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stock market Trading Based on market Sentiments and Reinforcement Learning

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Computers, Materials & Continua 2022年第1期70卷 935-950页

作者： K.M.Ameen Suhail Syam Sankar Ashok S.Kumar Tsafack Nestor Naglaa F.Soliman Abeer D.Algarni Walid El-Shafai Fathi E.Abd El-Samie Department of Computer Science&Engineering NSS College of EngineeringPalakkad678008KeralaIndia Department of Electronics and Communication Engineering NSS College of EngineeringPalakkad678008KeralaIndia Unitéde Recherche de Matière Condensée D’Electronique et de Traitement du Signal(URAMACETS)Department of PhysicsUniversity of DschangP.O.Box 67DschangCameroon Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

stock market is a place,where shares of different companies are *** is a collection of buyers’and sellers’*** this digital era,analysis and prediction in the stock market have gained an essential role in shaping today’s *** market analysis can be either fundamental or *** analysis can be performed either with technical indicators or through machine learning *** this paper,we report a system that uses a Reinforcement Learning(RL)network and market sentiments to make decisions about stock market *** system uses sentiment analysis on daily market news to spot trends in stock *** sentiment analysis module generates a unified score as a measure of the daily news about *** score is then fed into the RL module as one of its *** RL section gives decisions in the form of three actions:buy,sell,or *** objective is to maximize long-term future *** have used stock data of Apple from 2006 to 2016 to interpret how sentiments affect *** stock price of any company rises,when significant positive news become available in the public *** results reveal the influence of market sentiments on forecasting of stock prices.

关键词： Deep learning machine learning daily market news reinforcement learning stock market

stock market Index Prediction Using Machine Learning and Deep Learning Techniques

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Intelligent Automation & Soft Computing 2023年第8期37卷 1325-1344页

作者： Abdus Saboor Arif Hussain Bless Lord Y。Agbley Amin ul Haq Jian Ping Li Rajesh Kumar Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou313001China Abdul Wali Khan University Mardan Mardan23200Pakistan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Brain Institute Peshawar Peshawar25130Pakistan

stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult *** the objective of constructing an effective prediction model,both linear and machine learning tools have been investigated for the past couple of *** recent years,recurrent neural networks(RNNs)have been observed to perform well on tasks involving sequence-based data in many research *** this motivation,we investigated the performance of long-short term memory(LSTM)and gated recurrent units(GRU)and their combination with the attention mechanism;LSTM+Attention,GRU+Attention,and LSTM+GRU+*** methods were evaluated with stock data from three different stock indices:the KSE 100 index,the DSE 30 index,and the BSE *** results were compared to other machine learning models such as support vector regression,random forest,and k-nearest *** best results for the three datasets were obtained by the RNN-based models combined with the attention *** performances of the RNN and attention-based models are higher and would be more effective for applications in the business industry.

关键词： Machine learning deep learning stock market prediction data analysis

stock market's Response to Real Output Shocks in China: A VARwAL Estimation

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China & World Economy 2023年第5期31卷 1-25页

作者： Numan Ulki Kexing Wu Institute of Economic Studies Charles UniversityCzechia School of Commerce University of South AustraliaAustralia

This paper studies the connection between the stock market and real output in China and compares it with benchmark countries,employing a novel vector autoregression with asymmetric leads(VARwAL)*** makes two ***,it finds that the time profile of the Chinese stock market's response to real output shocks suggests no evidence of a distorted relationship due to manipulation of Chinese real output data or domination of the Chinese stock market by individual ***,the Chinese stock market is relatively more responsive to real output,in line with the larger share of manufacturing in the Chinese *** output and industrial profits,two different,less-manipulable time series,yield similar ***,it presents the first use of VARwAL impulse responses to detect stock market bubbles:VARwAL captures the 2015 bubble in China *** the full sample period,China's stock market appears to have been less prone to bubbles than the US stock market.

关键词： China real output shocks stock market VARwAL model

Modelling the dynamics of stock market in the gulf cooperation council countries:evidence on persistence to shocks

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Financial Innovation 2022年第1期8卷 1252-1273页

作者： Heni Boubaker Bassem Saidane Mouna Ben Saad Zorgati Institute of High Commercial Studies(IHEC)of Sousse LaREMFiQB.P.404054 SousseTunisia IPAG Business School IPAG LAB184 Boulevard Saint-Germain75006 ParisFrance

This study examines the statistical properties required to model the dynamics of both the returns and volatility series of the daily stock market returns in six Gulf Cooperation Council countries,namely Bahrain,Oman,Kuwait,Qatar,Saudi Arabia,and the United Arab Emirates,under different financial and economic *** empiri-cal investigation is conducted using daily data from June 1,2005 to July 1,*** analysis is conducted using a set of double long-memory specifications with some significant features such as long-range dependencies,asymmetries in conditional variances,non-linearity,and multiple seasonality or time-varying *** study indicates that the joint dual long-memory process can adequately estimate long-memory dynamics in returns and *** in-sample diagnostic tests as well as out-of-sample forecasting results demonstrate the prevalence of the Autoregressive Fractionally Integrated Moving Average and Hyperbolic Asymmetric Power Autoregressive Conditional Heteroskedasticity modeling process over other competing models in fitting the first and the second conditional moments of the market ***,the empirical results show that the proposed model offers an interesting framework to describe the long-range dependence in returns and seasonal persistence to shocks in conditional volatility and strongly support the estimation of dynamic returns that allow for time-varying correlations.A noteworthy finding is that the long-memory dependencies in the conditional variance processes of stock market returns appear important,asymmetric,and differ in their volatility responses to unexpected *** evidence suggests that these markets are not completely efficient in processing regional news,thus providing a sound alternative for regional portfolio diversification.

关键词： Long-memory Volatility process Asymmetric power Seasonality Forecast performance stock market

COVID-19 Pandemic Data Predict the stock market

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Computer Systems Science & Engineering 2021年第3期36卷 451-460页

作者： Abdulaziz Almehmadi Faculty of Computing and Information Technology University of TabukTabukSaudi Arabia

Unlike the 2007–2008 market crash,which was caused by a banking failure and led to an economic recession,the 1918 inﬂuenza pandemic triggered a worldwideﬁnancial *** usually affect the global economy,and the COVID-19 pandemic is no *** stock markets have fallen over 40%,and companies are shutting down,ending contracts,and issuing volun-tary and involuntary leaves for thousands of *** economic effects have led to an increase in unemployment rates,crime,and *** pandemics’economic effects,especially on the stock market,has not been urgent or feasible until ***,with advances in artiﬁcial intelligence(AI)and the inter-connectivity that social media provides,such research has become *** this paper,we propose a COVID-19-based stock market prediction system(C19-SM2)that utilizes social *** AI system enables economists to study how COVID-19 pandemic data inﬂuence social media and,hence,the stock market.C19-SM2 gathers COVID-19 infection and death cases reported by the authorities and social media data from a geographic area and extracts the sentiments and events that occur in that *** information is then fed to the support vector machine(SVM)and random forest and random tree classiﬁers along with current stock market ***,the system produces a projection of the stock market’s movement during the next *** tested the system with the Dow Jones Industrial Average(DJI)and the Tadawul All Share Index(TASI).Our system achieved a stock market prediction accuracy of 99.71%,substantially higher than the 89.93%accuracy reported in the related literature;the inclusion of COVID-19 data improved accuracy by 9.78%.

关键词： COVID-19 economic impact artiﬁcial intelligence sentiment analysis stock market

Social Media and stock market Prediction: A Big Data Approach

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Computers, Materials & Continua 2021年第5期67卷 2569-2583页

作者： Mazhar Javed Awan Mohd Shafry Mohd Rahim Haitham Nobanee Ashna Munawar Awais Yasin Azlan Mohd Zain Azlanmz School of Computing Faculty of EngineeringUniversity Teknologi MalaysiaJohorMalaysia Department of Software Engineering University of Management and TechnologyLahorePakistan Collage of Business Abu Dhabi UniversityAbu DhabiUnited Arab Emirates Oxford Center for Islamic Studies The University of OxfordOxfordUK The University of Liverpool Management School The University of LiverpoolLiverpoolUK Department of Computer Engineering National University of TechnologyIslamabadPakistan School of Computing UTM Big Data CentreUniversiti Teknologi MalaysiaJohorMalaysia

Big data is the collection of large datasets from traditional and digital sources to identify trends and *** quantity and variety of computer data are growing exponentially for many *** example,retailers are building vast databases of customer sales *** are working on logistics financial services,and public social media are sharing a vast quantity of sentiments related to sales price and *** of big data include volume and variety in both structured and unstructured *** this paper,we implemented several machine learning models through Spark MLlib using PySpark,which is scalable,fast,easily integrated with other tools,and has better performance than the traditional *** studied the stocks of 10 top companies,whose data include historical stock prices,with MLlib models such as linear regression,generalized linear regression,random forest,and decision *** implemented naive Bayes and logistic regression classification *** results suggest that linear regression,random forest,and generalized linear regression provide an accuracy of 80%-98%.The experimental results of the decision tree did not well predict share price movements in the stock market.

关键词： Big data analytics artificial intelligence machine learning stock market social media business analytics

stock market restrictions and corporate social responsibility: Evidence from IPO suspension in China

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China Journal of Accounting Research 2022年第1期15卷 122-140页

作者： Shanmin Li Ruoming Yang Jueting Zhou School of Business Sun Yat-sen University Advanced Institute of Finance Sun Yat-sen University

Improving corporate social responsibility(CSR)requires not only the efforts of firms themselves but also the support of the appropriate institutional *** paper assesses whether access to the stock market can promote firms’*** China’s suspension of IPOs in 2012-2014,we find that firms affected by the suspension show lower CSR in their listing *** later listing after the suspension ends,the greater reduction in ***,the effect of the IPO suspension is more serious for firms with financial constraints than for non-financially constrained ***,we show that the IPO suspension has an adverse impact on firms’liquidity and *** this suspension ends,firms’CSR activities recover within 1-2 ***,our conclusion enriches the literature on the factors influencing CSR and provides firm-level evidence of the adverse impact of an IPO suspension.

关键词： Corporate social responsibility IPO suspension Financial constraint stock market

Dynamics of volatility spillover between stock market and foreign exchange market: evidence from Asian Countries

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Financial Innovation 2016年第1期2卷 29-48页

作者： Khalil Jebran Amjad Iqbal Department of Management Studies University of MalakandChakdaraPakistan Dongbei University of Finance and Economics DalianChina

Background:The purpose of this study is to examine volatility spillover effects between stock market and foreign exchange market in selected Asian countries;Pakistan,India,Sri Lanka,China,Hong Kong and *** study considered daily data from 4th January,1999 to 1st January,***:This study opted EGARCH(Exponential Generalized Auto Regressive Conditional Heteroskedasticity)model for the purpose of analyzing asymmetric volatility spillover effects between stock and foreign exchange ***:The EGARCH analyses reveal bidirectional asymmetric volatility spillover between stock market and foreign exchange market of Pakistan,China,Hong Kong and Sri *** results reveal unidirectional transmission of volatility from stock market to foreign exchange market of *** analysis reveals no evidence of volatility transmission between the two markets in reference to ***:The result of this study provide valuable insights to economic policy makers for financial stability perspective and to investors regarding decision making in international portfolio and currency risk strategies.

关键词： Volatility spillover Asian Countries EGARCH Exchange rate stock market

Building stock market resilience through digital transformation:Using Google trends to analyze the impact of COVID-19 pandemic

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Frontiers of Business Research in China 2020年第3期14卷 257-277页

作者： Ding Ding Chong Guan Calvin MLChan Wenting Liu School of Business Singapore University of Social Sciences463 Clementi RdSingapore 599494Singapore

As the 2019 novel coronavirus disease(COVID-19)pandemic rages globally,its impact has been felt in the stock markets around the *** the gloomy economic outlook,certain sectors seem to have survived better than *** paper aims to investigate the sectors that have performed better even as market sentiment is affected by the *** daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first an a lyzed using a combination of hierarchical clustering and shape-based distance(SBD)*** sentiment is modeled from Google trends on the COVID-19 *** is then analyzed against the time series of daily closing stock prices using augmented vector autoregression(VAR).The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the ***,the stock price performance across sectors is differentiated by the level of the digital transformation of sectors,with those that are most digitally transformed,showing resili ence towards negative market sentime nt on the *** study contributes to the existing literature by incorporating search trends to an a lyze market sentiment,and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak.

关键词： Digital transformation stock market Search trends market sentiment 2019 novel coronavirus disease(COVID-19)