Price Changes Research Articles

Assessment of exchange rate determination in a mono-resource economy: A case of Nigeria

<p style="text-align:justify"><span style="font-size:16px"><span style="font-family:Times New Roman,Times,serif"><span dir="ltr" lang="EN-US"><span style="color:black">This study examined the determinants of the exchange rate in a mono-resource economy, during the period of currency and oil price fall in Nigeria. The aim of the study is to ascertain the potentiality of economic diversification (non-oil export) in saving the falling value of the domestic currency, as well as, its stability. The study employed the use of monthly data from the Central Bank of Nigeria and Nigeria National Petroleum Corporation from January 2008 to December 2020 using the ARDL and NARDL models to achieve the stated objectives. In the literature, several empirical studies analyzed the relationship between exchange rates, oil price, and oil export and came to the logical conclusion that a rise in the price of oil would bring about a rise in the exchange rate, all things being equal while the reverse is the case. To add to the existing body of knowledge this study did not only look at the relationship between the variables but also examined the determinants of the exchange rate in a mono-resource economy and ascertained the potentiality of economic diversification in bringing succor to the falling exchange rate in the midst of falling oil price. The NARDL result corroborates the findings of previous studies on the relationship between exchange rate, oil price, and the performance of non-oil export. It maintains that positive changes in oil prices would lead to the appreciation of domestic currency while negative changes in oil prices would lead to the depreciation of the domestic currency. With this analysis, the study recommends the diversification of the economy and revitalization of the non-oil sector. Since import proves to have a negative impact on the exchange rate, the revitalization of the non-oil sector stimulates domestic consumption and thus reduces import bills which raise the demand for the dollar and weakens the naira.</span></span></span></span></p>

Carbon emissions trading price forecasts by multi-perspective fusion

<p>The precise prediction of carbon emissions trading prices is the foundation for the stable and sustainable development of the carbon financial market. In recent years, influenced by a combination of factors such as the pandemic, trading regulations, and policies, carbon prices have exhibited strong random volatility and clear non-stationary characteristics. Traditional single-perspective prediction methods based on conventional statistical models are increasingly inadequate due to the homogenization of features and are struggling to adapt to China's regional carbon emissions trading market. Therefore, this paper proposes a multi-perspective fusion-based prediction method tailored to the Chinese market. It leverages carbon emissions trading information from key cities as relevant features to predict the price changes in individual cities. Inspired by the development of artificial intelligence, this paper implements various time series models based on deep neural networks. The effectiveness of the multi-perspective approach is validated through multiple metrics. It provides scientific decision-making tools for domestic carbon emissions trading investors, making a significant contribution to strengthening carbon market risk management and promoting the establishment and rational development of a unified carbon market in China.</p>

A Showcase for Resilient and Sustainable District Heating in Denmark

This presentation gives a description of the Danish district heating company, Hvide Sande District Heating, which has become independent of fossil fuels by using wind and solar energy. This has resulted in lower consumer heating prices in a time when other fossil fueled district heating plants are raising their heating prices due to higher fossil fuel prices.Besides participating in the Day-ahead market it also participates in the balancing markets.Hvide Sande is a small fishing town. The district heating plant provides heat to 1,700 consumers. From being a natural gas fired Combined Heat and Power plant, it has in recent years become more resilient by investing in a solar collector, wind turbines, a heat pump, an electrical boiler as well as more large thermal storages, and is now independent of natural gas.The two thermal storages of 2,000 m3 and 1,200 m3, respectively, can store around 200 MWh-heat, which allows flexible market-based productions of the different production units. The heat delivered to consumers can thus be produced many hours or days before delivery.Even in the Day-ahead market, the market-based productions are a challenge to plan. Because of the large thermal storages, the manager must look more days ahead as well as consider the heat amount in the thermal storages, when deciding the hourly bids for tomorrow. Biddings are based on forecasts more days ahead of wind velocity, solar radiation, ambient temperatures, and Day-ahead prices.Besides participating in the Day-ahead market it also participates in the balancing markets. However, to take advantage across more electricity markets of this flexibility, a vast digitalization of the plant using advanced bidding methods has been required. This presentation illustrates examples of the daily earnings the plant has had participating across more electricity markets. It is also important that the manager maintains a digital twin of the plant. The digital twin of the plant will in this presentation be used for simulating the resilience of the plant against large changes in electricity prices and fuel prices.

STOCK MARKET REACTIONS TO PUBLIC RELEASE OF ENVIRONMENTAL RANKINGS OF HEAVY POLLUTERS: THE ROLE OF ULTIMATE CONTROLLERS

Adopting the event study method, this paper examines how the stock market reacts to the public release of environmental performance rankings of heavy polluters. Specifically, we explore the impact of the public release of the 100 best and 100 worst companies in terms of environmental performance on the stock prices of companies involved, and the potential moderation of ultimate controllers. We find that the stock prices of the companies on the lists were negatively affected by the event. However, stock price changes are not significantly related to the relative rankings on the list. Furthermore, companies whose ultimate controllers possess a larger control–ownership wedge experienced a less severe fall in stock prices than did their counterparts. This study sheds light on the nuances of the financial implications of negative environmental publicity.

Web Application for Company Stock Price Prediction

This study contributes to predicting stock prices. In this paper, we have provided a novel machine learning and deep learning technique for stock prediction, which capitalizes on the complex emotional patterns of investors that can be extracted from Twitter data. By joining two separate datasets, such as Twitter sentiments and the associated stock prices, we have applied complex algorithms and evaluated this method, which classifies a user’s sentiment into a positive and negative one. It was possible to address the complexity of investor behavior within the highly unstable stock market environment. One of the most important problems that are encountered by developers is gaining the possibility to properly capture stock price fluctuations in the temporal dimension. It implies the necessity to utilize such reliable evaluation metrics as precision-recall factors for classification purpose and root mean squared error for regression purpose. by using these metrics, the performance of the predictive model can be evaluated in detail, which allows to draw meaningful permanent conclusions within the context of how well it reflects the complex relationship between investor sentiment and market dynamics. The results of the evaluation conducted as part of the process of validating the proposed hypothesis show that, indeed, the predictive accuracy improves significantly from the use of machine learning and deep learning algorithms if paired with Twitter users’ emotional sentiment data. This combination enhances the predictive capacity of the model and provides meaningful insights into how the changes in stock prices are impacted by the sentiment of investors. Moreover, in the process of this study, the explanation and detail consideration of the algorithms for feature engineering and data processing were provided, which stands crucial for increasing the predictive model’s accuracy The study has a valuable implication for both the financial analysis and technical spheres due to the resolution of these methods and presents a model for further research activities in stock prediction. To conclude, this interdisciplinary strategy that combines sentiment analysis and stock prediction not only advances our understanding of the investor behavior but also brings the opportunity for novel applications in the emerging field at the juncture of technology and finance. As a result, the current research sheds light on the intricate connection between the dynamics of the stock market and investor sentiment, thus offering new insights that can better inform predictive modeling research in the financial industry.

The Spillover Effect of Stock Market Sentiment Intensity and Uncertainty on Housing Market in the United States

Investor sentiment is a key indicator of market performance. Many have studied how investor sentiment affects market returns or returns of different categories of stocks. Besides, many also studied how information revealed in media can affect the housing market. This paper focuses on the sentiment revealed in social media, Tweet, by combining these two key factors that have not been studied. This paper studies the spillover effect of stock market sentiment on the housing market by applying Vector Autoregression. The VAR model helps explain the relationship between housing prices and stock market sentiment from tweets, where sentiments are divided into sentiment intensity and sentiment uncertainty. The series of sentiment intensity and series of housing prices interact with each other, and the change in sentiment in the last period spills over to the housing market which affects housing prices, but the change in housing price does not affect sentiment in reverse. Similarly, the series of sentiment uncertainty and the series of housing prices interact with each other, and the change in sentiment diversity in the last period also spills over to the housing market which affects housing prices, but the change in housing price does not affect sentiment in reverse.

Stock Price Analysis and Forecasting of Bank of China Based on ARIMA Model

The stock market is of great importance to the development of the national economy. The price changes of bank stocks have an important impact on the whole stock market. Therefore, it is of practical value to model the changes in bank stock prices and forecast the future trends. Currently, there is a limited number of studies examining the utilization of the ARIMA model in forecasting stock prices. It is crucial to investigate its level of accuracy. This study employs the ARIMA model to conduct an empirical analysis on the time series data of Bank of China's opening price from January 1st, 2024, to January 31st, 2024. The objective is to predict the stock price for the subsequent 28 days and compare it with actual values. The findings reveal that short-term stock price predictions based on the ARIMA model exhibit a high degree of precision. In conclusion, the ARIMA model can offer valuable insights and serve as a theoretical foundation for investors and decision-makers to some extent. This paper will provide reference for the subsequent research on stock price forecasting and ARIMA model, and fill the existing research gaps.

Food price trends during the COVID-19 pandemic in Brazil.

The present study aims to analyze the trends in food price in Brazil with emphasis on the period of the Covid-19 pandemic (from March 2020 to March 2022). Data from the Brazilian Household Budget Survey and the National System of Consumer Price Indexes were used as input to create a novel data set containing monthly prices (R$/Kg) for the foods and beverages most consumed in the country between January 2018 and March 2022. All food items were divided according to the Nova food classification system. We estimated the mean price of each food group for each year of study and the entire period. The monthly price of each group was plotted to analyze changes from January 2018 to March 2022. Fractional polynomial models were used to synthesize price changes up to 2025. Results of the present study showed that in Brazil unprocessed or minimally processed foods and processed culinary ingredients were more affordable than processed and ultra-processed foods. However, trend analyses suggested the reversal of the pricing pattern. The anticipated changes in the prices of minimally processed food relative to ultra-processed food, initially forecasted for Brazil, seem to reflect the impact of the Covid-19 pandemic on the global economy. These results are concerning as the increase in the price of healthy foods aggravates food and nutrition insecurity in Brazil. Additionally, this trend encourages the replacement of traditional meals for the consumption of unhealthy foods, increasing a health risk to the population.

Risk price decomposition and the output gap

AbstractWe employ a time‐varying price of risk model that allows us to track the change in prices of risk. We find that the output gap generates the time‐varying prices of market and momentum risks, but the exposures to the output gap have the opposite signs. In contrast, we do not observe that the output gap is linked to time variations in the prices of value and investment risks. We uncover that the output gaps impact the prices of market risk for European and Japanese portfolios, while there are weak relationships between the prices of momentum risk and output gaps.

Unraveling the Major Determinants behind Price Changes in Four Selected Representative Agricultural Products

This study aims to analyze the drivers behind price changes in agricultural products in Türkiye from 2002 to 2021, considering the impacts of three crises of different causes which are the global food crisis, the Russia–Türkiye aircraft crisis, and the COVID-19 pandemic. The potential factors are categorized into four subgroups: governmental effects, agricultural inputs, macroeconomic indicators, and climatic conditions. The selected agricultural goods for price change measurement include wheat and maize representing subsistence goods, and olive oil and cotton as marketing goods. The autoregressive distributed lag (ARDL) model is applied to observe both the short- and long-term impacts of the variables on price developments. The results suggest that government effectiveness, regulatory quality, nitrogen use, water price, money supply, exchange rate, and GDP under the related categories are the most effective factors in price changes. Among the variables under the category of climatic conditions, significant values are obtained only in the analysis of the temperature impact on olive oil. The analysis also reveals the variable impact of crises on the prices of the chosen products, depending on the goods involved. The maize and wheat analyses yield particularly noteworthy results. In the long run, nitrogen use demonstrates a substantial positive impact, registering at 29% for wheat and 19.47% for maize, respectively. Conversely, GDP exhibits a significant negative impact, with 26.15% and 20.08%. Short-term observations reveal that a unit increase in the governmental effect leads to a reduction in inflation for these products by 17.01% and 21.42%. However, changes in regulatory quality result in an increase in inflation by 25.45% and 20.77% for these products, respectively.

Accurate Price prediction by Double Deep Q-Network

For more than several decades, time series data have been in the center of attention for scholars to predict the future prices of the markets, the most fundamental and challenging of which has been the prediction of the price of the stock market. It is of great importance to note that the algorithms with the fewest errors in price predictions are more applicable. There have been more methods suggested for price prediction in the stock markets: time series data analysis, mathematical and statistical analysis, signal processing, pattern recognition and machine learning. One of the demerits of the aforementioned methods is failing to recognize sudden change of prices, in this regard, experiencing more errors is the consequence of such demerit. In this regard, to have the error solved, the DDQN algorithm, consisting of deep neural networks which includes LSTM-CNN layers, has been employed. Confronting price fluctuations, the agent has the privilege of having better performance by employing the advantages of LSTM-CNN layers. In this research, the algorithm has been carried out over Iranian Gold Market, including six various types of Gold, from 2009 to 2020. The results reveal the point that the given method is more precise in comparison with other suggested methods confronting sudden changes in prices.

A Regional Analysis of the Impacts of Changes in Housing Values on Property Tax Assessment During and After the 2008 Financial Crisis

This study examines the relationship between changing house prices and assessed value in the Detroit Michigan region. Using parcel-level data from 2008 through 2016, we construct a Hedonic Housing Price Index and an Assessed Value Index for 120 Michigan assessing jurisdictions in the five-county region surrounding Detroit (Macomb, Monroe, Oakland, Washtenaw, and Wayne). Our findings are consistent with the literature, indicating at least a three-year lag between changes in house prices and changes in property assessments. Communities considered to be distressed tended to over-assess properties before the crisis, but these communities recalibrated assessments to better align with market conditions after the financial crisis. Our evaluation also shows that while assessed values decreased more slowly than housing prices fell during the crisis, assessed values tended to fall to even deeper levels than housing prices over a longer time horizon. JEL Codes: H71, H83, G01

Empirical Assessment of Maize Price Dynamics and Market Integration in Selected Regional Markets of Benue State, Nigeria.

This study investigated how maize prices move in selected regional markets in Benue State, Nigeria. The researchers looked at monthly maize price data from January 2008 to December 2018 to determine if the price series in these different markets were connected. Specific tests were used to determine if the prices influenced each other over time (market integration) and, if so, which market (rural or urban) tended to lead the price changes in the state. The result revealed that maize prices eventually stabilized over time (became stationary) and that rural and urban markets were indeed connected. There was also evidence of some level of market integration in the short term. Interestingly, the price changes in one market didn't always directly cause changes in the other market. In some cases, price changes seemed to flow in both directions. Based on these results, this study therefore recommends that the all stakeholders provide information centers and use media channels to improve communication between maize producers and consumers in the state; as this would allow actors to make more informed decisions about maize marketing.

Exchange Rate Pass-Through and Inflation: Policy Insights for Vietnam Based on Comparative Analyses Across Asia

The exchange rate is a crucial macro indicator for any open economy, whereby any change in the rate would influence, to a certain extent, various socioeconomic facets. One of the significant impacts is revealed in the pass-through of exchange rate variations to price inflation, also known as the exchange rate pass-through (ERPT). This paper explores the effect of ERPT on domestic inflation in Vietnam by comparing with other Asian economies. Specifically, pass-through mechanisms are examined for every single price as well as a distribution chain from import price index (IMP), producer price index (PPI) to consumer price index (CPI). The empirical results show that exchange rate shocks positively influence the price changes in Vietnam as well as in other Asian countries. According to Cholesky decomposition, the exchange rate plays a limited role in regulating CPI movements. Besides, world commodity prices are found to have a significant impact on domestic inflation across the sample-countries. Our findings reveal profound implications for monetary policy management and price stability.

Intellectual capital and firm risk: evidence from an emerging economy

Purpose Intellectual capital (IC) plays a pivotal role in determining corporate risk profiles in the contemporary knowledge era. Consequently, this study aims to analyze the impact of IC on firm risk (FR) among the manufacturing companies listed on the Pakistan Stock Exchange (PSX). Design/methodology/approach The authors have adopted the modified value-added intellectual model which combines human capital efficiency, structural capital efficiency, efficiency of capital employed and relational capital efficiency. FR has been used as the dependent variable, measured as the standard deviation of the daily stock prices. The study has used panel data from a sample of 40 manufacturing companies listed in the KSE-100 Index from 2015 to 2021. Findings The results suggest that IC has a significant impact on the FR of manufacturing companies listed on the benchmark index of PSX. Moreover, this relationship is direct; thus, an increase in IC would also increase FR measured by the change in stock prices. Research limitations/implications The current study has only used linear techniques. Future researchers may consider investigating the impact of IC at varying levels of FR using nonlinear techniques. Practical implications This study provides corporate managers and policymakers valuable insight into the need to strike a balance between investment in IC and their FR, particularly in an emerging market context. Originality/value IC is frequently associated with firm performance. However, the relationship between IC and FR has generally been underexplored. This study adds to the strand of limited IC literature by investigating the impact of a modified IC model on FR in an emerging economy.

Comparative analysis of key factors influencing urban green space in Mashhad, Iran (1988–2018)

This paper analyzed the role of national economic factors, in addition to some key city-level variables, in the variation of the urban green space (UGS) in Mashhad City (Iran) during three decades (1998–2018). The correlation result revealed the effects of the increasing trend of land price, population rate, and construction of built-up areas in the decreasing trend of UGS in the study area (R from − 0.95 to -0.99 at p-value > 95%). Also, the country-level economic factors, i.e., GDP per capita, oil price, export total value, and FDI, represented the overall increasing trend from 1988 to 2018, correlating with the decrease of green space areas (R from − 0.76 to -0.92 at p-value > 75%). Some statistical analyses, such as the run-test, the skewness and kurtosis tests, the Kolmogorov-Smirnov test, and the ANOVA test were done to confirm the normality of the data distribution and reliability of the results. Ultimately, clustering the research variables based on the significance and confidence levels of the estimated correlation results revealed that the change in the oil price and national export values in the petroleum-dependent economy of Iran can be assumed as the lead key economic factors to fluctuate all city-level variables, particularly the UGS variations.

Deep learning in finance assessing twitter sentiment impact and prediction on stocks

The widespread adoption of social media platforms has led to an influx of data that reflects public sentiment, presenting a novel opportunity for market analysis. This research aims to quantify the correlation between the fleeting sentiments expressed on social media and the measurable fluctuations in the stock market. By adapting a pre-existing sentiment analysis algorithm, we refined a model specifically for evaluating the sentiment of tweets associated with financial markets. The model was trained and validated against a comprehensive dataset of stock-related discussions on Twitter, allowing for the identification of subtle emotional cues that may predict changes in stock prices. Our quantitative approach and methodical testing have revealed a statistically significant relationship between sentiment expressed on Twitter and subsequent stock market activity. These findings suggest that machine learning algorithms can be instrumental in enhancing the analytical capabilities of financial experts. This article details the technical methodologies used, the obstacles overcome, and the potential benefits of integrating machine learning-based sentiment analysis into the realm of economic forecasting.

Positive or Negative: The Heterogeneities in the Effects of Urban Regeneration on Surrounding Economic Vitality—From the Perspective of Housing Price

Urban regeneration is a sound, sustainable urban development strategy globally. In China, promoting urban regeneration has become the national sustainable urban strategy. Under the resource constraint, it is necessary to understand what benefits different types of urban regeneration projects can contribute to urban development. Much research has contributed to evaluating the benefits of urban regeneration on the project scale. The systematic investigation of their effects on surrounding economic vitality, especially the comparison between different types of projects, is relatively downplayed. This paper aims to evaluate and compare the effects of urban regeneration projects on surrounding economic vitality by calculating the change in housing prices. Chongqing is selected as the case city. Based on the housing transaction data from 2015 to 2021, a staggered difference-in-difference method is employed to capture the results. The finding shows that the overall effects are significantly negative, especially within a 400–800 m radius. Higher investment or better project location relates to stronger negative effects. Moreover, the differences in effects among the three types of urban regeneration projects are clearly revealed from diverse perspectives. It provides a valuable reference for policymakers and urban planners to make urban regeneration planning better by considering comprehensive benefits.

Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions.

The cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations. In this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly. To make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results.

Oil Price Fluctuation and their Impact on the Macroeconomic Variables: The Case of Kuwait

The fluctuations in the international prices of crude oil have been a major concern for many economies who depend mainly on oil exports. Oil price shocks are a major disturbance for the economies of oil producing countries. This study investigates the impact of oil price shocks on selected macroeconomic variables in Kuwait using secondary data covering the period of 2001 to 2022. The study adopted the Vector autoregression (VAR) method to analyze the response of Kuwait macroeconomic variables to shocks in oil prices. Oil price in the study is considered in 2 categories: the linear oil price and the non-linear oil price (positive and negative changes in oil price). Based on the findings, it is observed that oil price shocks significantly impact macroeconomic variables in Kuwait. It is found that positive changes in oil prices have a more positive and significant impact on the macroeconomic variables compared to the negative changes in oil price. It is however concluded that there is a need for more policy developments to better harness periods of positive oil price experiences, and develop hedging risk framework for periods of negative changes in oil price in order to curb the impact of oil price shocks on Kuwait’s macroeconomy.