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A.O., Bello y Kabari L.G. "Digital Signal Processing for Predicting Stock Prices". British Journal of Computer, Networking and Information Technology 4, n.º 2 (5 de septiembre de 2021): 12–21. http://dx.doi.org/10.52589/bjcnit-xnp3ubpl.
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With the exponential growth of big data and data warehousing, the amount of data collected from various stock markets around the world has increased significantly. It is now impossible to process and analyze data using mathematical techniques and basic statistical calculations to forecast trends such as closing and opening prices, as well as daily stock market lows and highs. The development of smart and automated stock market forecasting systems has made significant progress in recent years. Digital signal processing is required for analysis and preprocessing because of the accuracy and speed with which these large amounts of data must be processed and analyzed. In this paper, we evaluate some of these predictive algorithms based on three parameters such as speed, accuracy and complexity, we analyze the data using the dataset from kaggle.com and we implement these algorithms using pythons. The results of our analysis in this paper shows a significant correlation between the yearly prices until the year 2018 where there is a significant increase in stock price.
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Jankovics, Peter. "LONG -TERM CHANGES OF MAIN INPUT -OUTPUT PRICES IN THE HUNGARIAN BROILER SECTOR". Annals of the Polish Association of Agricultural and Agribusiness Economists XX, n.º 1 (4 de abril de 2018): 50–57. http://dx.doi.org/10.5604/01.3001.0011.7228.
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The article presents changes of the main input-output prices in the Hungarian broiler industry over a period of 30 years, and associated correlations. For the processing of long-term data, a linear regression function, correlation and regression analysis were used. The cereal prices correlate and their changes also correspond with a change in compound feed prices. A close correlation can be found between cereal price and broiler price, whilst the correlation shown between the compound feed price and broiler price is very close. During the examined period, the feed prices increased at a higher rate than the broiler price. It was also established that the current feed and energy price significantly affect day-old chick prices which corresponds with an increase in price of the broiler. Furthermore, a close relation can be found between energy and feed compound prices.
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chougale, Jeevan, Abhishek Shinde, Ninad Deshmukh, Dhananjay Sawant y Vaishali Latke. "House Price Prediction using Machine learning and Image Processing". Journal of University of Shanghai for Science and Technology 23, n.º 06 (18 de junio de 2021): 961–65. http://dx.doi.org/10.51201/jusst/21/05280.
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We demonstrate that these urban features can be recorded by street views and satellite image data and enhance the estimate of house prices. In order to estimate house prices in London, UK, we recommend a pipeline that uses a deep neural network model to automatically extract visual features from images. In calculating the house price model, we use typical housing characteristics, such as age, size, and accessibility, as well as visual features from Google Street View images and Bing aerial pictures. We see promising outcomes where learning to describe a neighborhood’s urban efficiency facilitates the estimation of house prices, even when generalizing to previously unseen London boroughs. We discuss the use of non-linear vs. linear approaches to combine these signals with traditional house pricing models and explain how the interpretability of linear models helps one to specifically derive the visual desirability of neighborhoods as proxy variables that are both of importance in their own right and can be used as inputs to other econometric methods. This is particularly useful as it can be extended elsewhere after the network has been trained with the training data, enabling us to produce vivid complex maps of the desirability of London streets.
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Ma, Ping y Wei Yang Diao. "An Empirical Analysis of Relative Oil Price Shocks and Chinese Net Processing Exports". Advanced Materials Research 347-353 (octubre de 2011): 3098–102. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.3098.
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This paper studies the effects of Chinese relative domestic oil prices on net processing exports. Using a set of monthly data ranging from 2002 to 2008, we identify a long-run equilibrium cointegrating relationship between the two inflationary series. The unidirectional short-run Granger causality is running from relative oil prices to net processing exports, while in the long-run, the Granger causality is bidirectional. What is noteworthy is that relative oil price shocks have long-run positive effects on Chinese net processing exports, indicating the existence of an energy cost-driven mechanism of endogenous technological change.
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Ekwunife, Ifunanya C. "Technology Focus: Natural Gas Processing and Handling (April 2021)". Journal of Petroleum Technology 73, n.º 04 (1 de abril de 2021): 34. http://dx.doi.org/10.2118/0421-0034-jpt.
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In 2020, the spot prices of natural gas hit a record low in the US, reaching the lowest annual average price in more than a decade. Based on US Energy Information Administration (EIA) data, the average annual spot price reported in 2020 was $2.05 per million British thermal units (MMBtu). In the first few months of the year, reports from the EIA showed that natural gas prices started declining amid mild winter temperatures that resulted in a decline in the demand for natural gas for space heating. In March 2020, following the onset of the COVID-19 pandemic, the already declining natural gas prices plummeted further. This decline continued through the first half of the year. The EIA reported the average monthly Henry Hub spot price in the first 6 months at $1.81/MMBtu. June saw the lowest monthly natural gas price in decades (Henry Hub price aver-aged $1.66/MMBtu). Natural gas prices recovered in the second half of the year as natural gas production decreased and global exports of liquefied natural gas increased. Natural gas consumption in the residential, commercial, and industrial sectors declined in 2020, according to the EIA. Milder winter temperatures were a major contributor in the first quarter of the year, but overall declining consumption was attributed to reduced economic activities as a result of the COVID-19 pandemic. On the other hand, the consumption of natural gas for electric power generation registered an overall increase of 2% more than the 2019 average. According to the EIA, citing S&P Global Platts, this increase was attributed to power producers switching to cheaper natural gas from coal to meet the increased demand for electric power for cooling as summer temperatures increased. The EIA in its Annual Energy Outlook 2021 projects that the industrial and electric power sectors and net exports will drive the growth in US energy consumption between 2020 and 2050. Natural gas consumption in other sectors is expected to increase steadily or remain flat. The EIA forecasts that natural gas production will increase as consumption increases and prices will stay low relative to past prices. The EIA expects continued growth in natural gas exports as natural gas production surpasses natural gas consumption. Globally, the International Energy Agency forecasts a recovery in global demand for natural gas in 2021 led by growth in the Asia Pacific region as emerging markets recover. The US will continue to play a significant role as one of the largest producers and contributors to natural gas supply growth. Recommended additional reading at OnePetro: www.onepetro.org. SPE 200300 - Overcoming Challenges in the Development of Underground Gas Storage by Ammar Alali, Saudi Aramco, et al. OTC 30602 - Offshore LNG and Gas Monetization by Femi Adeoye Alabi, Total SPE 200147 - Development of the Underground Gas Storage and Construction of the Salt Cavern Storage in China by Peng Chen, CNPC, et al.
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Spoden, Amanda L., James H. Buszkiewicz, Adam Drewnowski, Mark C. Long y Jennifer J. Otten. "Seattle’s minimum wage ordinance did not affect supermarket food prices by food processing category". Public Health Nutrition 21, n.º 9 (7 de febrero de 2018): 1762–70. http://dx.doi.org/10.1017/s1368980017004037.
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AbstractObjectiveTo examine the impacts of Seattle’s minimum wage ordinance on food prices by food processing category.DesignSupermarket food prices were collected for 106 items using a University of Washington Center for Public Health Nutrition market basket at affected and unaffected supermarket chain stores at three times: March 2015 (1-month pre-policy enactment), May 2015 (1-month post-policy enactment) and May 2016 (1-year post-policy enactment). Food items were categorized into four food processing groups, from minimally to ultra-processed. Data were analysed across time using a multilevel, linear difference-in-differences model at the store and price level stratified by level of food processing.SettingSix large supermarket chain stores located in Seattle (‘intervention’) affected by the policy and six same-chain but unaffected stores in King County (‘control’), Washington, USA.SubjectsOne hundred and six food and beverage items.ResultsThe largest change in average price by food item was +$US 0·53 for ‘processed foods’ in King County between 1-month post-policy and 1-year post-policy enactment (P < 0·01). The smallest change was $US 0·00 for ‘unprocessed or minimally processed foods’ in Seattle between 1-month post-policy and 1-year post-policy enactment (P = 0·94). No significant changes in averaged chain prices were observed across food processing level strata in Seattle v. King County stores at 1-month or 1-year post-policy enactment.ConclusionsSupermarket food prices do not appear to be differentially impacted by Seattle’s minimum wage ordinance by level of the food’s processing. These results suggest that the early implementation of a city-level minimum wage policy does not alter supermarket food prices by level of food processing.
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Li, Jung Bin y Chien Ho Wu. "An Efficient Neural Network Model with Taylor Series-Based Data Pre-Processing for Stock Price Forecast". Applied Mechanics and Materials 284-287 (enero de 2013): 3020–24. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3020.
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This study adopts popular back-propagation neural network to make one-period-ahead prediction of the stock price. A model based on Taylor series by using both fundamental and technical indicators EPS and MACD as input data is built for an empirical study. Leading Taiwanese companies in non-hi-tech industry such as Formosa Plastics, Yieh Phui Steel, Evergreen Marine, and Chang Hwa Bank are picked as targets to analyze their reasonable prices and moving trends. The performance of this model shows remarkable return and high accuracy in making long/short strategies.
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Zakiah, Zakiah. "Preferensi dan Permintaan Kedelai pada Industri dan Implikasinya terhadap Manajemen Usaha Tani". MIMBAR, Jurnal Sosial dan Pembangunan 28, n.º 1 (19 de junio de 2012): 77. http://dx.doi.org/10.29313/mimbar.v28i1.341.
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This paper studies demand and preference of soybean processing industry. We used two types of data: time series and primary data that obtained from soybean processing industry. The result shows that increasing of local soybean price will reduce demand for soybeans. Increasing of tempe price and imported soybean price will increase soybean demand, and statistically, the effect is significant. Increasing imported soybean prices should be decrese demand for soybeans at industry, but in this study does not decrease demand for soybeans. This is shows dependence of soybean processing industry in Banda Aceh on imported soybean. To increase local soybean production both in quality and quantity require better farming management, through technological improvements form production stage to harvest, marketing channel, institutional, and decent price for farmers.
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Razzakova, Ch M. y L. E. Ziganshina. "Change in affordability of medications in Kazan in 2011 and 2015 as a reflection of state initiatives to regulate drug prices". Kazan medical journal 98, n.º 5 (15 de octubre de 2017): 822–26. http://dx.doi.org/10.17750/kmj2017-822.
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Aim. Performing comparative analysis of drug prices in 2011 and 2015 in Kazan to assess the effectiveness of state initiatives to ensure the affordability of medicines.
Methods. The collection and processing of data was performed according to methodology developed by Health Action International and World Health Organization (WHO/HAI). We studied the availability and prices of 30 medicines in public and private pharmacies in Kazan in 2011 and 2015 and analyzed the procurement prices of the same medicines in inpatient hospitals. Recording and analysis were performed using standardized MS Excel WHO/HAI Workbook. Medicine prices were compared with international reference prices and were expressed as median price ratio.
Results. The analysis showed a decrease in medicine prices in 2015 compared to 2011. In public and private sectors median price ratio of the originator brands reduced by about 3 times, and of the lowest price generics reduced by 1.5 times. A decrease in procurement prices by more than 2 times for generics and more than 6 times for the original brands was also revealed in 2015 in comparison with 2011.
Conclusion. State initiatives to regulate drug prices contributed to the price reduction by 1.5-3 times in 2015 compared to 2011; changes in the procedures for the medicines procurement at the legislative level resulted in reduction of procurement prices by more than 2 times for generic drugs in 2015 compared to 2011.
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Eni, Yuli y Rudy Aryanto. "Analysis of Factors that Affect the Movement of Gold’s Price as Investment Alternatives in Indonesia". Advanced Science Letters 21, n.º 4 (1 de abril de 2015): 878–81. http://dx.doi.org/10.1166/asl.2015.5912.
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This study examined the dominant factors that affecting the price of gold. The factors examined are London Gold price returns, the return rate of USD—INR, JCI return, inflation rate, and the return of the EURO—USD currency, which individually or simultaneously can affect the price of gold. The purpose of this study was to investigate how influence the factors that are considered to affect the fluctuation of gold prices and gold prices predicted for the next period which can be used by investors to seek alternative investment to be made. The results will provide information to investors about gold price forecast both long-term and short-term. This study uses secondary data taken from several websites. Further data have been obtained, processed using the method of Multiple Linear Regression Model and the ECM with GARCH models, using e-views 8 and SPSS 22. As for the results obtained from the processing of the data is simultaneously the influence of variable returns no London Gold price, return rate USD—CAD, JCI return, inflation rate, and the return of the EURO currency—USD, with the return of gold in Indonesia. Individually, the variable returns the London Gold price and exchange rate USD—CAD who have an influence on the return of gold prices in Indonesia.
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Zhao, Lu-Tao, Li-Na Liu, Zi-Jie Wang y Ling-Yun He. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach". Sustainability 11, n.º 14 (17 de julio de 2019): 3892. http://dx.doi.org/10.3390/su11143892.
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The rapid fluctuations in global crude oil prices are one of the important factors affecting both the sustainable development and the green transformation of the global economy. To accurately measure the risks of crude oil prices, in the context of big data, this study introduces the two-layer non-negative matrix factorization model, a kind of natural language processing, to extract the dynamic risk factors from online news and assign them as weighted factors to historical data. Finally, this study proposes a giant information history simulation (GIHS) method which is used to forecast the value-at-risk (VaR) of crude oil. In conclusion, this paper shows that considering the impact of dynamic risk factors from online news on the VaR can improve the accuracy of crude oil VaR measurement, providing an effective tool for analyzing crude oil price risks in oil market, providing risk management support for international oil market investors, and providing the country with a sense of risk analysis to achieve sustainable and green transformation.
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Guntur, Mohammad, Julius Santony y Yuhandri Yuhandri. "Prediksi Harga Emas dengan Menggunakan Metode Naïve Bayes dalam Investasi untuk Meminimalisasi Resiko". Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 2, n.º 1 (17 de abril de 2018): 354–60. http://dx.doi.org/10.29207/resti.v2i1.276.
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The high low price of gold influenced by many factors such as economic conditions, inflation rate, supply and demand and much more. The Naïve Bayes algorithm is capable of generating a classification that is used to predict future opportunities. By using the Naïve Bayes Classifier algorithm obtained a prediction of gold prices that can help decision makers in determining whether to sell or buy gold. By using the Naïve Bayes Classifier algorithm obtained a prediction of gold prices that can help decision makers in determining whether to sell or buy gold. Gold data will be processed using Rapidminer software. Stages of processing are reading training data, calculating the mean and standard deviation, entering the test data and finding the density value of gauss and then looking for probability value. Based on the calculation that has been done, Naïve Bayes Classifier method is able to predict the price of gold for 1 day ahead or every day. With the results of this calculation is expected to help gold investment actors in increasing accuracy to predict gold prices for decision making.
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Murti, Ariani Trisna y Sri Andika Putri. "FAKTOR – FAKTOR YANG MEMPENGARUHI PERMINTAAN DAGING BROILER DI KOTA MALANG". BUANA SAINS 18, n.º 1 (3 de julio de 2018): 47. http://dx.doi.org/10.33366/bs.v18i1.937.
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The research was conducted on a number of consumers who bought broiler chicken and not sold again. The number of respondents in each market amounted to 100 respondents incidental sampling. Research location was chosen with consideration because the big market is in the middle of the city, while for dinoyo market because of its location on subdistrict and reside in the residential area. The data collected are primary and secondary data. The type of this research is quantitative descriptive, that is research which describes or describe characteristic from a state or object of research done through data collecting, data analysis and interpretation result of its analysis. The research method used survey method. Data processing from the results of research conducted using Cobb-Douglas function using SPSS version 16.0. Based on the results of research conducted on broiler consumers in Malang City can be concluded that the factors that affect the level of demand for broiler meat in the city of Malang is the price of the goods themselves (broiler meat prices), price of chicken meat, beef prices, cooking oil prices, incomes per capita, education and consumer tastes.
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Harper, Jayson K. y George M. Greene. "Fruit Quality Characteristics Influence Prices Received for Processing Apples". HortScience 28, n.º 11 (noviembre de 1993): 1125–28. http://dx.doi.org/10.21273/hortsci.28.11.1125.
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This study quantifies the discounts and premiums associated with various quality factors for processing apples (Malus domestica Borkh.). Discounts and premiums were estimated using a hedonic price model and quality data from a total of 137 samples representing three processing apple cultivars (45 `York Imperial', 43 `Rome Beauty', and 49 `Golden Delicious'). Price discounts in the sample were statistically significant for fruit size, bruising, bitter pit, decay, misshapen apples, and internal breakdown. Commonly cited defects, such as insect damage and apple scab, did not cause significant price discounts.
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Jaramillo-Morán, Miguel A. y Agustín García-García. "Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors". Energies 12, n.º 23 (22 de noviembre de 2019): 4439. http://dx.doi.org/10.3390/en12234439.
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In this paper, we forecast the price of CO2 emission allowances using an artificial intelligence tool: neural networks. We were able to provide confident predictions of several future prices by processing a set of past data. Different model structures were tested. The influence of subjective economic and political decisions on price evolution leads to complex behavior that is hard to forecast. We analyzed correlations with different economic variables related to the price of CO2 emission allowances and found the behavior of two to be similar: electricity prices and iron and steel prices. They, along with CO2 emission allowance prices, were included in the forecasting model in order to verify whether or not this improved forecasting accuracy. Only slight improvements were observed, which proved to be more significant when their respective time series trends or fluctuations were used instead of the original time series. These results show that there is some sort of link between the three variables, suggesting that the price of CO2 emission allowances is closely related to the time evolution of the price of electricity and that of iron and steel, which are very pollutant industrial sectors. This can be regarded as evidence that the CO2 market is working properly.
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Nilsen, Øivind A., Per Marius Pettersen y Joakim Bratlie. "Time-Dependency in Producers’ Price Adjustments: Evidence from Micro Panel Data". Review of Economics 69, n.º 2 (28 de agosto de 2018): 147–68. http://dx.doi.org/10.1515/roe-2018-0012.
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Abstract Existing micro evidence of firms’ price changes tends to show a downward sloping hazard rate – the longer the price of a product has remained the same, the less likely it is that the price will change. Using a panel of Norwegian plant- and product-specific prices, we also find a downward sloping hazard when applying a Kaplan–Meier model. After having controlled for both observed and unobserved characteristics, we find flat hazards with spikes in the first and twelfth months. This suggests time-dependent price-setting by at least some of the producers. The spike after 12 months might be explained by seasonal demand effects, but also by the pricing season effect related to information acquisition and processing, negotiation and signing of price contracts. The revealed price adjustment pattern is at odds with the predictions of the Calvo model, a central element in many dynamic stochastic general equilibrium models, as this assumes constant frequencies of price adjustments over time. Our empirical findings instead point to a modified Calvo model where firms in some periods experience lower menu costs. Finally, the empirical findings may have implications for the effectiveness of monetary policy interventions.
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Klepacka, Anna M., Wojciech J. Florkowski y Cesar Revoredo-Giha. "Can Family Farms Depend on Price Information? Testing Butter and Curd Price Integration in Poland". Agriculture 11, n.º 5 (11 de mayo de 2021): 434. http://dx.doi.org/10.3390/agriculture11050434.
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This study examines the integration of regional dairy markets in Poland, which is a major European dairy producing country. The analysis of prices is important, as many dairy farmers are members of dairy processing cooperatives, and their incomes are affected by the prices of two popular products: butter and curd. Moreover, the period of study included significant fluctuations in the world market and the termination of the milk quota system in the European Union (EU). The price records used in this study are from the two main milk-producing regions in the country: Northern and Central. The data were tested for stationarity and Granger causality before estimating a Vector Error Correction (VEC) model. Estimation results show that the removal of the milk quota lowered prices of butter and curd in the two regions. The relationships of the prices in both regions for butter markets were nearly perfect during the period January 2010–November 2017, but curd prices were found unintegrated. Impulse response analysis showed that the effect of shocks was mostly absorbed in a two-week period and prices returned to full equilibrium in about four to five weeks. This fast price adjustment indicates that both markets operate properly and no market participant can obtain gains above those offered at equilibrium.
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Panasolo, Alessandro, Franklin Galvão, Hermes Yukio Higachi, Edilson Batista de Oliveira, Fernando Campos de Oliveira, Carlos Augusto Wroblewski, Tatiana Maria Cecy Gadda y Camila Fossa Balbinot. "Urban green areas and real estate prices in Curitiba, Brazil". Revista Ibero-Americana de Ciências Ambientais 11, n.º 6 (6 de julio de 2020): 86–102. http://dx.doi.org/10.6008/cbpc2179-6858.2020.006.0008.
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We attempted to identify to which extent the implicit ecosystem service values of urban green areas impact real estate values in the city of Curitiba, Brazil. The study is based on spatial econometrics techniques and hedonic price theory applied to 43 urban green areas, highlighting three units: the Airumã Private Natural Heritage Reserve, the Teresa Urban Ecological Station, and the President Getulio Vargas Refinery. Information was obtained on the structural characteristics of more than 5,300 apartments and houses. The results of exploratory spatial data analysis (ESDA) and estimates from hedonic regression model parameters show that the presence of urban green areas contribute to the final property prices. The effects of proximity to urban green areas on the price of urban residential property are not homogeneous and stationary throughout urban spaces and can generate distinct spatial clusters of real estate prices: high-high and low-low. The used methodology proved to be efficient to assess the value of urban green areas with regard to the use of information, processing, data analysis, and results generated. Furthermore, it measured the impact of these areas on property prices and provided easily interpretable data that can be relevant for payments for ecosystem services policies at the local level.
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Sklyar, Alexander. "Mathematical model of the supply-demand system for raw materials". Теоретическая и прикладная экономика, n.º 1 (enero de 2021): 76–85. http://dx.doi.org/10.25136/2409-8647.2021.1.27680.
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The subject of this research is the processes of price formation for raw materials depending on the demand for end consumer products. The article reviews a mathematical model that is based on the principle of maximum utility. The proposed model is founded on the stage-by-stage determination of the production output and consumption of end products, as well as corresponding prices depending on the prices of used raw materials and semi-finished products. The prices for intermediate products and raw materials are formed depending on the need for end products output with their optimization by demand. The article provides the basic mathematical ration with regards to using principle of maximum utility applicable to the demand-supply model and its implementation in multi-stage production. The acquired results indicate weak dependence of production output and prices for end products on the cost of raw material in terms of advanced refining. With limited production capacity of raw materials, the dynamics of prices is well predicted. The results of modeling, compared to the available statistical data, indicate the adequacy of the proposed model to the unfolding economic processes. It is determined that the accuracy of price prediction for raw products with a significant volume of its subsequent processing is limited.
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Fernando, Julianto, Cindy Yulistia, Felisia Felisia y Mohd Nawi Purba. "Pengaruh Return on Investment, Net Profit Margin, Dividen Per Share dan Pertumbuhan Aset terhadap Harga Saham Perusahaan Manufaktur". Owner 5, n.º 1 (1 de febrero de 2021): 38–50. http://dx.doi.org/10.33395/owner.v5i1.334.
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A publicly listed company on the IDX, such as a manufacturing company, must issue shares that can be owned by investors. The stock price is very volatile and can change at any time, even though per Share and Asset Growth on Stock Prices of Manufacturing Companies listed on investors really want the stock price to rise and never go down so that investors do not suffer losses. The purpose of this study was to determine the effect of Return on Investment, Net Profit Margin, Dividend the Indonesia Stock Exchange 2016-2019. Quantitative research used in data processing with statistics. Research is causal. Collecting data using documentation and literature. The population of this research is 177 manufacturing companies listed on the Indonesia Stock Exchange 2016-2019.The research sample was 34 manufacturing companies listed on the Indonesia Stock Exchange 2016-2019. Multiple linear regression model. The result is that Return On Investment has no effect on stock prices in manufacturing companies listed on the Indonesia Stock Exchange 2016-2019. Net Profit Margin has no effect on stock prices in manufacturing companies listed on the Indonesia Stock Exchange 2016-2019. Dividend Per Share has an effect on stock prices in manufacturing companies listed on the Indonesia Stock Exchange 2016-2019. Asset growth has no effect on stock prices in manufacturing companies listed on the Indonesia Stock Exchange 2016-2019. Return On Investment , Net Profit Margin , Dividend Per Share and Asset Growth affect stock prices in manufacturing companies listed on the Indonesia Stock Exchange 2016-2019.
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Chen, Yiyuan, Yufeng Wang, Jianhua Ma y Qun Jin. "BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market". Energies 12, n.º 12 (12 de junio de 2019): 2241. http://dx.doi.org/10.3390/en12122241.
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For the benefit from accurate electricity price forecasting, not only can various electricity market stakeholders make proper decisions to gain profit in a competitive environment, but also power system stability can be improved. Nevertheless, because of the high volatility and uncertainty, it is an essential challenge to accurately forecast the electricity price. Considering that recurrent neural networks (RNNs) are suitable for processing time series data, in this paper, we propose a bidirectional long short-term memory (LSTM)-based forecasting model, BRIM, which splits the state neurons of a regular RNN into two parts: the forward states (using the historical electricity price information) are designed for processing the data in positive time direction and backward states (using the future price information available at inter-connected markets) for the data in negative time direction. Moreover, due to the fact that inter-connected power exchange markets show a common trend for other neighboring markets and can provide signaling information for each other, it is sensible to incorporate and exploit the impact of the neighboring markets on forecasting accuracy of electricity price. Specifically, future electricity prices of the interconnected market are utilized both as input features for forward LSTM and backward LSTM. By testing on day-ahead electricity prices in the European Power Exchange (EPEX), the experimental results show the superiority of the proposed method BRIM in enhancing predictive accuracy in comparison with the various benchmarks, and moreover Diebold-Mariano (DM) shows that the forecast accuracy of BRIM is not equal to other forecasting models, and thus indirectly demonstrates that BRIM statistically significantly outperforms other schemes.
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Lutz, Jack, Theodore E. Howard y Paul E. Sendak. "Stumpage Price Reporting in the Northern United States". Northern Journal of Applied Forestry 9, n.º 2 (1 de junio de 1992): 69–73. http://dx.doi.org/10.1093/njaf/9.2.69.
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Abstract Data collection, processing, and dissemination methods of stumpage price reports in the northern United States vary considerably among the states due to differing objectives, markets, traditions, and budget constraints. Data are collected primarily from limited segments of the market with little quality control exerted by the compiling agencies. Prices are reported in terms of species, timber quality, and major product, and range from detailed lists to gross aggregates. Important areas for improving the price reports include more rapid dissemination, broader sampling of transactions, improved quality control and statistical analysis, and increased computerization. North. J. Appl. For. 9(2):69-73.
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Burian, Jaroslav, Karel Macků, Jarmila Zimmermannová y Rostislav Nétek. "Sustainable Spatial and Temporal Development of Land Prices: A Case Study of Czech Cities". ISPRS International Journal of Geo-Information 9, n.º 6 (16 de junio de 2020): 396. http://dx.doi.org/10.3390/ijgi9060396.
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Only a limited number of studies have examined land price issues based on official land price maps. A very unique timeline of official land price maps (2006–2019) allowed research to be conducted on four Czech cities (Prague, Olomouc, Ostrava, and Zlín). The main aim of the research was to describe the links between land price, land use types, and macroeconomic indicators, and to compare temporal changes of these links in four cities of different size, type, and structure by using spatial data processing and regression analysis. The results showed that the key statistically significant variable in all cities was population size. The effect of this variable was mostly positive, except for Ostrava, as an example of a developing city. The second statistically significant variable affecting land prices in each city was discount rate. The effect of other variables differed according to the city, its characteristics, and stage of economic development. We concluded that the development of land prices over time was slightly different between the studied cities and partially dependent on local spatial factors. Nevertheless, stagnation in 2010–2011, probably as a consequence of the global economic crisis in 2009, was observed in each city. Changes in the monitored cities could be seen from a spatial point of view in similar land price patterns. The ratio of land area with rising prices was very similar in each city (85%–92%). The highest land prices were typically in urban centers, but prices rose only gradually. A much more significant increase in prices occurred in each city in their peripheral residential areas. The results of this study can improve understanding of urban development and the economic and spatial aspects of sustainability in land price changes.
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Ikraman, Muhamad y Sri Ernawati. "Pengaruh Kenaikan Harga Tahu Terhadap Minat Beli Masyarakat Di Kota Bima". JURNAL SOSIAL EKONOMI DAN HUMANIORA 6, n.º 2 (29 de diciembre de 2020): 101–4. http://dx.doi.org/10.29303/jseh.v6i2.84.
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In a factory, to provide a decision regarding product pricing is very important and not easy to do. Changes in prices that are very small or very large will cause significant impacts and changes to sales in a large enough quantity, so if there is a mistake in determining the selling price, the company will lose or lose customers because the specified selling price is too low or too high. from this study was to determine the effect of rising prices Interest of Buying in the city of Bima. This type of research is associative with the type of quantitative data from primary data sources. The study population is all people in the Bima City who have bought tofu products. Sampling of this study using a purposive sampling technique. Data analysis techniques used are validity and reliability, simple linear regression analysis, correlation coefficient, coefficient of determination (R2) and t test. Data processing and analysis was carried out using the Statistical Product and Service Solution (SPSS) program version 21.0. The results of this study prove that there is no effect of tofu price increase on people's Interest of Buying in the Bima City.
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Anastasia, Njo y Fabian Hidayat. "HUBUNGAN INDEKS HARGA PROPERTI RESIDENSIAL DAN KREDIT PERBANKAN". EKUITAS (Jurnal Ekonomi dan Keuangan) 3, n.º 1 (3 de diciembre de 2019): 95–111. http://dx.doi.org/10.24034/j25485024.y2019.v3.i1.3998.
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Demand and supply in housing market depends on macroeconomic conditions such as Gross Domestic Product, interest rates, and housing prices. Changes to these variables are related to changes in housing market. This study aims to examine the relationship of housing prices, Gross Domestic Product, mortgage interest rate to Banking Credit. Knowing the relationship will be useful in making strategic decisions related to property investment and portfolio management. Housing price using Residential Price Index in primary market will be grouped into three parts based on land area of residential property consist of small house type, medium house type, and big house type. Data processing using Auto Regressive Distribution Lag (ARDL) bound test model to test the relationship between variables. The result of the research shows that there is a significant long run cointegration on the variable of housing price, Gross Domestic Product, and mortgage interest to banking credit. Furthermore, in testing each housing price group, the test results also show the relationship between these variables.
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Dařena, František, Jonáš Petrovský, Jan Žižka y Jan Přichystal. "Machine Learning-Based Analysis of the Association Between Online Texts and Stock Price Movements". Inteligencia Artificial 21, n.º 61 (9 de mayo de 2018): 95. http://dx.doi.org/10.4114/intartif.vol21iss61pp95-110.
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The paper presents the result of experiments that were designed with the goal of revealing the association between texts published in online environments (Yahoo! Finance, Facebook, and Twitter) and changes in stock prices of the corresponding companies at a micro level. The association between lexicon detected sentiment and stock price movements was not confirmed. It was, however, possible to reveal and quantify such association with the application of machine learning-based classification. From the experiments it was obvious that the data preparation procedure had a substantial impact on the results. Thus, different stock price smoothing, lags between the release of documents and related stock price changes, five levels of a minimal stock price change, three different weighting schemes for structured document representation, and six classifiers were studied. It has been shown that at least part of the movement of stock prices is associated with the textual content if a proper combination of processing parameters is selected.
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Mumini, Omisore Olatunji, Fayemiwo Michael Adebisi, Ofoegbu Osita Edward y Adeniyi Shukurat Abidemi. "Simulation of Stock Prediction System using Artificial Neural Networks". International Journal of Business Analytics 3, n.º 3 (julio de 2016): 25–44. http://dx.doi.org/10.4018/ijban.2016070102.
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Stock trading, used to predict the direction of future stock prices, is a dynamic business primarily based on human intuition. This involves analyzing some non-linear fundamental and technical stock variables which are recorded periodically. This study presents the development of an ANN-based prediction model for forecasting closing price in the stock markets. The major steps taken are identification of technical variables used for prediction of stock prices, collection and pre-processing of stock data, and formulation of the ANN-based predictive model. Stock data of periods between 2010 and 2014 were collected from the Nigerian Stock Exchange (NSE) and stored in a database. The data collected were classified into training and test data, where the training data was used to learn non-linear patterns that exist in the dataset; and test data was used to validate the prediction accuracy of the model. Evaluation results obtained from WEKA shows that discrepancies between actual and predicted values are insignificant.
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Nuryatin, Atin. "Comparative Analysis of ARIMA and GARCH Methods to Predict Stock Prices". Almana : Jurnal Manajemen dan Bisnis 4, n.º 3 (17 de diciembre de 2020): 405–15. http://dx.doi.org/10.36555/almana.v4i3.1483.
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Investment has a very important role in economic growth, when investors invest, GDP tends to rise when investment falls, so GDP also tends to decline. Investors must be vigilant in investing in banking companies. One of the ways to predict stock prices with technical analysis is by using the ARIMA and GARCH methods. The purpose of this study is to determine whether the ARIMA and GARCH methods are accurate in predicting stock prices. The research method used in this research is descriptive and verification methods with a quantitative approach. Sources of data taken in this study are secondary data sources for the bank sub-sector found on the Indonesia Stock Exchange (IDX), namely the annual stock price reports for the years 2014, 2015, 2016, 2017, and 2018 as many as 39 companies. Processing data from this study using the ARIMA and GARCH methods with an evaluation of forecasting errors using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), or Mean Absolute Percentage Error (MAPE) analysis results using the E-View 9 program. shows that the ARIMA Method is accurate in predicting stock prices in 2015, 2016, and 2018. Meanwhile, the GARCH Method is accurate in predicting stock prices in 2014 and 2017.
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Mishra, R. y DA Kumar. "Price behaviour of major vegetables in hill region of nepal: an econometric analysis". SAARC Journal of Agriculture 10, n.º 2 (12 de marzo de 2014): 107–20. http://dx.doi.org/10.3329/sja.v10i2.18332.
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An attempt has been made to study the price behaviour of major vegetables in hill region of Nepal. The study is based on secondary data on wholesale monthly/weekly arrivals and prices for the period of 2000-01 to 2009-10. The seasonality in wholesale price was analyzed using multiplicative model; the Fourier analysis was used to analyze cyclical variation in wholesale prices and Autoregressive model to study the relationship between market arrival and price of vegetables in market of hill region of Nepal. It was found that during the post-harvest period, the wholesale price ruled very low while during the lean period, the prices were quite high which is due to seasonal and perishable nature of the vegetables. The entire vegetables registered the positive and increasing trend and periodicities of 2 to 3 years in the wholesale price of vegetables. The effect of lagged price on current wholesale price was positively significant and high in magnitude and significant negative response was observed for the relationship between wholesale price and market arrival for all the vegetables in the market of hill region. Therefore, improved market information system is a need of efficient vegetable markets in Nepal in order to enable farmers to make proper production and marketing decisions. Further, Government is required to create market infrastructure facilities like warehousing, processing, transportation, etc. for reducing the variation in prices of vegetables in the market of hill region of Nepal. DOI: http://dx.doi.org/10.3329/sja.v10i2.18332 SAARC J. Agri., 10(2): 107-120 (2012)
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Setiawan, Irwan. "ANALISIS EKSPLORASI DAN VISUALISASI PROFIL SUPERHOST AIRBNB KOTA MADRID DAN AMSTERDAM". JTT (Jurnal Teknologi Terapan) 6, n.º 2 (14 de octubre de 2020): 156. http://dx.doi.org/10.31884/jtt.v6i2.274.
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Superhost Airbnb is an experienced host and provides excellent service to its customers. Superhost has features that can increase the number of bookings and revenue. SuperHost profile is one thing that can be used as a reference for other hosts to improve the quality of service. In this study, an analysis of exploration and visualization of Airbnb hosts' data in the city of Amsterdam and the city of Madrid to find out the profile of superhost from the aspect of price and consumer reviews. The city of Amsterdam and the city of Madrid are chosen because the two cities are the leading destinations for tourists in Europe. The study was conducted using a machine learning approach that has four work steps, namely understanding business processes, data retrieval, data processing, and exploratory analysis and data visualization. The tools used in this study are Jupyter Notebook with the Python programming language. The results obtained from this study are superhost in Madrid, mostly offering rental prices in the price range of $60 - $80. They get the highest reviews from customers based on cleanliness, communication, and check-in. As for Amsterdam, the superhost offers the most rental prices in the price range above $ 140. Superhosts in this price range gets the most reviews from customers in all review groups.
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Laili, Nurul, Sri Hindarti y Dwi Susilowati. "ANALYSIS OF FACTORS AFFECTING THE PRICE FLUCTUATION OF CAYENNE PEPPER IN MALANG REGENCY". Agrisocionomics: Jurnal Sosial Ekonomi Pertanian 5, n.º 1 (17 de junio de 2021): 19–26. http://dx.doi.org/10.14710/agrisocionomics.v5i1.7123.
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This study aims to 1) Analyze the pattern of changes in commodity prices for spanish pepper in Malang District. 2) Analyzing the factors that influence fluctuations in the price of spanish pepper in Malang District. The research method used is quantitative method that uses secondary data in the form of time series obtained from several related agencies, namely the Central Statistics Agency of Malang District, Department of Industry and Trade, and Department of food crops, horticulture, and plantation in Malang District. Analysis of the data used is multiple linear regression with the dependent variable is the price at the consumer level from 2009-2018, while the independent variables use the data of the price of spanish pepper at the producer level, the amount of production, and the amount of consumption from 2009-2018. The study found that: 1) The development of the price of spanish pepper had a trend that tended to increase during the last 10 years. 2) From the results of data processing using multiple linear regression method with Eviews 9.0 application, it is found that the factor that significantly influences changes in the price of spanish pepper is the price at the producer level, while the amount of production of spanish pepper and the number of requests does not significantly affect the change in spanish pepper prices in Malang District.
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Mishra, Sibanjan. "Testing Martingale Hypothesis Using Variance Ratio Tests: Evidence from High-frequency Data of NCDEX Soya Bean Futures". Global Business Review 20, n.º 6 (26 de julio de 2019): 1407–22. http://dx.doi.org/10.1177/0972150919848937.
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The aim of the article is to examine the martingale hypothesis of market efficiency on high-frequency data of the soya bean futures traded in National Commodity and Derivatives Exchange (NCDEX) of India using multiple variance ratio (VR) tests from February 2015 to August 2015. The study employs high-frequency future prices of 5, 10, 15, 30 and 60 min time intervals mainly to decipher the efficiency of processing information by soya bean traders during intraday sessions of futures trading. The results of VR tests confirm that except prices of 5 and 10 min intervals which displays weak form of market efficiency, all other samples follow martingale hypothesis. The findings suggest that as information gets absorbed promptly in the intraday NCDEX soya bean futures prices, there exists fairly less opportunities to explore any trading strategy for profitable outcomes in the soya bean futures market in India.
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Mardianto, Mardianto y Juniyanti Juniyanti. "Analisis Pengaruh Kepemilikan Institusional, Koneksi Politik, Ukuran Perusahaan, ROE, dan Leverage terhadap Sinkronisitas Harga Saham". Global Financial Accounting Journal 4, n.º 2 (31 de octubre de 2020): 75. http://dx.doi.org/10.37253/gfa.v4i2.1228.
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This scientific work is written to examine the effect of the independent variable towards the synchronicity of stock prices variable. The independent variables referred to in the scientific work are political connections, institutional ownership, ROE, company size, leverage, skewness and kurtosis. Author will elaborate the results of the variables in this research.
The LQ45 company which is well known for its financial condition, growth prospects, and high transaction value has been chosen as sample used for this study. The sampling period ranged from 2014 to 2018, which is 5 (five) years. The financial statements and annual reports of the LQ45 company are downloaded through the site https://www.idx.co.id/. Whereas the company's information regarding weekly return needed for data processing of share price synchronicity is obtained through the website https://finance.yahoo.com/. The research data obtained are then processed using Eviews application version 10 and SPSS version 22.
Results indicate that the institutional ownership variable has significant negative effect on the synchronicity of stock price. While compant size and kurtosis variables has significant positive effect on the synchronicity of stock prices. Other variables, namely political connections, Return On Equity (ROE), leverage and skewness do not have a significant relationship to the variable synchrony of stock prices.
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Kopp, Thomas, Bernhard Brümmer, Zulkifli Alamsyah y Raja Sharah Fatricia. "Welfare implications of intertemporal marketing margin manipulation". British Food Journal 119, n.º 8 (7 de agosto de 2017): 1656–71. http://dx.doi.org/10.1108/bfj-11-2016-0572.
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Purpose In Indonesia, rubber is the most valuable export crop produced by small scale agriculture and plays a key role for inclusive economic development. This potential is likely to be not fully exploited. The observed concentration in the crumb rubber processing industry raises concerns about the distribution of export earnings along the value chain. Asymmetric price transmission (APT) is observed. The paper aims to discuss these issues. Design/methodology/approach This study investigates the price transmission between international prices and the factories’ purchasing prices on a daily basis. An auto-regressive asymmetric error correction model is estimated to find evidence for APT. In a subsequent step the rents that are redistributed from factories to farmers are calculated. The study then provides estimations of the size of this redistribution under different scenarios. Findings The results suggest that factories do indeed transmit prices asymmetrically, which has substantial welfare implications: around USD3 million are annually redistributed from farmers to factories. If the price transmission was only half as asymmetric as it is observed, the majority of this redistribution was re-diverted. Originality/value This study combines the approaches of non-parametric and parametric estimation techniques of estimating APT processes with a welfare perspective to quantify the distributional consequences of this intertemporal marketing margin manipulation. Especially the calculation of different scenarios of alternative price transmissions is a novelty. The data set of prices on such a disaggregated level and high frequency as required by this approach is also unique.
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Farboodi, Maryam y Laura Veldkamp. "Long-Run Growth of Financial Data Technology". American Economic Review 110, n.º 8 (1 de agosto de 2020): 2485–523. http://dx.doi.org/10.1257/aer.20171349.
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“Big data” financial technology raises concerns about market inefficiency. A common concern is that the technology might induce traders to extract others’ information, rather than to produce information themselves. We allow agents to choose how much they learn about future asset values or about others’ demands, and we explore how improvements in data processing shape these information choices, trading strategies and market outcomes. Our main insight is that unbiased technological change can explain a market-wide shift in data collection and trading strategies. However, in the long run, as data processing technology becomes increasingly advanced, both types of data continue to be processed. Two competing forces keep the data economy in balance: data resolve investment risk, but future data create risk. The efficiency results that follow from these competing forces upend two pieces of common wisdom: our results offer a new take on what makes prices informative and whether trades typically deemed liquidity-providing actually make markets more resilient. (JEL C55, D83, G12, G14, O33)
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Hasyim, Fuad y Resyta Aulia Ardityasari. "Derivative Analysis of Value Added to Stock Returns at Jakarta Islamic Index". BISNIS : Jurnal Bisnis dan Manajemen Islam 8, n.º 2 (30 de diciembre de 2020): 155. http://dx.doi.org/10.21043/bisnis.v8i2.8150.
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<p>This study aims to examine the effect of value added derivative such as economic value added (EVA), market value added (MVA) and refined economic value added (REVA) on stock return with stock price as an intervening variable. The object of this study are all Islamic stocks listed in the Jakarta Islamic Index (JII) in the period 2014-2019. This study using purposive sampling method and obtained by 11 companies. Data processing using panel regression with common, fixed and random modelling approach. The results show that economic value added (EVA) has no effect either on stock prices or stock returns, market value added (MVA) affects the stock price and stock return, while refined economic value added (REVA) has no effect on both. Then, stock prices are only able to mediate the effect of market value added (MVA) on stock return.</p>
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SAWITRI, MADE NITA DWI, I. WAYAN SUMARJAYA y NI KETUT TARI TASTRAWATI. "PERAMALAN MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK". E-Jurnal Matematika 7, n.º 3 (2 de septiembre de 2018): 264. http://dx.doi.org/10.24843/mtk.2018.v07.i03.p213.
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The purpose of the study is to forecast the price of rice in the city of Denpasar in 2017 using backpropagation neural network method. Backpropagation neural network is a model of artificial neural network by finding the optimal weight value. Artificial neural networks are information processing systems that have certain performance characteristics similar to that of human neural networks. This analysis uses time series data of rice prices in the city of Denpasar from January 2001 until December 2016. The results of this research, concludes that the lowest rice price is predicted in July 2017 at Rp9791.5 while the highest rice price in April 2017 for Rp9839.4.
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Lestari, Retno Martanti Endah y Putri Permatasari. "STUDI TERHADAP PEMBAGIAN DIVIDEN DAN DAMPAKNYA TERHADAP HARGA SAHAM PERUSAHAAN-PERUSAHAAN YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE 2011-2014". JIAFE (Jurnal Ilmiah Akuntansi Fakultas Ekonomi) 2, n.º 1 (1 de julio de 2016): 69–85. http://dx.doi.org/10.34204/jiafe.v2i1.537.
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The purpose of this study was to elucidate whether there is a role in influencing patterns of distribution of dividend stock prices. Data processing methods used were descriptive and comparative analysis. The results of this study indicate that not all issuers listed on the Stock Exchange dividends and the distribution of the dividend were varied. Of the 285 listed companies there are 13 issuers that pay dividends above Rp500 per share and most large issuers that pay dividends (MLBI). Issuers who regularly distribute dividends from 2011-2014 as many as 122 listed companies, with issuers who have an average dividend yield and the standard deviation is SQBB largest and the smallest is the SMMA. Of the 122 listed companies that distribute dividends on a regular basis, issuers that have a relative risk (covariance) is lowest that ASDM. After compared with stock prices, issuers that have a positive correlation between the distribution of dividends and stock prices is larger, ie 75.41%. From this study we can conclude that the theory says that the dividend distribution will affect the stock price can not be generalized. The dividend distribution does not necessarily affect the movement of the stock price still due to the dividend distribution of listed companies is negatively correlated with stock prices. In investing stock investors need not sticking to the distribution of dividends, since not all issuers that pay dividends positively correlated to the stock price.Keywords: dividend, stock price, listed on the Stock Exchange
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Mariyah, Mariyah, Saripah Nurfilah, Dina Lesmana y Agung Enggal Nugroho. "SOSIOECONOMIC ASPECT OF AGRIBUSINESS AFFECTED DURING COVID-19 PANDEMIC". Sosiohumaniora 23, n.º 1 (2 de marzo de 2021): 89. http://dx.doi.org/10.24198/sosiohumaniora.v23i1.29585.
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Agribusiness is a business in the agricultural sector that consists of providing input, production, marketing, processing and supporting which are interrelated and many actors are involved. The research purpose was to determine the socioeconomic aspect of agribusiness affected during the Covid-19 pandemic based on general public perception. This research was conducted in June-July 2020 by distributing questionnaires online from 18 June 2020 to 30 June 2020 using google form. The total respondents who filled in were 87 people. It’s consists of academics, practitioners, and the general public. Data were analyzed using qualitative analysis with a Likert scale. The results showed that agribusiness during the Covid-19 pandemic was positively impacted with an average score of 114.6. Socioeconomic aspects that have been positively affected, among others local input is sought, local input prices increase, import inputs are limited, food demand, food production, and output prices increase, food availability becomes the focus, local production sought after, imported products decreased, various marketing patterns varied, online order services and delivery order increased, marketing was increasingly creative, demand for fresh food increased, demand for processed food and household processing industries increased, prices for processed food increasing, processing innovations are increasingly diverse, online financing transactions, massive information dissemination, extensive sharing of agricultural experiences, price information increasing, multi-stakeholder meetings increasing, changes in agriculture policy. The research implication is that the Covid-19 pandemic provides an important lesson for agribusiness that agricultural is an important sector and the ability of business units to adapt and innovate is needed.
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Shabri, Ani y Ruhaidah Samsudin. "Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis". Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/854520.
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Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.
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Junaeni, Irawati. "Stock Prices Predicted by Bankruptcy Condition?" Binus Business Review 9, n.º 2 (31 de julio de 2018): 105–14. http://dx.doi.org/10.21512/bbr.v9i2.4103.
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This research had two objectives. First, it determined the prediction of the method of Altman Z-Score whether it could classify banking positions, bankruptcy, or financial distress in the go-public bank in Indonesia Stock Exchange. Second, it was to know the influence of value position of Altman Z-Score on the stock price. The population was 84 banking company listed on the Indonesia Stock Exchange in 2010-2015. The sampling method was purposive sampling. Moreover, data analysis method used was a simple regression analysis. For data processing, it used software Eviews 8. The Z-Score calculations predict the potential bankruptcy of go-public bank in 2010-2015. All results show that Z-Score has the small score of 1,81. It can be said there is a potential bankruptcy. For t-test, it can be concluded that Z-Score has the positive and significant effect on the stock price. The ability of Z-Score values in explaining the stock price is 95,50% while the remaining 4,50% is influenced by other variables that are not analyzed in the research. With some weaknesses of Altman’s Z-Score model, this research has the implication for management bank. It improves the financial performance for the future to avoid opportunity bankruptcy prediction. The results show how the effect of bankruptcy on banking stock prices.
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Choengthong, Suthijit, Weerasak Kongrithi, Suchart Choengthong y Benyapa Chuaymuang. "The Current Status and Future Trends of Farm Production and Marketing of Para-Rubber in the Upper East Coast Provinces of Southern Thailand". Advanced Materials Research 844 (noviembre de 2013): 30–33. http://dx.doi.org/10.4028/www.scientific.net/amr.844.30.
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The research aimed to: 1) describe the current status and future trends of para-rubber production and marketing in the study area; 2) identify the marketing costs of farmers; and 3) study the marketing system of para-rubber. Data were collected from 462 farmers, 108 middlemen, and 17 processors. The study area was 4 provinces in the Upper East Coast of Southern Thailand: Chumpon, Suratthani, Nakhonsrithammarat, and Patthalung. The surveys were conducted by using structured questionnaires, in-depth interviews, and workshops. The data were analyzed by descriptive statistics, cost analysis, and marketing channel analysis. From 2005 to 2010, planting areas increased but total yields declined, due to heavy rains and floods. The rubber prices fluctuated but had an increasing trend. On an average, the planting area was 2.7 ha and had been cultivated for 17 years. The major variety was RRIM 600. The majority of farmers (63%) sold most of their para-rubber in latex form, while rubber sheets (31%) or cup lumps (18%) dominated less often. Hired help was often paid by a split of sales revenue. The product price, relationship, and shipping distance affected selecting the sales channel. Most preferred self-transportation to middlemen (81%), who usually set the price lower than the central market price. The marketing costs were from sheet making, transportation, and deductions for low quality. Sheet making at 3.65 THB per kg was mainly labor costs (72%). The transportation cost of cup lumps, latex and, sheets were on average 1.04, 0.86, 0.48 THB per kg, respectively. The cup lumps had quality deductions of 11% average from full price, and cup lump wetness and contamination had affected 65% of farmers, while 46% had sold sheets with deductions averaging 2%. Fluctuating prices and lack of knowledge about market channels and prices were the major problem for 45% of the farmers. Farm producers mainly sold latex or dry rubber (sheets, cutting scraps, and cup lumps). The latex marketing channel was through collectors for processing plants, that could both be local or larger scale. The dry rubber marketing channels included also collectors, central market, and processing plants. Rubber processed to primary products (smoked sheets, block, and compound rubber) was mainly exported. The latex marketing channels are gaining importance because farmers prefer to sell in latex form. They are likely to sell to a local village collector close to the farm. The demand for local processing plants that transform latex to rubber sheets or block rubber as well as the latex processing plants will likely increase.
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Goceri, Evgin. "Future Healthcare: Will Digital Data Lead to Better Care?" New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, n.º 8 (9 de diciembre de 2017): 07–11. http://dx.doi.org/10.18844/gjapas.v0i8.2781.
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Currently, datasets used in bioinformatics and computational biology are high-dimensional, complex and multivariate. Analysis and processing of data is vital in medicine; however, manual analysis and pattern recognition with big data is difficult, and processing of large and weakly connected datasets is challenging. The increasing complexity of healthcare systems causes high health cost. To provide better healthcare services at reduced prices, computer-aided tools using smart approaches and context-aware computations are of great importance. Advancements in wireless network technology, mobile devices and pattern recognition applications help solve the cost problem of healthcare systems. In the future, patients will be able to participate in healthcare as their own health manager and observe important parameters like body fat amount and blood pressure. However, open issues related to this topic exist. In this paper, we present a survey of smart healthcare environments and smart hospitals and discuss some questions and challenges in this area. Keywords: Future healthcare, healthcare system, smart hospitals, smart environments.
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Komalawati, Ratna Winandi Asmarantaka, Rita Nurmalina y Dedi budiman Hakim. "VOLATILITAS DAN TRANSMISI HARGA DAGING SAPI DI INDONESIA: STUDI KASUS DI JAKARTA, BANDUNG, SEMARANG DAN SURABAYA". Buletin Ilmiah Litbang Perdagangan 15, n.º 1 (15 de julio de 2021): 127–56. http://dx.doi.org/10.30908/bilp.v15i1.491.
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Abstrak
Daging sapi merupakan salah satu komoditas strategis dengan harga yang cukup berfluktuasi. Fluktuasi harga daging sapi dapat berpengaruh terhadap produsen, konsumen, dan industri pengolahan daging sapi skala kecil. Besarnya perubahan harga daging sapi yang terjadi di suatu pasar dapat memengaruhi pasar lainnya dan dapat digunakan untuk mengetahui kekuatan suatu pasar. Kajian ini bertujuan untuk mengkaji volatilitas dan transmisi harga daging sapi di sentra konsumen Jakarta dan sentra produsen Bandung, Semarang dan Surabaya. Data yang digunakan adalah data harian daging sapi. Volatilitas harga harian daging sapi dianalisis dengan menggunakan model GARCH dan transmisi harga dikaji dengan menggunakan model VAR/VECM. Hasil kajian menunjukkan bahwa hanya harga daging sapi Jakarta yang memiliki volatilitas rendah namun persisten dalam jangka panjang. Perubahan harga daging sapi ditransmisikan dua arah dari Jakarta ke Bandung dan Semarang, dan hanya searah dari Jakarta ke Surabaya. Hasil analisis menunjukkan bahwa upaya stabilisasi harga daging sapi dapat dilakukan dengan menjaga ketersediaan daging sapi baik melalui impor (jangka pendek dan menengah) maupun upaya penyediaan bibit sapi dan sapi potong lokal dalam jangka panjang. Iklim usaha daging sapi yang kompetitif juga diperlukan agar ketidaksesuaian perubahan harga antar pasar dapat dikurangi.
Kata Kunci: Daging Sapi, Volatilitas, GARCH, Vector Auto Regression, Stabilisasi Harga
Abstract
Beef is one of the strategic commodities with fairly fluctuating prices. Fluctuations in beef prices could affect producers, consumers, and small-scale beef processing industries. The magnitude of changes in beef prices that occur in a market could affect other markets and could be used to determine the strength of a market. The purpose of this paper is to examine the volatility and transmission of beef prices in the consumer centers of Jakarta and the production centers of Bandung, Semarang and Surabaya. The data used is the daily data of beef. Daily price volatility of beef was analyzed using the GARCH model and price transmission was assessed using the VAR/VECM model. The results of the study show that only Jakarta beef prices have low volatility but are persistent in the long term. Changes in beef prices are transmitted in two directions from Jakarta to Bandung and Semarang, and only in one direction from Jakarta to Surabaya. The results of the analysis show that efforts to stabilize beef prices could be carried out by maintaining the availability of beef either through import (short and medium term) or efforts to provide cattle seeds and local beef cattle in the long term. A competitive beef business climate is also needed so that discrepancies in price changes between markets could be reduced.
Keywords: Beef, Volatility, GARCH, Vector Auto Regression, Price Stabilisation
JEL Classification: F12, F13, F15
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Domanski, Pawel D. y Mateusz Gintrowski. "Alternative approaches to the prediction of electricity prices". International Journal of Energy Sector Management 11, n.º 1 (3 de abril de 2017): 3–27. http://dx.doi.org/10.1108/ijesm-06-2013-0001.
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Purpose This paper aims to present the results of the comparison between different approaches to the prediction of electricity prices. It is well-known that the properties of the data generation process may prefer some modeling methods over the others. The data having an origin in social or market processes are characterized by unexpectedly wide realization space resulting in the existence of the long tails in the probabilistic density function. These data may not be easy in time series prediction using standard approaches based on the normal distribution assumptions. The electricity prices on the deregulated market fall into this category. Design/methodology/approach The paper presents alternative approaches, i.e. memory-based prediction and fractal approach compared with established nonlinear method of neural networks. The appropriate interpretation of results is supported with the statistical data analysis and data conditioning. These algorithms have been applied to the problem of the energy price prediction on the deregulated electricity market with data from Polish and Austrian energy stock exchanges. Findings The first outcome of the analysis is that there are several situations in the task of time series prediction, when standard modeling approach based on the assumption that each change is independent of the last following random Gaussian bell pattern may not be a true. In this paper, such a case was considered: price data from energy markets. Electricity prices data are biased by the human nature. It is shown that more relevant for data properties was Cauchy probabilistic distribution. Results have shown that alternative approaches may be used and prediction for both data memory-based approach resulted in the best performance. Research limitations/implications “Personalization” of the model is crucial aspect in the whole methodology. All available knowledge should be used on the forecasted phenomenon and incorporate it into the model. In case of the memory-based modeling, it is a specific design of the history searching routine that uses the understanding of the process features. Importance should shift toward methodology structure design and algorithm customization and then to parameter estimation. Such modeling approach may be more descriptive for the user enabling understanding of the process and further iterative improvement in a continuous striving for perfection. Practical implications Memory-based modeling can be practically applied. These models have large potential that is worth to be exploited. One disadvantage of this modeling approach is large calculation effort connected with a need of constant evaluation of large data sets. It was shown that a graphics processing unit (GPU) approach through parallel calculation on the graphical cards can improve it dramatically. Social implications The modeling of the electricity prices has big impact of the daily operation of the electricity traders and distributors. From one side, appropriate modeling can improve performance mitigating risks associated with the process. Thus, the end users should receive higher quality of services ultimately with lower prices and minimized risk of the energy loss incidents. Originality/value The use of the alternative approaches, such as memory-based reasoning or fractals, is very rare in the field of the electricity price forecasting. Thus, it gives a new impact for further research enabling development of better solutions incorporating all available process knowledge and customized hybrid algorithms.
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Hana, Sinta Kismi, Beby Mashito Batu Bara y Nina Angelia. "Evaluasi Anggaran Biaya Produksi Pada PT. Perkebunan Nusantara III Di Kota Medan". Jurnal Ilmu Pemerintahan, Administrasi Publik, dan Ilmu Komunikasi (JIPIKOM) 1, n.º 2 (26 de abril de 2019): 145–53. http://dx.doi.org/10.31289/jipikom.v1i2.154.
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The purpose of this research is to evaluate the production cost budget at PT. Perkebunan Nusantara III This research is a study that uses a qualitative approach with a descriptive method in question is to use information data obtained at the time of the study and from the field in the form of data that is written or oral from the parties involved. The results obtained that PT. Perkebunan Nusantara III has evaluated the calculation of production costs periodically, based on reports based on production prices, selling prices and profit and loss by determining harvest costs, maintenance costs, factory overhead costs, processing costs, and depreciation costs. budget prepared by PT. Perkebunan Nusantara III is not yet perfect enough because there are still many significant deviations both beneficial and adverse. This is the responsibility of managers to conduct more in-depth evaluations to make the realization of costs so as not to occur too far away. Conclusions through field research that PT. Perkebunan Nusantara III has made a production cost budget with a yearly period. PT. Perkebunan Nusantara III has evaluated the calculation of production costs periodically, based on reports based on production prices, selling prices and profit and loss by determining harvest costs, maintenance costs, factory overhead costs, processing costs, and depreciation costs. The budget prepared by PT. Perkebunan Nusantara III is not yet perfect enough.
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Infante, Saba, Luis Sánchez, Aracelis Hernández y José Marcano. "Sequential Monte Carlo Filters with Parameters Learning for Commodity Pricing Models". Statistics, Optimization & Information Computing 9, n.º 3 (22 de junio de 2021): 694–716. http://dx.doi.org/10.19139/soic-2310-5070-814.
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In this article, an estimation methodology based on the sequential Monte Carlo algorithm is proposed, thatjointly estimate the states and parameters, the relationship between the prices of futures contracts and the spot prices of primary products is determined, the evolution of prices and the volatility of the historical data of the primary market (Gold and Soybean) are analyzed. Two stochastic models for an estimate the states and parameters are considered, the parameters and states describe physical measure (associated with the price) and risk-neutral measure (associated with the markets to futures), the price dynamics in the short-term through the reversion to the mean and volatility are determined, while that in the long term through markets to futures. Other characteristics such as seasonal patterns, price spikes, market dependent volatilities, and non-seasonality can also be observed. In the methodology, a parameter learning algorithm is used, specifically, three algorithms are proposed, that is the sequential Monte Carlo estimation (SMC) for state space modelswith unknown parameters: the first method is considered a particle filter that is based on the sampling algorithm of sequential importance with resampling (SISR). The second implemented method is the Storvik algorithm [19], the states and parameters of the posterior distribution are estimated that have supported in low-dimensional spaces, a sufficient statistics from the sample of the filtered distribution is considered. The third method is (PLS) Carvalho’s Particle Learning and Smoothing algorithm [31]. The cash prices of the contracts with future delivery dates are analyzed. The results indicate postponement of payment, the future prices on different maturity dates with the spot price are highly correlated. Likewise, the contracts with a delivery date for the last periods of the year 2017, the spot price lower than the prices of the contracts with expiration date for 12 and 24 months is found, opposite occurs in the contracts with expiration date for 1 and 6 months.
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Prijanto, Budi, Rani Ferina Pulung y Agustin Rusiana Sari. "The Influence of Net Profit Margin On Stock Price with Earnings Per Share (Eps) As Moderating Variables". Journal of Economics, Finance and Accounting Studies 3, n.º 2 (18 de septiembre de 2021): 74–80. http://dx.doi.org/10.32996/jefas.2021.3.2.8.
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This study aims to investigate: the effect of Net Profit Margin (NPM) on stock prices and whether EPS is a moderating variable on the effect of NPM on stock prices. The case study was determined on the food and beverage sub-sector companies listed on the Indonesia Stock Exchange from 2015 to 2019. The population of this study was 26 companies, with the sampling technique used was the purposive sampling method. The use of this sampling technique resulted in 11 companies that met the criteria. The data analysis techniques used include simple regression (t test), multiple regression (F test), and interaction-type moderation tests using Moderated Regression Analysis. Data processing was carried out with the help of the IBM SPSS Ver 22 program. The findings of this study were that NPM had an effect on stock prices and EPS became a moderating variable (strengthened) on the effect of NPM on stock prices.
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Supriatini, Kadek Ayu y Ni Luh Gede Erni Sulindawati. "Non Performing Loan, Loan to Deposit Ratio, Good Corporate Governance, Net Interest Margin, Return on Assets, Capital Adequacy Ratio dan Economic Value Added Terhadap Harga Saham". Ekuitas: Jurnal Pendidikan Ekonomi 9, n.º 1 (29 de junio de 2021): 50. http://dx.doi.org/10.23887/ekuitas.v9i1.26756.
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Banking is one of the companies that have the role to support the economy. Conceptually the purpose of the research is to observe financial performance. To find out the health of banks, financial ratios are used through the Risk Based Bank Rating (RBBR) approach and through the performance value tool with the Ecconomic Value Added approach. Thus the results of this study are intended to determine the effect of Non Performing Loan, Loan to Deposit Ratio, Good Coorporate Governance, Net Interest Margin, Return On Asset, Capital Adequacy Ratio and Economic Value Added on the Bank's Stock Price. This type of research is to use quantitative because the use of data is in the form of numbers. Data acquisition is secondary in the financial statements. population use, namely overall listing on the Indonesia Stock Exchange specifically for the period 2014-2018. Sampling by purposive sampling through certain criteria. The number of samples produced was 23 banks in five years. Data processing using multiple linear regression techniques through SPSS version 20. The results showed that partially there were negative influences of Non-Performing Loans, Loan To Deposit Ratio, Good Corporate Governance variables on Stock Prices, while Net Interest Margin, Return On Assets, and Economic Value Added variables had positive effects on Stock Prices. While the Capital Adequacy Ratio has no effect on the Share Price.
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Lukasevich, I. "Efficient Market Hypothesis and Fractal Market Hypothesis: evidence from Russian stock exchange". Management and Business Administration, n.º 2 (5 de julio de 2021): 62–80. http://dx.doi.org/10.33983/2075-1826-2021-2-62-80.
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This paper presents the results of the study of the fulfillment of the key conditions and prerequisites of the hypotheses of the efficiency and fractality of price behavior in financial markets for the period 1997–2021. Its relevance is due to the high volatility of the Russian stock market and its imperfections, which lead to significant price deviations.
On the example of the analysis of the dynamics of the MOEX stock index, the method of testing the dynamics of prices on large arrays of real data with the use of statistical data processing methods and modern information technologies is demonstrated.
The article concludes that the nature of the Russian market as a whole has a fractal character. At the same time, the assumptions underlying the hypothesis of information efficiency of the market are not fulfilled.