Showing papers on "Stock exchange published in 2020"

Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis

Abstract: The nature of stock market movement has always been ambiguous for investors because of various influential factors. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms. Four stock market groups, namely diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange, are chosen for experimental evaluations. This study compares nine machine learning models (Decision Tree, Random Forest, Adaptive Boosting (Adaboost), eXtreme Gradient Boosting (XGBoost), Support Vector Classifier (SVC), Naive Bayes, K-Nearest Neighbors (KNN), Logistic Regression and Artificial Neural Network (ANN)) and two powerful deep learning methods (Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators from ten years of historical data are our input values, and two ways are supposed for employing them. Firstly, calculating the indicators by stock trading values as continuous data, and secondly converting indicators to binary data before using. Each prediction model is evaluated by three metrics based on the input ways. The evaluation results indicate that for the continuous data, RNN and LSTM outperform other prediction models with a considerable difference. Also, results show that in the binary data evaluation, those deep learning methods are the best; however, the difference becomes less because of the noticeable improvement of models' performance in the second way.

Deep Learning for Stock Market Prediction

Abstract: The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.

Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong

Abstract: Stock prediction via market data analysis is an attractive research topic. Both stock prices and news articles have been employed in the prediction processes. However, how to combine technical indicators from stock prices and news sentiments from textual news articles, and make the prediction model be able to learn sequential information within time series in an intelligent way, is still an unsolved problem. In this paper, we build up a stock prediction system and propose an approach that 1) represents numerical price data by technical indicators via technical analysis, and represents textual news articles by sentiment vectors via sentiment analysis, 2) setup a layered deep learning model to learn the sequential information within market snapshot series which is constructed by the technical indicators and news sentiments, 3) setup a fully connected neural network to make stock predictions. Experiments have been conducted on more than five years of Hong Kong Stock Exchange data using four different sentiment dictionaries, and results show that 1) the proposed approach outperforms the baselines in both validation and test sets using two different evaluation metrics, 2) models incorporating prices and news sentiments outperform models that only use either technical indicators or news sentiments, in both individual stock level and sector level, 3) among the four sentiment dictionaries, finance domain-specific sentiment dictionary (Loughran–McDonald Financial Dictionary) models the news sentiments better, which brings more prediction performance improvements than the other three dictionaries.

The Impact of the COVID-19 on the Financial Markets: Evidence from China and USA

Abstract: Globally, the COVID - 19 shock is severe even compared to the Great Financial Crisis in 2007–08. However, the impact of the COVID - 19 on the financial markets has never been researched. The present study is undertaken to investigate the impact of the COVID - 19 on the Financial Markets from the period dated 1st March 2020 to 25th March 2020 in China and the USA. The study applied a Simple regression model to investigate the impact of the COVID - 19 on the Financial Markets during the period from dated 1st March 2020 to 25th March 2020 in China and the USA. Time series data from China COVID - 19 Statistics Reports and Trading Economics from 1st March 2020 to 25th March 2020 for China and the USA were employed by the study. The study used the Shanghai Stock Exchange as a sample for China and the New York Dow Jones as a sample for the USA. In the process of investigating the impact of the COVID - 19 on the financial markets the study assumes the COVID - 19 Confirmed cases to be the independent variable while Shanghai Stock Exchange and New York Dow Jones to be dependent variables of the study in China and USA. The study findings were in actual fact very interesting. The study findings revealed that there is a positive significant relationship between the COVID - 19 confirmed cases and all the financial markets (Shanghai stock exchange and New York Dow Jones) from 1st March 2020 to 25th March 2020 in China and the USA. That means the COVID - 19 had a significant impact on the financial markets from 1st March 2020 to 25th March 2020 in China and the USA.

How corporate social responsibility boosts firm financial performance: The mediating role of corporate image and customer satisfaction

Abstract: A great number of studies have been conducted to examine the direct impact of corporate social responsibility on firm's financial performance, but this direct relationship seems to be spurious and imprecise. Therefore, the main purpose of this study is to investigate the intervening role of corporate image and customer satisfaction on the relationship between corporate social responsibility and financial performance. Data is collected from 229 companies listed on Pakistan stock exchange using simple random sampling technique. Structural equation modelling has been used for the measurement model and for hypotheses testing. Results indicate that corporate image and customer satisfaction partially mediate the association between corporate social responsibility and financial performance. The study concludes that corporate social responsibility significantly affects the firm's financial performance by developing a positive image among the stakeholders and decreasing overall costs. This study will help management of organizations to realize the importance of corporate social responsibility.

A comprehensive evaluation of ensemble learning for stock-market prediction

Abstract: Stock-market prediction using machine-learning technique aims at developing effective and efficient models that can provide a better and higher rate of prediction accuracy. Numerous ensemble regressors and classifiers have been applied in stock market predictions, using different combination techniques. However, three precarious issues come in mind when constructing ensemble classifiers and regressors. The first concerns with the choice of base regressor or classifier technique adopted. The second concerns the combination techniques used to assemble multiple regressors or classifiers and the third concerns with the quantum of regressors or classifiers to be ensembled. Subsequently, the number of relevant studies scrutinising these previously mentioned concerns are limited. In this study, we performed an extensive comparative analysis of ensemble techniques such as boosting, bagging, blending and super learners (stacking). Using Decision Trees (DT), Support Vector Machine (SVM) and Neural Network (NN), we constructed twenty-five (25) different ensembled regressors and classifiers. We compared their execution times, accuracy, and error metrics over stock-data from Ghana Stock Exchange (GSE), Johannesburg Stock Exchange (JSE), Bombay Stock Exchange (BSE-SENSEX) and New York Stock Exchange (NYSE), from January 2012 to December 2018. The study outcome shows that stacking and blending ensemble techniques offer higher prediction accuracies (90–100%) and (85.7–100%) respectively, compared with that of bagging (53–97.78%) and boosting (52.7–96.32%). Furthermore, the root means square error (RMSE) recorded by stacking (0.0001–0.001) and blending (0.002–0.01) shows a better fit of ensemble classifiers and regressors based on these two techniques in market analyses compared with bagging (0.01–0.11) and boosting (0.01–0.443). Finally, the results undoubtedly suggest that an innovative study in the domain of stock market direction prediction ought to include ensemble techniques in their sets of algorithms.

Stock Market Response during COVID-19 Lockdown Period in India: An Event Study

Abstract: The research investigates the impact of the lockdown period caused by the COVID-19 to the stock market of India. The study examines the extent of the influence of the lockdown on the Indian stock market and whether the market reaction would be the same in pre- and post-lockdown period caused by COVID-19. Market Model Event study methodology is used. A sample of 31 companies listed on Bombay Stock Exchange (BSE) are selected at random for the purpose of the study. The sample period taken for the study is 35 days (24 February-17 April, 2020). An event window of 35 days was taken with 20 days prior to the event and 15 days during the event. The event (t1) being the official announcement of the lockdown. The results indicate that the market reacted positively with significantly positive Average Abnormal Returns during the present lockdown period, and investors anticipated the lockdown and reacted positively, whereas in the pre-lockdown period investors panicked and it was reflected in negative AAR. The study finds evidence of a positive AR around the present lockdown period and confirms that lockdown had a positive impact on the stock market performance of stocks till the situation improves in the Indian context.

Corporate Governance and Firms Financial Performance in the United Kingdom

Abstract: The objective of this study is to examine empirically the impact of good corporate governance on financial performance of United Kingdom non-financial listed firms. Agency theory and stewardship theory serve as the bases of a conceptual model. Five corporate governance mechanisms are examined on two financial performance indicators, return on assets (ROA) and Tobin's Q, employing cross-sectional regression methodology. The conclusion drawn from empirical test so performed on 252 firms listed on London Stock Exchange for the year 2014 indicates a positive or a negative relationship, but also sometimes no effect, of corporate governance mechanisms impact on financial performance. The implications are discussed. Thereby, so distinguishing effects due to causes, we present a proof that, when the right corporate governance mechanisms are chosen, the finances of a firm can be improved. The results of this research should have some implication on academia and policy makers thoughts.

Measures that matter: an empirical investigation of intellectual capital and financial performance of banking firms in Indonesia

Abstract: Purpose This study aims to investigate the intellectual capital–financial performance relationship using two models, namely the conventional Value-Added Intellectual Coefficient (VAIC) model and the adjusted Value-Added Intellectual Coefficient (A-VAIC) model Design/methodology/approach This study is designed as a quantitative research focusing on the relationship between intellectual capital and financial performance of the banking industry in Indonesia As many as 114 data are derived from the publicly listed banks on the Indonesia Stock Exchange for the period of 2012–2017 The multiple regression analysis is employed to test the hypotheses studied Findings In general, the result confirms that intellectual capital affects financial performance Although not all hypotheses of the study are supported by either the VAIC model or the A-VAIC model, the results provide a deeper and new insight on how each component of intellectual capital efficiency (human capital, structural capital, capital employed, innovation capital) relates to financial performance (return on asset, return on equity, asset turnover, price to book ratio) The results also justify that further improvements in measuring intellectual capital are still needed in the future Research limitations/implications This study limits its generalization since the sample is only in the Indonesian banking industry Notwithstanding the limitation, the results imply that the Indonesian banking managers need to be aware of intellectual capital management because of its strategic role in enhancing financial performance Practical implications This study contributes to the intellectual capital literature by providing empirical evidence on the use of both models, namely the conventional VAIC and the A-VAIC in the Indonesian banking industry research setting which is never been studied before Social implications This study has the social implication to the enhancement of the quality life of the society The higher the quality of intellectual capital in the banking firms, the better the banks serve the needs of the community Originality/value This study contributes to the IC literature by providing empirical research on the use of the VAIC model and the A-VAIC model in the Indonesian banking industry

Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement

Abstract: Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market Investment in the stock market has a big risk associated with it Minimizing prediction error reduces the investment risk Machine learning (ML) models typically perform better than statistical and econometric models Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement Eight different stock data from three stock exchanges (NYSE, NASDAQ, and NSE) are randomly collected and used for the study Each data set is split into training and test set Ten-fold cross validation accuracy is used to evaluate the ML models on the training set In addition, the ML models are evaluated on the test set using accuracy, precision, recall, F1-score, specificity, and area under receiver operating characteristics curve (AUC-ROC) Kendall W test of concordance is used to rank the performance of the tree-based ML algorithms For the training set, the AdaBoost model performed better than the rest of the models For the test set, accuracy, precision, F1-score, and AUC metrics generated results significant to rank the models, and the Extra Trees classifier outperformed the other models in all the rankings

The Impact Of COVID-19 Pandemic On The Financial Performance Of Firms On The Indonesia Stock Exchange

Abstract: The COVID-19 pandemic has harmed the national economy and caused a decline in various businesses' financial performance. This study aims to examine the impact of the COVID-19 pandemic on firms' financial performance listed on the Indonesia Stock Exchange. The research samples included 214 companies, which were divided proportionally into nine sectors or 49 sub-sectors. Data analysis used was the Wilcoxon Signed Rank Test. The results show an increase in the leverage ratio and short-term activity ratio but a decrease in the public companies' liquidity ratio and profitability ratio during the COVID-19 pandemic. There was no significant difference in the liquidity ratio and leverage ratio. However, the public companies' profitability ratio and short-term activity ratio differed significantly between before and during the COVID-19 pandemic. The sector that experienced an increase in liquidity ratio, profitability ratio, and short-term activity ratio but a decrease in the leverage ratio was the consumer goods sector. In contrast, the sectors experiencing a decrease in the liquidity and profitability ratios were property, real estate and building construction, finance, trade, services, and investment sectors.

Multifractal Detrended Fluctuation Analysis (MF-DFA) of Stock Market Indexes. Empirical Evidence from Seven Central and Eastern European Markets

Abstract: In this paper, we present a comparative investigation of the multifractal properties of seven Central and Eastern European (CEE) stock markets using recent financial data up to August 2018 by employing seasonal and trend decompositions before applying multifractal detrended fluctuation analysis. We find that stock indices returns exhibit long-range correlations, supporting the idea that the stock markets in question are not efficient markets and have not reached a mature stage of market development. The results of the paper are of interest to investors looking for opportunities in these stock exchanges and also to policy makers in their endeavour of realizing institutional reforms in order to increase stock market efficiency and to support the sustainable growth of the financial markets.

Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models

Abstract: Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models. For the purpose of our study, we have used NIFTY 50 index values of the National Stock Exchange (NSE) of India, during the period December 29, 2014 till July 31, 2020. We have built eight regression models using the training data that consisted of NIFTY 50 index records during December 29, 2014 till December 28, 2018. Using these regression models, we predicted the open values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then, augment the predictive power of our forecasting framework by building four deep learning-based regression models using long-and short-term memory (LSTM) networks with a novel approach of walk-forward validation. We exploit the power of LSTM regression models in forecasting the future NIFTY 50 open values using four different models that differ in their architecture and in the structure of their input data. Extensive results are presented on various metrics for the all the regression models. The results clearly indicate that the LSTM-based univariate model that uses one-week prior data as input for predicting the next week open value of the NIFTY 50 time series is the most accurate model.

Stock Market Reactions to COVID-19 Pandemic Outbreak: Quantitative Evidence from ARDL Bounds Tests and Granger Causality Analysis

Abstract: This paper examines the linkages in financial markets during coronavirus disease 2019 (COVID-19) pandemic outbreak. For this purpose, daily stock market returns were used over the period of December 31, 2019-April 20, 2020 for the following economies: USA, Spain, Italy, France, Germany, UK, China, and Romania. The study applied the autoregressive distributed lag (ARDL) model to explore whether the Romanian stock market is impacted by the crisis generated by novel coronavirus. Granger causality was employed to investigate the causalities among COVID-19 and stock market returns, as well as between pandemic measures and several commodities. The outcomes of the ARDL approach failed to find evidence towards the impact of Chinese COVID-19 records on the Romanian financial market, neither in the short-term, nor in the long-term. On the other hand, our quantitative approach reveals a negative effect of the new deaths' cases from Italy on the 10-year Romanian bond yield both in the short-run and long-run. The econometric research provide evidence that Romanian 10-year government bond is more sensitive to the news related to COVID-19 than the index of the Bucharest Stock Exchange. Granger causality analysis reveals causal associations between selected stock market returns and Philadelphia Gold/Silver Index.

Sustainability reporting and performance of MENA banks: is there a trade-off?

Abstract: Sustainability reporting has been widely adopted by firms worldwide given stakeholders’ need for more transparency on environmental, social and governance (ESG) issues. This study aims to investigate the relationship between ESG and bank’s operational (return on assets [ROA]), financial (return on equity [ROE]) and market performance (Tobin’s Q) in a group of emerging countries in the Middle East and North Africa (MENA) region.,This study examines 59 banks listed on the stock exchanges of MENA countries over a period of 10 years (2008-2017). Only conventional banks with all data for at least two years are included in the sample. The core independent variable is ESG scores, and the dependent variables are ROA, ROE and Tobin’s Q. This study uses bank- and country-specific control variables to measure the relationship between sustainability reporting and bank’s performance.,The findings from the empirical results demonstrate a significant positive impact of ESG on performance and economic benefits to shareholders. However, the relationship between ESG disclosures varies individually; unlike the majority of published research, the authors found that social performance plays a negative role in determining bank’s profitability and value. Furthermore, the authors present evidence in support of the impact of bank- and country-specific factors in determining bank’s performance.,To the best of the authors’ knowledge, this is the first study to investigate the impact of sustainability reporting on banks’ performance in the MENA region. It provides evidence that questions the positive relationship between sustainability reporting and financial measures of performance.

Inside the Mind of a Stock Market Crash

Abstract: We analyze how investor expectations about economic growth and stock returns changed during the February-March 2020 stock market crash induced by the Covid-19 pandemic, as well as during the subsequent partial stock market recovery. We surveyed retail investors who are clients of Vanguard at three points in time: (i) on February 11-12, around the all-time stock market high, (ii) on March 11-12, after the stock market had collapsed by over 20%, and (iii) on April 16-17, after the market had rallied 25% from its lowest point. Following the crash, the average investor turned more pessimistic about the short-run performance of both the stock market and the real economy. Investors also perceived higher probabilities of both further extreme stock market declines and large declines in short-run real economic activity. In contrast, investor expectations about long-run (10-year) economic and stock market outcomes remained largely unchanged, and, if anything, improved. Disagreement among investors about economic and stock market outcomes also increased substantially following the stock market crash, with the disagreement persisting through the partial market recovery. Those respondents who were the most optimistic in February saw the largest decline in expectations, and sold the most equity. Those respondents who were the most pessimistic in February largely left their portfolios unchanged during and after the crash.

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques.

Abstract: Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. This leads to the research of finding the most effective prediction model that generates the most accurate prediction with the lowest error percentage. This paper reviews studies on machine learning techniques and algorithm employed to improve the accuracy of stock price prediction.

Effects of corporate environmental responsibility strength and concern on innovation performance: The moderating role of firm visibility

Abstract: Based on stakeholder theory, this research aims to examine the effects of the two dimensions of corporate environmental responsibility (CER), which are CER strength and CER concern, on firm innovation performance, and the moderating effect of firm visibility on these relationships. Using data on Chinese firms listed on Shenzhen stock exchange from 2006 to 2015, this research finds that CER strength positively affects firm innovation performance while CER concern negatively affects innovation performance. These relationships are stronger for firms with greater visibility. This research provides insights for understanding the relationship between CER and innovation performance and has important managerial implications for firms to manage their environmental behaviors and improve innovation performance to achieve sustainable development.

A hybrid approach for portfolio selection with higher-order moments: Empirical evidence from Shanghai Stock Exchange

Abstract: Skewness and kurtosis, the third and fourth order moments, are statistics to summarize the shape of a distribution function. Recent studies show that investors would take these higher-order moments into consideration to make a profitable investment decision. Unfortunately, due to the difficulties in solving the multi-objective problem with higher-order moments, the literature on portfolio selection problem with higher-order moments is few. This paper proposes a new hybrid approach to solve the portfolio selection problem with skewness and kurtosis, which includes not only the multi-objective optimization but also the data-driven asset selection and return prediction, where the techniques of two-stage clustering, radial basis function neural network and genetic algorithm are employed. With the historical data from Shanghai stock exchange, we find that the out-of-sample performance of our model with higher-order moments is significantly better than that of traditional mean-variance model and verify the robustness of our hybrid algorithm.

Time series data analysis of stock price movement using machine learning techniques

Abstract: Stock market also called as equity market is the aggregation of the sellers and buyers. It is concerned with the domain where the shares of various public listed companies are traded. For predicting the growth of economy, stock market acts as an index. Due to the nonlinear nature, the prediction of the stock market becomes a difficult task. But the application of various machine learning techniques has been becoming a powerful source for the prediction. These techniques employ historical data of the stocks for the training of machine learning algorithms and help in predicting their future behavior. The three machine learning algorithms used in this paper are support vector machine, perceptron, and logistic regression, for predicting the next day trend of the stocks. For the experiment, dataset from about fifty stocks of Indian National Stock Exchange’s NIFTY 50 index was taken, by collecting stock data from January 1, 2013, to December 31, 2018, and lastly by the calculation of some technical indicators. It is reported that the average accuracy for the prediction of the trend of fifty stocks obtained by support vector machine is 87.35%, perceptron is 75.88%, and logistic regression is 86.98%. Since the stock data are time series data, another dataset is prepared by reorganizing previous dataset into the supervised learning format which improves the accuracy of the prediction process which reported the results with support vector machine of 89.93%, perceptron of 76.68%, and logistic regression of 89.93%, respectively.