Future Stock Price Research Articles

The Relationship between Stock Prices and Silver Future Prices Based on VAR Model

Despite recent dramatic increases in the prices of coal, oil, natural gas, and other fossil energy futures, some of which have hit successive record highs, related stocks have seen substantial decreases. The price trend of commodities is typically driven by commodity futures, which serve as price discoverers. There is a relationship between futures and stocks in the market, meaning that when the price of the futures contract for a particular commodity rises, so will the price of the stock of the company that produces the commodity because investors anticipate rising earnings. But does the price of futures vary when the stock price does? Based on the findings of the VAR model and Granger causality test, this study concludes that the stock price, whether it be the current price or the historical earnings, does not significantly affect the futures price. Futures prices have a significant impact on future stock prices. This would facilitate investment decision-making.

Stock Price Prediction System

To determine the future stock value of a company is the main purpose of stock price prediction there is a continuous change in the price of stocks which is affected by different industries and market conditions. The high dimensionality of data is a challenge for machine learning models because highly correlated dimensions/attributes may exert influence on precision of the model. PCA is used to reduce dimensionality to fit linear regression algorithm for future stock price prediction. Linear regression algorithm is used prior to and subsequent to implementation of Principal Component Analysis on the Tesla stock price data. Results manifest that production of machine learning models can be boosted by PCA, reducing the correlation and appropriate selection of principal components for high redundancy of data. Root mean square value and R-square value is used for assessment. Keywords: Principal component analysis, Linear regression, Root mean square error, r square value.

Google Trends and Technical Indicator based Machine Learning for Stock Market Prediction

The stock market often attracts investors to invest, but it is not uncommon for investors to experience losses when buying and selling shares. This causes investors to hesitate to determine when to sell or buy shares in the stock market. The accurate stock price prediction will help investors to decide when to buy or sell their shares. In this study, we propose a new approach to predicting stocks using machine learning with a combination of features from stock price features, technical indicators, and Google trends data. Three well-known machine learning algorithms such as Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Multiple Linear regression are used to predict future stock prices. The test results show that the SVR outperformed the MLP and Multiple Linear Regression to predict stock prices for Indonesian stocks with an average MAPE is 0.50%. The SVR can predict the stock price close to the actual price.

Stock Price Prediction Using Machine Learning

Stock price prediction approach using Long Short-Term Memory (LSTM) networks. The proposed method utilizes historical stock price data as input and trains an LSTM model to predict future stock prices. The LSTM network architecture includes multiple layers of LSTM cells, which can capture long-term dependencies and patterns in the input data. The effectiveness of the proposed approach is evaluated using real-world stock price data from the S&P 500 index. Experimental results demonstrate that the LSTM-based approach can achieve promising prediction accuracy compared to other traditional methods. The proposed method can potentially benefit financial analysts, investors, and traders by providing useful insights for decision-making. Key Words: Financial analysis, Stock-Market, LSTM, Future prediction, Machine learning

Progress and prospects of data-driven stock price forecasting research

With the rapid development of social economy and the continuous improvement of stock market, stock investment has become more and more widely concerned. Stock price prediction has become an important research direction in the field of cognitive computing in engineering. Data-driven stock price forecasting aims to predict future stock price trends based on historical values and textual data, which can effectively help people reduce risks and improve returns in the process of stock investment. The article reviews the literature on stock price forecasting methods, and classifies stock price forecasting methods from two different perspectives of model and feature. According to different model angles, the existing stock price prediction methods can be divided into statistical analysis methods, traditional machine learning methods and deep learning methods. According to different characteristic angles, the existing stock price prediction methods can be divided into those based on numerical data and those based on text mixed with numerical data. Finally, we summarize the research challenges faced by stock price prediction and provide future research directions.

Depoliticization and Stock Price Crash Risk: Evidence from China

ABSTRACT Using the enactment of Document No. 18 as a quasi-natural event, we take a sample of Chinese A-share listed companies from 2010 to 2017 to test the impact of depoliticization on stock price crash risk based on the difference-in-differences (DID) model. Our results show that depoliticization reduces future stock price crash risk more significantly among non-state-owned enterprises. Furthermore, depoliticization can mitigate stock price crash risk through the effects of financing needs, censorship risk and agency cost.

Improving Strategy Implementation Using Blockchain Smart Contracts

Every organization develops a strategy that needs to be implemented and is undertaken through project delivery. In essence, project requirements should exactly replicate an organization’s strategy. In reality, this does not happen, and behavioral factors deviate the project delivery from the strategic objectives. This occurs as project stakeholders can have competing objectives. Resultantly, requirements that are implemented through projects that are less aligned to the strategy. This article develops a game theoretic model to simulate why such deviations occur. That explains the difference between strategy development and implementation. This article also explains how the implementation of a blockchain smart contract can eliminate this difference and help an organization implement their strategy effectively. The difference between the overall strategy and the final implementation can make a significant difference in the overall value of the company because if it does not implement strategy effectively, it could lose market share, causing reduced revenue and a significant future stock price fall.

Fundamental Stock Analysis in COVID-19 Vaccines Industry

The COVID-19 keeps rampant in the human society from 2018 till now. Under the spread of the contagion, coronavirus vaccines are crucial factors to control the outbreak of the disease. Meanwhile, whether investing in companies developing COVID-19 vaccines becomes the research focus among investors. In this paper, fundamental stock analysis is applied as a method to investigate which companies in COVID-19 vaccine industry has the most promising prospect and will perform better in the future. The analysis of stocks is based on four aspects: valuation, profitability, growth, and payout, where the corresponding indicators are price to earnings ratio, earning per share growth, return on asset and equity, and dividend yield. After comparison of indicators of the main companies in the U.S. vaccine industry, suitable underlying assets are suggested to different types of investors. However, there are some limitations for using financial ratios to predict the future stock price in consideration of social fluctuations and fiscal policies. Therefore, further research ought to focus on how to combine fundamental stock analysis with technical method and social factors. These results shed light on guiding further exploration of stock selection based on fundamental analysis.

Stock Price predication based on Linear Regression, RNN, LSTM

The prediction of stock price has always been a topic of concern by the financial analysis industry. With the progress of science and technology, the prediction methods of stock price are becoming more and more rich and advanced. From classical linear regression to the state-of-art neural network scenarios, from mathematics to AI deep learning, the introduction of computers and the development of programming also provide a new direction for the prediction of stock prices. This study takes the stock of Johnson & Johnson in the past five years as an example, selects the open, high, low price of the stock as the explanatory variable, and chooses the close price as the explanatory variable to establish a linear regression model. This study also establishes RNN and LSTM models to predict stock prices. Subsequently, the R2, RMSE, MAE of the model are evaluated, where LSTM model have the best R2 score and the best prediction of future stock price. These results shed light on guiding further exploration to the short-term stock price prediction.

A State-of-the-Art Review of Probabilistic Portfolio Management for Future Stock Markets

Portfolio management has long been one of the most significant challenges in large- and small-scale investments alike. The primary objective of portfolio management is to make investments with the most favorable rate of return and the lowest amount of risk. On the other hand, time series prediction has garnered significant attention in recent years for predicting the trend of stock prices in the future. The combination of these two approaches, i.e., predicting the future stock price and adopting portfolio management methods in the forecasted time series, has turned out to be a novel research line in the past few years. That is, to have a better understanding of the future, various researchers have attempted to predict the future behavior of stocks and subsequently implement portfolio management techniques on them. However, due to the uncertainty in predicting the future, the reliability of these methodologies is in question, and it is unclear to what extent their results can be relied upon. Therefore, probabilistic approaches have also entered the research arena, and attempts have been made to incorporate uncertainty into future forecasting and portfolio management. This issue has led to the development of probabilistic portfolio management for future data. This review paper begins with a discussion of various time-series prediction methods for stock market data. Next, a classification and evaluation of portfolio management approaches are provided. Afterwards, the Monte Carlo sampling method is discussed as the most prevalent technique for probabilistic analysis of stock market data. The probabilistic portfolio management method is applied to future Shanghai Stock Exchange data in the form of a case study to measure the applicability of this method to real-world projects. The results of this research can serve as a benchmark example for the analysis of other stock market data.

Corporate tax avoidance and stock price crash risk: the moderating effects of corporate governance

PurposeThis study aims to investigate the relationship between corporate tax avoidance and stock price crash risk and the moderating effects of corporate governance.Design/methodology/approachThis study investigates the relationship between corporate tax avoidance and stock price crash risk using the sample consisting of listed firms in Vietnam for the period of 2011–2020 using panel regressions.FindingsThe authors find that there is a positive relationship between tax avoidance and stock price crash risk. Foreign ownership weakens the impacts of tax avoidance on stock price crash risk, while managerial ownership strengthens the impacts. Female Chief Executive Officers (CEOs) and female chairpersons weaken this relationship. Board gender diversity and state ownership have insignificant moderating impacts.Practical implicationsThese findings could help the stock market build better internal monitoring mechanisms to reduce the impacts of tax avoidance on future stock price crash risk. Investors can recognize the characteristics of corporate governance, especially foreign ownership, managerial ownership, female CEOs and female chairpersons when making investment decisions. The policy makers should consider policies to attract foreign investment and support women entrepreneurship.Originality/valueThis paper contributes to the literature on the impacts of tax avoidance on stock price crash risk in emerging countries. This paper is the first to investigate the influence of corporate governance mechanisms including state ownership, foreign ownership, female CEOs and chairpersons and board gender diversity on this relationship.

Withholding Bad News in the Face of Credit Default Swap Trading: Evidence from Stock Price Crash Risk

AbstractCredit default swaps (CDSs) are a major financial innovation related to debt contracting. Because CDS markets facilitate bad news being incorporated into equity prices via cross-market information spillover, CDS availability may curb firms’ information hoarding. We find that CDS trading on a firm’s debt reduces the future stock price crash risk. This effect is stronger in active CDS markets, when the main lenders are CDS market dealers with securities trading subsidiaries, or when managers have more motivation to hoard information. Our findings suggest that debt market financial innovations curtail the negative equity market effects of firms withholding bad news.

Extraction of Technical Indicators and Data Augmentation-Based Stock Market Prediction Using Deep LSTM Integrated Competitive Swarm Feedback Algorithm

In recent decades, time-series analysis and value forecasting have grown in research fields. Making a significant decision in the stock market prediction strategy requires knowledge that may be gained by forecasting with time-series data. Various prediction methods are developed to forecast future stock prices, but accurate prediction with the time-series data using external factors still results in a difficult task. An effective prediction approach is designed in this paper using the adopted Competitive Swarm Feedback Algorithm-based Deep Long Short-Term Memory (CSFA-based Deep LSTM) classifier for predicting the stock market data. The CSFA is the integration of the Competitive Swarm Optimizer (CSO) and Feedback Artificial Tree (FAT) algorithm. Then, technical indicators’ extraction, feature fusion and data augmentation steps are carried out. The Deep LSTM achieved higher prediction results than the other traditional classifiers. The proposed method achieved lower MAE, lower MSE, and minimum RMSE with the values of 0.1418, 0.1119, and 0.2557.

Peramalan Return Saham Menggunakan Model Integrated Moving Average

A popular investment that is in great demand among investors is stocked. Stocks are another type of financial instrument offering returns but carrying a higher risk level. Price time series are more difficult to manage than return time series. To equip investors with the knowledge to forecast future stock prices, mathematical models can be used to simulate stock price fluctuations. The time series method, especially the Integrated Moving Average (IMA) model, is a model that can be used to observe changes in stock prices. The Integrated Moving Average (IMA) model will be used in this study to simulate stock returns. The Integrated Moving Average (IMA) model is a Moving Average model that is carried out with a differencing process or an Autoregressive Integrated Moving Average model with a value of Autoregressive being zero. This study uses secondary data simulations from secondary sources, such as data on daily business stock prices for one year, to conduct a literature review and test experiments. The Integrated Moving Average (IMA) model is used in data processing, especially to test the differencing data process. The results obtained are the IMA (1,1) model with the following equation Zˆt = Zt-1 + 0, 5782at-1, which can be used to anticipate future stock returns. Based on these results, it is expected that investors can predict the value of shares within a certain period of time.

Predicting stock market using machine learning: best and accurate way to know future stock prices

Predicting stock market using machine learning: best and accurate way to know future stock prices

The impact of short sale prospect on income smoothing

PurposeThe purpose of this paper is to examine the impact of short sale prospect on future income smoothing.Design/methodology/approachThis study examines how short sale prospect impacts future income smoothing. This study follows prior research and uses two measures of income smoothing. One is the correlation between the change in prediscretionary income and the change in discretionary accruals. The other is the variability of earnings relative to the variability of cash flows.FindingsThis study finds that short sale prospect has a negative impact on future income smoothing. This finding is robust to use different measures of short sale prospect and income smoothing and to subsample tests. Additional analysis reveals that short sale prospect, by curbing income smoothing, reduces future stock price crash risk.Originality/valueTo the best of the authors’ knowledge, this study is the first to examine the impact of short selling on firms’ subsequent smoothing of reported income. This study contributes to the earnings quality literature by demonstrating the governance role of short selling on future earnings smoothness.

The executive pay gap and stock price crash risk: Promotion or suppression?

In recent years, cases of stock price crash have continued to emerge. However, yet little research to date has investigated the compensation incentives of top management team (TMT) affect the risk of stock price crash. Nor has research considered the impact of the executive pay gap on the stock price crash risk. Especially, as the "egalitarianism" was broken in the compensation system, and the increase of the degree of marketization of salaries, the executive pay gap has shown an expanding trend. Under this circumstance, we would systematically examine the association between the extent of executive pay gap and its future stock price crash risk. Based on the sample of A-Share non-financial listed companies in Shanghai and Shenzhen Stock Exchange, we used firm FE regression method to empirically examine the relationship of the internal and external compensation gaps of executives and crash risk, as well as its contigency variables and inner mechanism. The empirical results show that there is a U-shaped relationship between the internal and external pay gap of executives and future crash risk. After passing the endogenous test and the robustness test, the conclusion still holds. Further research shows that the U-shaped relationship between the pay gap and crash risk is more pronounced, when firms are affiliated with the non-state-owned enterprise or its compensation fairness is lower. Finally, the quality of information disclosure plays a mediation effect when executive pay gap affects stock price crash risk. According to the economic and behavior perspectives, we explored the impact of compensation structure on stock price crash risk from the pay gap of executives for the first time, and extended the emerging literature of forecasting future stock price crash risk and executive pay gap. In addition, a key implication of our findings is that more guidance for firms is provided to design the compensation structures and to reduce stock price crash risk.

ANALISIS REGRESI FAKTOR PANEL DINAMIS BLUNDELL-BOND DENGAN ESTIMASI SYSTEM-GENERALIZED METHOD OF MOMENT PADA SAHAM FARMASI DI BEI

The pharmaceutical sector has become a concern during the Covid-19 pandemic because of the large use of drugs. Companies need to improve financial performance to increase their share prices and investors need analysis to predict future stock prices. This study aims to analyze the influence of stock prices on 10 pharmaceutical companies on the Indonesia Stock Exchange during the third quarter of 2020 to the third quarter of 2021. Based on previous research, the factors that are thought to have an effect on changes in stock prices are internal financial ratios (ROA, ROE, NPM, GPM, EPS, PER, BV, PBV, DAR, DER, CR, QR, Cash Asset Ratio) and external inflation, exchange rates, interest rates. The method used in this research is dynamic panel factor regression analysis with GMM (Generalized Method of Moment) estimation. Factor analysis to reduce the independent variables to form a factor score which is then entered into the regression. The regression model was obtained from the comparison of Arellano-Bond GMM and Blundell-Bond System. The GMM system is the development of Arellano-Bond which will produce more efficient estimates when the sample time series is short. The results of the study were obtained 3 factor scores with a total variance of 81.757% from the elimination of 6 variables that had MSA <0.5. The best model is the Blundell-Bond Twostep System which fulfills the model assumptions with RMSE 803.276.

Predicting TSLA Stock Price Based on LSTM and GRU Models

In recent years, advancements in artificial intelligence technologies have facilitated their successful application in time series forecasting and analytical tasks. With the objective of fostering growth in the stock market, this study employs two well-established deep learning forecasting models, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), to predict TESLA stock prices. Initially, historical data is utilized to project future stock prices, and a comparative analysis of the two models is conducted, concluding that a hybrid LSTM+GRU model demonstrates superior performance compared to a standalone LSTM model when provided with identical data. Furthermore, to enhance prediction accuracy, time series data is smoothed using either moving average or exponential average techniques, resulting in a significant improvement in the performance of the LSTM model as compared to both its previous performance and the LSTM+GRU model.

Stock Price Prediction Model Using Deep Learning Optimization Based on Technical Analysis Indicators

Stock price prediction is one of the processes of analyzing and determining stock prices in the future. With technical analysis, future stock price predictions can be predicted through the pattern of fluctuations in the stock price in the past. In this study, the researcher predicts the stock price for the next week using the Deep Learning method, namely the Multilayer Perceptron, and combined with the day-shifting method. To expect the results of this stock, the author also observes the model's usefulness and proposes a Mean Error to Mean Price Ratio (MEMPR) to increase the insights processed by the model. Then to find out the accuracy of stock price predictions for each algorithm, testing is carried out using stock data which consists of new data which is then carried out by a training process to get an absolute error value. The experimental results show that the model can predict stock prices with an R2 metric of 0.995.