Showing papers on "Stock exchange published in 2021"

A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction

Abstract: Whether stock prices are predictable has been the center of debate in academia. In this paper, we propose a hybrid model that combines a deep learning approach with a sentiment analysis model for stock price prediction. We employ a Convolutional Neural Network model for classifying the investors’ hidden sentiments, which are extracted from a major stock forum. We then propose a hybrid research model by applying the Long Short-Term Memory (LSTM) Neural Network approach for analyzing the technical indicators from the stock market and the sentiment analysis results from the first step. Furthermore, this work has conducted real-life experiments from six key industries of three time intervals on the Shanghai Stock Exchange (SSE) to validate the effectiveness and applicability of the proposed model. The experiment results indicate that the proposed model has achieved better performance in classifying investor sentiments than the baseline classifiers, and this hybrid approach performs better in predicting stock prices compared to the single model and the models without sentiment analysis.

Nexus between financial innovation and bankruptcy: evidence from information, communication and technology (ict) sector

Abstract: This study examines the reliability of Altman’s Z-score model to predict the financial failure of the ICT sector in Pakistan. Data for 11 PSE-listed (Pakistan Stock Exchange) ICT companies were col...

Co-movement of energy prices and stock market return: environmental wavelet nexus of COVID-19 pandemic from the USA, Europe, and China.

Abstract: This work aims to study the time-frequency relationship between the recent COVID-19 pandemic and instabilities in oil price and the stock market, geopolitical risks, and uncertainty in the economic policy in the USA, Europe, and China. The coherence wavelet method and the wavelet-based Granger causality tests are applied to the data (31st December 2019 to 1st August 2020) based on daily COVID-19 observations, oil prices, US-EPU, the US geopolitical risk index, and the US stock price index. The short- and long-term COVID-19 consequences are depicted differently and may initially be viewed as an economic crisis. The results illustrate the reduced industrial productivity, which intensifies with the increase in the pandemic's severeness (i.e., a 10.57% decrease in the productivity index with a 1% increase in the pandemic severeness). Similarly, indices for oil demand, stock market, GDP growth, and electricity demand decrease significantly with an increase in the pandemic severeness index (i.e., a 1% increase in the pandemic severeness results in a 0.9%, 0.67%, 1.12%, and 0.65% decrease, respectively). However, the oil market shows low co-movement with the stock exchange, exchange rate, and gold markets. Therefore, investors and the government are recommended to invest in the oil market to generate revenue during the sanctions period.

Punishment by Securities Regulators, Corporate Social Responsibility and the Cost of Debt

Abstract: This study examines whether penalties issued to Chinese listed companies by securities regulators for violations of corporate law affect the cost of debt, and the moderating role of corporate social responsibility (CSR) fulfillment on this relationship. Our sample consists of firms listed on Shanghai and Shenzhen stock exchanges from 2011 to 2017 and the data are collected from the announcements of China Securities Regulatory Commission. The findings are as follows: (1) punishment announcements by regulatory authorities increase the cost of debt; and (2) the effect of punishment announcements on the cost of debt is partially offset by prior CSR performance. These findings are shown to be robust. The reputation insurance effect of CSR is more pronounced in state-owned enterprises and in an institutional environment with low marketization, a weak legal environment, and low information transparency. The findings support the reputation insurance hypothesis of CSR and employ the cost of debt as a governance mechanism.

Stock market analysis using candlestick regression and market trend prediction (CKRM)

Abstract: Stock market data is a time-series data in which stock value varies depends on time. Prediction of the stock market is an endeavor to assess the future value of a company’s stock rate which will increase the investor’s profit. The accurate prediction of stock market analysis is still a challenging task. The proposed system predicts stock price of any company mentioned by the user for the next few days. Using the predicted stock price and datasets collected from various sources regarding a certain equity, the overall sentiment of the stock is predicted. The prediction of stock price is done by regression and candlestick pattern detection. The proposed system generates signals on the candlestick graph which allows to predict market movement to a sufficient level of accuracy so that the user is able to judge whether a stock is a ‘Buy/Sell’ and whether to short the stock or go long by delivery. The prediction accuracy of the stock exchange has analyzed and improved to 85% using machine learning algorithms.

Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series

Abstract: Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modelled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of four years: 2015 – 2018. Based on the NIFTY data during 2015 – 2018, we build various predictive models using machine learning approaches, and then use those models to predict the “Close” value of NIFTY 50 for the year 2019, with a forecast horizon of one week, i.e., five days. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual “Close” values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network (CNN) with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.

From National Marketplaces to Global Providers of Financial Infrastructures: Exchanges, Infrastructures and Structural Power in Global Finance

Abstract: This paper analyses the role of (stock and derivative) exchanges as powerful actors in global finance While most IPE accounts of exchanges analyse ‘exchanges as marketplaces’ and focus on equity m

An Econometric Approach Regarding the Impact of Fiscal Pressure on Equilibrium: Evidence from Electricity, Gas and Oil Companies Listed on the New York Stock Exchange

Abstract: The matter of fiscal pressure is more current than ever in most countries around the world for various reasons. In the first place, disruptive phenomena such as financial crises put tremendous pressure on worldwide economies. Secondly, high taxes trigger an overall reduction in the level of investments aiming at creating stable and well-paid jobs. Thirdly, the income generated by the majority of taxpayers is subject to excessive taxation, which may fuel tax evasion acts. On these grounds, the article is the first empirical research investigating the impact of fiscal pressure on the financial equilibrium of energy companies listed on the New York Stock Exchange. The sample included 88 electricity, gas, and oil companies from around the world, which were analyzed over a time span of 16 years, including the periods before, during, and after the 2008 global financial crisis. The methodology entailed estimating econometric models via Panel Least Squares (cross-section weights) with and without time fixed effects. Empirical results showed that fiscal pressure had a stronger impact on the short-term and long-term equilibrium of electricity and oil companies than on the equilibrium of gas companies. The study can serve as a compass for the managers of energy companies interested in estimating the evolution of company equilibrium state when considering other potential financial downturns.

Corporate governance and financial distress: Asian emerging market perspective

Abstract: The purpose of this paper is to examine the impact of corporate governance index (PAKCGI) on firm financial distress for a sample of 152 non-financial firms listed at Pakistan Stock Exchange (PSX) over the period from 2003 to 2017.,To examine the impact of PAKCGI on financial distress (Altman Z-Score), random effect model is applied. The PAKCGI is a self-constructed index based on the five important factors of corporate governance practices, i.e. board of directors, audit committees, right of shareholders, disclosures and risk management. The binary coding approach is adopted for the construction of PAKCGI. Altman Z-Score model is used as a proxy for financial distress indicator. The absolute value of Altman Z-score has been taken as financial distress indicator.,The outcomes of the study indicate a positive impact of PAKCGI on risk of firms’ financial distress. The positive coefficient of PAKCGI implies that the good corporate practices work as catalyst to reduce risk of financial distress in Pakistan. A significant negative impact of block holders on financial distress suggests that the concentrated block ownership take monopolistic decision to protect their interests. It has also been observed that significant positive impact of institutional ownership on financial distress exists in the Pakistani listed firms. Furthermore, this study also reveals that significant negative association between board size, CEO duality and financial distress indicator.,The findings may encourage the Pakistani listed companies to follow and implement good corporate governance practices, which would lead to increase the confidence of investors, regulators and stakeholders.,The current study extends the corporate governance literature by examining the relationship between the corporate governance attributes and the financial distress status of Pakistani listed companies. From the academic perspective, this paper adds to the knowledge concerning the association between corporate governance practices and risk of financial distress in emerging markets.

The relationship between integrated reporting and corporate environmental performance: A green trial

Abstract: Integrated reporting is a fairly recent phenomenon in the corporate reporting realm. Its dawn marks a new age of corporate reporting where financial and non‐financial information and their interrelation create an integrated and holistic approach for telling a value creation story. In tandem with this transformation, business sustainability in general and environmental performance, in particular, are also gaining prominence in the corporate landscape. This scholarly article investigates the relationship between integrated reporting and corporate environmental performance. A panel‐data is used to carry out the study using a sample of 110 firms listed on the Johannesburg Stock exchange for the years 2014–2018, where Integrated Reporting was first mandated. The empirical results are robust and consistent with our predictions in that integrated reporting is found positively associated with corporate environmental performance. Our findings pave the way for a new stream of literature on the transformation and the connectivity functions of integrated reporting.

Corporate governance and firm performance: empirical evidence from Jordan

Abstract: This study aims to assess the effect of director board and audit committee attributes and ownership structure on firm performance. In general, resource dependency and agency theories have underlined the superior performance of firms equipped with stronger Corporate Governance (CG) versus those of deficient governance. Concurrently, the study delineated the provisions of ownership structure provision, specifically foreign ownership and institutional ownerships, thus describing the component denoting the structural significance in explicating firm performance.,The current study implemented an empirical approach involving the construction of extensive CG measures thus, subjected to 81 non-financial firms listed on the Amman Stock Exchange spanning the period of 2014–2018.,The current study identified the positive and significant relationship between the board of directors and audit committee characteristics with the firm performance measures tested, namely, return on equity (ROE) and Tobin’s Q. In terms of ownership structure, both foreign and institutional ownerships yielded a significant and positive relationship with ROE. Meanwhile, Tobin’s Q led to an insignificant and negative relationship between both ownership types and firm performance measures.,The analytical outcomes substantiate the possibility of enhanced performance shown by growing global firms because of the implementation of CG mechanisms, specifically because of the practices resulting in minimised agency costs.,The current study offers novel evidence detailing the impact of CG effectiveness towards performance and its implementation in emerging markets following the minimal amount of scholarly efforts on the topic. It is a timely contribution towards the current understanding of the relationship linking governance and performance for the purpose of ensuring the adoption and imposition of a strong corporate governance code by the government.

Stagpression: The Economic and Financial Impact of the COVID-19 Pandemic

Abstract: This paper formulates an analytical framework to understand the spatiotemporal patterns of epidemic disease occurrence, its relevance, and implications to financial markets activity The paper suggests a paradigm shift: a new multi-dimensional geometric approach to capture all symmetrical and asymmetrical strategic graphical movement Furthermore, it introduces the concept of stagpression, a new economic phenomenon to explain the uncharted territory the world economies and financial markets are getting into The Massive Pandemic Contagious Diseases Damage on Stock Markets Simulator (φ-Simulator) to evaluate the determinants of capital markets behavior in the presence of an infectious disease outbreak The model investigates the impact of Covid-19 on the performance of ten stock markets, including S&P 500, TWSE, Shanghai Stock Exchange, Nikkei 225, DAX, Hang Seng, U K -FTSE, KRX, SGX, and Malaysia-FTSE

Corporate environmental performance and financing constraints: An empirical study in the Chinese context

Abstract: Between 2006 and 2017, 2,965 Chinese firms listed on the Shanghai/Shenzhen Stock Exchange have been studied to investigate whether better corporate environmental performance (CEP) leads to better access to capital and mitigates firms' financing constraints. It is hypothesized that better access to finance can be attributed to the increased government support due to enhanced firm political legitimacy and market legitimacy. Event studies find that the firms with better CEP suffer significantly lower finance constraints, and the evidence from the studies proves that firms' political legitimacy and market legitimacy are important in mitigating finance constraints. The results of the studies are confirmed by using two alternative measures of capital constraints and CEP, an instrumental variable approach, and a simultaneous equations approach.

Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models

Abstract: Today, stock market has important function and it can be a place as a measure of economic position. People can earn a lot of money and return by investing their money in the stock exchange market. But it is not easy because many factors should be considered. So, there are many ways to predict the movement of share price. The main goal of this article is to predict stock price indices using artificial neural network (ANN) and train it with some new metaheuristic algorithms such as social spider optimization (SSO) and bat algorithm (BA). We used some technical indicators as input variables. Then, we used genetic algorithms (GA) as a heuristic algorithm for feature selection and choosing the best and most related indicators. We used some loss functions such as mean absolute error (MAE) as error evaluation criteria. On the other hand, we used some time series models forecasting like ARMA and ARIMA for prediction of stock price. Finally, we compared the results with each other means ANN-Metaheuristic algorithms and time series models. The statistical population of research have five most important and international indices which were S&P500, DAX, FTSE100, Nasdaq and DJI.

Modelling Stock Markets by Multi-agent Reinforcement Learning

Abstract: Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS) These statistical tools are based on modelling agents trading via a centralised order book, in order to emulate complex and diverse market phenomena These past financial models have all relied on so-called zero-intelligence agents, so that the crucial issues of agent information and learning, central to price formation and hence to all market activity, could not be properly assessed In order to address this, we designed a next-generation MAS stock market simulator, in which each agent learns to trade autonomously via reinforcement learning We calibrate the model to real market data from the London Stock Exchange over the years 2007 to 2018, and show that it can faithfully reproduce key market microstructure metrics, such as various price autocorrelation scalars over multiple time intervals Agent learning thus enables accurate emulation of the market microstructure as an emergent property of the MAS

Measuring the Impact of Intellectual Capital on the Financial Performance of the Finance Sector of India

Abstract: In the current scenario, intellectual capital has been recognised as a vital corporate asset because the conventional performance measurement techniques are incapable of measuring intangible dimensions of corporate performance. It is a challenge, especially for knowledge-driven firms, to measure the impact of intangibles on their financial performance. This study tries to explore the impact of intellectual capital on the financial performance of knowledge-driven firms of India. For conducting the study, Bombay Stock Exchange’s finance index has been taken for a period ranging from 2009 to 2018, and the Value Added Intellectual Coefficient (VAIC™) methodology has been used to measure the intangible aspects of these firms. The results reveal that Value Added Intellectual Coefficient has an insignificant association with the profitability and productivity of the sample companies. While among the components of Value Added Intellectual Coefficient, the capital employed efficiency has a significant positive relationship only with the profitability of the financial sector. In the case of productivity, all the components of intellectual capital have an insignificant effect on the financial companies of India. The SCE remain insignificant for all the financial performance measures, whereas human capital efficiency is substantial only for enhancing the return on assets of the sample companies.

A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction

Abstract: The stock market is very unstable and volatile due to several factors such as public sentiments, economic factors and more Several Petabytes volumes of data are generated every second from different sources, which affect the stock market A fair and efficient fusion of these data sources (factors) into intelligence is expected to offer better prediction accuracy on the stock market However, integrating these factors from different data sources as one dataset for market analysis is seen as challenging because they come in a different format (numerical or text) In this study, we propose a novel multi-source information-fusion stock price prediction framework based on a hybrid deep neural network architecture (Convolution Neural Networks (CNN) and Long Short-Term Memory (LSTM)) named IKN-ConvLSTM Precisely, we design a predictive framework to integrate stock-related information from six (6) heterogeneous sources Secondly, we construct a base model using CNN, and random search algorithm as a feature selector to optimise our initial training parameters Finally, a stacked LSTM network is fine-tuned by using the tuned parameter (features) from the base-model to enhance prediction accuracy Our approach's emperical evaluation was carried out with stock data (January 3, 2017, to January 31, 2020) from the Ghana Stock Exchange (GSE) The results show a good prediction accuracy of 9831%, specificity (09975), sensitivity (08939%) and F-score (09672) of the amalgamated dataset compared with the distinct dataset Based on the study outcome, it can be concluded that efficient information fusion of different stock price indicators as a single data source for market prediction offer high prediction accuracy than individual data sources

Quantifying the High-Frequency Trading

Abstract: We use stock exchange message data to quantify the negative aspect of high-frequency trading, known as “latency arbitrage.” The key difference between message data and widely-familiar limit order book data is that message data contain attempts to trade or cancel that fail. This allows the researcher to observe both winners and losers in a race, whereas in limit order book data you cannot see the losers, so you cannot directly see the races. We find that latency-arbitrage races are very frequent (about one per minute per symbol for FTSE 100 stocks), extremely fast (the modal race lasts 5-10 millionths of a second), and account for a remarkably large portion of overall trading volume (about 20%). Race participation is concentrated, with the top 6 firms accounting for over 80% of all race wins and losses. The average race is worth just a small amount (about half a price tick), but because of the large volumes the stakes add up. Our main estimates suggest that races constitute roughly one-third of price impact and the effective spread (key microstructure measures of the cost of liquidity), that latency arbitrage imposes a roughly 0.5 basis point tax on trading, that market designs that eliminate latency arbitrage would reduce the market's cost of liquidity by 17%, and that the total sums at stake are on the order of $5 billion per year in global equity markets alone. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

COVID-19 and stock exchange return variation: empirical evidences from econometric estimation.

Abstract: This research looked at the effects of COVID-19 on a number of the world's most important stock exchanges, as well as the empirical relation between the COVID-19 wave and stock market volatility. In order to plan proper portfolio diversification in international financial markets, researchers must examine COVID-19 anxiety in relation to stock market volatility. The stock market volatility connected with the COVID-19 pandemic was measured using AR(1)-GARCH(1,1). COVID-19 fear, according to our research, is the ultimate driver of public attention and stock market volatility. The findings show that throughout the pandemic, stock market performance and GDP growth both declined significantly due to average increases. Furthermore, a 1% increase in COVID-19 causes a 0.8% and 0.56% decline in stock return and GDP, respectively. The stock market, on the other hand, showed a slight movement in GDP growth. Furthermore, the COVID-19 pandemic reported cases index, death index, and global panic index all influenced public perceptions of purchasing and selling. As a result, rather than investing in stocks, it is recommended that you invest in gold. The research also makes policy recommendations for important stakeholders. We look to examine how stock returns respond dynamically to unanticipated changes in the COVID-19 scenarios, as well as the uncertainty that comes with a pandemic. Using daily data from Canada and the USA, we conclude that a spike in COVID-19 instances has a negative impact on the stock market in general. Furthermore, in both the increase and decline scenarios in Canada, the stock return reactions are asymmetric. The disparity is due to the unfavorable impact of the pandemic's unpredictability. We also discovered that uncertainty had a negative impact on the US stock market. The magnitude, however, is insignificant.

Predicting Stock Price Using Two-Stage Machine Learning Techniques

Abstract: Stock market forecasting is considered to be a challenging topic among time series forecasting. This study proposes a novel two-stage ensemble machine learning model named SVR-ENANFIS for stock price prediction by combining features of support vector regression (SVR) and ensemble adaptive neuro fuzzy inference system (ENANFIS). In the first stage, the future values of technical indicators are forecasted by SVR. In the second stage, ENANFIS is utilized to forecast the closing price based on prediction results of first stage. Finally, the proposed model SVR-ENANFIS is tested on 4 securities randomly selected from the Shanghai and Shenzhen Stock Exchanges with data collected from 2012 to 2017, and the predictions are completed 1–10, 15 and 30 days in advance. The experimental results show that the proposed model SVR-ENANFIS has superior prediction performance than single-stage model ENANFIS and several two-stage models such as SVR-Linear, SVR-SVR, and SVR-ANN.

The Influence of Liquidity and Solvency on Performance within the Healthcare Industry: Evidence from Publicly Listed Companies

Abstract: Any lucrative economic activity implies aiming at obtaining a profit, including companies in the healthcare industry. The present study analyzes the extent to which financial liquidity and financial solvency influenced the performance of 34 healthcare companies that are publicly traded on the New York Stock Exchange. The period of analysis spanned from Q4 2005 to Q4 2020. The research methodology favored a complex approach by running econometric models with two-stage least squares (2SLS) panel and panel generalized method of moments (GMM). Empirical evidence showed that the financial indicators current liquidity ratio, quick liquidity ratio, and debt to equity ratio significantly influenced company performance measured by return on assets, gross margin ratio, operating margin ratio, earnings before interest, tax, depreciation, and amortization. Strategies intended to improve business performance based on liquidity and solvency insights are also addressed.

Multi-period portfolio optimization using coherent fuzzy numbers in a credibilistic environment

Abstract: In this paper, we use an extension of fuzzy numbers, called coherent fuzzy numbers, to model asset returns and an investor’s perception of the stock market (pessimistic, optimistic, or neutral) simultaneously. Two multi-period multi-objective portfolio optimization models are formulated using mean absolute semi-deviation and Conditional Value-at-Risk (CVaR) as risk measures, respectively. We aim to provide more flexibility to the investor in specifying the risk tolerance and devise optimum investment plans for different investment horizons. The proposed models also incorporate bound, cardinality, and skewness constraints for each investment period to capture various stock market scenarios. A real-coded genetic algorithm is employed to solve the resultant models. Two real-life case studies involving 20 assets of the National Stock Exchange (NSE), India, and another involving 50 assets listed in the S&P 500 and NASDAQ-100 indexes have been provided to illustrate the efficacy and advantages of the models. An in-sample and out-of-sample analysis have been done for both the models to analyze the performance in the real-world scenario. The conclusion drawn from the analysis strongly emphasizes on accurately assessing the current stock market prospects, i.e., adopting the right attitude (pessimistic, optimistic, or neutral), is of paramount importance and must be included in the portfolio optimization problem.