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.

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.

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.

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.

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.

Exploring Patent Effects on Higher Stock Price and Stock Return Rate-A Study in China Stock Market

Based on the valid patent data and stock price data of China A-shares,the patent effects of four patent species including the invention publication,the invention grant,the utility model grant,and the design grant,on the stock price and the stock return rate were analyzed via analysis of variance(ANOVA).It was proved that the A-shares having new patents of any patent species shown the higher stock price mean and the higher stock return rate mean than those A-shares having no new patents did.The A-shares having new design grants were found to show the highest stock price mean among the A-shares having new patents of any patent species.The A-shares in the group of top 25%patent count of either the invention publication or the invention grant shown the highest stock return rates mean than those A-shares in other groups of less patent count did.The invention grant,following the general concept,showed its excellent patent effect.The design grant,beyond the expectation,also showed patent effects on the higher stock price and the higher stock return rate.The finding would improve the state of the art in the patent valuation and the listing company evaluation.

ARIMA and Facebook Prophet Model in Google Stock Price Prediction

We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’predictions.We first examine the stationary of the dataset and use ARIMA(0,1,1)to make predictions about the stock price during the pandemic,then we train the Prophet model using the stock price before January 1,2021,and predict the stock price after January 1,2021,to present.We also make a comparison of the prediction graphs of the two models.The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.

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.

Forecasting Tesla’s Stock Price Using the ARIMA Model

The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock market’s prediction of the trend of stock prices helps in grasping the operation law of the stock market and the influence mechanism on the economy.The autoregressive integrated moving average(ARIMA)model is one of the most widely accepted and used time series forecasting models.Therefore,this paper first compares the return on investment(ROI)of Apple and Tesla,revealing that the ROI of Tesla is much greater than that of Apple,and subsequently focuses on ARIMA model’s prediction on the available time series data,thus concluding that the ARIMA model is better than the Naïve method in predicting the change in Tesla’s stock price trend.

Investor Attention,Analyst Optimism,and Stock Price Crash Risk

This paper used the A-shares listed companies in China as samples,constructed a comprehensive indicator of investor attention,and conducted an empirical analysis on the correlations among investor attention,analyst optimism,and stock price crash risk.The results indicated that investor attention aggravates the stock price crash risk and has a positive effect on analyst optimism.Meanwhile,the analyst optimism plays a mediating role in the positive correlation between investor attention and stock price crash risk.In addition to that,institutional investor attention also has direct and indirect effects on the crash risk.

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.

Prediction of BRIC Stock Price Using ARIMA,SutteARIMA,and Holt-Winters

The novel coronavirus has played a disastrous role in many countries worldwide.The outbreak became a major epidemic,engulfing the entire world in lockdown and it is now speculated that its economic impact might be worse than economic deceleration and decline.This paper identifies two different models to capture the trend of closing stock prices in Brazil(BVSP),Russia(IMOEX.ME),India(BSESN),and China(SSE),i.e.,(BRIC)countries.We predict the stock prices for three daily time periods,so appropriate preparations can be undertaken to solve these issues.First,we compared the ARIMA,SutteARIMA and Holt-Winters(H-W)methods to determine the most effective model for predicting data.The stock closing price of BRIC country data was obtained from Yahoo Finance.That data dates from 01 November 2019 to 11 December 2020,then divided into two categories-training data and test data.Training data covers 01 November 2019 to 02 December 2020.Seven days(03December 2020 to 11December 2020)of datawas tested to determine the accuracy of the models using training data as a reference.To measure the accuracy of the models,we obtained the means absolute percentage error(MAPE)and mean square error(MSE).Prediction model Holt-Winters was found to be the most suitable for forecasting the Brazil stock price(BVSP)while MAPE(0.50)and MSE(579272.65)with Holt-Winters(smaller than ARIMA and SutteARIMA),model SutteARIMA was found most appropriate to predict the stock prices of Russia(IMOEX.ME),India(BSESN),and China(SSE)when compared to ARIMA and Holt-Winters.MAPE andMSE with SutteARIMA:Russia(MAPE:0.7;MSE:940.20),India(MAPE:0.90;MSE:207271.16),and China(MAPE:0.72;MSE:786.28).Finally,Holt-Winters predicted the daily forecast values for the Brazil stock price(BVSP)(12 December to 14 December 2020 i.e.,115757.6,116150.9 and 116544.1),while SutteARIMA predicted the daily forecast values of Russia stock prices(IMOEX.ME)(12 December to 14 December 2020 i.e.,3238.06,3241.54 and 3245.01),India stock price(BSESN)(12 December to 14 December 2020 i.e.,.45709.38,45828.71 and 45948.05),and China stock price(SSE)(11 December to 13 December 2020 i.e.,3397.56,3390.59 and 3383.61)for the three time periods.

Stock Price Prediction Using Predictive Error Compensation Wavelet Neural Networks

:Machine Learning(ML)algorithms have been widely used for financial time series prediction and trading through bots.In this work,we propose a Predictive Error Compensated Wavelet Neural Network(PEC-WNN)ML model that improves the prediction of next day closing prices.In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs.An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence.The performance of the proposed model is evaluated using six different stock data samples in the New York stock exchange.The results have demonstrated significant improvement in forecasting accuracy in all cases when the second network is used in accordance with the first one by adding the outputs.The RMSE error is 33%improved when the proposed PEC-WNN model is used compared to the Long ShortTerm Memory(LSTM)model.Furthermore,through the analysis of training mechanisms,we found that using the updated training the performance of the proposed model is improved.The contribution of this study is the applicability of simultaneously different time frames as inputs.Cascading the predictive error compensation not only reduces the error rate but also helps in avoiding overfitting problems.

Corporate pledgeable asset ownership and stock price crash risk

We investigate how a firm’s corporate pledgeable asset ownership(CPAO)affects the risk of future stock price crashes.Using pledgeable asset ownership and crash risk data for a large sample of U.S.firms,we provide novel empirical evidence that a firm’s risk of a future stock price crash decreases with an increase in its pledgeable assets.Our main findings are valid after conducting various robustness tests.Further channel tests reveal that firms with pledgeable assets increase their collateral value,thereby enhancing corporate transparency and limiting bad news hoarding,resulting in lower stock price crash risk.Overall,the results show that having more pledgeable assets enables easier access to external financing,making it less likely that managers will hoard bad news.

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.

Japanese Stock Price Determination： What Is Important？

This paper analyzes empirically the relationship between recent Japanese stock prices and macroeconomic variables For the Japanese stock prices, the level and rate of that are both used for analysis. The results show that exchange rates have not been a significant determinant of the ＂level＂ of Japanese stock prices, but on the other hand, have been a significant determinant of its ＂rate＂. The ＂rate＂ has been used to analyze determinants of the stock prices. However, it is dangerous to use it without taking into account its level. Many stock traders usually look at the level. Interest rates, especially the domestic interest rate, have not impacted the Japanese stock prices. Japanese interest rates have been quite low as the Bank of Japan has implemented quantitative easing instead of interest-based policies. Recently, the U.S. stock prices, particularly in the first few days after a change, have been significantly influencing the Japanese 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.