Impact of Environmental,Social,and Governance(ESG)Factors on Stock Prices and Investment Performance

This study examines the relationship between Environmental,Social,and Governance(ESG)factors and stock prices as well as investment performance.ESG factors have become increasingly relevant in investment decisions as investors prioritize companies with sustainable practices.Using a sample of publicly-traded companies,this research analyzes the impact of ESG factors on stock prices and investment returns.The findings suggest that companies with strong ESG performance tend to have higher stock prices and better investment performance than those with weak ESG performance.The study also highlights the significance of the individual components of ESG,such as environmental policies and corporate governance practices,on stock prices and investment returns.Overall,this research provides valuable insights for investors seeking to incorporate ESG factors into their investment decision-making processes.

Currency exposures of the oil and natural gas stock prices in the Hushen-300 stock market： A nonlinear model approach

The paper embarks to investigate the relationship between currency risk and stock prices of the oil and natural gas exploitation industry in the value-weighted Hushen-300 stock market, by applying the standard Capital Asset Pricing Model （CAPM） and nonlinear exchange rate exposure model to the Renminbi against US dollar. The results show that the currency exposure does vary in the oil-gas stock prices throughout the bull and bear market. The study suggests that the models of the equilibrium exchange rate exposure must be extended to considering the nonlinear exchange rate exposure, the regime periods of bull and bear market, and the industry types that is sensitive to the currency exposures. The nonlinear dynamic relationship between the exchange rate changes and the Chinese energy stock prices throughout the bull and bear market add to the recent empirical evidences that foreign exchange markets and stock markets are closely correlated.

Bankruptcy Probability and Stock Prices： The Effect of Altman Z-Score Information on Stock Prices Through Panel Data

There is an extensive branch of literature that examines the success of Altman＇s Z-score in predicting bankruptcy or financial distress. The goal of this research paper is to investigate the stock price performance of firms that exhibit a large probability of bankruptcy according to the model of Airman. Regardless of the validity of Airman＇s Z-score, we utilize a new empirical design that relates stock price movements to Altman＇s Z-score. We focus and examine, through the methodology of panel data, whether stocks that have a high probability of bankruptcy underperform stocks with a low probability of bankruptcy or if there are differences in the way the markets react to the financial health of the sample firms.

Stock prices and economic activity nexus in OECD countries:new evidence from an asymmetric panel Granger causality test in the frequency domain

This study investigates the stock price–economic activity nexus in 12 member countries of the Organization for Economic Cooperation and Development(OECD)by employing monthly data over the period 1981:1–2018:3.For this purpose,the study uses Granger causality in the frequency domain in the panel setting by decomposing the symmetric and asymmetric fluctuations.This methodology determines whether the predictive power of interested variables is concentrated on quickly,moderately,or slowly fluctuating components.Our findings show that the stock prices have predictive power for future long-term economic activity in the panel setting.However,economic activity has more reliable information for stock prices for negative components.Additionally,empirical findings for asymmetric shocks are not fully consistent with those of symmetric ones.Besides,the country-specific results provide different causal linkages across members and frequencies.These findings may provide valuable information for policymakers to design proper and effective policies in OECD countries regarding the stock market and economic activity nexus.

Do the RMB exchange rate and global commodity prices have asymmetric or symmetric effects on China’s stock prices?

With the rapid expansion of the RMB exchange rate’s floating range,the effects of the RMB exchange rate and global commodity price changes on China’s stock prices are likely to increase.This study uses both auto regressive distributed lag(ARDL)and nonlinear ARDL(NARDL)approaches to explore the symmetric and asymmetric effects of the RMB exchange rate and global commodity prices on China’s stock prices.Our findings show that without considering the critical variable of global commodity prices,there is no cointegration relationship between the RMB exchange rate and China’s stock prices,and the coefficient of the RMB exchange rate is not statistically significant.However,when we introduce global commodity prices into the NARDL model,the result shows that the RMB exchange rate has a negative effect on China’s stock prices,that there indeed exists a long-run cointegration relationship among the RMB exchange rate,global commodity prices,and stock prices in the NARDL model,and that global commodity price changes have an asymmetric effect on China’s stock prices in the long run.Specifically,China’s stock prices are more sensitive to increases than decreases in global commodity prices.Thus,increases in global commodity prices cause China’s stock prices to decline sharply.In contrast,the same magnitude of decline in global commodity prices induces a smaller increase in China’s stock prices.

A Panel Data Analysis of Corporate Attributes and Stock Prices for Indian Manufacturing Sector

Stock prices have always been considered as unpredictable phenomena due to their dynamic patterns. Identifying the forces that contribute to variations of stock prices is probably one of the most researched areas in finance. This study relates stock prices to the stock volatility （measured by beta） and to corporate attributes, i.e., size, liquidity, profits, leverage, and returns. The study is based on manufacturing sector in India, and it is based on a sample of 3,027 manufacturing companies during the periods from 1991-1992 to 2006-2007 collected from the Centre for Monitoring Indian Economy （CMIE） database. The regressions were performed with the dummies for time effect and firm effect separately and then for both effects together. Panel data models have been used to estimate the stock prices equation. The model finds out fixed and random effects between independent and explanatory variables and analyzes them through Hausman test. The paper also studies multicollineairity that may exist amongst the selected variables. The study shows that volatility （represented by Beta）, profit （represented by earnings per share （EPS））, and size （represented by market capitalization （MCAP）） significantly influence the stock prices （at the level of 5%）. Panel data analysis using Hausman test supports the fixed effect model.

The Prediction of Stock Prices Based on PCA and BP Neural Networks

There are many factors to influence stock prices indeed. The research method combining models and examples is applied to study how the factors affect stock prices here. Firstly, the principal component analysis is used to deal with a set of variables as the input of a BP Neural Network. Therefore, not only is the number of variables less, but also most of the information of original variables is kept. Then, the BP Neural Network is established to analyze and predict stock prices. Finally, the analysis of Chinese stock market illustrates that the method predicting stock prices is satisfying and feasible.

The interaction between stock prices and interest rates in Turkey:empirical evidence from ARDL bounds test cointegration

This paper demonstrates a significant,long-running relationship between stock prices and domestic interest rates in Turkey’s financial markets for the period of 2001 M1-2017 M4.Cointegration analysis is investigated using the autoregressivedistributed lag bounds(ARDL Bounds)test and vector autoregressive cointegration.Additionally,cointegrating equations such as the fully modified ordinary least square,dynamic ordinary least squares,and canonical cointegrating regression are applied to check the long-run elasticities in the concerned relationship.The ARDL Bounds and Johansen Cointegration test results show that,dynamically,both prices are significantly related to each other.The cointegrating equation outcomes demonstrate elasticities whereby both coefficients have negative signs.Additionally,the same results are corroborated by the impulse response where all variables respond negatively to each other.

A Mathematical Model Reveals That Both Randomness and Periodicity Are Essential for Sustainable Fluctuations in Stock Prices

Is it true that there is an implicit understanding that Brownian motion or fractional Brownian motion is the driving force behind stock price fluctuations? An analysis of daily prices and volumes of a particular stock revealed the following findings: 1) the logarithms of the moving averages of stock prices and volumes have a strong positive correlation, even though price and volume appear to be fluctuating independently of each other, 2) price and volume fluctuations are messy, but these time series are not necessarily Brownian motion by replacing each daily value by 1 or –1 when it rises or falls compared to the previous day’s value, and 3) the difference between the volume on the previous day and that on the current day is periodic by the frequency analysis. Using these findings, we constructed differential equations for stock prices, the number of buy orders, and the number of sell orders. These equations include terms for both randomness and periodicity. It is apparent that both randomness and periodicity are essential for stock price fluctuations to be sustainable, and that stock prices show large hill-like or valley-like fluctuations stochastically without any increasing or decreasing trend, and repeat themselves over a certain range.

ON THE INCREMENTS DISTRIBUTION OF STOCK PRICES

In this paper,the models of increment distributions of stock price are constructed with two approaches. The first approach is based on limit theorems of random summation. The second approach is based on the statistical analysis of the increment distribution of the logarithms of stock prices.

Comparative Analysis of Machine Learning Models for Stock Price Prediction: Leveraging LSTM for Real-Time Forecasting

The research focuses on improving predictive accuracy in the financial sector through the exploration of machine learning algorithms for stock price prediction. The research follows an organized process combining Agile Scrum and the Obtain, Scrub, Explore, Model, and iNterpret (OSEMN) methodology. Six machine learning models, namely Linear Forecast, Naive Forecast, Simple Moving Average with weekly window (SMA 5), Simple Moving Average with monthly window (SMA 20), Autoregressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM), are compared and evaluated through Mean Absolute Error (MAE), with the LSTM model performing the best, showcasing its potential for practical financial applications. A Django web application “Predict It” is developed to implement the LSTM model. Ethical concerns related to predictive modeling in finance are addressed. Data quality, algorithm choice, feature engineering, and preprocessing techniques are emphasized for better model performance. The research acknowledges limitations and suggests future research directions, aiming to equip investors and financial professionals with reliable predictive models for dynamic markets.

Stock Price Prediction Based on the Bi-GRU-Attention Model

The stock market, as one of the hotspots in the financial field, forms a data system with a huge volume of data and complex relationships between various factors, making stock price prediction an area of keen interest for further in-depth mining and research. Mathematical statistics methods struggle to deal with nonlinear relationships in practical applications, making it difficult to explore deep information about stocks. Meanwhile, machine learning methods, particularly neural network models and composite models, which have achieved outstanding results in other fields, are being applied to the stock market with significant results. However, researchers have found that these methods do not grasp the essential information of the data as well as expected. In response to these issues, researchers are exploring better neural network models and combining them with other methods to analyze stock data. Thus, this paper proposes the ABiGRU composite model, which combines the attention mechanism and bidirectional gated recurrent unit (GRU) that can effectively extract data features for stock price prediction research. Models such as LSTM, GRU, and Bi-LSTM are selected for comparative experiments. To ensure the credibility and representativeness of the research data, daily stock price indices of BYD are chosen for closing price prediction studies across different models. The results show that the ABiGRU model has a lower prediction error and better fitting effect on three index-based stock prices, enhancing the learning efficiency of the neural network model and demonstrating good prediction stability. This suggests that the ABiGRU model is highly adaptable for stock price prediction.

The potential harms of goodwill impairment avoidance: Evidence based on future performance and stock prices

The rapidly increasing volume of goodwill assets in the capital market generates potential risks due to the possibility of an untimely recognition of goodwill impairment.In this paper,we investigate the financial consequences of goodwill impairment avoidance based on firms’future performance and stock prices.Using Chinese A-share listed firms with goodwill balances,we find that avoiding goodwill impairments negatively affects a firm’s performance growth and increases its risk of a future stock price crash.These adverse effects continue for the three years following the goodwill impairment avoidance.Our results indicate that goodwill impairment avoidance has detrimental impacts on a firm’s future performance and stock price and that these impacts are persistent.Our conclusions are helpful for regulators on how to prevent the risks hidden in goodwill impairment recognition and maintain the stable development of the financial market.

Harmonious Stock Prices

Stock price movements in China still remain highly harmonious, in spite of the many significant regulatory and structural changes over the recent years. A survey of the literature reveals that harmony in the stock price movements is related to a few salient features in China＇s capital market： high ownership concentration, high incidence of the use of pyramidal ownership structure, significant state ownership, and a lack of active institutional investors. In addition, we also point out that harmonious stock prices may generally result from low intensity of private information acquisitions by risk arbitrageurs.

The impact of bond rating downgrades on common stock prices in China

In this paper,we examine how bond rating downgrades affect common stock prices in China by using the data of all the bond rating downgrades in China during the period from 1 January 2008 to 30 May 2016.To provide empirical evidence for the theory in Goh and Ederington(1993),we classify the samples according to the downgrade reasons and the bonds’time to maturity and examine the abnormal returns of each group in different windows.The empirical results show that the downgrades due to deteriorating financial prospects have a negative effect on stock prices and that this effect lags behind.The downgrades due to leverage changes have no significant effect on stock prices.Meanwhile,the variation in the decrease in stock prices due to rating downgrades of bonds that will mature within three years is significantly larger than that of those which will mature after more than three years.

Carbon emission trading system and stock price crash risk of heavily polluting listed companies in China:based on analyst coverage mechanism

This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements.Hence,the study identifies that heavily polluting enterprises in China have severe off-balance sheet carbon reduction risks before implementing the carbon emission trading system(CETS).Through the staggered difference-in-difference(DID)model and the propen-sity score matching-DID model,the impact of CETS on reducing the risk of stock price crashes is examined using data from China’s A-share heavily polluting listed companies from 2007 to 2019.The results of this study are as follows:(1)CETS can significantly reduce the risk of stock price crashes for heavily polluting companies in the pilot areas.Specifically,CETS reduces the skewness(negative conditional skewness)and down-to-up volatility of the firm-specific weekly returns by 8.7%and 7.6%,respectively.(2)Heterogeneity analysis further shows that the impacts of CETS on the risk of stock price crashes are more significant for heavily polluting enterprises with the bear market condition,short-sighted management,and intensive air pollution.(3)Mechanism tests show that CETS can reduce analysts’coverage of heavy polluters,reducing the risk of stock price crashes.This study reveals the role of CETS from the stock price crash risk perspective and helps to clarify the relationship between climatic risk and corporate financial risk.

Demand and Tight Stocks Driving Prices Higher

World cotton production and consumption are forecast to roughly balance at 25.1 million tons in 2010/11,as a result of a 15% rebound in production and a 2% increase in mill use.World ending stocks

Stock market and macroeconomic variables:new evidence from India

Understanding the relationship between macroeconomic variables and the stock market is important because macroeconomic variables have a systematic effect on stock market returns.This study uses monthly data from India for the period from April 1994 to July 2018 to examine the long-run relationship between the stock market and macroeconomic variables.The empirical findings suggest that standard cointegration tests fail to identify any relationship among these variables.However,a transformation that extracts the actual functional relationship between these variables using the alternating conditional expectations algorithm of(J Am Stat Assoc 80:580–598,1985)identifies strong evidence of cointegration and indicates nonlinearity in the long-run relationship.Further,the continuous partial wavelet coherency model identifies strong coherency at a lower frequency for the transformed variables,establishing the fact that the long-run relationship between stock prices and macroeconomic variables in India is nonlinear and time-varying.This evidence has far-reaching implications for understanding the dynamic relationships between the stock market and macroeconomic variables.

The Changes in World Oil Prices, Monetary Factors, and Foreign Index Toward Composite Index Movement： Indonesian Case for the Period of 2005-2011

Capital market is one of the drivers of the economy through the formation of capital investor excess as well as an indicator of a country＇s economy. Movement of stock price index is often influenced by many factors, derived from the company＇s performance, monetary factor, and changes in world oil prices. This study highlights the problem in world oil prices due to political turmoil in the Middle East. The samples are taken from the Jakarta Composite Stock Price Index （JCI）, oil prices, Indonesian inflation rate, Certificate of Bank Indonesia＇s （CBI） rate, and the reserve assets, during the period from January 2005 to December 2011 （84 months）. Using the data published by the Bank of Indonesia, reports of the Central Bureau of Statistics （Biro Pusat Statistik, BPS）, and other relevant sources, the data analyzed through the Eviews 7.1. The main objective of this study is to examine the effect of oil prices, foreign stock price index, and monetary variables （inflation rate, CBI rate, country＇s foreign reserves, and others） toward the JCI analyzed through the error correction model （ECM）. Hypothesis testing with the F-test for the 95% confidence level indicates that the oil price, exchange rate （Indonesian Rupiah （IDR）/United States Dollar （USD））, CBI rate, foreign exchange reserves, the Dow Jones Index, and the Taiwan stock index, both simultaneously as well as partially have a significant influence on the JCI.

The Voice of Physics in Finance:A Glance on the Theoretical Application of Heat Equation to Stock Price Diffusions

Stock price volatility is considered the main matter of concern within the investment grounds.However,the diffusivity of these prices should as well be considered.As such,proper modelling should be done for investors to stay healthy-informed.This paper suggest to model stock price diffusions using the heat equation from physics.We hypothetically state that,our model captures and model the diffusion bubbles of stock prices with a better precision of reality.We compared our model with the standard geometric Brownian motion model which is the wide commonly used stochastic differential equation in asset valuation.Interestingly,the models proved to agree as evidenced by a bijective relation between the volatility coefficients of the Brownian motion model and the diffusion coefficients of our heat diffusion model as well as the corresponding drift components.Consequently,a short proof for the martingale of our model is done which happen to hold.