Stock Market Prices Research Articles

The Effect of Inflation and Rupiah Exchange Rate on Stock Prices with Dividends as Moderators

Objectives: The choice to pay dividends is a strategic one for every business. It's the foundation on which stock market investments are made. Dividends paid out by a firm are more highly valued by shareholders than capital profits. Due to the inherent risk associated with investing, investors would rather receive compensation in the now than in the future. The company's future success may be seen in its consistent dividend payments. The goal of this research was to examine the moderating role of dividends in a market vulnerable to inflation and the rupiah exchange rate. The focus is on dividend-paying firms trading on the IDX between 2013 and 2021.Methodology: The associative quantitative approach used here relies on previously collected data. The purposive sampling method was used in the sampling process. This study used 153 samples over 9 time periods, following the criteria. Both single- and multi-linear regression are used in the study.Finding: The findings in the study are inflation and rupiah exchange rates on stock prices have a negative effect and simultaneously affect stock prices while dividends per share can moderate the connection between inflation and stock prices.Conclusion: Both inflation and the rupiah exchange rate have been shown to have a negative effect on stock prices. Inflation has been shown to have a negative influence on stock prices while the rupiah exchange rate also has this effect. According to the findings of this journal, a combined effect of inflation and the value of the rupiah against other currencies may be seen in stock market prices. Furthermore, it has been shown that this effect can occur. The MRA research also shows that dividends per share have a moderating influence on the connection between inflation and stock prices, as well as the correlation between the rupiah exchange rate and stock prices.

Comparative analysis of machine learning and deep learning techniques for prediction of the stock market

In recent years, there has been a continuing search for reliable instruments that can predict trends in financial markets and activities related to investments. In the past, academics have used traditional methods to forecast the investment worth of equities by analyzing metrics such as the financial records of companies from both a fundamental and technical point of view. The effectiveness of these strategies could decrease as market information asymmetry continues to rise and high-frequency trading becomes increasingly prevalent. Researchers have developed novel methodologies as a result of the progress that has been made in the field of artificial intelligence technology. One of these methodologies is the application of neural networks for forecasting. In the meantime, data visualization is becoming increasingly common, which could make it easier to conduct an in-depth analysis of the advantages and disadvantages presented by various models. The purpose of this research is to evaluate the performance of machine learning and deep learning strategies, including logistic regression, support vector machine, multi-layer perceptron and convolution neural networks, in forecasting stock market prices where various data visualization techniques are utilized for investigation. The findings from error analysis demonstrate that convolutional neural networks operate superbly.

Forecast stock price based on GRA-LoGo model of information filtering networks

The considerable fluctuation of the stock market caused by COVID-19 tends to have a tremendous and long-lasting adverse impact on the economy. In this work, we propose a novel methodology to investigate this impact on the Chinese medical stock market. We examine changes in the stock network structure using the Triangulated Maximally Filtered Graph (TMFG), which is computationally faster and more adaptable to enormous datasets. Additionally, we develop the LoGo model, which combines a local-global approach in its construction, to predict the stock prices of the Chinese medical stock market. In addition to traditional predictors, we incorporate daily new infected numbers as an additional predictor to reflect the impact of COVID-19. We select data from the 2019-2020 period and divide it into two datasets: one for the period during COVID-19 and another for the period before COVID-19. Firstly, we compute the grey correlation coefficients between stocks instead of standard correlation coefficients. We use these coefficients to build the TMFG, enabling us to identify which stocks played the leading roles. Subsequently, we choose six stocks to build the price prediction models. Compared with the LSTM and SVR models, the LoGo models demonstrates higher accuracy, achieving an average accuracy of 71.67 percent. Furthermore, the execution time of the Logo models is 200 times faster than that of the SVR models and 50 times faster than that of the LSTM models.

Evaluation of Stock Closing Prices using Transformer Learning

Predicting stock markets remains a critical and challenging task due to many factors, such as the enormous volume of generated price data, instant price data changes, and sensitivity to human sentiments, wars, and natural disasters. Since the previous three years of the COVID-19 pandemic, forecasting stock markets is more difficult, complex, and problematic for stock market analysts. However, technical analysts of the stock market and academic researchers are continuously trying to develop innovative and modern methods for forecasting stock market prices, using statistical techniques, machine learning, and deep learning-based algorithms. This study investigated a Transformer sequential-based approach to forecast the closing price for the next day. Ten sliding window timesteps were used to forecast next-day stock closing prices. This study aimed to investigate reliable techniques based on stock input features. The proposed Transformer-based method was compared with ARIMA, Long-Short Term Memory (LSTM), and Random Forest (RF) algorithms, showing its outstanding results on Yahoo Finance data, Facebook Intra data, and JPMorgan's Intra data. Each model was evaluated using Mean Absolute Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE).

A Brief Analysis of The Impact of Investor Sentiment on Stock Prices in The Chinese Stock Market

This article describes the challenges that behavioral finance poses to traditional financial assumptions, from questioning the rationality of human behavior to explaining the classifications and three indicators of investor sentiment in behavioral finance. Furthermore, it confirms the combination of theory and practice by examining turnover ratio as a proxy indicator, the trading volume of closed-end funds, and stock price regression. It demonstrates that investor sentiment significantly influences stock prices. Moreover, it indicates that both investor sentiment and stock prices can mutually affect each other. Among the investor sentiment indicators, turnover ratio has a stronger impact on stock prices, while the trading volume of closed-end funds exhibits certain predictive functionality.

Lapsation Risk: Takaful Operators In Malaysia

Insurance serves as a tool for risk management. Takaful is an Islamic insurance where the concepts of mutual cooperation (ta’awun) and donation (tabarru’) are used as the main pillars. Apart from the usual market and credit risk, lapsation risk is one of the main risks faced by a life insurer. It is important to determine factors that may influence policy lapsation as it could affect the solvency and the profitability of insurance or a Takaful operator. Therefore, the relationship between surrender rates and economic variables (Unemployment Rate, Real Interest Rate, Growth Domestic Product (GDP), Inflation - Annual % change in Consumer Price Index and Stock Market Price Index (%) ) were examined for Takaful operator ordinary life and unit investment link products. Out of sample forecast was performed for 2019 to 2020 surrender rates for Takaful operator ordinary life and unit investment link products. Panel data model was applied for both purposes as the method is viable to overcome small sample size issue. The empirical findings revealed that the economic variable displayed a different impact on the surrender rate for both products. Both ordinary life and investment linked products exhibited a significant relationship between unemployment rate and surrender rate; in which higher unemployment rate led to increased surrender rate. Stock also had a statistically significant impact on the surrender rate of both products. Meanwhile, interest rate had an insignificant link with the surrender rate of both products. This outcome is in agreement with several past studies that reported no real relationship between interest rate and surrender rate; thus disregarding the infamous interest rate hypothesis. Lastly, Gross Domestic Product (GDP) displayed an insignificant correlation with the surrender rate of both products. By using Pool OLS model, the trend for ordinary life product decreased in 2020 for all Takaful operators. As for investment linked products, a decreasing trend was noted from 2019 to 2020; the lowest and the highest forecast rates in 2019 were ascribed to Takaful Ikhlas and Takaful Malaysia, respectively.

Predicting Automobile Stock Prices Index in the Tehran Stock Exchange Using Machine Learning Models

This paper analyses the performance of machine learning models in forecasting the Tehran Stock Exchange's automobile index. Historical daily data from 2018-2022 was pre-processed and used to train Linear Regression (LR), Support Vector Regression (SVR), and Random Forest (RF) models. The models were evaluated on mean absolute error, mean squared error, root mean squared error and R2 score metrics. The results indicate that LR and SVR outperformed RF in predicting automobile stock prices, with LR achieving the lowest error scores. This demonstrates the capability of machine learning techniques to model complex, nonlinear relationships in financial time series data. This pioneering study on a previously unexplored dataset provides empirical evidence that LR and SVR can reliably forecast automobile stock market prices, holding promise for investing applications.

Dynamic relationship between volume and volatility in the Chinese stock market: evidence from the MS-VAR model

Since market uncertainty, or volatility, serves as a crucial gauge for assessing the traits of market fluctuations, the link between stock market volume and price continues to be a focal point of interest in finance. This study examines the dynamic, nonlinear correlations between Chinese stock volatility, trading volume, and return using a hybrid approach that combines the Markov-switching regime with the vector autoregressive model (MS-VAR). The empirical findings are as follows: (1) The Chinese stock market can be divided into three regional systems: steady downward, steady upward, and high volatility. The three states have similar frequencies of occurrence, and their corresponding stable probabilities are not high, indicating that the Chinese stock market is unstable. (2) Asymmetric dynamic relationships exist between market volatility, investment return, and trading volume. For different regimes, while the effect of trading volume on volatility and return appears to be insignificant, the impacts of volatility and return on trading volume are considerably strong. (3) A regime-dependent, contemporaneous correlation between volatility and return is observed, which also reflects the behavior of the Chinese stock market “chasing up and down”. However, a positive contemporaneous correlation always exists between volatility and trading volumes in different regimes, indicating that uncertainty in the Chinese stock market is closely related to information inflow.

Blockchain-Based Secure Stock Market Price Prediction Using Next Generation Optimized LSTM Model

Stock forecasting has long drawn people’s attention because the stock market is a crucial source of financing for publicly traded corporations and a sizable investment market. To fully use the evidence from dissimilar typical prices and recover the stock forecasting effect, a Blockchain-based secure stock value forecasting model TL-EMD-LSTM-MA (TELM) is projected. Other methods are selected for prediction according to the oscillation frequencies of the details, and the high-frequency components use the depth of the transfer learning method to train the stacked LSTM. Deep transferable learning-trained stacked LSTMs incorporate data from several equities and have a deeper understanding of the marketplace or commerce, which can significantly lower forecasting mistakes. Furthermore, it is possible to more accurately estimate the low-frequency components and the overall trend of the stock by employing the average movement approach. 500 stocks in the stock market are shown, as well as indices such as the Stock Exchange Index and Stock Exchange Component Index, the outcomes demonstrate that compared with other models, TELM has the least prediction error and the highest goodness of fit. Finally, simulate the stock trading process based on the stock closing price predicted by TELM, and the results show that TELM investment has low risk and high returns.

Stock price momentum modeling using social media data

Alternative data has rapidly gained popularity as a means of observing investor reactions to current stock market events, analyzing sector movements, and developing investment strategies. Signals observed from alternative data may add valuable information to conventional market analysis, especially when there exists some information asymmetry in the market, to which small-cap stocks are particularly sensitive. Our research uses alternative textual data obtained from social media to predict the price momentum of small-cap stocks, which are known to be sensitive to market events and retail investors’ reactions. We directly address the challenges in the analysis of texts, especially in the Korean language, such as the use of jargon, acronyms, and typos, by taking a two-fold approach to preprocessing. Then, we extract the social keywords and label them by two standards: word momentum and correlation with stock price movement. Using these social keywords as social indicators derived from the retail investors trading small-cap stocks, in addition to the conventional technical indicators, we propose a price momentum forecast model which classifies whether the closing price of a given small-cap stock will increase by more than 10% in the next 5 business days. Experimental results show that our approach successfully extracts social keywords that play a significant role in determining the price momentum in the market, and our selected social indicators are intuitive to human understanding. Furthermore, we consider example investment scenarios and run tests by training various classifiers with the social indicators and computing cumulative returns. Our results are very promising and show that the investment scenarios with social indicators outperform the benchmark considerably. We anticipate that our work will expand the scope of using alternative data for stock market analysis and price momentum forecasts by incorporating social indicators extracted from social media.

Value Relevance and Integrated Reporting Sustainability Approach: Evidence from Listed Non-Financial Companies in Australia

The purpose of the study is to investigate the impact of Integrated Reporting on value performance of listed companies in Australia based on the adoption of the King III report. The study considers 38 non- financial companies for a period of time ranging from 2008 to 2020. The technique employed for analysis is sub- divided into three parts: firstly, the research compares the market valuation coefficients of summary accounting information over a three-year period before the adoption of IR (2008-2010) against the first three years (2011- 2013) after the mandatory IR adoption and then compares it with the current period (2014-2020) to ascertain any improvement, deterioration, similarities or differences in the IR adoption system. Secondly, a System GMM regression analysis was applied to ascertain the impact of IR on value relevance and sustainability on Australian listed companies. During the period of pre- adoption of the King III report, there were no significant relationship between book value of equity and equity and did not influence the value relevance of Australian markets since the King III report was not yet introduced in the accounting system of entities during that period. Secondly, during the period of adoption of the King III report, similar results were exhibited in terms of magnitude and direction during the pre- adoption of the report. BE and EAR are found to be negative and insignificant to value relevance. During the post- period of the implementation of the report, a downfall in the level of net assets and leverage was pointed out. Australian firms might employ internal financing to finance their operations and investments. The findings support evidence of (Marx and Mohammadali- Haji, 2014; Babourdakos, 2016). Further, Australian companies experienced risk disclosures, challenges and uncertainties that they might encounter in the future but also find new mitigation techniques to diminish them. Ultimately, a drop in the value relevance of net assets might be occurred by risk disclosures for instance credit risk or business risks. Regarding sustainability performance, during the adoption of King III report, there was no relationship between sustainability and market value information. Hence, theoretically speaking, it can be deduced that the principles regarding sustainability reporting were complex and unclear compared to financial reporting. However, the post adoption of King III stated that sustainability impacted positively on market value which explicated the variation in stock market prices and hence was value relevant in supporting the shareholders’ value-enhancing role of corporate sustainability (Ali & Jadoon, 2022). The unique of this study emanates from that both a contextual and methodological aspect employing the System GMM for conducting panel analysis.

Effective Stock Market Pricing Prediction Using Long Short Term Memory-Upgraded Model (LSTM-UP) on Evolving Data Sets

Effective Stock Market Pricing Prediction Using Long Short Term Memory-Upgraded Model (LSTM-UP) on Evolving Data Sets

THE EFFECT OF CAPITAL STRUCTURE, LIQUIDITY, PROFITABILITY, ACTIVITY, AND INSTITUTIONAL OWNERSHIP ON COMPANY VALUE AMONG MANUFACTURING COMPANIES LISTED ON INDONESIA STOCK EXCHANGE DURING 2017-2022

The value of a company that go public reflects the market price of the company's stocks. With the development of the company for the better, it will certainly affect the value of the company which will also increase. This study aimed at determining the effect of Debt to Equity Ratio (DER), Current Ratio (CR), Return on Equity (ROE), Total Assets Turnover (TATO), and Institutional Ownership (INST) on the Value of Manufacturing Companies listed in Indonesia Stock Exchange for the period 2017-2020. The sampling technique in this study used purposive sampling so that 56 companies were obtained. The research analysis method used was Panel Data Regression. The results showed that DER had a positive and significant effect on firm value, while CR, ROE, TATO, and INST had no influence on firm value.

Systematic Review of Nexus between Exchange Rate Fluctuations and Stock Market Price Movements: Flow Oriented Theory, Stock Oriented Theory and Portfolio Balance Theory

The study investigated literatures on nexus between exchange rate fluctuations and stock market price movements: flow oriented theory, stock oriented theory and portfolio balance theory. It was discovered that many studies predominantly found out statistically significant positive nexus between exchange rate fluctuations and stock market price movements (Parsva & Lean, 2017; Mechri, Ben Hamad, De Peretti & Charfi, 2018; Morales-Zumaquero & Sosvilla-Rivero, 2018; Bahrnani-Oskooee & Saha,2016; Ho & Huang,2015; Sui & Sun,2016; Mroua & Trabelsi,2019; Nazakat & Kashif,2017; Yildirim & Adali, 2018; Fapetu, Adeyeye, Seyingbo & Owoeye,2017; Okechukwu, Mbadike, Geoffrey & Ozurunma,2019; Bagh, Azad, Razzaq, Liaqat & Khan,2017; Alzyoud, Wang & Basso, 2018) while very scanty and few studies revealed a negative significant relationship and statistically insignificant. Various studies conducted shows different results on asymmetric and symmetric effects and causality: bidirectional, unidirectional and no causality between exchange rate fluctuations and stock market price movements. Some results support the ?ow oriented theory, some are for the stock oriented theory and portfolio balance theory while others believe that three theories are functional in an economy. Variation in these studies could be as a result of methodology used, market condition, risk and other factors that are peculiar to each economy. The study discovered other factors namely GDP of a country, level of employment, companies business internal ?nancing structure, investment inflow, inflation rates, oil prices, government expenditure, in?ation rate, interest rate, and exchange rate that have effect on stock market movements. The empirical studies examined recommended as follows: Regulators should consider developing sound policy measures to prevent ?nancial risk. The public who desire to invest must utilize the information of important macro-economic variable in particular exchange rates vol

The impact of covid-19 vaccination on stock markets: a comparative analysis of the USA and China

ABSTRACT This study aimed to reveal the emergence of the vaccine against COVID-19 and its influence on the stock markets. In line with this purpose, this study seeks answers to some questions, whether vaccination supports the recovery of financial markets or not, and in which direction the effect is similar in the context of developed and developing countries, considering the United States and China samples. Fourier-based cointegration and causality tests were used to reveal the effect of COVID-19 vaccination on stock markets in the paper, which used daily data between March-2021 and September-2022. According to the results vaccination rates triggered the stock market prices in the U.S.A.. Moreover obtained findings show that the Chinese stock market and COVID-19 vaccination act together in the long term during the data range examined. Finally, it seems possible to state that it would be beneficial for investors investing in both the U.S.A. and China markets.

COVID-19 and commodity effects monitoring using financial & machine learning models

This article focuses on examining the effects of the COVID-19 pandemic and gold prices on the stock market. It primarily analyzes the relationship between COVID-19 cases and stock market prices, along with the impact on various commodity elements such as gold, oil, Chinese RMB, and US Dollar prices. These commodity elements are considered essential indicators of a country's financial health, and the study investigates how the increase in COVID-19 cases affects these financial elements. The research incorporates financial models, machine learning algorithms, and a financial Gaussian mixture model for data analysis and comparison. The findings shed light on the correlation between the virus, trading outcomes, and the importance of Karachi Stock Exchange-100 index data in preventing market crashes. The study also explores the implications of emergencies on the finance sector and provides insights for future financial predictions and the impact of social disasters on the economy.

Reactions of Stock Prices to Changes in Exchange Rate and Monetary Policy Rate of Central Banking System: Symmetry or Asymmetry

This study examines the reactions of stock prices to variations in the monetary policy rate of the central banks and exchange rate shock, which is if the responses are similar or differ in a comparative analysis between developed and emerging stock markets. Data were obtained on daily stock market prices, exchange rates, and variations in policy rate. The panel non-linear autoregressive distributed lag (NARDL) estimation method was utilized for this study. The NARDL showed the existence of a significant asymmetric long-run link between stock market price, exchange rate, and variation in monetary policy rate variables. For developing countries, the long-run results show that a percentage rise in the currency rate stimulated a 0.596% increase in the price of stock while it resulted in a 0.574% drop in the prices of stock. The same results were obtained for the short-run analysis. Also, the results show that a positive shock to the policy rate led to a 0.26% drop in the price of stock in the long term while it induced a 0.35% rise in the prices of stock. These estimates are similar to those obtained for developed economies. By and large, the findings imply that variations in the currency and monetary policy rate of the central banks have significant implications for stock market prices. There is a need for central monetary authorities to base effective regulations of exchange and monetary policy rates on market mechanisms while stock market investors are advised to frequently study the behavior of the two variables when dealing with the stock market.

Sequential monitoring of stock market price changes

In 2016, Eugene Fama mentioned that he wanted a systematic way of identifying and predicting a stock market bubble. This paper develops a statistical method to sequentially monitor the stock market price changes. A new simple boundary function is proposed and asymptotic properties are established. In the empirical application, we successfully detect that the U.S. dot-com bubble occurred around June 1997 using the S&P 500 and Nasdaq indices.

The failure of online endorsement systems in investment communities: evidence from Yahoo! Finance

PurposeIn prior literature, online endorsement system allowing the users to “like” or “dislike” shared information is found very useful in information filtering and trust elicitation in most social networks. This paper shows that such systems could fail in the context of investment communities due to several psychological biases.Design/methodology/approachThis study develops a series of regression analyses to model the “like”/“dislike” voting process and whether or not such endorsement distinguishes between valuable information and noise. Trading simulations are also used to validate the practical implications of the findings.FindingsThe main findings of this research are twofold: (1) in the context of investment communities, online endorsement system fails to signify value-relevant information and (2) bullish information and “wisdom over the past event” information receive more “likes” and fewer “dislikes” on average, but they underperform in stock market price discovery.Originality/valueThis study demonstrates that biased endorsement may lead to the failure of the online endorsement system as information gatekeeper in investment communities. Two underlying mechanisms are proposed and tested. This study opens up new research opportunities to investigate the causes of biased endorsement in online environment and motivates the development of alternative information filtering systems.

Associating deep learning and the news headlines sentiment for Bursa stock price prediction

Accurate stock price prediction is appealing to academics, economists, and financial analysts for its potential to increase profits. Although remarkable progress has been made in stock prediction accuracy, studies to explore the relationship between public sentiments and the prediction of stock price movement based on online news portals in Malaysia context are limited. Therefore, this study aims to determine whether news sentiments influence the movement of the Bursa stock price. The stock prediction model was implemented using long short-term memory (LSTM), with stock data from Bursa Malaysia between January 2017 and April 2022, and the root mean squared error (RMSE) value was calculated. In addition, LSTM prediction model was compared to the decision tree algorithm, and LSTM performed significantly better than the decision tree, particularly when using the New York stock exchange (NYSE) dataset. Furthermore, sentiment analysis was carried out using a Malaysian online news portal's business and financial news. The findings showed that i) news has a significant impact on Malaysian stock market price movement; ii) the RMSE of the LSTM model was improved by adding a parameter (news polarity values); and iii) the RMSE value generated is less than one for every company stock and is influenced by stock price and price change magnitude.