credit available for the purpose of buying stocks. The goal of this paper is to measure the impact of changing margin requirement on stock returns' volatility in Dhaka Stock Exchange (DSE). The impact of margin requirement on stock price volatility has been extensively studied with mixed and ambiguous results. Using daily stock returns, we found mixed evidence that SEC's margin requirements have significant impact on market volatility in DSE.

Stock market moves will not shake Beijing's resolve

2015 ◽  
Keyword(s):  
Stock Market ◽  
Market Volatility ◽  
Capital Account ◽  
Stock Market Volatility ◽  
Reserve Currency ◽  
Chinese Firms ◽  
Content Type ◽  
Mechanical Process ◽  
Downward Pressure

Significance Beijing's supportive policies are likely to stabilise the market, and structural tailwinds will probably reflate it eventually. Stock market volatility is likely to have limited ramifications for the wider economy unless it is prolonged. Impacts Downward pressure will last until margin traders have unwound their positions, a mechanical process over which Beijing has little control. Foreign-listed Chinese firms may reconsider plans to de-list abroad and re-list in China. Capital account and currency liberalisation will resume, in pursuit of Beijing's goal of achieving IMF reserve currency status.

Effects of Daylight-Saving Time Changes on Stock Market Returns and Stock Market Volatility: Rebuttal

2013 ◽  
Vol 112 (1) ◽  
pp. 89-99 ◽  
Author(s):  
Mark J. Kamstra ◽  
Lisa A. Kramer ◽  
Maurice D. Levi
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Market Returns ◽  
Stock Market Returns ◽  
Daylight Saving Time ◽  
Daylight Saving ◽  
Time Changes ◽  
Saving Effect

In a 2011 reply to our 2010 comment in this journal, Berument and Dogen maintained their challenge to the existence of the negative daylight-saving effect in stock returns reported by Kamstra, Kramer, and Levi in 2000. Unfortunately, in their reply, Berument and Dogen ignored all of the points raised in the comment, failing even to cite the Kamstra, et al. comment. Berument and Dogen continued to use inappropriate estimation techniques, over-parameterized models, and low-power tests and perhaps most surprisingly even failed to replicate results they themselves reported in their previous paper, written by Berument, Dogen, and Onar in 2010. The findings reported by Berument and Dogen, as well as by Berument, Dogen, and Onar, are neither well-supported nor well-reasoned. We maintain our original objections to their analysis, highlight new serious empirical and theoretical problems, and emphasize that there remains statistically significant evidence of an economically large negative daylight-saving effect in U.S. stock returns. The issues raised in this rebuttal extend beyond the daylight-saving effect itself, touching on methodological points that arise more generally when deciding how to model financial returns data.

Forecasting Stock Market Volatility: Evidence from Six Emerging Markets

2013 ◽  
Vol 14 (2) ◽  
pp. 68-93
Author(s):  
Naliniprava Tripathy ◽  
Ashish Garg
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Markets ◽  
Stock Price ◽  
Market Volatility ◽  
Future Market ◽  
Investment Strategies ◽  
Stock Market Volatility ◽  
Efficient Management ◽  
Asymmetric Garch

This paper forecasts the stock market volatility of six emerging countries by using daily observations of indices over the period of January 1999 to May 2010 by using ARCH, GARCH, GARCH-M, EGARCH and TGARCH models. The study reveals the positive relationship between stock return and risk only in Brazilian stock market. The analysis exhibits that the volatility shocks are quite persistent in all country’s stock market. Further the asymmetric GARCH models find a significant evidence of asymmetry in stock returns in all six country’s stock markets. This study confirms the presence of leverage effect in the returns series and indicates that bad news generate more impact on the volatility of the stock price in the market. The study concludes that volatility increases disproportionately with negative shocks in stock returns. Hence investors are advised to use investment strategies by analyzing recent and historical news and forecast the future market movement while selecting portfolio for efficient management of financial risks to reap benefits in the stock markets.

scholarly journals The effect of GST announcement on stock market volatility: evidence from intraday data

2019 ◽  
Vol 16 (3) ◽  
pp. 313-328
Author(s):  
Razali Haron ◽  
Salami Mansurat Ayojimi
Keyword(s):  
Stock Market ◽  
Global Financial Crisis ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Cost Of Living ◽  
Empirical Estimation ◽  
Content Type ◽  
Goods And Services ◽  
Intraday Data ◽  
The Cost

Purpose The purpose of this paper is to examine the effect of GST announcements (pre and post) on Malaysian stock market index. This study also utilised intraday data to look into intraday market volatility post-GST announcement. Design/methodology/approach Both daily closing prices and intraday data of different frequencies are used to capture the extent of stock market volatility as well as the subsided period of the volatility. The period of study ranges from June 2009 to November 2016 and empirical estimation is based on the GARCH (1, 1) model for the pre- and post-GST announcements. Findings Persistent market volatility in the post-GST announcement is empirically recorded and the volatility is higher in the post-GST announcement than the pre-GST announcement. This demonstrates the unwillingness and reaction of the market towards the tax policy implementation. Market expectation on GST implementation towards the increase in the cost of living following the increase in the prices of goods and services in Malaysia is empirically supported in the post-GST announcement. Practical implications The finding on this study is consistent with the expectation of the market that GST implementation will increase the price of the goods and services and hence increase the cost of living. This is supported by a noticeable increase in the stock market volatility in the post-GST announcement. Although GST announcement could be classified as a scheduled announcement, unwillingness to accept the policy prevails as shown by the increase in the stock market volatility. Originality/value The effects of Asian and global financial crisis are the major focus of past studies on stock market volatility, whereas this study examines and highlights the effect of the GST announcement on stock market volatility and the use of intraday data to further examine the nature of the volatility.

scholarly journals Effects of Daylight Savings Time Changes on Stock Market Volatility

2010 ◽  
Vol 106 (2) ◽  
pp. 632-640 ◽  
Author(s):  
M. Hakan Berument ◽  
Nukhet Dogan ◽  
Bahar Onar
Keyword(s):  
United States ◽  
Stock Market ◽  
Stock Returns ◽  
Conditional Variance ◽  
Time Effect ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Stock Volatility ◽  
Time Effects ◽  
Time Changes

The presence of daylight savings time effects on stock returns and on stock volatility was investigated using an EGARCH specification to model the conditional variance. The evidence gathered from the major United States stock markets for the period between 1967 and 2007 did not support the existence of the daylight savings time effect on stock returns or on volatility. Returns on the first business day following daylight savings time changes were not lower nor was the volatility higher, as would be expected if there were an effect.

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