Price Fluctuations Research Articles

Literature Review: Evidence of Day-of-the-Week Effect in Cryptocurrency Market

This study provides a comprehensive analysis of the anomaly of day-of-the-week effect found in cryptocurrency market. Its primary objective is to synthesize existing literature and offer an overview of the current understanding of this phenomenon, while identifying trends and research gaps. A systematic literature review was conducted across three major databases—Emerald Insight, Science Direct, and JSTOR—using the keywords "cryptocurrencies" or "cryptocurrency" and "day of the week effect." After applying inclusion and exclusion criteria, 10 relevant articles were identified, and an additional 5 studies were included through manual data extraction from included studies, resulting in a total of 15 articles analyzed. The findings reveal increasing evidence of dayof-the-week effect in cryptocurrency markets, with specific days exhibiting significantly higher or lower returns. However, the exact patterns and underlying drivers of this effect remain underexplored. This review emphasizes the need for additional research to gain a deeper understanding of the time-based anomalies in cryptocurrency price fluctuations and their effects on investors and policymakers.

Dynamic Price Monitoring in E-Commerce: Leveraging Web Scraping, Data Analysis, and Email Automation for Dynamic Pricing Insights

This paper focuses on the development of a web application that leverages web scraping, data storage, and email automation technologies to track real-time price changes for specific products on e-commerce platforms. Built using Next.js for the frontend, MongoDB as the database, and Nodemailer for email notifications, the system aims to provide users with a comprehensive and user-friendly interface to monitor price fluctuations. By scraping data from product pages, the application gathers key pricing metrics, including the average, lowest, highest, and current prices, and presents this information visually to help users make informed purchasing decisions. By utilizing the combination of web scraping and real-time data analysis, this project provides a dynamic solution for both consumers and businesses, offering valuable insights into price changes and trends in the competitive e-commerce landscape.

Predicting Gold Prices Through Machine Learning with Economic Indicators and Feature Selection Methods

The enormous fluctuation of the gold price in international markets gives predictive analysis an indispensable place for business firms, economists, and investors. With this in mind, this research aims to construct a machine learning model for gold price prediction based on past information and identify some leading patterns affecting the prices of such a commodity. The model thus covers a range of related financial and economic factors, be it past gold prices and inflation metrics, oil price vagaries, worldwide market indexes, and interest rate movements. Using machine learning algorithm approaches like Support Vector Machine (SVM), Random Forest, as well as Linear Regression approaches, the system is set to identify complex relationships prevailing in the data and provides predictions for short-term movements and long-term movements concerning price. The model will be trained and validated using historical data that includes the history of the gold price. In this respect, the findings of the study are relevant to stakeholders in financial forecasting who could make informed investment decisions. Additionally, the paper has outlined the method for feature selection that determines which factors are dominant in explaining the fluctuation of the gold price. The goal of the model, therefore, is to enhance its performance through prediction accuracy reduction in noise, recursive elimination of features, and application of correlation analysis. As a result, a robust evaluation of several machine algorithms, including support vector machines, allows the selection of a suitable model that best solves the prediction task. This approach to forecasting will therefore aid in the creation of instruments for risk management, automated trading techniques, and financial strategies for an individual as well as for organizations. Keywords: Support Vector Machine, Random Forest, machine learning, and gold price prediction; economic indicators, feature selection, and linear regression in the field of financial forecasting; erratic behaviour; and risk management.

Financial Indicators Shaping Share Price Movement: Insight from the Dhaka Stock Exchange SME Board

This study intends to explore the parameters that influence the share price movement of companies listed under the SME board on Dhaka Stock Exchange (DSE). The small capital platform, popularly known as the SME board is a specialized to trade share of Small and Medium Sized Enterprises (SMEs). Based on secondary data, the present study used three independent variables, such as Earning Per Share (EPS), Price Earning (P/E) ratio, and Net Asset Value (NAV) per share to evaluate the fluctuations of share price in the market. All the companies listed on the DSE SME board were taken into consideration for the current study. Relevant Data concerning dependent and independent variables were collected from the DSE website. Average closing price of selected shares were collected during March 15, 2022–March 14, 2023. The study performed descriptive statistics, correlation, and regression analysis using Statistical Package for Social science (SPSS) software. The study revealed that the fluctuations of share price of SME companies was significantly influenced by EPS, Net Asset NAV per share, and P/E ratio. This maiden study is expected to have implications to companies intending to be listed, investors, regulators, and other stakeholders in Bangladesh.

Analysis of Micro Business Marketing Strategy in Facing Competition

This study is motivated by the challenges faced in the development of micro-enterprises at Bukit Sion Market, largely due to the high level of competition. Many businesses struggle to determine appropriate financing models and product offerings, as the market is saturated with similar products offered by competitors. The objective of this research is to analyze the marketing strategies employed by micro-enterprises and identify the obstacles they face in dealing with competition at Bukit Sion Market, Lahomi District. The research employs a descriptive qualitative method. The findings reveal that all food stalls adopt marketing strategies that include market selection, product planning, pricing, distribution systems, and marketing communication. Effective marketing strategies, such as offering discounts, special promotions, or hosting promotional events, have been shown to significantly boost sales. However, the food stalls encounter several challenges, including intensified competition due to the emergence of similar businesses offering comparable products, a lack of product innovation and promotion, fluctuations in raw material prices, as well as issues with product quality consistency, customer service, and overall customer experience.

Modeling and Analyzing Carbon Emission Market Volatility and Impact: Evidence from Guangdong Province, China

This research investigates the volatility of carbon prices in Guangdong’s emission trading market, a critical element of China’s broader climate strategy aimed at reducing greenhouse gas emissions and promoting sustainable development. This study applies ensemble empirical mode decomposition (EEMD) to analyze the complex interactions between carbon price fluctuations and various economic factors, including energy prices and environmental regulations. By decomposing the data, we identify key trends and cycles within the market, providing a clearer understanding of both short-term volatility and long-term market trends. Our findings reveal that regulatory policies play a pivotal role in shaping carbon market dynamics, with shifts in regulations leading to significant price volatility. Additionally, fluctuations in global energy prices, especially oil and coal, are found to have a considerable impact on carbon price movements, further complicating the market’s stability. This underscores the interconnected nature of the carbon trading market with broader economic and environmental factors, both domestic and international. The findings provide valuable insights for policymakers and market participants, underscoring the importance of stable carbon markets for promoting the transition to a low-carbon economy and achieving broader sustainability goals.

A Novel Deep Learning Model for Stock Prediction Integrating Historical Prices, Core Volatility Trends and Financial News

Accurate stock price forecasting is crucial for investors and analysts aiming to make informed decisions and mitigate risks in financial markets. While deep learning models have proven effective in identifying complex patterns in quantitative data, they often struggle to account for non-quantitative factors such as corporate events, market sentiment, and regulatory changes that can cause unpredictable stock price movements. To address this challenge, an ensemble model is introduced that integrates historical price data, financial news, and stock fundamentals to predict next-day stock prices. The model combines one-dimensional Convolutional Neural Networks (1D CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs), applied independently to each data stream and concurrently as a unified ensemble, to capture both short-term and long-term market behaviors. NVIDIA (NVDA) stock was selected for testing due to its high volatility and rapid price fluctuations, presenting a challenging case for predictive modeling. Using data spanning from April 2014 to March 2024, the model achieved a coefficient of determination (R²) of 0.983 and a mean absolute error (MAE) of $12.72. These results suggest that the ensemble approach effectively captures both quantitative and qualitative factors, making it a promising tool for improving stock price prediction in volatile market conditions.

Reinforcement Learning-Enhanced Adaptive Scheduling of Battery Energy Storage Systems in Energy Markets

Battery Energy Storage Systems (BESSs) play a vital role in modern power grids by optimally dispatching energy according to the price signal. This paper proposes a reinforcement learning-based model that optimizes BESS scheduling with the proposed Q-learning algorithm combined with an epsilon-greedy strategy. The proposed epsilon-greedy strategy-based Q-learning algorithm can efficiently manage energy dispatching under uncertain price signals and multi-day operations without retraining. Simulations are conducted under different scenarios, considering electricity price fluctuations and battery aging conditions. Results show that the proposed algorithm demonstrates enhanced economic returns and adaptability compared to traditional methods, providing a practical solution for intelligent BESS scheduling that supports grid stability and the efficient use of renewable energy.

Multisource Data-Driven Carbon Price Composite Forecasting Model: Based on Feature Selection and Multiscale Prediction Strategy

Accurate prediction of carbon trading prices is crucial for guiding investors to make informed decisions and assisting governments in formulating scientific carbon trading policies. This paper introduces a multisource data-driven carbon price composite forecasting model, aimed at enhancing prediction accuracy through advanced data processing and analysis methods. The model initially employs a secondary decomposition strategy, including Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) methods, to decompose the original data series into three sub-sequences of different frequencies. Subsequently, it utilizes the Partial Autocorrelation Function (PACF) and Random Forest algorithm for feature selection to determine the input variables for different frequency sequences and conducts in-depth analysis and selection of influencing factors, including unstructured data. Furthermore, the model employs a multiscale forecasting strategy, combining Particle Swarm Optimization (PSO) enhanced Bidirectional Long Short-Term Memory (BiLSTM) and Extreme Gradient Boosting (XGBoost) models, along with the Autoregressive Integrated Moving Average (ARIMA) method, to predict based on the characteristics of different frequency components. Finally, the forecasts are integrated using PSO-BiLSTM to form a comprehensive forecast. Notably, given the high correlation between the trend series and influencing factor variables, the model jointly predicts them. A case study based on the Guangdong carbon market in China demonstrates that the proposed composite forecasting model outperforms other benchmark models, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values of 0.4009, 0.2699, and 0.5183%, respectively. This forecasting model provides an effective tool for predicting and analyzing carbon price fluctuations, offering new insights for precise carbon market price predictions.

Rancang Bangun Aplikasi Untuk Prediksi Harga Bitcoin Menggunakan Algoritma Long Short-Term Memory

Bitcoin, as the first cryptocurrency launched in 2009, has experienced rapid growth and significant volatility.The high price fluctuations of Bitcoin have attracted the attention of investors and traders worldwide.Predicting Bitcoin prices poses a challenge due to its inherent complexity and volatility. Various methodsare employed to predict Bitcoin prices, including fundamental analysis, technical analysis, and deeplearning techniques. This study explores the use of neural networks, specifically Long Short-Term Memory(LSTM), as a tool for predicting Bitcoin prices. Long Short-Term Memory is a type of recurrent neuralnetwork (RNN) designed to address the vanishing and exploding gradient problems present in traditionalRNNs and can learn long-term dependencies in data. The LSTM model used in this research comprises 50neurons, 400 epochs, a batch size of 32, and utilizes Adam optimization. Model evaluation indicates thatLSTM performs well, with a Mean Square Error (MSE) of 2688. Additionally, the model achieves a MeanAbsolute Percentage Error (MAPE) accuracy of 97.77%. Based on these predictions, Bitcoin's price isexpected to decrease over the next month, with an estimated price of approximately 66,191.00 on June 15,2024, and around 64,271.64 on July 15, 2024

Deep Convolutional Transformer Network for Stock Movement Prediction

The prediction and modeling of stock price movements have been shown to possess considerable economic significance within the finance sector. Recently, a range of artificial intelligence methodologies, encompassing both traditional machine learning and deep learning approaches, have been introduced for the purpose of forecasting stock price fluctuations, yielding numerous successful outcomes. Nonetheless, the identification of effective features for predicting stock movements is considered a complex challenge, primarily due to the non-linear characteristics, volatility, and inherent noise present in financial data. This study introduces an innovative Deep Convolutional Transformer (DCT) model that amalgamates convolutional neural networks, Transformers, and a multi-head attention mechanism. It features an inception convolutional token embedding architecture alongside separable fully connected layers. Experiments conducted on the NASDAQ, Hang Seng Index (HSI), and Shanghai Stock Exchange Composite (SSEC) employ Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), accuracy, and Matthews Correlation Coefficient (MCC) as evaluation metrics. The findings reveal that the DCT model achieves the highest accuracy of 58.85% on the NASDAQ dataset with a sliding window width of 30 days. In terms of error metrics, it surpasses other models, demonstrating the lowest average prediction error across all datasets for MAE, MSE, and MAPE. Furthermore, the DCT model attains the highest MCC values across all three datasets. These results suggest a promising capability for classifying stock price trends and affirming the DCT model’s superiority in predicting closing prices.

Fair Energy Trading in Blockchain-Inspired Smart Grid: Technological Barriers and Future Trends in the Age of Electric Vehicles

The global electricity demand from electric vehicles (EVs) increased by 3631% over the last decade, from 2600 gigawatt hours (GWh) in 2013 to 97,000 GWh in 2023. The global electricity demand from EVs will rise to 710,000 GWh by 2030. These EVs will depend on smart grids (SGs) for their charging requirements. Like EVs, SGs are a booming market. In 2021, SG technologies were valued at USD 43.1 billion and are projected to reach USD 103.4 billion by 2026. As EVs become more prevalent, they introduce additional complexity to the SG landscape, with EVs not only consuming energy, but also potentially supplying it back to the grid through vehicle-to-grid (V2G) technologies. The entry of numerous independent sellers and buyers, including EV owners, into the market will lead to intense competition, resulting in rapid fluctuations in electricity prices and constant energy transactions to maximize profit for both buyers and sellers. Blockchain technology will play a crucial role in securing data publishing and transactions in this evolving scenario, ensuring transparent and efficient interactions between EVs and the grid. This survey paper explores key research challenges from an engineering design perspective of SG operation, such as the potential for voltage instability due to the integration of numerous EVs and distributed microgrids with fluctuating generation capacities and load demands. This paper also delves into the need for a synergistic balance to optimize the energy supply and demand equation. Additionally, it discusses policies and incentives that may be enforced by national electricity carriers to maintain grid reliability and manage the influx of EVs. Furthermore, this paper addresses emerging issues of SG technology providing primary charging infrastructure for EVs, such as incentivizing green energy, the technical difficulties in integrating diverse hetero-microgrids based on HVAC and HVDC technologies, challenges related to the speed of energy transaction processing during fluctuating prices, and vulnerabilities concerning cyber-attacks on blockchain-based SG architectures. Finally, future trends are discussed, including the impact of increased EV penetration on SGs, advancements in V2G technologies, load-shaping techniques, dynamic pricing mechanisms, and AI-based stability enhancement measures in the context of widespread SG adoption.

Reflections of Economic Shocks and Price of Crude Oil on Gold Price Volatility within Asian Nations

The focus of this study is to examine the effects of economic shocks along with the crude oil price on gold prices in Asian countries. This study utilizes a regression analysis to measure the relationship between the macroeconomic indicators, crude oil price, and gold price in six selected nations from 1990 until 2022. The validity of the data was diagnostically checked by performing a VIF procedure, autocorrelation test, and normality test on residual, and data were analyzed via EViews software. The analysis showed mixed findings, however, a major variable that positively affects the fluctuation of the gold price is “the crude oil price” and it reflects global economic stability in the six countries. A sudden rise in crude oil prices might trigger conflicts in oil-exporting countries and limit access to material sources. This situation may force investors to move their funds into a safe-haven commodity like gold. The fluctuations in crude oil prices are not only a response to current market conditions but also alarming a lot more fundamental problems in an economy, which include approaching economic recessions, currency devaluation and a major financial crisis. For practical implications, this study suggests that policymakers pay extra attention to this commodity besides other economic indicators like inflation, interest and currency rates, and a GDP rate to ensure a sustainable economic condition. To the best of the authors’ knowledge, this study is the first that examines the economic shocks along with crude oil prices pre-and-post COVID-19 pandemic focusing on a few Asian countries.

Coal price, economic growth and electricity consumption in China under the background of energy transition

With the increasing share of renewable energy generation in China's electricity consumption and the dramatic fluctuation of coal prices in recent years, the discussion of the relationship between coal prices, economic growth and electricity consumption in China becomes more relevant. This paper collects monthly data from 2016 to 2023, uses industrial value added to represent economic growth, and explore the relationship between the three through a time-varying parameter vector autoregressive (TVP-VAR) model, which shows that: (1) The mutual impact effects among coal prices, economic growth, and electricity consumption vary across short-, medium- and long-term. This finding underscores the importance of considering different time horizons when evaluating the interactive effects of these variables. (2) Compared to 2017 and 2022, the lagged impact effects among the three have undergone certain changes, indicating that the understanding of their relationships should be contextualized within specific developmental phases. (3) The relationship between coal prices, economic growth, and different categories of electricity consumption exhibits heterogeneous characteristics, suggesting that recognition of these relationships should be based on specific types of power generation. This research enhances the understanding of the dynamic relationship between coal prices and the macroeconomy in China at the current stage.

Analysis of Factors Influencing Artwork Pricing

Artworks, as unique commodities, possess both properties and aesthetic value. For consumers, understanding the factors affecting artwork pricing can mitigate the risks associated with market pitfalls. For collectors intending to sell artworks, knowing the factors influencing the pricing of artworks can help them better assess the value of their collections and make more informed decisions on when, where and how to sell. This paper analyzes the influencing factors on art prices from three perspectives: intrinsic attributes of artworks, demand dynamics, and special elements of the art market. Each factor's variation can lead to significant fluctuations in art prices.

Electricity Price Bubbles and Global Crisis: Does Financial Development Make a Difference?

The aim of this study is to investigate the existence of electricity price bubbles and identify the factors that contribute to their formation by conducting a comparative analysis between a developed country, Germany, and an emerging country, Romania. We employ the generalized sup ADF test, utilizing bi-annual data spanning from 2007 to 2022. Our findings reveal the presence of 2 distinct electricity price bubbles in both Germany and Romania. In Germany, the first bubble emerged in the second half of 2008, coinciding with the onset of the global economic crisis. Conversely, Romania did not experience a bubble as a result of the economic crisis during this period. The second electricity price bubble in both countries was primarily driven by Russia’s decision to halt gas supplies to Europe. Additionally, the surge in energy consumption following the economic recovery from the COVID-19 pandemic played a significant role. This research contributes to the broader discourse on energy economics by elucidating the mechanisms behind electricity price fluctuations in different economic contexts. The comparative approach underscores the importance of tailored policy responses that consider country-specific factors while addressing global challenges. As energy markets continue to evolve, such studies will be instrumental in guiding strategic decisions and fostering sustainable economic growth. JEL Classification: Q41, E31

The impact of social media discourse on financial performance of e-commerce companies listed on Borsa Istanbul

This research investigates how changes in public discourse on social media (particularly Twitter, now X) influence financial performance of companies listed on the e-commerce XTCRT index of Borsa Istanbul amid the COVID-19 pandemic. Utilising a unique 4-layer network structure, the study integrates daily closing prices, highest and lowest values, and trading volume data of these companies with Twitter discussions on six categories: culture, economy, health, politics, technology, world affairs. By using advanced computational methods such as multilayer network analysis, long short-term memory architecture, nonlinear autoregressive distributed lag analysis, we find a high responsiveness of the e-commerce index to social media content, particularly in discussions related to health, technology, world affairs. The study underscores that social media discussions significantly shape index composition, resulting in noticeable shifts in market dynamics, including fluctuations in stock prices, trading volumes, and market sentiment. Our study advocates for a more advanced approach to market analysis that incorporates the dynamics of online public discussions.

Seasonal price variation analysis of palm oil: case study of Nigeria (2016–2022)

To enhance the benefits derived from palm oil processing by the farmers, there is a need for the producers (farmers) and buyers of palm oil to understand the seasonal price for making informed decisions on when to buy and sell the commodity. In order to determine price at the appropriate time of the season, the study looked at the seasonal price variation analysis of palm oil and the relationship between producers and buyers. This was done by understanding buyers’ requirements, producers’ effort, measurements taken, and contracting with buyers’ credit from customers. A multiphase sampling strategy was used. Organized interviews and questionnaires were utilized to collect primary data, while the National Bureau of Statistics provided secondary time series data on the average monthly retail price of palm oil from 2016 to 2022. The need for bio-fuel, production growth, governmental regulations, market dynamics, and the seasonality of palm oil products are then identified by the study as important factors influencing palm oil price fluctuation throughout the year. The study then suggests that there’s a need for economic diversification to reduce reliance on a single sector. This study will serve as a basis for future research on palm oil price variation analysis.

From Field to Market: The Challenges Faced by Onion Growers in Chitradurga, Karnataka, India

Horticultural crops are integral to the Indian economy, serving as a major source of employment, and supplying vital raw materials to the food processing sector. Their cultivation leads to enhanced farm profitability, driven by increased yields and improved market opportunities. Moreover, the export of horticultural produce significantly contributes to foreign exchange earnings, further strengthening India's economic framework. Onion grows extensively across the nation in a variety of climates. India is the world's second-largest producer of onions. It ranks first in terms of acreage, third in terms of exports, and contributes roughly 22.83% to global production. The present study was conducted in 2023 in the Chitradurga district of Karnataka, aimed to identify the challenges faced by onion growers and gather their recommendations for addressing these issues. A total of 120 farmers were selected for the study, comprising sixty small and sixty big farmers. Utilizing an ex post facto research design, data were collected through a pre-tested, structured interview method. The findings highlighted several key constraints in onion cultivation, including limited awareness of nutrient management, price fluctuations in the market, and inadequate access to credit. The majority of farmers recommended the implementation of a minimum support price for onions and timely provision of credit at low-interest rate to mitigate these challenges.

Does US Dollar Supply Trigger House Price Fluctuations? An International Comparison

Cyclical fluctuations in asset prices are closely related to the overall stability of the macroeconomy and have thus become a significant topic in academic and professional exploration. Among these fluctuations, changes in the supply of the U.S. dollar, the global reserve, and primary settlement currency have a broad and far-reaching impact on international financial markets. Since COVID-19, asset prices across various countries have shown differing trends due to fluctuations in the U.S. dollar supply. Housing prices, a key focus in asset pricing research, have been affected to varying degrees. This thesis utilizes seven house price indices from representative cities worldwide from 2020 to 2023 to compare the differential impacts across various countries using an empirical approach. The results indicate that the impact of the U.S. dollar money supply (M2) diminishes in a gradient from the U.S. to East Asia, and then to Europe.