Stock Price Fluctuations Research Articles

Optimization of Stock Portfolios Using Goal Programming Based on the Kalman-Filter Method

Long-term stock investment development is carried out by means of portfolio optimization. Selection of stocks for portfolios is not only based on high-value stock prices but also takes into account their fluctuations. Estimation of future stock price fluctuations has an indirect impact on future portfolio formation. This research has implemented the Kalman filter method to obtain the best estimation results from various stock prices with a high degree of accuracy. The results are then used to form a stock portfolio on the basis of Goal Programming. This study has compared the optimization results with the real value of stock prices. The results obtained, Kalman filter-based Goal Programming is more effective for predicting future portfolios compared to the Goal Programming method with a return difference of Rp. 178,039,848. This suggests that optimization with the Kalman Filter-based Objective Programming can be used as a tool to determine future stock portfolios.

PERAMALAN INDEKS HARGA SAHAM MENGGUNAKAN ENSEMBLE EMPIRICAL MODE DECOMPOSITION (EEMD)

Stock price fluctuations make investors tend to hesitate to invest in stock markets because of an uncertain situation in the future. One method that can solve these problems is to use forecasting about the stock prices in the future. Generally, the huge size of data non linear and non stationary, and it is difficult to be interpreted in concrete. This problem can be solved by performing the decomposition process. One of decomposition method in time series data is Ensemble Empirical Mode Decomposition (EEMD). EEMD is process decomposition data into several Intrinsic Mode Function (IMF) and the IMF residue. In this research, this concept applied to data Stock Price Index in Property, Real Estate, and Construction from July 1, 2019 to July 30, 2020 as many as 272 data. Based on the results of data processing, as many as 6 IMF and IMF remaining were used as IMF forecasting and the IMF remaining in the future. The forecast was performed by choosing the best model of each IMF component and IMF remaining, used ARIMA and polynomial trend. Keywords: Time Series Data, Stock Price Index, EEMD, ARIMA, Polynomial Trend.

The Impact of Margin Trading on Stock Volatility: Based from 2014 to 2016 Shanghai and Shenzhen 300 Index

The paper study the impact of margin trading on the volatility of the stock market, We selected 469 observation values among the daily Shanghai and Shenzhen 300 index from May 2014 to March 2016. the Granger causality test results are obtained for the model. Empirically study shows that one of the factors affecting stock price fluctuation does include margin trading business, and shows a negative correlation, which plays a more stable role in the stock market.

The Impact of Margin Trading on Stock Volatility: Based on 2014 to 2016 Shanghai and Shenzhen 300 Index

The paper study the impact of margin trading on the volatility of the stock market, We selected 469 observation values among the daily Shanghai and Shenzhen 300 index from May 2014 to March 2016. the Granger causality test results are obtained for the model. Empirically study shows that one of the factors affecting stock price fluctuation does include margin trading business, and shows a negative correlation, which plays a more stable role in the stock market.

Board attributes and corporate philanthropy behavior during COVID‐19: A case from China

This study examines the relationship between board attributes and corporate philanthropic behavior for combating the COVID‐19 epidemic and stock price fluctuations The results show that the philanthropic behavior has a positive effect on the stock prices; that companies with female leaders are more likely to engage in philanthropic behavior; and that the proportion of female's directors is positively correlated with philanthropic behavior. Additionally, the results show that board size is negatively correlated with philanthropic behavior.

Characteristics of 2020 stock market crash: The COVID-19 induced extreme event.

A sudden fall of stock prices happens during a pandemic due to the panic sell-off by the investors. Such a sell-off may continue for more than a day, leading to a significant crash in the stock price or, more specifically, an extreme event (EE). In this paper, Hilbert-Huang transformation and a structural break analysis (SBA) have been applied to identify and characterize an EE in the stock market due to the COVID-19 pandemic. The Hilbert spectrum shows a maximum energy concentration at the time of an EE, and hence, it is useful to identify such an event. The EE's significant energy concentration is more than four times the standard deviation above the mean energy of the normal fluctuation of stock prices. A statistical significance test for the intrinsic mode functions is applied, and the test found that the signal is not noisy. The degree of nonstationarity test shows that the indices and stock prices are nonstationary. We identify the time of influence of the EE on the stock price by using SBA. Furthermore, we have identified the time scale ( τ) of the shock and recovery of the stock price during the EE using the intrinsic mode function obtained from the empirical mode decomposition technique. The quality stocks with V-shape recovery during the COVID-19 pandemic have definite τ of shock and recovery, whereas the stressed stocks with L-shape recovery have no definite τ. The identification of τ of shock and recovery during an EE will help investors to differentiate between quality and stressed stocks. These studies will help investors to make appropriate investment decisions.

Uncovering the impacts of structural similarity of financial indicators on stock returns at different quantile levels

The mining industry is the upstream industry of national economic development. In recent years, the mining financial market has rapidly developed. Stock returns have always been a hot topic for investors and researchers. There are many factors affecting stock prices, such as market supply and demand, national policies and the price fluctuations of other financial products. The most essential factor affecting stock price fluctuation is the operating condition of the listed companies that issue shares. A series of financial indicators comprehensively and systematically reflect listed companies' current earnings and future development potential. However, there are few studies on the structure of financial indicators of listed mining companies. Therefore, we take listed mining companies as the research object to explore whether the structural similarity of their financial indicators can uncover their stock returns. The research findings are as follows: (1) The strength of the structural similarity of financial indicators has a significant negative impact on annual returns, so enterprise managers should adjust their business model and attach importance to personalized development. (2) Similar media capabilities have a negative impact on companies with high annual stock returns and a positive impact on companies with low annual stock returns. (3) The impact of similar cohesion on annual returns varies across different networks and it has a positive effect on the earnings of low-returning companies in operation network. A specific analysis should be carried out according to different financial indicator networks to diversify investment.

Analisis Laba atas Ekuitas dan Inflasi terhadap Stock Price

The phenomenon of several companies experiencing stock price fluctuations can affect inflation and return on equity. The research objective is to implement the analysis of return on equity and inflation in stock prices. The population in the survey was 49 consumer goods industries in 2015-2019. Samples were carried out by purposive sampling for 10 business units. Research data analyzers with the help of self-testing and concurrent testing. The joint test results show the impact of return on equity, inflation is not significant. Meanwhile, the return on equity and inflation simultaneously have an impact on share prices.
 Keywords: Return On Equity; Inflation; Stock Price.

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction.

The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).

Stock trading rule discovery with double deep Q-network

Stock market serves as an important indicator of today’s economy. Predicting the price fluctuation of stocks and acquiring the maximum gains has been the main concern of investors. In recent years, deep learning models are widely applied to stock market prediction and have achieved good performances. However, the majority of these deep learning based models belong to supervised learning methods and are not capable of dealing with long-term targets. Therefore, in this paper we proposed a deep reinforcement learning based stock market trading model, which is suitable for predicting stock price fluctuation and stock transactions. We carefully devise the reward function and deep learning based policy network, which enables the model to capture the hidden dependencies and latent dynamics in the stock data. In order to evaluate the superiority of the proposed model, stock price trend forecasting and transaction is conducted on randomly selected stocks and stock market indices. Experiment results demonstrate that our model outperforms baseline methods on several indicators.

Stock Sale Induced by Anxiety in the Face of Risk

It is observed that stock price fluctuations are slowly in upward phases like bubble, but fast in downward phases like its burst. This paper provides a new theoretical explanation of this phenomenon, especially why stock price drops sharply, based on the timing of stock sales. Investors tend to be more risk averse when the timing of decision making is closer for the present. This tendency can explain that the timing of stock sales is different according to risk aversion. When investors become excessive risk averse, they tend to sell stocks more in the present. This result is consistent with asymmetric patterns of stock price fluctuation when investors are moderate risk averse in normal economic conditions, but excessive risk averse in abnormal economic condition like the fear of financial crises.

BOND AS A SAFE HAVEN DURING MARKET CRASH: EXAMINATION OF COVID-19 PANDEMIC IN ASEAN-5

The central aim of this paper is to examine the effectiveness of corporate bonds and government bonds against the stock price fluctuation in ASEAN-5 countries (Indonesia, Malaysia, Philippines, Thailand and Singapore). Highlighting an important event of COVID-19, we investigate whether bonds provide protection by utilizing Q-REG method. The noteworthy finding is that bonds consistently act as a hedge for Malaysia and Philippines, while regarded as a diversifier for Indonesia’s capital market. However, this study observed that corporate bonds successfully become a strong safe haven for Thailand and Singapore capital market. Similar result was also found for Singapore government bonds which provides a valuable return during the pandemic of COVID-19.

Praktik Manajemen Laba dan Reaksi Pasar Setelah Event Pergantian Chief Executive Officer (CEO)

This research is to prove after the change of chief executive officer (CEO) of earnings management practices and market reaction. The total sample taken was using the nonprabability sampling method with a purposive sampling technique of 48 companies on the IDX which included the LQ45 index. The analysis technique used is simple linear regression and paired sample t-test on the DA and PER values of the company. Based on the results of the analysis found that there is no effect of earnings management on market reaction after one and two years of CEO turnover. These results prove that there is no important information on the announcement of CEO turnover, so it is less able to make significant stock price fluctuations. The next result is no difference in both earnings management and market reaction that occurs one and two years after CEO turnover.
 Keywords: Chief Executive Officer (CEO); Earning Management; Market Reaction; Price Earning Ratio (PER).

GARCH Model For Forecasting Stock Return Volatility In The Infrastructure , Utilities And Transportation Sectors

The stock market is continuously changing with uncertainties that can create risks. Prompt information dissemination and rapid capital flow will cause stock price fluctuations, causing volatility in stock prices. This research examines the behavior of volatility patterns in the infrastructure, utility, and transportation sectors using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. This study uses monthly data from January 2014 to December 2019. The results show that the volatility of all stocks in the study is influenced by the previous month's error and volatility return. Investors and securities analysis can use these results in making decisions to invest in the infrastructure, utilities, and transportation sectors.

Research on the stock correlation networks and network entropies in the Chinese green financial market

Green financial stock is an important embodiment of the social sustainable value. The complex network theory provides an important research paradigm for studying the stock correlations and the dynamic evolution characteristics in the green financial market. Considering that a few scholars have studied the green financial network, we fill this gap by constructing the stock correlation network model based on the correlations of stock price fluctuations to analyze the network centrality and its influencing factors in the Chinese green financial market. Subsequently, it is also applied to explore the dynamic evolution characteristics of the stock correlation networks and the correlations between the network structures and different types of network entropies in the Chinese green financial market. In the light of empirical research, we can obtain the following results. First, the variables about corporate sustainable development, corporate green innovation output, corporate social responsibility, corporate return on net assets, and corporate property rights play positive roles in promoting the network centrality ranking of individual enterprises in the stock correlation networks of the Chinese green financial market, while the corporate age is negatively related to the network centrality ranking. Second, the stock correlation networks of the Chinese green financial market show the small-world feature in the dynamic evolution process. Third, network entropies can effectively depict the change direction of network structures over time in the Chinese green financial market. Finally, to maintain the stable operation of the green financial market, more and more importance is attached by the regulatory authorities to these green financial stocks that are “too related to fail”.

FAKTOR-FAKTOR YANG BERPENGARUH TERHADAP HARGA SAHAM SYARIAH PT. WIJAYA KARYA, TBK., DAN PT. ANEKA TAMBANG, TBK.

The stock price always changes. Investors can find out the factors that influence it with a fundamental analysis of the company's financial statements. This study aims to determine whether the Return On Assets, Return On Equity, and Debt to Equity Ratio affect the stock price fluctuation of PT. Wijaya Karya, Tbk. and PT. Aneka Tambang, Tbk. in 2016-2018. The research method used is descriptive quantitative, then this study uses the population and samples in the selection of research objects. The population consisted of 30 companies registered in JII, the sampling method was purposive sampling and obtained PT. Wijaya Karya, Tbk. and PT. Aneka Tambang, Tbk. The research data is secondary data obtained from the annual financial reports of the two research samples. The data analysis technique used is multiple regression. The results of the study concluded that both simultaneously and partially ROA, ROE, and DER did not have a significant effect on the stock price of PT. Wijaya Karya, Tbk. and PT. Aneka Tambang, Tbk. 2016-2018 period.

Financial Sequence Prediction Based on Swarm Intelligence Algorithms of Internet of Things

In order to accurately predict the financial time series and stock price fluctuation, in this study, metadata was used to ensure the objectivity of data for most of the swarm intelligence algorithms of the Internet of Things. The results show that the combination of theoretical methods and financial time series forecasting model can significantly improve the forecasting effect. At the same time, the proposed fuzzy theory also gives a totally different perspective to the modeling of financial time series which has stochastic and uncertain characteristics. Therefore, swarm intelligence algorithm and fuzzy theory are effectively combined, and their combination is very useful for the improvement of two financial fuzzy time series models. Because of their combination, the problem of stock price forecasting with different orders and different factors is well solved. Besides, it also provides two new solutions for such problems respectively as well as additional model choices for investors to avoid risks and improve returns.

Research on the relationship between investor network interaction and stock price fluctuation——Take “SSE e interaction” as an example

Whether the stock price fluctuation in emerging markets such as China is dominated by “information efficiency” or “noise” has aroused many scholars’ disputes. Based on the “SSE e interaction” Q & A data, this paper uses the fixed effect model to study the impact of “SSE e interaction” on the stock price synchronization from 3 perspectives: the lag of the company’s response, the pertinence and the negative emotional tendency of investors. The research found that the targeted response of listed companies to investors’ questions in the “SSE e interaction” significantly improved the synchronization of stock prices, and the lag of the response may be the result of selective and tendentious information dissemination. The negative sentiment of investors has certain information content, but excessive negative sentiment may bring noise to the market. Our research shows that information and “noise” coexist in China’s capital market, but “noise” is still the dominant factor in stock price fluctuations.

Research on Application of Multifractal Spectrum and Neural Network in Financial Market

With the deepening of the financial market’s reform and innovation, to better explain the market's stock price fluctuations, this article uses the traditional efficient market hypothesis to study further. This study is showed that the Shanghai Stock Exchange Index, the Shenzhen Stock Exchange Index, and the Nasdaq Index have the characteristics of "tip fat tail ", which indicates that the distribution is not normal. Based on the theory of multifractal spectrum, empirical analysis and comparison results of multifractal characteristics, three indexes are carried out to analyze the impact of the maturity of different financial markets on stock price fluctuations. Finally, the multifractal spectrum parameters and daily return rate as input variables, an average accuracy rate of 99.436%, are obtained for the Shanghai Composite Index forecast for the next 30 days, using a 5-layer neural network model. It has particular practical significance for controlling and managing financial risks.

Equity pledge of major shareholders, quality of information disclosure and synchronization of stock price

The synchronization of stock price is an important index to measure the information of stock price. Compared with the countries with mature capital market, the synchronization of stock price in China is on the high side, and the fluctuation of stock price often presents the phenomenon of “rising and falling together”. This paper studies the impact of pledge of major shareholders’ equity on the synchronization of stock price, and further explores the role of information disclosure quality. Based on the data of China’s A-share listed companies in 2013-2019, the empirical study shows that: there is a large shareholder equity pledge in enterprises, and the synchronization of stock price is higher, and with the increase of the proportion of large shareholder equity pledge, the impact is more significant; the quality of information disclosure plays a significant negative role in the relationship between the synchronization of large shareholder equity pledge and stock price, easing the impact of large shareholder equity pledge the impact on stock price synchronization.