Effects of Financial Market Variables on Stock Prices:A Review of the Literature

Stock market plays a pivotal role in firms’expansion and turns economic growth.In the literature,because of the importance of stock markets to the real economy,the smooth and risk-free operation of the stock market has attracted significant attention.The finance literature contains a large number of studies that examine the stock price behaviour with some emphasis on the determinants of the relationship between the equity prices and the financial market activities.The present study reviews the previous works of the effect of financial market variables and stock price.Five selected financial market variables,market capitalization,earnings per share,price earnings multiples,dividend yield,and trading volume are reviewed in this study.In the past literature,there are the opinions of the positive significant relationship between market capitalization and stock price.To find the relationship between dividend yield and stock price,there are two broad schools of thoughts.Both of the relevance and irrelevance theory of Gordon and Modigliani have the strong evidence in the current literature that keeps on the dilemma and provides the scopes for future research.Price-earnings multiples are analyzed in the past literature by using different variables.Based on that,it is evidenced that price-earnings multiples have a negative significant effect on stock price.The reviewed studies state the cointegrating relationship between the stock price and the trading volume as the trading volume is a source of risk.

Prediction of the Bombay Stock Exchange (BSE) Market Returns Using Artificial Neural Network and Genetic Algorithm

Stock Market is the market for security where organized issuance and trading of Stocks take place either through exchange or over the counter in electronic or physical form. It plays an important role in canalizing capital from the investors to the business houses, which consequently leads to the availability of funds for business expansion. In this paper, we investigate to predict the daily excess returns of Bombay Stock Exchange (BSE) indices over the respective Treasury bill rate returns. Initially, we prove that the excess return time series do not fluctuate randomly. We are applying the prediction models of Autoregressive feed forward Artificial Neural Networks (ANN) to predict the excess return time series using lagged value. For the Artificial Neural Networks model using a Genetic Algorithm is constructed to choose the optimal topology. This paper examines the feasibility of the prediction task and provides evidence that the markets are not fluctuating randomly and finally, to apply the most suitable prediction model and measure their efficiency.

Research on the Dynamic Volatility Relationship between Chinese and U.S. Stock Markets Based on the DCC-GARCH Model under the Background of the COVID-19 Pandemic

This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.

China’s Monetary Policy Impacts on Money and Stock Markets

This study investigated the impact of China’s monetary policy on both the money market and stock markets,assuming that non-policy variables would not respond contemporaneously to changes in policy variables.Monetary policy adjustments are swiftly observed in money markets and gradually extend to the stock market.The study examined the effects of monetary policy shocks using three primary instruments:interest rate policy,reserve requirement ratio,and open market operations.Monthly data from 2007 to 2013 were analyzed using vector error correction(VEC)models.The findings suggest a likely presence of long-lasting and stable relationships among monetary policy,the money market,and stock markets.This research holds practical implications for Chinese policymakers,particularly in managing the challenges associated with fluctuation risks linked to high foreign exchange reserves,aiming to achieve autonomy in monetary policy and formulate effective monetary strategies to stimulate economic growth.

The Impact of Short Selling Disclosure Regulatory Constraint on the Lending Market and Stock Ownership

We examine the impact of the short sell disclosure(SSD)regime on the stock lending market and investor behaviors,employing a staggered difference-indifference(DiD)methodology.Our research reveals that the introduction of the disclosure regime enhances market transparency,resulting in a diminished appeal of stock ownership in the lending market for active investors.This shift is accompanied by a reduction in information leakage risks and longer loan durations.Specifically,our analysis reveals a significant decrease in the risk of loan recall by 4.87%,accompanied by an average increase of 23.72%in loan duration for short selling activities.Furthermore,the cost associated with short-sell disclosure causes a decline in both lending supply and short demand.

Securing Stock Transactions Using Blockchain Technology: Architecture for Identifying and Reducing Vulnerabilities Linked to the Web Applications Used (MAHV-BC)

This paper deals with the security of stock market transactions within financial markets, particularly that of the West African Economic and Monetary Union (UEMOA). The confidentiality and integrity of sensitive data in the stock market being crucial, the implementation of robust systems which guarantee trust between the different actors is essential. We therefore proposed, after analyzing the limits of several security approaches in the literature, an architecture based on blockchain technology making it possible to both identify and reduce the vulnerabilities linked to the design, implementation work or the use of web applications used for transactions. Our proposal makes it possible, thanks to two-factor authentication via the Blockchain, to strengthen the security of investors’ accounts and the automated recording of transactions in the Blockchain while guaranteeing the integrity of stock market operations. It also provides an application vulnerability report. To validate our approach, we compared our results to those of three other security tools, at the level of different metrics. Our approach achieved the best performance in each case.

The Effect of Dividends and Earnings per Share on the Stock Market Value by Moderating Bank Size

The aim of this study was to investigate the effect of dividend distributions and earnings per share by moderating bank size as measured by its total assets on the stock market value of banks operating in Jordan during the period between 2011 and 2016.The hypotheses of the study were tested based on multiple and hierarchical regression method.The most important result of the study is that the earnings per share is the strongest variable that helps in predicting the stock market value of the bank shares,in addition to the significant effect of bank size as measured by its total assets.

Stock Market Reaction to Yearly Earnings Announcements for Firms in Financial Distress:Evidences from Italy

The purpose of this paper is to feed the debate regarding investor’s reaction to relevant financial information releases as yearly earnings announcements(EAs)with a specific focus on financial distressed firms.Using the event study methodology and adopting two well-known tests in the literature,we analyzed Italian listed companies in the period of 2008-2016,to detect whether there is a market reaction to EAs releases for firms in financial distress,adopting as a measure of financial distress the presence in the audit report of a going concern opinion(GCO).In the Italian legislation,the GCO must be communicated immediately to the market and this can be done before,simultaneously or after EAs.The achieved results shed light on the negative impact of EAs of distressed firms receiving a GCO.On the other hand,the possibility that negative abnormal returns are mainly due to the GCO release cannot be neglected.Hence,through additional tests,we found that effects of EAs are more persistent and significant than GCOs,in accordance with the prevailing literature,which sees,on average,EAs predominant information for investors.Our study is pioneering in disentangling possible effects of confounding events for the Italian stock market.The EAs superior effect confirms the dynamics characterizing weak and small equity markets as Italy where,before GCOs releases,some relevant and more precise information(such as earnings magnitude)is often held by shareholders because of the high percentage of family firms and/or concentrated ownership,demonstrating also the weakness of auditor profession if compared with other developed countries.

Investigations into Optimization Models of Crude Oil Distillation Column in the Context of Feed Stock and Market Value

This paper proposes optimization models of crude oil distillation column for both limited and unlimited feed stock and market value of known products prices. The feed to the crude distillation column was assumed to be crude oil containing naphtha gas, kerosene, petrol and diesel as the light-light key, light key, heavy key and heavy-heavy key respectively. The models determined maximum concentrations of heavy key in the distillate and light key in the bottom for limited feed stock and market condition. Both were impurities in their respective positions of the column. The limiting constraints were sales specification concentration of light key in the distillate [ ], heavy key in the bottom [ ] and an operating loading constraint of flooding above the feed tray. For unlimited feed stock and market condition, the optimization models determined the optimum separation [ and ] and feed flow rate that would give maximum profit with minimum purity sales specification constraints of light key in the distillate and heavy key in the bottom as stated above. The feed loading was limited by the reboiler capacity. However, there is need to simulate the optimization models for an existing crude oil distillation column of a refinery in order to validate the models.

Study of the Discount on Private Placements and Risk of Stock Market Crash in Listed Companies

With the gradual completion of the split-share structure reform,private placement has gradually become the mainstream of refinancing. One of the points that the practical and theoretical circles are widely concerned about is that the private placement price is often higher than the market price at the time of the private placement. High discounts are often accompanied by the transmission of benefits,and the increase in insider information will lead to the risk of a stock market crash? This paper intends to use the data of A-share listed companies from 2006 to 2015 to empirically study the relationship between the discount on private placements and the risk of stock market crash. At the same time,this paper examines whether the degree of information asymmetry plays a regulatory role in the relationship between the discount on private placements and the risk of stock market crash. This paper provides a certain reference for the regulatory authorities to improve the relevant laws and regulations in the private placement,and to provide a certain reference for the protection of the interests of small and medium-sized investors.

An Empirical Assessment of the Impact of Nigerian all Share Index, Market Capitalization, and Number of Equities on Gross Domestic Product

A stock exchange is an exchange where stock brokers and traders can buy and sell shares of stock, bonds, and other securities. All listings are included in the Nigerian Stock Exchange All Shares index. In terms of market capitalization, the Nigerian Stock Exchange is the third largest stock exchange in Africa. Objectives: The paper assesses the impact of Nigerian Stock Market (all share index, market capitalization, and number of equities) on Gross domestic product (Economic Growth). Materials and Methods: Regression analysis and ordinary least square technique were employed. Result and Discussion: The series was stationary at 1%, 5%, and 10% α level;the residuals were normally distributed but not serially correlated at 5% α level. All Share Index, Market Capitalization and Total Number of listed Equities have a joint and individual significant effect on Economic Growth (Gross Domestic Product) with Total Number of listed Equities having a negative (opposite) linear relationship with the Gross Domestic Product. The Durbin-Watson statistics (R2 = 0.9910 = 1.3686) suggest that the model is not spurious and it is devoid of positive and negative autocorrelation (DW = 1.3686 > dl = 1.07 and DW = 1.5033 ?-?du = 2.17). Therefore, it can produce meaningful result when used for forecasting a positive relationship between gross domestic product, all share index and market capitalization with a 99.1% R-square value. Significant Positive connection between all share index, market capitalization, the number of equities and gross domestic product suggests that government policies and bills aimed towards rapid development of the capital market should be initiated.

Survey of feature selection and extraction techniques for stock market prediction

In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price predictions.Several review papers in the literature have focused on various ML,statistical,and deep learning-based methods used in stock market forecasting.However,no survey study has explored feature selection and extraction techniques for stock market forecasting.This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications.We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022.We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles.We also describe the combination of feature analysis techniques and ML methods and evaluate their performance.Moreover,we present other survey articles,stock market input and output data,and analyses based on various factors.We find that correlation criteria,random forest,principal component analysis,and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

Hot Events Detection of Stock Market Based on Time Series Data of Stock and Text Data of Network Public Opinion

With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and enables investors to quickly identify relevant financial events that may lead to stock market volatility. However, in the research of event detection in the financial field, many studies are focused on micro-blog, news and other network text information. Few scholars have studied the characteristics of financial time series data. Considering that in the financial field, the occurrence of an event often affects both the online public opinion space and the real transaction space, so this paper proposes a multi-source heterogeneous information detection method based on stock transaction time series data and online public opinion text data to detect hot events in the stock market. This method uses outlier detection algorithm to extract the time of hot events in stock market based on multi-member fusion. And according to the weight calculation formula of the feature item proposed in this paper, this method calculates the keyword weight of network public opinion information to obtain the core content of hot events in the stock market. Finally, accurate detection of stock market hot events is achieved.

A Simplified Approach for Implementing Capital Gain Tax in Stock Marketing

The sustainability of a country inevitably depends on proper taxation system. To date, there are many taxes implemented by the ruling authorities of a country. The taxes that are sourced from stock markets or share markets are paramount to better govern a country. The capital gain tax (CGT), which is incurred in disposing the shares or stocks owned by an investor or an institution, is one of the taxes implemented in stock markets. Though in the past many attempts have been made to properly streamline the CGT, the methodologies or the approaches used in the implementation of CGT, even in the United States, are not well-grounded from a scientific point of view. Therefore, in this paper, a simplified approach based on the assumption that the CGT is implemented on a yearly basis is proposed. The CGT is calculated for each stock owned by an investor or an institution. The approach is implemented using an open access platform: AMP (Apache-MySQL-PHP). Subsequently, the proposed approach is tested using some hypothetical data. The proposed approach, which is easy-to-use, practical and un-biased, is of use to any country that is willing to progress towards the sustainability. Moreover, the proposed approach with the current technology will enhance the developing nations which have large size of informal economy, on designing and implementing effective tax policies and administrations.

Stock Market Prediction Using Generative Adversarial Networks(GANs):Hybrid Intelligent Model

The key indication of a nation’s economic development and strength is the stock market.Inflation and economic expansion affect the volatility of the stock market.Given the multitude of factors,predicting stock prices is intrinsically challenging.Predicting the movement of stock price indexes is a difficult component of predicting financial time series.Accurately predicting the price movement of stocks can result in financial advantages for investors.Due to the complexity of stock market data,it is extremely challenging to create accurate forecasting models.Using machine learning and other algorithms to anticipate stock prices is an interesting area.The purpose of this article is to forecast stock market values to assist investors to make better informed and precise investing decisions.Statistics,Machine Learning(ML),Natural language processing(NLP),and sentiment analysis will be used to accomplish the study’s objectives.Using both qualitative and quantitative information,the study developed a hybrid model.The hybrid model has been handled with GANs.Based on the model’s predictions,a buy-or-sell trading strategy is offered.The conclusions of this study will assist investors in selecting the ideal choice while selling,holding,or buying shares.

Stock Market Index Prediction Using Machine Learning and Deep Learning Techniques

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

A Survey on Stock Market Manipulation Detectors Using Artificial Intelligence

A well-managed financial market of stocks,commodities,derivatives,and bonds is crucial to a country’s economic growth.It provides confidence to investors,which encourages the inflow of cash to ensure good market liquidity.However,there will always be a group of traders that aims to manipulate market pricing to negatively influence stock values in their favor.These illegal trading activities are surely prohibited according to the rules and regulations of every country’s stockmarket.It is the role of regulators to detect and prevent any manipulation cases in order to provide a trading platform that is fair and efficient.However,the complexity of manipulation cases has increased significantly,coupled with high trading volumes,which makes the manual observations of such cases by human operators no longer feasible.As a result,many intelligent systems have been developed by researchers all over the world to automatically detect various types of manipulation cases.Therefore,this review paper aims to comprehensively discuss the state-of-theart methods that have been developed to detect and recognize stock market manipulation cases.It also provides a concise definition of manipulation taxonomy,including manipulation types and categories,as well as some of the output of early experimental research.In summary,this paper provides a thorough review of the automated methods for detecting stock market manipulation cases.

Cross-Sectional Variations of Illiquidity on Stock Returns,Idiosyncratic Volatility Biases in the Shanghai A’Share Stock Market

Under the NTS Reform(Non-Tradable Share Reform),this paper explores the cross-sectional relations between illiquidity and stock returns by considering the idiosyncratic volatility biases in the Shanghai A’Share stock market.Differing from prior studies,stock returns are decreasing in a stock’s illiquidity both before and after the NTS Reform.Regarding the negative relation between illiquidity and stock returns,we find that stock returns show no clear relation with illiquidity after controlling for idiosyncratic volatility biases.Furthermore,we use residual approach to eliminate the effect of idiosyncratic volatility,and find there exists a positive relation between illiquidity and stock returns after the NTS Reform.

The Research of the Correlation between Stock Market and Macroeconomy--Based on Comparison of Chinese and American Stock Markets

Since 2008,the economic volume of the United States has gradually decreased from 30% of world GDP to 20%-25%,and China has risen from 7% to 15%.However,face at a fast-growing economy,China's stock market has been sluggish,contrast strongly to the US's thriving stock market.This paper studies the correlation between stock market and macroeconomy,based on the perspective of stock market and macroeconomy between China and the United States.This article takes China and the United States from 1999 to early 2017 as the time frame.Choosing the Shanghai Stock Exchange securities market,the S&P 500 index and the macroeconomy indicators and policies of China and the United States as research objects,using a comparative method to study the interactive relationship between the two major economies.In addition,this paper analyzes the parts of macroeconomy and microlisted companies in economic and financial theory,and innovatively applies the four different aspects of macroeconomy of total seven indicators to more fully represent the macroeconomy.This paper establishes the VAR model,impulsive response,and variance decomposition to explore the interaction between trends of the stock market and macroeconomic trends.The research results show that the stock market trend is positively related to the macroeconomic trend.China's stock market is greatly affected by capital,and the reason why the US stock market can develop better under the condition that the macroeconomic development is not as good as China's,because of the unique status of the US dollar.Finally,this paper combines descriptive analysis and empirical analysis results to propose policy recommendations for China's stock market and macroeconomic development.

Stock market prediction using deep learning algorithms

The Stock Market is one of the most active research areas,and predicting its nature is an epic necessity nowadays.Predicting the Stock Market is quite challenging,and it requires intensive study of the pattern of data.Specific statistical models and artificially intelligent algorithms are needed to meet this challenge and arrive at an appropriate solution.Various machine learning and deep learning algorithms can make a firm prediction with minimised error possibilities.The Artificial Neural Network(ANN)or Deep Feedforward Neural Network and the Convolutional Neural Network(CNN)are the two network models that have been used extensively to predict the stock market prices.The models have been used to predict upcoming days'data values from the last few days'data values.This process keeps on repeating recursively as long as the dataset is valid.An endeavour has been taken to optimise this prediction using deep learning,and it has given substantial results.The ANN model achieved an accuracy of 97.66%,whereas the CNN model achieved an accuracy of 98.92%.The CNN model used 2-D histograms generated out of the quantised dataset within a particular time frame,and prediction is made on that data.This approach has not been implemented earlier for the analysis of such datasets.As a case study,the model has been tested on the recent COVID-19 pandemic,which caused a sudden downfall of the stock market.The results obtained from this study was decent enough as it produced an accuracy of 91%.