Stock Price Research Articles

Deep Learning-Based Stock Price Prediction Using LSTM Model

The stock market is a vital component of the broader financial system, with its dynamics closely linked to economic growth. The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets. By examining historical transaction data, latent opportunities for profit can be uncovered, providing valuable insights for both institutional and individual investors to make more informed decisions. This study focuses on analyzing historical transaction data from four banks to predict closing price trends. Various models, including decision trees, random forests, and Long Short-Term Memory (LSTM) networks, are employed to forecast stock price movements. Historical stock transaction data serves as the input for training these models, which are then used to predict upward or downward stock price trends. The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements. The LSTM-based deep neural network model, in particular, demonstrates a commendable level of predictive accuracy. This conclusion is reached following a thorough evaluation of model performance, highlighting the potential of LSTM models in stock market forecasting. The findings offer significant implications for advancing financial forecasting approaches, thereby improving the decision-making capabilities of investors and financial institutions.

A new look at the finance–environment nexus: How yield spread affects environmental quality in the United States

AbstractEnhancing environmental quality is currently a key focus for policymakers. Furthermore, researchers are also prioritizing the investigation of novel variables that can influence environmental quality. The yield spread is commonly used as a leading indicator for business cycles in the macro‐finance literature, providing information about future expectations regarding business cycles. The yield spread also presents information about the borrowing costs of firms. None of the existing studies in the environmental economics literature has tested the impact of the yield spread on environmental quality, implying the level of the yield spread has not been considered a variable that can influence environmental quality. This study aims to fill this gap by providing evidence on the effect of yield spread (between 2‐ and 10‐year government bonds) on environmental quality in the United States from June 1976 to September 2023. The results indicate that an increase in the yield spread leads to a rise in environmental destruction. These results suggest two considerable consequences. First, optimistic expectations today lead to the overuse of resources and environmental destruction. Second, firms' higher borrowing costs may be forcing them to use less environmentally friendly production methods to offset more expensive financing costs and increase their profits. This study asserts that companies should enhance their environmental sensitivity, and policymakers should adopt a more proactive stance, based on the results of this study and other research that demonstrates the detrimental impacts of environmental regulation violations and poor environmental performances on companies' stock prices and financial performances.

Improving out-of-sample forecasts of stock price indexes with forecast reconciliation and clustering

In this paper, we propose a novel approach to improving forecasts of stock market indexes by considering common stock prices as hierarchical time series, combining clustering with forecast reconciliation. We propose grouping the individual stock price series in various ways including via metadata and using unsupervised learning techniques. The proposed approach is applied to the Dow Jones Industrial Average Index and the Standard & Poor 500 Index and their component stocks, and the results obtained with different grouping approaches are compared. The results empirically demonstrate that the combined use of clustering and reconciliation improves the forecast accuracy of the stock market indexes and their constituents.

The spillover and contagion effects of sovereign risk on stock markets

This study examines the spillover effects of sovereign risk on international stock markets using a dynamic spatial Durbin model (SDM) on a panel of forty countries from 2009Q1 to 2024Q2. The findings show that an increase in sovereign risk, as measured by CDS spreads, leads to a significant decrease in both local and foreign stock prices, with 0.027 percent reductions across foreign markets on average. These effects are transmitted through geographical, and economic and financial channels, with exchange rates, gross fixed capital formation, and industrial production playing crucial roles in influencing stock market performance. Additionally, the negative (positive) spillover effects from rising gross fixed capital formation and sovereign risk (industrial production) demonstrate how foreign markets are more impacted than domestic ones. The study highlights the interconnected nature of global financial markets, reinforcing the need for coordinated international policy responses to mitigate the transmission of sovereign risk across borders. These findings provide new insights into how global economic factors influence stock market volatility, particularly through specific transmission channels, underscoring the importance of multilateral collaboration in managing sovereign risk.

The Influence Of Economic Value Added (EVA), Return On Assets (ROA) On Stock Prices With Market Value Added (MVA) As A Moderating Variable

The aim of this research is to ascertain the influence of Economic Value Added (EVA), Return on Assets (ROA) on stock prices with Market Value Added (MVA) as a moderating variable in retail sector companies on the Indonesian Stock Exchange for the 2017-2022 period. The research methodology used is a quantitative method. The research population and sample consisted of 30 data. In this research, the data collection techniques used in this research were documentation and library research techniques. Data were analyzed using panel data regression, Microsoft Excel 2010 and Eviews Version 12 were used to process the data. Findings: The results of the t test (partial) on the Economic Value Added (EVA) variable (X1) obtained a calculated t value of 0.434167 < t table, namely 2.048407142 and a sig value. 0.6676 > 0.05, then H1 is rejected. The results of the t test (partial) on the Return on Assets (ROA) variable obtained a calculated t value of 12.77570 > t table, namely 2.048407142 and a sig value. 0.0000 < 0.05, then H2 is accepted. The results of the t test (partial) on the moderating variable Market Value Added (MVA) are able to moderate Economic Value Added (EVA) and obtain a sig level value. is 1.288399 > 0.05 and the sig.prob value is 0.2094 < 0.05, then H3 is accepted. The results of the t test (partial) on the Market Value Added (MVA) moderating variable are able to moderate the Return on Assets (ROA) to obtain a sig level value. is -0.916624 < 0.05 and the sig.prob value is 0.3681 > 0.05, then H4 is rejected.

Rx for Legal Troubles: CEO Medical Degree and Corporate Litigation

ABSTRACTResearch Question/IssueThis study examines the relationship between CEOs' medical degrees (MDs) and corporate litigations targeting US pharmaceutical firms.Research Findings/InsightsCEOs with a medical background are significantly more susceptible to securities class actions, while facing fewer patent lawsuits. The results indicate that the lack of management skills among MD CEOs comes at the cost of firm value. After lawsuit announcements, companies led by MD CEOs experience a significantly negative effect on their stock prices. Markets show no tolerance for failures by MD CEOs, as evidenced by the impact of clinical trial disappointments.Theoretical/Academic ImplicationsThe findings contribute to our understanding of the impact of CEOs' medical backgrounds on corporate litigation outcomes. The results suggest that when examining how a CEO's qualifications affect legal issues and the value of a company, we should consider the unique context of having an MD.Practitioner/Policy ImplicationsPractically, the study implies that firms should carefully consider the balance of managerial and medical expertise in CEO appointments, recognizing the potential legal and financial consequences. Policymakers might see it as beneficial to encourage a mix of different skills among top executives. This could make pharmaceutical companies more resilient in the face of legal issues, which, in turn, helps maintain confidence among investors.

The Effect of Firm Size, Current Ratio, and Debt to Equity Ratio on Share Price in Food and Beverage Subsector Manufacturing Companies Listed on the Indonesia Stock Exchange

The purpose of this study is to understand how company size, liquidity, and capital structure can affect stock prices in the food and beverage industry in Indonesia, so as to provide practical guidance for stakeholders in making financial and investment decisions. This research method uses a sample of 18 manufacturing companies in the food and beverage subsector on the IDX in the 2019-2022 period. Sample selection is carried out by purposive sampling technique, which is a technique that considers and determines samples based on certain criteria. The purposive sampling technique was chosen to ensure that the samples taken are relevant and in accordance with the research objectives. The results of this study show that in the number that means that the independent variable is only able to explain the bound variable of 0.299 or 29.9%. *The Stock Price variable can be explained by 29.9% by variables such as company size, debt to equity ratio, current ratio, return on equity while the remaining 70.1% can be influenced by other factors that are not studied.

Partial hedging in credit markets with structured derivatives: a quantitative approach using put options

This study develops a novel method for mitigating credit risk through the use of structured derivatives, focusing in particular on the use of European put options as a strategic hedging tool. Inspired by the work of Merton (1974), our approach introduces the concept of default triggered by the stock price ST breaching a predefined barrier B. By establishing a distributional equivalence between an existing default model and P(ST<B) for a given time T, we demonstrate the potential for reducing the necessary capital allocation for a projected loss X(T) by partially hedging with a European put option. We formulate and solve an optimization problem w.r.t. a specific risk measure to determine the optimal strike price for the option, and our numerical analysis confirms a reduction in the Solvency Capital Requirement (SCR) in markets with and without jumps. Our findings provide (insurance) companies with a pragmatic approach to mitigating losses while maintaining their current risk management framework.

The Effect of Fundamental Factors and Earnings Growth on Stock Prices in Food and Beverage Subsector Companies on the IDX for the 2019-2023 Period

This study aims to determine and analyze the effect of Fundamental Factors consisting of Earning Per Share (EPS), Price to Book Value (PBV), Price to Earning Ratio (PER) and Earnings Growth on Share Prices in food and beverage subsector companies listed on the Indonesia Stock Exchange for the 2019-2023 period. The sample used in this study is the financial statements of food and beverage subsector companies for the period 2019 to 2023. The method used in determining the sample is by using purposive sampling method, and obtained as many as 13 companies. Data analysis used in research with the Eviews program. The results showed that EPS has a significant effect on stock prices, PBV has a significant effect on stock prices, PER has a significant effect on stock prices, earnings growth has no significant effect on stock prices. While simultaneously EPS, PBV, PER and earnings growth have a significant effect on stock prices. The implication in this study is that the signaling theory of the research results affects the rise and fall of stock prices. In view of the high fundamental factors, it shows a positive signal for the company, especially in stock prices. However, it does not apply to earnings growth because if earnings growth is high, it will have a bad effect on stock prices as well as for investors, but investors can look deeper into financial ratios.

Pengaruh Faktor-Faktor Ekonomi Makro terhadap Jakarta Islamic Index (JII) Periode 2010-2019

This study aims to determine and analyze the influence of macroeconomic factors such as Gross Domestic Product (GDP), Inflation Rate, SBI Interest Rate, Rupiah / US Dollar exchange rate on the JII (Jakarta Islamic Index) Stock Price Index on the Indonesia Stock Exchange for the period 2010-2019. This study uses an associative quantitative approach which is intended to examine the relationship and influence between macroeconomic factors variables on the JII stock price index variable. Meanwhile, the analysis method used is multiple linear regression. The results of this study indicate that only the rupiah/US dollar exchange rate has a significant negative effect on the JII stock price index. While macroeconomic factors such as Gross Domestic Product, Inflation Rate, SBI Interest Rate have no significant effect on the JII stock price index.

Dividend Policy and Share Price Volatility of Listed Construction Firms at the Nairobi Securities Exchange, Kenya

The inherent volatility in all stock market scares low risk takers from the financial market and is capable of deterring investments. One of the factors that drives investors’ reactions to stock pricing is the dividend policy. This study examined the influence of dividend policy on the volatility of stock prices of construction firms listed at the Nairobi Securities Exchange for the period spanning five (5) years from 2019 to 2023. The study was dealt with on the following objectives; to examine the influence of dividend pay-out on share price volatility of listed construction firms at the NSE, Kenya, to determine the influence of dividend yield on share price volatility of listed construction firms at the NSE, Kenya. The study was guided by the: Dividend relevance theory, and dividend irrelevance theory. This study adopted a correlational research design while dealing with the research objectives. A census method was employed in data collection before analysis of the audited financial statements that was downloaded from the firm websites in arriving at detailed and well informed conclusions by employing Multiple regression, Correlation and ANOVA techniques using the Statistical Package for Social Sciences (SPSS) to establish the existence (or lack of it) of the relationship between the independent variables and the dependent variable of the study. The findings revealed that dividend policy influenced share price volatility by; dividend pay-out ratio (r=0.111, β=0.036, t=4.7240); and dividend yield (r= -0.018, β= -0.003, t= 2.7130). The study recommended that potential and existing investors should opt for listed firms in the construction sector with a high dividend payout ratio compared to the earnings generated by the firms; Investors to invest in firms with high dividend payouts. The study achieved its main objective. Keywords: Dividend Policy, Dividend Pay-out, Dividend Yield, Share Price Volatility

A Novel NLP-based Stock Market Price Prediction and Risk Analysis Framework

Stock market prediction is an interesting and complex problem that has recently been in the limelight, thanks to the significant accuracy achieved by deep learning models. However, a complete platform with prediction and risk analysis ability is unavailable. In the current work, we present a novel framework for investment analysis designed to create ease for investors and provide a confidence measure along with the stock price to depict the risk involved in investing in stocks of a particular company. The model integrates two different approaches successfully to improve accuracy significantly. The model inputs two sources – a stock price dataset depicting the original scores as numerals and textual data extracted from Reddit news articles. The traditional problem of stock price prediction is dealt with using LSTMs on individual stock prices. At the same time, the confidence is represented by a risk value calculated intelligently using XGBoost and LSTM output. We have deployed natural language processing techniques for performing sentiment and subjectivity analyses, which are then used to extract features for further investigation in the study. The results show that an accuracy of 94% for stock trend prediction can be achieved using PCA as the feature extractor with tuned parameters for XGBoost and around 76% accuracy for stock price prediction with a tuned LSTM. It removes the hassle for investors to research the project or company they want to invest in and provides all relevant analysis and data.

A Stock Prediction Method Based on Multidimensional and Multilevel Feature Dynamic Fusion

Stock price prediction has long been a topic of interest in academia and the financial industry. Numerous factors influence stock prices, such as a company’s performance, industry development, national policies, and other macroeconomic factors. These factors are challenging to quantify, making predicting stock price movements difficult. This paper presents a novel deep neural network framework that leverages the dynamic fusion of multi-dimensional and multi-level features for stock price prediction, which means we utilize fundamental trading data and technical indicators as multi-dimensional data and local and global multi-level information. Firstly, the model dynamically assigns weights to multi-dimensional features of stocks to capture the impact of each feature on stock prices. Next, it applies the Fourier transform to the global features to capture the long-term trends of the global environment and dynamically fuses these with local and global features of the stocks to capture the overall market environment’s impact on individual stocks. Finally, temporal features are captured using an attention layer and an RNN-based model, which incorporates historical price data to forecast future prices. Experiments on stocks from various industries within the Chinese CSI 300 index reveal that the proposed model outperforms traditional methods and other deep learning approaches in terms of stock price prediction. This paper proposes a method that facilitates the dynamic integration of multi-dimensional and multi-level features in an efficient manner and experimental results show that it improves the accuracy of stock price predictions.

Integrating ChatGPT with Salesforce for Real-Time Market Insights on Accounts

This paper explores the integration of ChatGPT, an AI-driven natural language processing model, with Salesforce to enhance sales teams' access to real-time market information about specific accounts. By integrating ChatGPT with Salesforce through API calls, users can fetch the latest market trends, news articles, stock prices, financial data, legal filings, and acquisitions related to specific accounts directly on the Account detail page. This integration not only improves decision-making and customer engagement but also offers a scalable, automated solution that saves time and enhances data accuracy. The paper includes coding examples, architecture diagrams, and use cases to demonstrate the implementation, and it discusses key performance indicators (KPIs) for assessing its effectiveness in real-world applications. Keywords: Salesforce, ChatGPT, AI Integration, Real-Time Data, Account Insights, API, Market Trends, CRM, Natural Language Processing, Automation

Analyzing Amazon Stock Volatility: Market Returns and Macroeconomic Factors

Against the backdrop of globalization and changing financial markets, investors and policymakers need accurate market volatility forecasts to guide decision-making. Traditional forecasting models are difficult to fully explain the complex market volatility, thus requiring more refined research methods. This study provides an in-depth analysis of the impact of market returns and macroeconomic indicators on Amazon stock volatility by constructing a two-factor model to provide more accurate forecasts. A quantitative approach is used to construct the model, with market returns represented by the CAPM and selected macroeconomic indicators introduced as the second factor. The model parameters are estimated from historical data and empirical analyses verify the predictive effectiveness of the model in different market environments. Preliminary results show that market returns and macroeconomic indicators significantly influence Amazon stock volatility. Market factors dominate volatility in the short term, while macroeconomic factors may have a more significant impact in the long term. This finding contributes to a better understanding of Amazon stock price drivers and provides investors with more targeted decision support.

The Impact of Managers' Self-interested Behavior on the Firm's Strategic Decision-making

Based on agency theory, this paper investigates the impact of managerial self-interest, especially managerial temporal myopia, on corporate strategic decision-making. This myopic behavior can lead to investment myopia, which in turn affects investment decisions. Moreover, it may result in financial distress and stock price crashes, ultimately affecting financing decisions. Firstly, it explores how managers exploit information asymmetry to advance their personal interests, consequently undermining the long-term value of the company. Next, the study examines the specific manifestations and causes of managerial temporal myopia, explaining how short-term behaviors in investment, financing, and debt management affect corporate decision-making. Finally, the paper proposes strategies to mitigate managerial self-interest, including curbing the tendency towards short-term gains, enhancing information disclosure, and restricting managerial expansion without adequate supervision. This analysis aims to provide valuable guidance for corporate governance, helping enterprises to make scientifically sound strategic decisions while considering managerial self-interest. The objective is to avoid long-term losses caused by myopic behavior.

Asymmetric impact of the COVID-19 on the Moroccan stock exchange

PurposeThis study aims to investigate the asymmetric impact of the COVID-19 pandemic on the stock market returns of companies listed on the Casablanca Stock Exchange.Design/methodology/approachTo achieve this objective, we utilized the SymCovid series, which comprises two sub-series: one representing the deterioration of the pandemic situation (NEG) and the other representing the improvement of the pandemic situation (POS). We employed the Nonlinear Autoregressive Distributed Lag model, incorporating Bounds Testing as proposed by Pesaran and Shin (1999) and Pesaran et al. (2001), to explore the asymmetry of the pandemic’s impact on stock prices listed on the Casablanca stock exchange.FindingsOur analysis using the NARDL econometric model reveals an asymmetric effect of COVID-19 on stock prices. Notably, we observe that stock prices react more strongly to a worsening pandemic situation than to an improvement, on average. Furthermore, our main findings indicate that while the improvement in the pandemic situation has no significant long-term impact on stock prices, it does exhibit a significant positive effect in the short term. Conversely, the deterioration in the pandemic situation has a more pronounced negative effect on stock prices in the long term than the short term.Originality/valueOur study fills a gap in the existing literature by focusing on the impact of the COVID-19 pandemic on stock returns in the context of the Casablanca Stock Exchange, which has been relatively understudied compared to other regions such as Asia, Europe and the Americas. We go beyond previous research by examining whether stock returns exhibit asymmetric responses to changes in the pandemic situation, highlighting potentially unique dynamics in emerging market economies during crises and providing valuable information for investors, policymakers and researchers.

The Impact of Dividend Payments on Stock Price: Empirical Evidence From Companies Listed on the Brazilian Stock Exchange

The Impact of Dividend Payments on Stock Price: Empirical Evidence From Companies Listed on the Brazilian Stock Exchange

Liquidity, Solvability, Profitability, Size of Company Impact on Stock Prices In Health Service Companies - Hospitals With Dividend Policy As An Intervening Variable Listed On Indonesia Stock Exchange 2018 - 2022 Period

Liquidity, Solvability, Profitability, Size of Company Impact on Stock Prices In Health Service Companies - Hospitals With Dividend Policy As An Intervening Variable Listed On Indonesia Stock Exchange 2018 - 2022 Period

Analysis of machine learning methods applied to financial problems

With machine learning being widely used in many fields. In this paper, we study how machine learning methods can be used to make predictions in financial problems and analyze the applications.Machine learning is a branch of artificial intelligence that enables computer systems to learn from data and make decisions or predictions without being explicitly programmed at every step. Machine learning models are trained on large amounts of data to discover patterns and relationships in the data and use these patterns to make predictions or perform tasks. The purpose of this post is to illustrate the role machine learning plays in dissertation research.Machine learning can also be used for smart urbanization, industrial automation, fintech, healthcare, agricultural modernization, cybersecurity, etc. For example, extracting valuable information and knowledge from large amounts of data to help businesses and organizations make better decisions. By analyzing historical data, machine learning can predict future trends and behaviors, such as stock prices, house prices, weather, etc.