The chaotic behavior among the oil prices, expectation of investors and stock returns: TAR-TR-GARCH copula and TAR-TR-TGARCH copula

This paper has two aims. The first one is to investigate the existence of chaotic structures in the oil prices, expectations of investors and stock returns by combining the Lyapunov exponent and Kolmogorov entropy, and the second one is to analyze the dependence behavior of oil prices, expectations of investors and stock returns from January 02, 1990, to June06, 2017. Lyapunov exponents and Kolmogorov entropy determined that the oil price and the stock return series exhibited chaotic behavior. TAR-TR-GARCH and TAR-TR-TGARCH copula methods were applied to study the co-movement among the selected variables. The results showed significant evidence of nonlinear tail dependence between the volatility of the oil prices, the expectations of investors and the stock returns. Further, upper and lower tail dependence and comovement between the analyzed series could not be rejected. Moreover, the TAR-TR-GARCH and TAR-TR-TGARCH copula methods revealed that the volatility of oil price had crucial effects on the stock returns and on the expectations of investors in the long run.

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.

Brent prices and oil stock behaviors: evidence from Nigerian listed oil stocks

Background:Given the shale oil glut that culminated in the most recent and continuing oil price drop from June 2014 and the global financial crisis of 2008 that triggered a cyclical downturn in oil prices and stock market activity,this study investigates the impact of Brent oil price shocks on oil related stocks in Nigeria.Methods:This study uses a vector autoregressive(VAR)model with the impulse response function and the forecast variance decomposition error.Findings:The empirical evidence reveals that oil price shocks have a negative impact on Nigerian oil and gas company stocks.In theory,this situation should apply to oil importing countries and is therefore uncharacteristic of an oil exporting country like Nigeria.Conclusions:The findings suggest that oil companies operating in Nigeria should diversify their investments to protect their business from single-sector market forces,and can also embrace the advantages of outsourcing some of their operations to specialist providers to increase flexibility and reduce operating costs.Finally,for vertically integrated oil and gas companies,oil price hedging and energy risk management will be beneficial because it will mean that these companies will take a position in the crude oil futures market.This will allow for better cash flow management and flexibility.Originality/value:This study extends the existing literature in two distinct ways.First,it provides,to the best of our knowledge,the first examination of the impact of oil price shocks on stock market activities with a focus on the market returns of oil and gas companies listed in the Nigerian Stock Exchange.Second,this study uses daily data because high frequency data contain more information than lower frequency data does,and lower frequency data average out too much important information.

The time‑varying effects of oil prices on oil-gas stock returns of the fragile five countries

This study analyzes oil price exposure of the oil–gas sector stock returns for the fragile five countries based on a multi-factor asset pricing model using daily data from 29 May 1996 to 27 January 2020.The endogenous structural break test suggests the presence of serious parameter instabilities due to fluctuations in the oil and stock markets over the period under study.Moreover,the time-varying estimates indicate that the oil–gas sectors of these countries are riskier than the overall stock market.The results further suggest that,except for Indonesia,oil prices have a positive impact on the sectoral returns of all markets,whereas the impact of the exchange rates on the oil–gas sector returns varies across time and countries.

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.

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.

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.

G-Contractive Sequential Composite Mapping Theorem in Banach or Probabilistic Banach Space and Application to Prey-Predator System and A &H Stock Prices

Theorems of iteration g-contractive sequential composite mapping and periodic mapping in Banach or probabilistic Bannach space are proved, which allow some contraction ratios of the sequence of mapping might be larger than or equal to 1, and are more general than the Banach contraction mapping theorem. Application to the proof of existence of solutions of cycling coupled nonlinear differential equations arising from prey-predator system and A&H stock prices are given.

The Impact of US Stock Market on the Co-Movements of BRIC Stock Markets—Evidence from Linear Conditional Granger Causality

This paper investigates the impact of the US stock market on the co-movements among the BRIC stock markets using conditional Granger causality which allows a comprehensive exploration on direct and indirect causality. The results from linear conditional causality test show a strong influence of the US stock market on the co-movements of BRIC. Our findings identify the US stock market which is the main inner factor making major contributions to the co-movements among the BRIC stock markets. Further, this study provides robust evidence that the co-movements cannot be significantly influenced by the common information factor. These findings show a more complete picture of the relationships between the US and the BRIC stock markets, offering important implications for policymakers and investors.

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.

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.

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.

Deflation and Stock Prices

While the literature on inflation and stock prices is plentiful,there is little literature on deflation and stock prices.This paper explores the empirical data and makes a theoretical analysis of the likely impact on stock prices when expectations change from inflation to deflation.Deflation has a bad name among some economists and most investors.However,from a stock market perspective,deflations’bad name may not be well-deserved.Several observations support this:1)The 1930s was a statistical outlier and not representative for a deflationary period and deflation does not seem to create recessions,causality goes the other way;2)real stock returns are positive and around average in the periods leading up to and following the onset of deflation;3)when moving from low inflation to mild deflation,P/E ratios are virtually unchanged;and 4)peak P/E ratios seem to be reached at inflation rates close to zero.The author proposes three possible explanations for the seemingly disconnect between the empirical data and the“default”ex ante belief of most economists and investors:availability heurist,deflation illusion,and tax related issues in connection with the tax hypothesis.

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.

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.

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