Changes In Market Prices Research Articles

Penerapan Deep Learning Menggunakan Gated Recurrent Unit Untuk Memprediksi Harga Minyak Mentah Dunia

Crude oil is a much-needed energy for the whole world. Each country is inseparable from the use of crude oil for use in various sectors, such as transportation, so that the price of world crude oil is the most important variable for the world. Fluctuations in oil prices will cause various problems, such as inflation, changes in market prices, and others. Therefore, the prediction of world crude oil prices is very important as a consideration for decision making. This study implements deep learning using the Gated Recurrent unit model. The data used is the price of Brent crude oil with a total of 5834 data, starting from January 4, 2000 to December 19, 2022. The parameters used are the number of GRU units, batch size, and lookback. The best model produced in this study is the GRU model with hyperparameters consisting of 30 lookbacks, 50 GRU units, and 256 batch sizes with the lowest MAPE value among the other models, which is 2.25%. The MAPE value states that predictions using the GRU model are said to be very good at predicting world crude oil prices

Household production and consumption impacts of foot and mouth disease at the Uganda-Tanzania border.

Foot-and-mouth disease (FMD) is a highly contagious viral disease that is endemic in East Africa. FMD virus infection incurs significant control costs and reduces animal productivity through weight loss, lowered milk yield, and potentially death but how household's respond to these losses may differentially affect household income and food consumption. To address this, we use unique data from a FMD outbreak to assess how household production and consumption activities change from before to during the outbreak. Data came from a 2018 survey of 254 households in selected Tanzanian wards and sub-counties in Uganda. The data includes household recall of before and during an outbreak in the past year on livestock and livestock product sales, milk and beef consumption, as well as related changes in market prices. We apply both difference-in-difference and change in difference ordinary least squares regressions with fixed effects to evaluate the impact of FMD on household production and consumption. We find that households reported the largest reductions in livestock and livestock product sales, followed by reduced milk consumption and animal market prices. The changes in household income from livestock sales appears to be driven by FMD virus infection within the household herd while changes in market prices of substitute protein sources are primary associated with changes in milk and beef consumption. The role of widespread market price effects across both infected and uninfected herds and countries, tends to suggest that stabilizing prices will likely have a large impact on household nutritional security and income generation. We also propose that promoting diversity in market activity may mitigate differing impacts on households in FMD endemic regions.

An empirical assessment of asset value function for capital market price changes

An empirical assessment of asset value function for capital market price changes

Day-Ahead Electricity Market Price Forecasting Considering the Components of the Electricity Market Price; Using Demand Decomposition, Fuel Cost, and the Kernel Density Estimation

This paper aims to improve the forecasting of electricity market prices by incorporating the characteristics of electricity market prices that are discretely affected by the fuel cost per unit, the unit generation cost of the large-scale generators, and the demand. In this paper, two new techniques are introduced. The first technique applies feature generation to the label and forecasts the transformed new variables, which are then post-processed by inverse transformation, considering the characteristic of the fuel types of marginal generators or prices through two variables: fuel cost per unit by the representative fuel type and argument of the maximum of Probability Density Function (PDF) calculated by Kernel Density Estimation (KDE) from the previous price. The second technique applies decomposition to the demand, followed by a feature selection process to apply the major decomposed feature. It is verified using gain or SHapley Additive exPlanations (SHAP) value in the feature selection process. In the case study, both showed improvement in all indicators. In the Korean Electricity Market, the unit generation cost for each generator is calculated monthly, resulting in a step-wise change in the electricity market price depending on the monthly fuel cost. Feature generation using the fuel cost per unit improved the forecasting by eliminating monthly volatility caused by the fuel costs and reducing the error that occurs at the beginning of the month. It improved the Mean Squared Percentage Error (MAPE) of 3.83[%]. Using the argument of the maximum PDF calculated by KDE improved the forecasting during the test period, where discrete monthly variations were not included. The resulting MAPE was 3.82[%]. Combining these two techniques resulted in the most accurate performance compared to the other techniques used, which had a MAPE of 3.49[%]. The MAPE of the forecasting with the decomposed data of the original price was 4.41[%].

The spillover effect between Chinese crude oil futures market and Chinese green energy stock market

With the increasing severe pollution, the new energy industry is greatly favored by the government and investors. Using the static network connectedness method of Diebold and Yilmaz (2009, 2012, 2014) and the dynamic network connectedness approach of Antonakakis et al. (2020), this paper discusses the return and volatility spillover effects between China's crude oil futures market and 7 Chinese green energy stock markets. In terms of return spillover effects, we find firstly that Chinese green energy stock is able to dominate the price changes in the crude oil market, and the dominate role of natural gas stock market is stronger. Secondly, rather than changing the dominant role of the green energy stock market on crude oil futures market price changes, the outbreak of COVID-19 in 2020 strengthened that dominant role. For the volatility spillover effects, the results of static volatility spillover index show that the crude oil futures market volatility is mainly dominated by the green energy equity market, but the dynamic connectedness indices results show that the volatility in the international energy market can strengthen the dominant role of the crude oil futures on the volatility of the green energy stock market. Finally, we can find that both the outbreak of COVID-19 in 2020 and the online operation of the Chinese carbon trading market in 2021 can strengthen the dominant role of the green energy stock market on the crude oil futures market volatility.

A multi-agent virtual market model for generalization in reinforcement learning based trading strategies

Many studies have successfully used reinforcement learning (RL) to train an intelligent agent that learns profitable trading strategies from financial market data. Most of RL trading studies have simplified the effect of the actions of the trading agent on the market state. The trading agent is trained to maximize long-term profit by optimizing fixed historical data. However, such approach frequently results in the trading performance during out-of-sample validation being considerably different from that during training. In this paper, we propose a multi-agent virtual market model (MVMM) comprised of multiple generative adversarial networks (GANs) which cooperate with each other to reproduce market price changes. In addition, the action of the trading agent can be superimposed on the current state as the input of the MVMM to generate an action-dependent next state. In this research, real historical data were replaced with the simulated market data generated by the MVMM. The experimental results indicated that the trading strategy of the trained RL agent achieved a 12% higher profit and exhibited low risk of loss in the 2019 China Shanghai Shenzhen 300 stock index futures backtest.

Bihevioralna perspektiva strategije inflacionog ciljanja - slučaj Srbije

Regarding inflation, the lion's share of theory and literature refers to the analysis of its economic determinants. In our empirical analysis, we emphasized some behavioural factors embodied in the short-term inflation expectations of banks and companies, which are regarded as one of the essential factors for reaching desired current inflation dynamics. We considered whether and how the Central Bank can, through inflation targeting as monetary strategy, nudge the inflation expectations of market participants in the preferred direction. In the paper, we looked into the performances of inflation targeting in the case of Serbia and considered potential explanations of the given (un)success from both a neoclassical and a behavioural theoretical perspective. We found that in the case of Serbia market participants' expectations in one year ahead strongly influences the actual year-on-year inflation rate. Obviously less influence on actual inflation that comes from inflation expectations of the real economic sector in comparison to the financial sector could be attributed to both economic and psychological phenomenon of downward price rigidity in the internal environment of companies, which adjust with a delay to changes in market prices. The success of inflation targeting soundly depends on the way the Central Bank manages the formation and influences the movements of inflation expectations of market participants, especially banks which could be seen as professional forecasters.

Green Supply Chain Decision and Management under Manufacturer’s Fairness Concern and Risk Aversion

This study focuses on the impact of a supply chain manufacturer’s fairness concern and risk aversion on the green supply chain and constructs a two-level green supply chain consisting of a manufacturer and a retailer. It compares three models: the manufacturer is a risk-neutral and fair-neutral, the manufacturer is a risk-averse and fair-neutral, and the manufacturer is a risk-averse and fair-concerned decision maker. In three cases, this paper examines how the manufacturer’s risk aversion and fairness concerns the impact product green level, retail price, and the wholesale price in the green supply chain. Consumers are sensitive to pricing strategies and product green level. The results are as follows: the manufacturer’s risk aversion leads to an increase in the retailer’s profit, and the manufacturer’s profit decreases with the increase in the risk aversion coefficient. Second, when demand meets certain conditions and the manufacturer has advantageous fairness concerns, the manufacturer benefits from fairness concerns, the retailer’s profit decreases with the manufacturer’s fairness concerns coefficient, and the manufacturer’s risk aversion will lead to a decline in the product green level. Third, when the manufacturer believes that the risk is relatively large, they reduce the technology investment of green products; thus, the fairness concerns of the manufacturer will affect the investment of green products and increase the green level of products. For enterprises: When supply chain members face various risks, they need to consider the specific needs of consumers and other members’ behavioral preferences. They can put forward a more scientific prediction of market demand and rational decision making. For customers: they should not only pay attention to changes in market prices, but should also pay attention to changes in the behavioral preferences of supply chain members, as customers are in line with their own interests based on the choice of high-quality green production.

Coordinated bidding of multi-product charging station in electricity markets using rolling planning and sample average approximation

Recently, there has been an urgent tendency to steer the transport system towards zero-emission. Along natural gas vehicles, electric and hydrogen vehicles represent a promising way to a low-carbon system. Since the development of these vehicles relies on advances of refueling stations, their economic and operational aspects need to be considered. Therefore, this paper addresses a multi-product charging station for refilling electric, hydrogen and natural gas vehicles. The station participates in day-ahead and intra-day markets where the market price is subject to the uncertainty. For charging station participating in sequential markets with different prices, it is necessary to consider the coordinated bidding. Taking into account the sequential clearing of these markets and the gradual realization of market prices, the bidding problem is formulated as a two-stage stochastic program. In order to solve this large-scale stochastic program with a huge number of scenarios, the rolling planning method in combination with sample average approximation and SCENRED tool are applied. The study aims to attain the optimal operation of devices and bidding curves of the charging station in order to maximize its profit. The obtained average optimality gap based on Monte Carlo sampling approach, which is less than 1%, proves the effectiveness of our proposed algorithm in solving this large scale stochastic optimization. The submitted bidding curves to the intra-day market are found to be dependent on changes in the day-ahead market prices. Tornado diagrams indicate that the selling price and demand of electricity are ranked as the most effective factors on profit.

Design Analysis and Implementation of Stock Market Forecasting System using Improved Soft Computing Technique

In this paper, a stock market prediction model was created utilizing artificial neural networks. Many people nowadays are attempting to predict future trends in bonds, currencies, equities, and stock markets. It is quite challenging for a capitalist and an industry to forecast changes in stock market prices. Due to the numerous economic, political, and psychological aspects at play, forecasting future value changes on the stock markets is quite challenging. In addition, stock market forecasting is a difficult endeavor because it relies on a wide range of known and unknown variables. Many approaches, including technical analysis, fundamental analysis, time series analysis, and statistical analysis are used to attempt to predict the share price; however, none of these methods has been demonstrated to be a consistently effective prediction tool. Artificial neural networks (ANNs), a subfield of artificial intelligence, are one of the most modern and promising methods for resolving financial issues, such as categorizing corporate bonds and anticipating stock market indexes and bankruptcy (AI). Artificial neural networks (ANN) are a prominent technology used to forecast the future of the stock market. In order to understand financial time series, it is often essential to extract relevant information from enormous data sets using artificial neural networks. An outcome prediction neural network with three layers is trained using the back propagation method. Analysis shows that ANN outperforms every other prediction technique now available to academics in terms of stock market price predictions. It is concluded that ANN is a useful technique for predicting stock market movements globally.

Research on investment planning of power-hydrogen system considering the multi-stakeholder benefit

As the penetration of renewable energy sources continues to increase, the stability of the grid is being challenged. Solutions are needed to curb fluctuations in renewable energy generation and ensure a stable electricity supply. As a clean and efficient secondary energy source, hydrogen energy plays an important role in the power system. However, the development of hydrogen energy storage systems is currently limited by technology and costs, making it difficult to scale up. How to invest to make hydrogen storage economical is worth studying. This paper constructs a regional microgrid system, considering the economics of investment by renewable energy generators (REGs) and integrated energy operators (IEOs), and the sensitivity of the payback period under conditions of market adjustment and technological progress. A combination of the Gurobi and Particle Swarm Optimization (PSO) algorithm is used to solve the investment optimization model, and Shapley is used to allocate multi-intelligence benefits. The results show that: (1) the investment returns and payback periods of REG are within a reasonable range; (2) IEO is hardly economical in the presence of market price changes and technological advances. On this basis, this paper constructs a win-win investment model for REG and IEO and explores the distribution of benefits under their cooperation model. The results show an increase in return on investment of 0.34% for REG and 1% for IEO following investments by REG in partnership with IEO.

Weather shocks and food price seasonality in Sub-Saharan Africa: Evidence from Niger

Semi-Arid regions of Sub-Saharan Africa are vulnerable to weather shocks, especially rural areas where households depend on rainfed agriculture and access to stable food markets are limited by high transaction costs. Understanding how weather shocks affect rural Sub-Saharan African households through both production and market price changes is crucial in designing sustainable and effective programs to increase resilience. Weather shock impacts on agricultural production and market prices are well documented in Africa. But little is known about the impacts of weather shocks on market price seasonality, which is an important determinant of food security. We investigate the distinct impacts of positive and negative rainfall shocks on millet production and millet price seasonality in Niger using district-level longitudinal production and price data, along with high-resolution rainfall data. We find that a one standard deviation decrease in seasonal rainfall from historical averages is associated with declines in millet market price initially after harvest, but strong upward pressure on market prices 6 months after harvest. As a result, drought exacerbates existing price seasonality, which in turn can amplify negative impacts on households. Social protection programs need to account for potential increases in seasonal price variability in the design of programs to enhance household resilience to weather shocks.

A state space modeling for proactive management in equity investment

This paper proposes a novel state-space approach to explain stock market dynamics driven by different types of trading, which leads to a new promising scheme for proactive risk management in financial investment. Particularly, it is assumed that the current price changes are formulated through daily trading by multiple types of traders, each of whom follows a specific investment strategy based on technical indicators and a fuzzy logic using past data of stock prices, volumes and yield curves. Moreover, the current price changes are represented by a linear combination of those multiple trading types, where the coefficients corresponding with the size of impact on the price changes are regarded as time-varying state variables to be sequentially estimated under a state-space framework. Thereby, this work develops a new factor decomposition method on price changes from a perspective of different traders’ demand and supply to analyze the current situations and potential risks in financial markets. In empirical experiments, it is shown that the implementation of particle filtering algorithm makes it possible to replicate market price changes. Further, new signals based on the estimated states are developed, which are applied to proactive risk management in financial investment. Especially, it has been found that the demands of yield curve-based traders subtracting those of trend-followers could be a promising signal of stock market crashes, which has successfully enhanced simple buy-and-hold strategy of SP, as well as constant proportion strategies.

Investigating the Impact of Bank-specific Determinants on Stock Price of Listed Commercial Banks: Evidence from Emerging Economy

This paper attempts to investigate the impact of bank-specific determinants on the stock price of Dhaka Stock Exchange (DSE) listed commercial banks in Bangladesh. Several bank-specific factors such as Book to Market Ratio, Return on Equity, Price/Earnings to Growth Ratio, Cash Flow per Share, Debt to Equity Ratio, Earnings per Share, Dividend per Share, Bank Size and Institutional Ownership Percentage have been analyzed for this study and tried to see the impact of each variable on the change in market price of share of 10 listed commercial banks spanning from 2011 to 2020. In empirical discussion four estimation models such as Fixed effects, Random effects, GLS, and Pooled OLS approach have been used to ensure the robustness of the models. To examine the validity of the models chosen for this study, the model specification test and many diagnostic checks were used, including the test of heteroskedasticity, the cross-sectional dependence test, the test of autocorrelation and the unit root test, etc. Our investiagtion reveals that only Bank Size and Book to Market Value explanatory variables are found significantly responsible for fluctuation in the change in share price of banks, contributing to the current literature by revealing the importance of bank-specific factors that include all metrics in their calculation. As a result, Bangladesh's DSE-listed banks should take appropriate measures to boost their stock values, such as attempting to improve the ratio that has a positive impact on the share price while decreasing the ratio that has a negative impact.

Gender Wage Gap, Gender Norms, and Long-Term Care: A Theoretical Framework

ABSTRACT Population aging in developed and developing economies has led to increasing number of older persons in need of care, posing a challenge to the social arrangements of care and creating important aggregate economic implications. This article proposes a simple theoretical framework to evaluate the interplay of gender norms and the gender wage gap, as well as specific characteristics of the paid care market such as occupational segregation and market power rents. By incorporating a degree of substitutability between women’s and men’s care work, the model shows how declines in the gender wage gap have small effects on the division of long-term care work in the presence of persistent gender norms. The study also shows that market power dynamics, in conjunction with gender norms, perpetuate reliance on women’s provision of unpaid care. The model has important implications for policies promoting gender-egalitarian household division of labor and affordable access to quality long-term care. HIGHLIGHTS The market logic of the paid care service sector must be analyzed in conjunction with gender norms. A declining gender wage gap does not translate to more equal sharing of long-term care work due to persistent traditional gender norms. Social norms shape the response of the distribution of care work to changes in market prices and perpetuate reliance on women’s unpaid care. Gender-aware policies should encourage egalitarian social norms to reduce women’s unpaid care burden.

A Solution Management in Handling Economic Inequality Among Agrarian Communities

Agriculture has been an inherent part of human civilization for centuries. Therefore, this study aims to analyze the relationship between agricultural production and agricultural community income. The pattern of their relationship followed the dynamics of the Lotka-Volterra model. Two of the six critical phenomena that might occur: first, if the farmer's crop were abundant, the farmers' income would increase rapidly; second, if the farmers' yields decreased, the farmer's income would automatically decrease. Low and unstable farmer income could lead to poor productivity and low economic growth. Thus, monitoring and early warning were essential to effectively prevent possible negative impacts on agricultural product production, consumption, and changes in market prices. Sustainable agricultural systems included maintenance or enhancement of environmental natural resources, food supply, and social welfare. In brief, good agricultural management could maintain the balance of the ecosystem, contribute to the economy continuously, and increase agrarian communities' economic resilience. Keywords: agrarian community; ecosystem; harvest cycle; income; Lotka-Volterra

개별 주택가격 데이터를 이용한 주택가격 변동률 분포의 특성 분석*

This study departs from the convention of using only the house price index when analyzing the housing market price change, and examines the market through the lens of house price change distribution. By using a panel data of individual house prices, I find the following. First, house price change distribution has a positive skewness; so, the median is smaller than the mean. Second, when the price index falls, the individual price also falls; however, when the price index rises, the deviation between individual price change increases. Third, even when the price index rises by 10%~20%, 5.2% of the individual houses experience a price fall. Last, the large, high price houses in the capital area shows high price volatility; so, when the price index rises, their price rises more than the average houses, and when the price index falls, their price falls more than the average houses. The results of this study can be utilized in risk management in financial institutions.

Valuing Personal Consumption: Cost Versus Value and the Impact of Insurance

"An ideal income tax would perhaps differentiate among individuals according to their talents for using funds in consumption; but ... income taxes must [be based upon] measurable quantities! ... [c]onsumption, as an element of income, must be measured ... by outlays for consumption purposes."Andy is excited. He has finally been able to get the money together for an expensive new car. After years of making do with a used car or cheaper models he is about to pick up a new $50,000 Mercedes sedan. But alas! Andy's dream of years of driving comfort are soon shattered. The car is a lemon. It is constantly in the shop for repairs wasting Andy's time and eventually costing him several thousand dollars. In the end, it never drives as well as expected.Would an ideal tax system, as Simons suggests, take some account of Andy's misfortune? Should we, if we could, somehow determine what the car is really worth and allow Andy a deduction for the difference between that amount and his outlay of $50,000? Alternatively, should we ideally allow a deduction for the extraordinary cost of repairs, the amount spent beyond what would normally be expected? Is our failure to take account of Andy's misfortune a concession to administerability rather than a matter of principle? This article explores the answer to that question as well as whether recoveries, from insurance or otherwise, should be taxable, if such losses are disallowed.There are a number of reasons why there might be a great deal of difference between the amount of enjoyment derived from a consumer purchase and the cost to the individual on the market. Consumer surplus, circumstance of use, variation in quality, events subsequent to purchase, and changes in market price may explain the discrepancy between actual enjoyment and cost.

Forecasting Method of Stock Market Volatility Based on Multidimensional Data Fusion

The volatility of the stock market is related to the vital interests of stockholders and is essential for maintaining a stable financial environment. Through the analysis of data changes, excellent professional traders can extract information about the direction of stock changes, whether it is worth investing, and long-term or short-term trading. This article aims to study the forecasting methods of stock market volatility, by integrating multiparty data, in-depth analysis of the direction of data changes, predicting the price changes of the stock market, and better guiding stockholders’ investment. This paper proposes a multisource data fusion method to analyze the stock market price changes and find the best risk prediction method. The experimental results in this paper show that multisource data fusion can better help the stock market predict stock changes and reduce financial investment risks by 20%. Comparing the obtained prediction results with the real data, the MSE predicted by the ARIMA model is calculated to be 2.35. It provides a new idea for effectively analyzing nonstationary time series data with complex trend fusion characteristics by rationally screening feature signals and trend signals and modeling probability distribution.

Effect of Patent Expiry on the Performance of Innovator Multinational Pharmaceutical Companies in a Low Middle Income Country

Patent expiry or loss of exclusivity exposes innovator pharmaceutical companies to changes in the market dynamics brought about by increased production of generics by rival companies after patent expiration. This current study focused on the effect of generic products manufacturing and competitive market pressures, price changes, and changes in sales volumes and profitability of innovator multinational pharmaceutical companies after patent expiry. The methodology of this study involved a descriptive survey design and utilized both qualitative and quantitative techniques for data collection, analysis, and presentation. Primary data were collected using the key informants' in-depth interviews and survey questionnaires. The top management, including regional managers, general managers, and directors of each of the eight companies participating in this study, were interviewed to gather the qualitative data. Thirty-six respondents comprising of Product Development Managers and Business Supervising Managers responded to a survey questionnaire through purposive sampling. Findings depicted a significant effect of patent expiry on the generic production and subsequent decline in the performance of multinational innovator companies in the pharmaceutical industry. This study recommends that multinational innovator companies operating in low-income countries, such as Kenya, develop strategic policies to tap into the market by leveraging generic production through collaborative manufacturing with generic companies to share revenues.