Stock Price Fluctuations Research Articles

Transmission Mechanism of Stock Price Fluctuation in the Rare Earth Industry Chain

The transmission of stock price fluctuations of listed companies in the rare earth industry has complex characteristics. Mastering its transmission law is of great meaning to understand the relationship between the upstream and downstream of the rare earth industry chain and market investment. This article uses the time series of daily closing prices of stocks in the global rare earth industry chain in the past ten years as the research object. The Granger causality test and complex network theory were used to construct the risk transmission network of the industrial chain. We have identified the key stocks in the network of stock price fluctuation in the rare earth industry chain and obtained the transmission path of stock price fluctuation. According to the results: (1) The stocks of Chinese and Japanese listed companies considerably influence the transmission of the stock price fluctuation in the rare earth industry chain. (2) The transmission distance of the stock price fluctuation of each network is relatively small, and the transmission speed is relatively fast. (3) The fluctuation of stock price in the rare earth industry chain is mainly transmitted from the upstream and midstream links to the midstream and downstream links.

Footsie, yeah! Share prices and worker wellbeing

PurposeA small literature has shown that individual wellbeing varies with the price of company stock, but it is unclear whether this is due to wealth effects amongst those holding stock, or more general effects on sentiment, with individuals taking rising stock prices as an indicator of improvements in the economy. The authors contribute to this literature by using two data sets to establish the relationship between share prices on the one hand and worker wellbeing on the other.Design/methodology/approachFirst, the authors use over 20 years of British panel data to show that employee happiness and job satisfaction moves with share prices among those whose pay is partly determined by company fortunes. The authors then examine share price movements and employee stock holding in a single corporation and provide suggestive evidence that an increase in the firm’s stock price increases the well-being of those who belong to its employee share purchase plan (ESPP). These effects are greatest among those making the largest monthly contributions to the program who have the most to gain (or lose) from stock price fluctuations. There is also tentative evidence that the well-being effects of a higher share price are larger for those who hold more shares. Taken together these results suggest that, although stock price movements have little effect on well-being in the population at large, the well-being of those holding stock in their own company rises when the price of that stock is higher, suggesting the effects of share prices work at least partly via changes in wealth.FindingsTaken together these results suggest that the wellbeing effects of share prices work at least partly via changes in wealth.Research limitations/implicationsThe authors cannot be certain that the job satisfaction movements they see are causally linked to share plan participation and bonus receipt. Future research might fruitfully examine the mechanisms at play, and whether the effects identified here are linked to differences in employee motivation and effort over the business cycle.Practical implicationsFirms may wish to consider the appropriateness of linking their workers’ pay to firm performance through share plans or profit shares to establish whether this improves worker wellbeing.Social implicationsThe utility of workers may increase where firms offer some compensation via a share plan or profit share.Originality/valueThe literature suggests a link between share price movements and worker wellbeing, but the reasons for the link are contested. Using two very different data sources, the authors are able to show that share price increases induce higher worker wellbeing, at least in part, through wealth effects.

Firm-specific, macroeconomic factors and stock price risk for Jordanian banks

Internal (firm-specific) and external (macroeconomic) determinants of stock price fluctuations are vital for investors seeking to invest their money in a firm’s stocks. Thus, the main aim of this study is to explore macroeconomic and firm-specific factors that influence stock price fluctuations for all conventional banks in Jordan in 2010–2019. Ordinary least squares multiple regression (panel data) is applied for data analysis. The results report that trading volume (TV), dividend yield (DY), and Gross Domestic Product (GDP) have a positive effect on stock price volatility, while stock price volatility is statistically negatively affected by return on assets (ROA), dividend payout ratio (DPR), and price-earnings ratio (PE). On the other hand, money supply (MS) does not affect stock price volatility. Paying more dividends can reduce stock risk and, in turn, reduce stock price volatility. The findings can benefit current and potential investors, firm managers, brokers, dealers, portfolio managers, regulatory bodies, policy makers, and researchers.

Predicting stock price movement using effective Thai financial probabilistic lexicon

Predicting stock price fluctuation during critical events remains a big challenge for many researchers because the stock market is extremely vulnerable and sensitive during such time. Most existing works rely on various numerical data of related factors which can impact the stock price direction. However, very few research papers analyzed the effect of information appearing in financial news articles. In this paper, a novel probabilistic lexicon based stock market prediction (PLSP) algorithm is proposed to predict the direction of stock price movement. Our approach used the proposed thai financial probabilistic lexicon (ThaiFinLex) derived from Thai financial news and stock market historical prices. The PLSP development consists of three steps. Firstly, we constructed ThaiFinLex by extracting event terms from news articles and calculating their associated probability of increasing/decreasing values of stock prices. Then, event terms with bad prediction performance were filtered out. Finally, the stock price directions were predicted using the PLSP and the remaining effective event terms. Our results indicated that the proposed model can be used for predicting stock price movement. The performance is as high as 83.33% when PLSP is used to predict stocks from the financial sector.

Use GARCH Models to Build a Econometric Model to Predict Average Daily Closing Prices of the Iraqi Stock Exchange for the Period 2013-2016

This research aims to build a standard model for the analysis and prediction of the average daily closing price fluctuations for companies registered in the Iraq Stock Exchange for the period 07/01/2013 to 30/06/2016, using the conditional generalized Heteroscedasticity Generalized Autoregressive (GARCH) models. As these models deal with the fluctuations that occur in the financial time series. The results of the analysis showed that the best model for predicting the volatility of average closing prices in the Iraq Stock Exchange is the EGARCH model (3,1), depending on the statistical criteria used in the preference between the models (Akaike Information Criterion, Schwarz Criterion), and these models can provide information for investors in order to reduce the risk resulting from fluctuations in stock prices in the Iraqi financial market.

Stock Prediction Based on Optimized LSTM and GRU Models

Stock market prediction has always been an important research topic in the financial field. In the past, inventors used traditional analysis methods such as K-line diagrams to predict stock trends, but with the progress of science and technology and the development of market economy, the price trend of a stock is disturbed by various factors. The traditional analysis method is far from being able to resolve the stock price fluctuations in the hidden important information. So, the prediction accuracy is greatly reduced. In this paper, we design a new model for optimizing stock forecasting. We incorporate a range of technical indicators, including investor sentiment indicators and financial data, and perform dimension reduction on the many influencing factors of the retrieved stock price using depth learning LASSO and PCA approaches. In addition, a comparison of the performances of LSTM and GRU for stock market forecasting under various parameters was performed. Our experiments show that (1) both LSTM and GRU models can predict stock prices efficiently, not one better than the other, and (2) for the two different dimension reduction methods, both the two neural models using LASSO reflect better prediction ability than the models using PCA.

Predictability of Stock Price Fluctuations Based on Business Relationships: A Comparison of Normal and the COVID-19 Pandemic Periods in Japan

The stock prices of a company are significantly influenced by changes of its business relationships. However, the effectiveness of stock price prediction based on such inter-firm business relationships has been partially confirmed in limited region and/or timeframe cases. In particular, it has not been verified under highly volatile market conditions such as those caused by the COVID-19 pandemic. To address these issues, we analyzed the impact of supplier–customer relationships on stock prices in the case of the Japanese stock market using The Fama-French three-factor model and publicly available information of business relationships. The subjects were classified into two conditions—normal and COVID-19—and the stock price predictability associated with changes of stock prices of related companies for both short and long holding periods. As a result, the significance of stock price predictability was confirmed on a daily and monthly basis in the given region. In addition, specific factors including a volatile event caused by a customer company, a stock price downturn, and the company size of a customer particularly improved stock price predictability in the pandemic.

Analisis indikator makro ekonomi terhadap Jakarta Islamic Index

Stock price fluctuations in various sectors continue to show a decline because of the Covid-19 pandemic. This is because of its nature, which is strongly influenced by external changes or fundamental information. Here, the ability of investors to understand will influence investors' decisions to invest. This study aims to determine the effect of macroeconomic indicators on the Jakarta Islamic Index, the observation period 2015-2021, using monthly time series data. Macro indicators used in analyzing the stock of the Jakarta Islamic Index are inflation, interest rates, industrial production growth, crude oil, exchange rates, and the industry average Dow Jones as global index variables. The analysis technique used is multiple linear regression. Simultaneous test results show that the macro variable indicators and DJIA have a significant effect on JII. The results of the partial test show that inflation and DJIA have a significant positive effect on JII, while the exchange rate and crude oil have a significant positive effect on JII, for the variables of industrial production growth and interest rates have no significant effect on JII.

The sustainability of stock price fluctuations: Explanation from a recursive dynamic model.

The sustainability of stock price fluctuations indicated by many empirical studies hardly reconciles with the existing models in standard financial theories. This paper proposes a recursive dynamic asset pricing model based on the comprehensive impact of the sentiment investor, the information trader and the noise trader. The dynamic process of the asset price is characterized and a numerical simulation of the model is provided. The model captures the features of the actual stock price that are consistent with the empirical evidence on the sustainability of stock price fluctuations. It also offers a partial explanation for other financial anomalies, for example, asset price’s overreaction, asset bubble and the financial crisis. The major finding is that investor sentiment is the key factor to understand the sustainability of stock price fluctuations.

The COVID-19 Outbreak and Oil Stock Price Fluctuations: Evidence From China

This study explores the relation between Chinese oil stock price volatility and the COVID-19 pandemic using an autoregressive conditional heteroskedasticity model and its generalization. We show that the COVID-19 outbreak has a positive and weakly persistent impact on oil stock volatility.

Media Sentiment, Government Supervision Strategy, and Stock Price Fluctuation Risk

From the cross perspective of communication science and administration management, based on complex network theory, this paper constructs a model of stock price fluctuation risk contagion, which comprehensively considers media sentiment and government supervision strategy, and deeply analyzes the contagion mechanism of stock price fluctuation risk under the interaction of media sentiment and government supervision strategy. The main conclusions are as follows: The stock association network established by random way is more likely to cause contagion of stock price fluctuation risk. Media sentiment tendency, media sentiment intensity, and media attention persistence have positive “U” relationship, inverted “U” relationship, and positive correlation with contagion intensity of stock price fluctuation risk, respectively. There is a negative correlation between the strength, persistence, and timeliness of government supervision and the contagion intensity of stock price fluctuation risk. There is a positive correlation between market noise and contagion intensity of stock price fluctuation risk, and market noise has a restraining effect on media sentiment and government supervision strategy. In addition, the stock price fluctuation risk is inherent risk in the stock market, which cannot be eliminated by adjusting media sentiment and government supervision strategy, but its contagion intensity can be effectively controlled.

Research on the Stock Correlation Network Entropy and Its Influencing Factors in the Chinese Interbank Market

The stability of the interbank market is an essential guarantee for the sustainable development of society. Network entropy theory provides a critical research paradigm for the study of the stability of the interbank market. Considering that few scholars have conducted in-depth analysis on the influencing factors of the network entropy’s evolution process, this paper constructs the stock correlation network entropy model in the Chinese interbank market based on the correlation of stock price fluctuation. It focuses on analyzing the influencing factors in the dynamic evolution process of the stock correlation network entropy. In the light of empirical research, we can obtain the following results. First, the interbank market network’s aggregation coefficient, the interbank market network’s centrality, and the bank stock’s return rate play a positive role in the dynamic evolution of stock correlation network entropy in the Chinese interbank market. Second, the bank stock return’s volatility is negatively correlated with its network entropy. To maintain the stability of the financial market, the supervision department can monitor the stability of the interbank market by constructing the stock correlation network entropy.

A Novel Synergetic LSTM-GA Stock Trading Suggestion System in Internet of Things

The Internet of Things (IoT) play an important role in the financial sector in recent decades since several stock prediction models can be performed accurately according to IoT-based services. In real-time applications, the accuracy of the stock price fluctuation forecast is very important to investors, and it helps investors better manage their funds when formulating trading strategies. It has always been a goal and difficult problem for financial researchers to use predictive tools to obtain predicted values closer to actual values from a given financial data set. Leading indicators such as futures and options can reflect changes in many markets, such as the industry’s prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. In this research, a trading strategy for finding stock trading signals is proposed that combines long short-term memory neural networks with genetic algorithms. This new framework is called long short-term memory neural network with leading index, or LSTMLI for short. We thus take the stock markets of the United States and Taiwan as the research objects and use historical data, futures, and options as data sets to predict the stock prices of these two markets. After that, we use genetic algorithms to find trading signals for the designed stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.

A Long Short-Term Memory Network Stock Price Prediction with Leading Indicators

The accuracy of the prediction of stock price fluctuations is crucial for investors, and it helps investors manage funds better when formulating trading strategies. Using forecasting tools to get a predicted value that is closer to the actual value from a given financial data set has always been a major goal of financial researchers and a problem. In recent years, people have paid particular attention to stocks, and gradually used various tools to predict stock prices. There is more than one factor that affects financial trends, and people need to consider it from all aspects, so research on stock price fluctuations has also become extremely difficult. This paper mainly studies the impact of leading indicators on the stock market. The framework used in this article is proposed based on long short-term memory (LSTM). In this study, leading indicators that affect stock market volatility are added, and the proposed framework is thus named as a stock tending prediction framework based on LSTM with leading indicators (LSTMLI). This study uses stock markets in the United States and Taiwan, respectively, with historical data, futures, and options as data sets to predict stock prices in these two markets. We measure the predictive performance of LSTMLI relative to other neural network models, and the impact of leading indicators on stock prices is studied. Besides, when using LSTMLI to predict the rise and fall of stock prices in the article, the conventional regression method is not used, but the classification method is used, which can give a qualitative output based on the data set. The experimental results show that the LSTMLI model using the classification method can effectively reduce the prediction error. Also, the data set with leading indicators is better than the prediction results of the single historical data using the LSTMLI model.

ANALISIS PROFITABILITAS DAN LEVERAGE TERHADAP RETURN SAHAM PADA SEKTOR PERTAMBANGAN BATU BARA

The current condition of the capital market is full of uncertainty, which can be seen from fluctuations in stock prices and Returns. The volatility of the company’s stock price can indicate that investors have not obtained optimal stock Returns. This study aims to examine the effect of profitability and Leverage on stock Returns in the coal mining sector listed on the IDX in 2017-2019. This research uses associative research with a quantitative approach. The population used is all coal mining sectors listed on the IDX in 2017-2019 and the sample is used for purposive sampling that is 16 companies. Secondary data is used by accessing the official IDX website then downloading the annual financial reports of the coal mining sector for 2017-2019. Multiple linear regression was used as analysis in this study with statistical software. The results of the study state that profitability and Leverage have no effect on stock Returns in the coal mining sector which is listed on the Indonesia Stock Exchange in 2017-2019.
 Keywords: Leverage, Profitabilitas, Return Saham

Volatility Analysis of Select Blue Chip Companies Listed in Nifty 50 Index of India

The stock market is sometimes referred to as a "mirror" of the country's economic health since it acts a barometer to measure the economic growth. A potential buyer sets a price, known as an offer price, for a share of stock in the stock market, and a potential seller asks for a fixed price for it. When buying or selling in the market, both the buyer and the seller agree on the asking price or bid price. A trade occurs when the bid and ask prices are equal. As a result, stock valuation is extremely important in day-to-day stock exchange transactions. A company's success is reflected in its stock price. Companies with strong results are thought to have strong demand for their stock, which will drive up the price, and vice versa. Stock price fluctuations are influenced by rumours, speculation, and short-selling, among other manipulation practices. The present research on the above backdrop will make an analysis on the volatility of blue chip companies listed in the NIFTY 50 of National Stock Exchange in India.

Research on Emotional Leverage Effect of Capital Flow of Well-known Investment Institutions

China’s capital market started late, compared with developed countries, China’s individual investors account for a larger proportion. Because individual investor does not have the specialized investment knowledge and the mature investment psychology, its investment behavior often will receive the external factor the interference to produce “The herd effect”, therefore, the well-known investor institutions can rely on the reputation effect brought by their own popularity to influence the stock price fluctuation by influencing the investor sentiment, thus exceeding the influence degree of the corresponding buying and selling fund flow. This paper defines the above process as the emotional leverage effect of well-known investment institutions. In this paper, we use principal component analysis to construct the index of investor sentiment, and use correlation test to find that the well-known investment institutions, by virtue of their own reputation, make their capital flow have a significant impact on investor sentiment, there is a significant positive correlation between investor sentiment and the fluctuation of individual stock prices in the industry. Small and medium-sized investors will suffer investment losses because of their blind faith in the trading strategies of well-known institutions. In order to further enhance the protection of investors’ interests, this paper puts forward suggestions to improve the protection mechanism of small and medium-sized investors’ rights and interests from the perspective of information disclosure and policy supervision.

딥러닝 기법을 활용한 중국 주식시장 예측 연구

Business Analytics is a way of gathering large amounts of data, extracting information, and improving the efficiency of decision making. The global stock market produces large amounts of data every day, and stock prices are influenced by many factors, such as market indicators, economic and industrial conditions. Predicting the direction of stock price fluctuations is an important issue for investors. This study predicts the trend of the stock price in the China Stock Market based on the Deep Learning Method. Five industries were chosen from the China Stock Market, and a total of 82 companies were selected. The five industries are the basic materials, consumer goods, services, healthcare and financial industries. From 2002 to 2015, 3,523 daily stock price data were collected. Twenty-one macroeconomic indicators were included as independent variables. This study predicts stock price using neural network analysis and least squares method. Further studies should be conducted to select key prediction indicators and to improve the accuracy of neural network models.

Implementation of ARIMA Method in Predicting Stock Price Fluctuations of PT Bank Central Asia Tbk

In stock trading activities, the share price always fluctuates, it is caused by the demand and supply of the shares. The condition of stocks that continue to experience fluctuations makes investors who want to invest in stocks need to pay attention and study in advance the past data of the company to be selected to know how the company's stock price fluctuations in the next period.This study aims to determine the model of ARIMA Box-Jenkins from the closing price of PT Bank Central Asia Tbk shares, then forecast the obtained model. The data used in this study is the data series of the closing price of PT Bank Central Asia Tbk's daily shares from October 1, 2020 to February 26, 2021. The forecasting method used is the ARIMA Box-Jenkins method. As a result of this study, the appropriate ARIMA Box-Jenkins model for the closing price of PT Bank Central Asia Tbk's shares is the ARIMA model (0,2,1). The predicted value of the model is that the closing price of PT Bank Central Asia Tbk shares has decreased.
 
 Pada aktivitas perdagangan saham, harga saham selalu mengalami fluktuasi, hal itu disebabkan oleh adanya permintaan dan penawaran atas saham tersebut. Kondisi saham yang terus mengalami fluktuasi membuat para investor yang ingin melakukan investasi saham perlu memperhatikan dan mempelajari terlebih dahulu data masa lalu dari perusahaan yang akan dipilih untuk mengetahui bagaimana fluktuasi harga saham perusahaan pada periode selanjutnya. Penelitian ini bertujuan untuk menentukan model ARIMA Box-Jenkins dari harga penutupan saham PT Bank Central Asia Tbk, kemudian melakukan peramalan dari model yang diperoleh. Data yang digunakan dalam penelitian ini adalah data deret waktu harga penutupan saham harian PT Bank Central Asia Tbk dari 1 Oktober 2020 sampai 26 Februari 2021. Metode peramalan yang digunakan adalah metode ARIMA Box-Jenkins. Hasil dari penelitian ini, model ARIMA Box-Jenkins yang sesuai untuk harga penutupan saham PT Bank Central Asia Tbk adalah model ARIMA (0,2,1). Nilai hasil prediksi dari model tersebut yaitu harga penutupan saham PT Bank Central Asia Tbk mengalami penurunan.

The Impact of the COVID-19 on Economic Sustainability—A Case Study of Fluctuation in Stock Prices for China and South Korea

The coronavirus disease (COVID-19) pandemic has had a global impact on lives, livelihoods, and economies. This study investigates whether a contagious infectious disease can affect the prices of the Chinese and Korean stock markets. Specifically, we aim to discover discrepancies in the impact of COVID-19 on the stock prices of China and South Korea through panel data. To test these discrepancies, we first regressed the stock indices on confirmed cases and deaths. We then validated the stability of coefficients over the past days. The empirical results show that (1) responses of stock indices are stable and impulsive and (2) response patterns toward COVID-19 events considerably vary across nations, especially in the counties such as China and South Korea.