Price Movements Research Articles

Connectedness between sustainable exchanged‐traded funds in the presence of the Ukrainian war: Does political risk matter?

AbstractThis paper analyses the connectivity between geopolitical risk (GPR) and several segments of exchanged‐traded funds, aligned with the United Nations Sustainable Development Goals (SDGs), in order to understand the implications of risk associated with wars, political tensions and terrorist acts in the dynamics generated by this type of asset. We studied the behaviour of assets representative of 6 SDGs and 2 GPR indices, over a period of approximately 14 and a half years, characterized by several market phases, with special emphasis on the pandemic crisis and the war in Ukraine. Resorting to dynamic analysis, based on several multivariate models, it was possible to identify spillover effects in phases corresponding to turbulence in financial markets, in particular in the downward movements of prices of sustainable assets, generated from the geopolitical threat index. The results obtained are relevant for understanding the effect of GPR on sustainable investment, being of interest to various market actors.

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Accurately forecasting the trajectory of stock prices holds crucial significance for investors in mitigating investment risks and making informed decisions. Candlestick charts visually depict price information and the trends in stocks, harboring valuable insights for predicting stock price movements. Therefore, the challenge lies in efficiently harnessing candlestick patterns to forecast stock prices. Furthermore, the selection of hyperparameters in network models has a profound impact on the forecasting outcomes. Building upon this foundation, we propose a stock price prediction model SSA-CPBiGRU that integrates candlestick patterns and a sparrow search algorithm (SSA). The incorporation of candlestick patterns endows the input data with structural characteristics and time series relationships. Moreover, the hyperparameters of the CPBiGRU model are optimized using an SSA. Subsequently, the optimized hyperparameters are employed within the network model to conduct predictions. We selected six stocks from different industries in the Chinese stock market for experimentation. The experimental results demonstrate that the model proposed in this paper can effectively enhance the prediction accuracy and has universal applicability. In comparison to the LSTM model, the proposed model produces an average of 31.13%, 24.92%, and 30.42% less test loss in terms of MAPE, RMSE and MAE, respectively. Moreover, it achieves an average improvement of 2.05% in R2.

The price behavior characteristics of China and Europe carbon emission trading market based on the perspective of time scaling and expected returns.

China has the world's largest carbon market in terms of greenhouse gas emissions, but its system needs to be improved and enhanced. In comparison, the European carbon market stands as the most mature and well-developed carbon market globally. Carbon trading prices, serving as a barometer for the carbon market, are significantly influenced by investor behavior. Therefore, it is necessary to analyze the characteristics of carbon trading prices in both China and Europe, considering the impact of investor trading intervals and psychological expected returns. This study utilizes the Zipf method to characterize the dynamic behavior of carbon trading prices between China and Europe, conducting a comparative analysis. The results show distinctive asymmetry in the behavior of carbon trading prices in both markets. In the Chinese market, when τ < 277, the absolute deviation da(τ, ε) value gradually changes but consistently indicates a bullish trend. However, when τ ≥ 277, the da(τ, ε) value surges rapidly, reflecting a pronounced bullish sentiment among investors toward carbon trading prices in China. In the European market, within the sample period, regardless of variations in τ and ε, the da(τ, ε) value shows a linear upward trend, indicating a significant overall bullishness in prices. This suggests a higher probability of long-term bullishness in carbon trading prices. Investors' investment time scale (τ) and expected returns (ε) both influence the behavior of carbon trading prices in both China and Europe. Generally, a longer τ implies a higher probability of bullishness. As for ε, higher values lead to more extreme judgments on price movements, resulting in greater distortion in carbon trading prices. Short-term investors (τ<1 month) anticipate extreme fluctuations, exhibiting random behavior when ε < 0.15 and converging rapidly to extreme values of 1 or 0 when ε ≥ 0.15. Long-term investors (τ>quarter) are less biased, expressing a bullish outlook on both Chinese and European carbon prices. With increasing ε, the probability of bullishness either increases or decreases rapidly until reaching the saturation point. Once saturated, there is no further distortion in carbon price behavior. Furthermore, the Chinese carbon market displays a positive trend in carbon trading prices and a higher probability of long-term bullishness. For the European market, lower expected returns contribute to considerable carbon trading price fluctuations, exacerbating risk and uncertainty. The results of this study contribute to understanding the diverse trading behaviors in Chinese and European carbon markets and provide guidance for avoiding extreme volatility in carbon trading prices.

On the long-run properties of income and stock prices: the stability of the “golden ratios”

PurposeThe challenge of predicting changes in aggregate income and stock prices is one that has occupied the research agendas of economists. This paper aims to use the consumption–income ratio and the dividend–price ratio to predict future income and stock prices.Design/methodology/approachTo examine the stability of the consumption–income ratio and the dividend–price ratio, the authors run a two-variable, two-lag reduced-form VAR in the vein of Cochrane (1994), using a lag of each respective ratio as exogenous to the VAR. Additionally, the authors estimate an AR(4) model for income and prices.FindingsThe consumption–income ratio and the dividend–price ratio remain key to understanding future movements in income and stock prices. The consumption–income ratio significantly predicts future income in the USA, and aggregate income is easier to predict than consumption in the VAR model. The dividend–price ratio does not significantly predict future price growth. Consumption and dividend shocks have lasting impacts on income and prices.Originality/valueThe consumption–income ratio and the dividend–price ratio are still key to understanding future movements in income and stock prices. The consumption–income ratio significantly predicts future income in the USA, and aggregate income is easier to predict than consumption in the VAR model. However, the dividend–price ratio does not significantly predict future price growth, a change from previous research from the 1990s, despite the increasing complexity of stock markets. Consumption and dividend shocks have lasting impacts on income and prices and appear to be significant drivers in both the short- and long-run variance in income and prices.

Investigating the Co-Movement and Asymmetric Relationships of Oil Prices on the Shipping Stock Returns: Evidence from Three Shipping-Flagged Companies from Germany, South Korea, and Taiwan.

In the last 2 years, there has been a significant upswing in oil prices, leading to a decline in economic activity and demand. This trend holds substantial implications for the global economy, particularly within the emerging business landscape. Among the influential risk factors impacting the returns of shipping stocks, none looms larger than the volatility in oil prices. Yet, only a limited number of studies have explored the complex relationship between oil price shocks and the dynamics of the liner shipping industry, with specific focus on uncertainty linkages and potential diversification strategies. This study aims to investigate the co-movements and asymmetric associations between oil prices (specifically, West Texas Intermediate and Brent) and the stock returns of three prominent shipping companies from Germany, South Korea, and Taiwan. The results unequivocally highlight the indispensable role of oil prices in shaping both short-term and long-term shipping stock returns. In addition, the research underscores the statistical significance of exchange rates and interest rates in influencing these returns, with their effects varying across different time horizons. Notably, shipping stock prices exhibit heightened sensitivity to positive movements in oil prices, while exchange rates and interest rates exert contrasting impacts, one being positive and the other negative. These findings collectively illuminate the profound influence of market sentiment regarding crucial economic indicators within the global shipping sector.

Features of the development of the agricultural marketing system using information technologies

Forecasting prices for agricultural products is a complex, end-to-end and dynamic task of agricultural marketing. With constant development and changes in data sources, data types, data quality, data processing methods, model building methods, and model evaluation methods, agricultural price forecasting methods will also be updated and improved. An overview of the use of digital technologies is proposed, leading to profound changes in the functioning of economic factors, including in the agro-industrial complex. The article shows that the digital transformation (digitalization) of the agro-industrial market will lead to a change in the sectoral structure of the agro-industrial complex due to the replacement of traditional industries with new ones based on forecasts obtained using artificial intelligence systems. The paper provides an overview of specialized applied software products that are available on the Internet and are used to analyze financial data. An overview of modern information technologies, which allow convenient processing, systematization and visualization of data, as well as forecasting the movement of asset prices, was carried out. Scientific abstraction, analysis and synthesis methods, quantitative and qualitative assessment methods, which were used to determine the content of marketing concepts and its features in the activities of agro-industrial enterprises, as well as the method of source analysis served as the scientific and theoretical basis for deepening the research. When selecting sources for analysis, a search was made in open databases available in the global information network Internet, on the basis of which neural network training was carried out. The main functionalities of the created system are described, including a visual comparison of historical data, price movement forecasting using machine learning, and recommendations on the optimal content of the investment portfolio. The results obtained in the work substantiate the following basic propositions: - digitalization leads not only to technological, but also to organizational transformations in the agro-industrial complex; - the result of digital transformation will be a profound change in the model of the organization of the agro-industrial complex and its role in the national economy; - agribusiness needs to take measures to support the digital transformation of the agro-industrial market and the adaptation of the agro-industrial complex to the new digital system.

Global oil price and stock markets in oil exporting and European countries: Evidence during the Covid-19 and the Russia-Ukraine war

The relationship between oil price movements and stock markets during the COVID-19 pandemic and the geopolitical crisis like the ongoing Russian-Ukraine war is yet unexplored extensively. This study therefore examines the return-correlation effects of oil prices on stock markets and their spillover effects in oil-exporting and European countries using daily closing data. After estimating the GARCH process, we employ the static and dynamic Markov Switching model that allow the relationship between oil price and stock market to switch between two regimes coined the COVID-19 and the Russia-Ukraine war periods. The static model shows stock price returns to respond positively and significantly to oil price returns in Italy, Germany and the US during the Covid-19 period while the response is significantly positive only for US in the Russia-Ukraine war period. As regards the volatility spillover, significant spillover is found from stock to oil market for Nigeria, vice versa for Saudi Arabia and bi-directional volatility spillover found for the US, Italy and Germany during the COVID-19 period. The policy implication is that Nigeria and Saudi Arabia should prioritize financial policy and energy policy respectively while US, Italy and Germany should adopt policy coordination to stabilize oil-stock market volatility during low oil price period like the COVID-19 period.

Sharia Economic Review of Gold Selling Price Determination

This research aims to find out how Sharia Economic Review on Determining Gold Selling Prices (Case Study at the Cempaka Indah Gold Shop, Talang Babat Village). and the results of this research; First, gold traders have a very strong position in the marketing mechanism for gold buying and selling transactions because the level of stability of the gold business is very stable. In this gold buying and selling transaction, gold traders carry out price dualism. The price of gold sold by traders to buyers is always very different from the price of gold sold by buyers to traders. Second, the price of gold for consumers is always higher than the standard price because gold as jewelry is more expensive because of the aesthetic value and complexity of the jewelry design. Third, the government never sets the price of gold so respondents' answers are still very ambiguous. Because they do not yet understand the dominance and power of gold traders in marketing mechanisms and gold price movements. Fourth, the practices carried out by East Tanjung Jabung Marketing to increase or decrease the price of gold are not in accordance with Islamic law

The Influence of Environmental, Social, and Governance (ESG) Disclosure on Firm Value: An Asymmetric Information Perspective in Indonesian Listed Companies

The research explored how Environmental, Social, and Governance (ESG) disclosure impacted company value, with information asymmetry as the mediator. Data collection involved content analysis of sustainability and annual reports, supplemented by market data, including quarterly stock prices collected immediately after the publication of the sustainability report. The measurement for ESG disclosure used the index scoring method with disclosure indicators based on technical guidelines from SEOJK No. 16 of 2021. Meanwhile, measuring information asymmetry applied the bid-ask spread formula, and company value employed the approximate Tobin's Q formula. Then, market data utilized quarterly stock prices taken in the period immediately after the sustainability report was published to observe market reactions reflected in stock price movements. The population consisted of public companies listed on the Indonesian Stock Exchange from 2019 to 2021, with purposive sampling by selecting only companies that provided information relevant to ESG indicators. Data collection resulted in 286 analysis units from 109 companies. Using SPSS 27.0 and the Hayes Macro process for path analysis, the findings indicate that ESG disclosure has no impact on information asymmetry. Similarly, information asymmetry does not affect firm value or mediate the relationship between ESG disclosure and firm value. However, it should be noted that ESG disclosure has a negative impact on company value. The novelty of the research lies in the use of instruments to assess the quality of ESG disclosure and the utilization of information asymmetry as a mediator between ESG disclosure and company value.

STOCK MARKET PRICE PREDICTION USING MACHINE LEARNING TECHNIQUES

Predicting stock market prices is a challenging task in the financial sector, where the Efficient Market Hypothesis (EMH) posits the impossibility of accurate prediction due to the inherent uncertainty and complexity of stock price behaviour. However, introducing Machine Learning algorithms has shown the feasibility of stock market price forecasting. This study employs advanced Machine Learning models that can predict stock price movements with the right level of accuracy if the correct parameter tuning and appropriate predictor models are developed. In this research work, the LSTM model, which is a type of Recurrent Neural Network (RNN), time series forecasting Facebook Prophet algorithm and Random Forest Regressor model have been implemented on 10 Dhaka Stock Market (DSEbd) listed companies and six international giants for predicting the stock and forecasting the future price. The dataset of domestic companies is extracted from the graphical representation of the DSEbd website, and the international companies' dataset is imported from Yahoo Finance. In this experiment, Facebook Prophet demonstrates a long period of forecasting with reasonable accuracy, capturing daily, weekly, and yearly seasonality, including holiday effects for market trend analysis. Remarkably, the LSTM model exhibits significant accuracy, yielding the best results with evaluation metrics, including RMSE (0.35), MAPE (0.50%), and MAE (0.30). The experimental results underscore the efficiency of LSTM for future stock forecasting, observed over 15 days of upcoming market prices. A comparison of the results shows that the LSTM model efficiently forecasts the next day's closing price.

Examining the relationship between bitcoin and altcoins

Purpose- The purpose of this study is to examine the long and short-term relationship between Bitcoin and altcoins selected based on their market capitalization through an empirical analysis. For this purpose, the daily data of Bitcoin and nine altcoins consisting of Ether, Ripple, Tether, Litecoin, Monero, Stellar, Dash, Nem, Dogecoin for the period 07/08/2015-08/01/2020 were used. Methodology- The long-run relationship between Bitcoin and altcoins is first analyzed by Vector Autoregression (VAR) analysis. Granger causality test was utilized to determine the short-run causality relationship between the variables. The tests were conducted with the Eviews program. Findings- According to the results of the VAR analysis conducted to investigate the long-run relationship, there is a long-run relationship between Dogecoin, Dash, Litecoin, Nem, Stellar and Ripple and Bitcoin. After determining the long-run relationship between the variables, the relationships between the variables were analyzed with the help of impulse response functions. Impulse response function shows the effect of a one-unit shock to one variable on the other variable. Accordingly, when the results of impulse response functions are analyzed; it is seen that a one-unit random shock in Bitcoin has a negative effect on Ripple, Nem, Litecoin, Dash, Litecoin, Dogecoin in the first two periods, the effect decreases in the second period, and this effect disappears in the third period. A random shock to Bitcoin causes a positive effect on Stellar that lasts for two periods. This positive effect ends in the third period. After analyzing the relationship between Bitcoin and altcoins with impulse response functions, the source of the changes in the variance of the variables is analyzed through variance decomposition. According to the variance decomposition results, the effect of Bitcoin on Dogecoin is 25% in the first period and 22% in the other periods. The variance decomposition of Dash shows that approximately 18% of the change in standard deviation was caused by Bitcoin in the first period and this percentage increased to 25.5% in the following periods. Litecoin's variance decomposition results show that 33% of the change in standard deviation from the first period to the last period was caused by Bitcoin. It is observed that approximately 8% of the change in Nem's standard deviation in the first period was caused by Bitcoin, while this rate increased to 21.5% in the last period. From the first period to the last period, 13.5% of the change in Stellar's standard deviation was caused by Bitcoin. When the variance decomposition of Ripple is analyzed, it is observed that 10% of the difference in the standard deviation is due to Bitcoin. This situation continued similarly from the first period to the last period. Following the VAR analysis, Granger causality test was conducted to explain the short-term relationship between the variables. According to the test results, there is a bidirectional Granger causality between Bitcoin and all altcoins. Accordingly, when Bitcoin is taken as the dependent variable, it is the Granger cause of Ether, Ripple, Tether, Litecoin, Monero, Stellar, Dash, Nem, Dogecoin. When the Granger causality relationship between altcoins is analyzed, a causality relationship was observed from Tether to Stellar, while no causality was found from Stellar to Tether. Similarly, while Granger causality is observed from Tether to Ripple, there is no causality from Ripple to Tether. The variance decomposition of Stellar and Ripple shows that Tether does not contribute to the change in standard deviation. The variance decomposition test supports the Granger test results. All altcoin variables except these are Granger causes of each other. Conclusion- At the end of the study, according to the results of the VAR analysis to determine the long-run relationship, there is a long-run relationship between Dogecoin, Dash, Litecoin, Nem, Stellar and Ripple and Bitcoin. There is no long-run relationship between Tether, Monero, Ether and Bitcoin. According to the Granger causality analysis test results conducted to observe the short-term relationship, there is a bidirectional Granger causality between Bitcoin and all altcoins. Accordingly, when Bitcoin is taken as the dependent variable, it is the Granger cause of Ether, Ripple, Tether, Litecoin, Monero, Stellar, Dash, Nem, Dogecoin. As a result, it is observed that Bitcoin has a short-term relationship with all 9 altcoins subject to the study, and a long-term relationship with Dogecoin, Dash, Litecoin, Nem, Stellar and Ripple. These results show that the price movements in Bitcoin have an impact on altcoins. Keywords: Bitcoin, altcoin, cryptocurrency, causality analysis, VAR analysis. JEL Codes: G17, G10, C58

Market Sentiment Analysis Based on Image Processing With Put-Call Volatility Gap Surface

Analyzing the market sentiment and forecasting movements in asset prices is extremely important and has been attempted by researchers and market practitioners. Asset volatilities, regardless of historical ones or implied from the option prices, are crucial barometers of the market. And this research proposes a new approach that combines image processing and machine learning to capture the relationships between sentiment-related features and asset movement. The proposed research is based on the tick-level SPY options transactions, and the dataset contains around 1.5 million trading records. Specially, we obtained the gap between the call surface and the put surface as the second-level implied volatility surface (IVS). After adopting the traditional convolutional neural network (CNN) to compare the predictive effects among implied volatility (IV) call surface, IV put surface, and IV gap surface, the results indicate that the IV gap provides the most significant predictability. Besides, our project creatively proposes the interframe difference approach to optimized CNN and a recurrent CNN (RCNN) to fully utilize spatial and temporal features of the IV surface data. According to experiment results, the directional accuracy of prediction ranges from 67.20% to 72.05% for asset movement forecasting at the millisecond level.

Stock price forecasting in Indonesia stock exchange using deep learning: a comparative study

In 2022, the Indonesia stock exchange (IDX) listed 825 companies, making it challenging to identify low-risk companies. Stock price forecasting and price movement prediction are vital issues in financial works. Deep learning has previously been implemented for stock market analysis, with promising results. Because of the differences in architecture and stock issuers in each study report, a consensus on the best stock price forecasting model has yet to be reached. We present a methodology for comparing the performance of convolutional neural networks (CNN), gated recurrent units (GRU), long short-term memory (LSTM), and graph convolutional networks (GCN) layers. The four layers types combination yields 11 architectures with two layers stacked maximum, and the architectures are performance compared in stock price predicting. The dataset consists of open, highest, lowest, closed price, and volume transactions and has 2,588,451 rows from 727 companies in IDX. The best performance architecture was chosen by a vote based on the coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute percent error (MAPE), and f1-score. TFGRU is the best architecture, producing the finest results on 315 companies with an average score of RMSE is 553.327, MAPE is 0.858, and f1-score is 0.456.

Comparative analysis of machine learning techniques for cryptocurrency price prediction

The emergence of cryptocurrencies has revolutionized the concept of digital currencies and attracted significant attention from financial markets. Predicting the price dynamics of cryptocurrencies is crucial but challenging due to their highly volatile and non-linear nature. This study compares the performance of various models in predicting cryptocurrency prices using three datasets: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models analyzed include Moving Average (MA), Logistic Regression (LR), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). The objective is to uncover underlying patterns in cryptocurrency price movements and identify the most accurate and reliable approach for predicting future prices. Through the analysis, it could be observed that MA, LR, and ARIMA models struggle to capture the actual trend accurately. In contrast, LSTM and CNN-LSTM models demonstrate strong fit to the actual price trend, with CNN-LSTM exhibiting a higher level of granularity in its predictions. Results suggest that deep learning architectures, particularly CNN-LSTM, show promise in capturing the complex dynamics of cryptocurrency prices. These findings contribute to the development of improved methodologies for cryptocurrency price prediction.

Effectiveness of the Introduction Capital Market Activities to the Younger Generation Through the Support of Internship Programmes

Stock trading on the capital market is one of the investment activities. The capital market has become one of the places where transactions on securities can be carried out. Given the importance of expertise in this field, there is an internship for students to apply the knowledge previously acquired from the Capital Market School. The highly expected output of the internship activity is that students are able to read stock price movements, where they understand when it is time to buy stocks and when it was time to sell stocks, to be able to make a profit. Participants who take part in this internship will have skills in Opening Stock Account Administration up to the release of RDN, Selling Buy Stock Practices, Analysis of Stock Price Movement Transaction Graphs, and Knowledge of Advanced Material for BPPE Certification.

A systematic literature survey on recent trends in stock market prediction.

Prediction of the stock market is a challenging and time-consuming process. In recent times, various research analysts and organizations have used different tools and techniques to analyze and predict stock price movements. During the early days, investors mainly depend on technical indicators and fundamental parameters for short-term and long-term predictions, whereas nowadays many researchers started adopting artificial intelligence-based methodologies to predict stock price movements. In this article, an exhaustive literature study has been carried out to understand multiple techniques employed for prediction in the field of the financial market. As part of this study, more than hundreds of research articles focused on global indices and stock prices were collected and analyzed from multiple sources. Further, this study helps the researchers and investors to make a collective decision and choose the appropriate model for better profit and investment based on local and global market conditions.

Comparing Long Short-Term Memory and Random Forest Accuracy for Bitcoin Price Forecasting

Bitcoin’s daily value fluctuations are very dynamic. Understanding its rapid and intricate price movements demands advanced techniques for processing complex data. This research aims to compare the accuracy of two machine learning methods, Random Forest (RF) and Long Short-Term Memory (LSTM), in predicting Bitcoin price. This research employs RF and LSTM algorithms to forecast Bitcoin prices using a two-year Yahoo Finance dataset. The evaluation metrics used were accuracy based on Mean Absolute Percentage Error (MAPE) and computational power (CPU-Z). As a result of this research, the LSTM model demonstrates higher accuracy compared to the RF model. MAPE reveals LSTM’s precision of 99.8% and RF’s accuracy of 90.1%. Regarding computational time and resources, RF shows slightly better performance than LSTM. The visual comparison further emphasizes LSTM’s better performance in predicting Bitcoin prices, highlighting its potential for informed decision-making in cryptocurrency trading. This research contributes valuable insights into the effectiveness, strengths, and weaknesses of LSTM and RF models in predicting cryptocurrency trends.

Stock Valuation of Telecommunication Provider in Saudi Arabia with the Case Study of Tawal Spin-Off of Saudi Telecom Company (STC)

This study investigates the stock valuation implications of the strategic spin-off plan involving Tawal, the biggest telecom tower infrastructure provider in Saudi Arabia, a 100% owned subsidiary of the Saudi Telecommunications Company (STC). Saudi Public Investment Fund (PIF) has sent a nonbinding offer to acquire 51% of Tawal's shares from the STC group, which is aligned with the Saudi Vision 2030 strategy. The findings of this research contribute nuanced insights into the relationship between spin-offs and stock valuation within the context of the telecommunications industry. By scrutinizing the anticipated impact of the Tawal spin-off on STC stock price, this study provides valuable information for investors and the STC group navigating the strategic decisions and their implications for the stock value. The research employs a comprehensive stock valuation framework with absolute-relative valuation, internal-external analysis, and risk assessment to gauge the potential effects on STC's respective stock values of the Tawal spin-off. Comparative analyses conducted within the telecommunications sector in Saudi Arabia offer valuable benchmarks for assessing the performance of STC Group. According to the intrinsic valuation approach and the Discounted Cash Flow model, STC stock price is undervalued compared to the stock price of 19th November 2023 by 38.3 SAR. Assuming the spin-off failed, obtaining the fair price at 41.5 SAR per share based on the DCF model and 54.2 SAR based on the relative valuation PE ratio. If the Spin-off is agreed upon and concluded, the fair price is valued at 42.6 SAR per share based on the DCF model. Sensitivity analysis and Monte Carlo simulation resulted in two results: the gross profit margin is a sensitive variable of the stock price movement, and the buy option is preferable. In line with that, STC has a solid holding organization, is financially healthy, and has sustained a competitive advantage. STC is expected to deploy the liquidity into investment and expansion in the region and globally, generating a higher return to satisfy the investor interest.

Analysis of the Potential and Volatility of Large Chili and Cayenne Chili After Covid-19 in Yogyakarta City

Chili is a category of 10 agricultural commodities and a staple in Indonesia. Price movements that go up and down or fluctuate are certainly a problem in themselves, where chili as a strategic commodity is one of the main determinants of the dynamics of national inflation. Regarding the policy of protecting farmers with restrictions on chili imports, chili stocks become more stable in the country so that local prices can compete with imported products. This observe uses secondary information obtained from the Bank Indonesia website to determine volatility and potential. The cause of this observe is related to the importance of volatility in large chili and cayenne pepper as well as the potential considering that they are the main commodities in agriculture and price projections in October 2023. This study uses quantitative descriptive analytical tools using ARIMA and GARCH time series models to predict time patterns and calculate potential with Location Quotient (LQ) analysis. This research shows volatility in Big Red Chili and Red Cayenne Chili and shows price forecasting that is experiencing a downtrend

De-noising classification method for financial time series based on ICEEMDAN and wavelet threshold, and its application

This paper proposes a classification method for financial time series that addresses the significant issue of noise. The proposed method combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and wavelet threshold de-noising. The method begins by employing ICEEMDAN to decompose the time series into modal components and residuals. Using the noise component verification approach introduced in this paper, these components are categorized into noisy and de-noised elements. The noisy components are then de-noised using the Wavelet Threshold technique, which separates the non-noise and noise elements. The final de-noised output is produced by merging the non-noise elements with the de-noised components, and the 1-NN (nearest neighbor) algorithm is applied for time series classification. Highlighting its practical value in finance, this paper introduces a two-step stock classification prediction method that combines time series classification with a BP (Backpropagation) neural network. The method first classifies stocks into portfolios with high internal similarity using time series classification. It then employs a BP neural network to predict the classification of stock price movements within these portfolios. Backtesting confirms that this approach can enhance the accuracy of predicting stock price fluctuations.