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Journal articles on the topic 'Formation of the price'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Kopytets, N., S. Pashko, and V. Voloshyn. "Current trends in meat price formation." Ekonomìka ta upravlìnnâ APK, no. 2(159) (November 24, 2020): 55–63. http://dx.doi.org/10.33245/2310-9262-2020-159-2-55-63.

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The article examines the main trends in meat price formation.It is determined that the methodology and tradition of price functioning have evolved historically in terms of understanding the nature and characteristics of the price. The level of product price contains the conflicting interests of different parts (producer and consumer). It is generalized that the formation of livestock products prices is influenced by supply and demand.It is confirmed that price is a factor that creates demand in case of the low consumers’ purchasing power. The process of prices forming on livestock products is based on the general principles of pricing. However, there are certain features of pricing stipulated from the specifics of production and processing of livestock products.Among the main factors influencing the prices formation on livestock products should be noted the following: natural and climatic conditions, price disparity in agriculture, the presence of a large number of households which deal with raising cattle and poultry,high level of production costs, the presence of multiple links in the production chain, a short period of product storage, a large proportion of low-income population.It is proved that the livestock product prices in market conditions must respond quickly to any changes in the production chain. The analysis of the price situation on the meat market was carried out.It was found that in Ukraine during the study period there is a tendency of increasing purchasing, wholesale and consumer prices.In the first half-year of 2020, there were significant changes in the price situation.The results confirm the trends at the world meat market.It is proved that the situation at the domestic meat market depends on the state of the global market. It is noted that in the future the price situation at the meat market will depend on the purchasing power of the population, the proposal of main meat types, the exchange rate of the national currency, production and export volumes. Keywords: price, demand, supply, meat market, purchase prices, wholesale prices, consumer prices, beef, pork, poultry meat.

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2

Khasanah, Siti Mir'atul, Mochammad Maksum, and Endy Suwondo. "Trend Analysis of Red Chili Price-Formation Models." agriTECH 40, no. 1 (March 5, 2020): 57. http://dx.doi.org/10.22146/agritech.45946.

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Red chili’s characteristic flavor has been a popular element in Indonesian cuisine. A large and continuous demand for red chili is inconsistent with production volumes, causing frequent and extreme price fluctuations throughout the year. This study explores the changing trends in red chili prices to identify the influencing factors. The study was conducted in the Sleman district of Yogyakarta, Indonesia. Time-series datasets of monthly production rates and prices of chili for 3 years were subject to multiple linear regression analysis. The study found a rising trend in prices in the Sleman Regency from January 2014 to December 2016. The factors significantly influencing the red chili prices was the price of cayenne pepper. The production cost of chili, the price of tomatoes, and the price of chili for the previous 2 months had only partial and nonsignificant effects. The timing of great Muslim celebrations, such as Eid Al-Fitr and Eid Al-Adha had no significant effect on the price of red chili. However, Christmas and New Year events were associated with higher prices.

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3

Blažková, Ivana, and Pavel Syrovátka. "Price formation and transmission along the food commodity chain." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60, no. 4 (2012): 31–36. http://dx.doi.org/10.11118/actaun201260040031.

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The article is focused on analysis of price transmission along the wheat commodity chain in the Czech Republic, with the distinction on wheat products with low value added (wheat flour), respectively high value added (wheat rolls). The degree of vertical price transmission is measured to identify potential market failures, because asymmetric price transmission can be the result of existence of market power within the food commodity chain. The data basis is made up from monthly prices on partial markets of the analyzed commodity chain published by Czech Statistical Office and Ministry of Agriculture of the Czech Republic. The monitored time period is from January 2000 till October 2009. The analysis is based on calculation of the price transmission elasticity coefficient (evaluation of price transmission along the chain) and the intensity of dependency of positive and negative inter-market price differences (evaluation whether positive or negative price changes are better transmitted among particular vertical markets). Time lag is tested as well. The assessment of price transmission along the wheat commodity chain confirmed the existence of market power especially on the retail stage and low impact of price changes of farm prices on final consumer food prices.

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4

Domberger, Simon. "Industrial price formation." International Journal of Industrial Organization 7, no. 2 (June 1989): 316–17. http://dx.doi.org/10.1016/0167-7187(89)90028-3.

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5

Makarov, Igor, and Antoinette Schoar. "Price Discovery in Cryptocurrency Markets." AEA Papers and Proceedings 109 (May 1, 2019): 97–99. http://dx.doi.org/10.1257/pandp.20191020.

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We ask which markets drive bitcoin prices and how price discovery happens across different exchanges. Does the greater exuberance for cryptocurrencies outside the United States affect prices only on local markets or does it impact price formation on global cryptocurrency markets? We document significant heterogeneity in which price formation happens across exchanges and time. When markets are more integrated, shocks to prices on all exchanges contribute to price discovery. However, when markets become segmented, those exchanges that have large arbitrage spreads relative to the US price, i.e. where investors are more exuberant become much less important for price discovery.

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6

Salamin, O. "Actual problems of price formation for agricultural products." Scientific Messenger of LNU of Veterinary Medicine and Biotechnologies 21, no. 93 (November 16, 2019): 17–22. http://dx.doi.org/10.32718/nvlvet-e9304.

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The problems of prices formation for agricultural products are analyzed. In Ukraine, prices have been liberalized without market environment, unlike economically developed countries, where the market environment has evolved over a long period of time. This has led to high price volatility, which is very difficult for manufacturers to adapt. They cannot develop business plans and marketing programs. Due to high risk, bank loans cannot be attracted. The effectiveness of direct financial support programs is decreasing. They did not ensure the overcoming of monopoly tendencies in the field of product purchases and stabilization of the prices of efforts of state agricultural management and public organizations aimed at establishing marketing cooperatives. The cooperative products are sold to the processing plants at the same prices at which they would be harvested and sold without setting up a cooperative. Such cooperatives only simplify the activities of processing enterprises for the procurement of raw materials. Cooperatives, which carry out the processing of products, are viable ones that influence the level of prices and stabilize them. This forms a self-regulatory vertical marketing system of the cooperative type. Without government intervention, prices are acceptable that are acceptable both, for the production and promotion and sale of products to the end consumer. Prices are promptly revised in the light of market conditions and problems that arise at individual levels of the supply chain. Auction results have a significant impact on the overall level and dynamic price changes. The information on the results of the bidding affects the price level, which is formed by all other alternative sales channels. Monopolistic tendencies by individual market participants in the field of agricultural purchases are often only possible due to the lack of information on market conditions from individual producers and possible price in alternative distribution channels. All wholesale agricultural markets established in Ukraine are of a supply nature. They operate in large cities and create the conditions for manufacturers to produce manufactured products, but require trading operations throughout the day. Such markets are more appropriate for intermediary structures that operate on a permanent basis. For the manufacturers of products, wholesale wholesale markets that are created in the areas of production of raw materials are more acceptable. The founders of such markets are usually sales cooperatives. Large cooperative lots of homogeneous products can be offered in such markets by cooperatives in such markets. Auctions may be organized and conducted for such products.

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7

SZNAJD-WERON, K., and R. WERON. "A SIMPLE MODEL OF PRICE FORMATION." International Journal of Modern Physics C 13, no. 01 (January 2002): 115–23. http://dx.doi.org/10.1142/s0129183102003000.

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A simple Ising spin model, which can describe the mechanism of price formation in financial markets is proposed. In contrast to other agent-based models, the influence does not flow inward from the surrounding neighbors to the center site, but spreads outward from the center to the neighbors. The model thus describes the spread of opinions among traders. It is shown via standard Monte Carlo simulations that very simple rules lead to dynamics that duplicate those of asset prices.

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8

Komin, A. N. "Price Formation and Wages." Problems in Economics 32, no. 9 (January 1990): 37–44. http://dx.doi.org/10.2753/pet1061-1991320937.

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9

Kaya, Ayça, and Qingmin Liu. "Transparency and price formation." Theoretical Economics 10, no. 2 (May 2015): 341–83. http://dx.doi.org/10.3982/te1566.

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10

Loannides, Chris, and David Whitmarsh. "Price formation in fisheries." Marine Policy 11, no. 2 (April 1987): 143–45. http://dx.doi.org/10.1016/0308-597x(87)90006-6.

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11

Barten, A. P., and L. J. Bettendorf. "Price formation of fish." European Economic Review 33, no. 8 (October 1989): 1509–25. http://dx.doi.org/10.1016/0014-2921(89)90075-5.

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12

Hörner, Johannes, Stefano Lovo, and Tristan Tomala. "Belief-free price formation." Journal of Financial Economics 127, no. 2 (February 2018): 342–65. http://dx.doi.org/10.1016/j.jfineco.2017.11.004.

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13

Yasinska, A. "Law and Statutory Regulation of Transfer Price Formation in Ukraine." Economics, Entrepreneuship, Management 3, no. 2 (2016): 83–87. http://dx.doi.org/10.23939/eem2016.02.083.

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14

Cahyaningrum, Anisha Wirasti. "FORMATION OF COMPOSITE GLOBAL COMMODITY PRICES AS AN INFLATION INDICATOR FOR EAST JAVA." East Java Economic Journal 2, no. 2 (August 5, 2021): 210–17. http://dx.doi.org/10.53572/ejavec.v2i2.20.

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With the average contribution of imports to Gross Regional Domestic Product (GRDP) in the last five years reaching 19.1%, the dynamics of global commodity prices also influence the economic performance of East Java, including the movement of inflation. A composite indicator of global commodity prices is needed to find out the impact of changes in various global commodity prices on inflation in East Java. By adopting the Bank Indonesia methodology in forming a composite global price known as the Imported Inflation Price Index (IHIM) which has considered the method of forming a global composite price created by the IMF (IMF Commodity Price Index), the compilation of East Java global price composites also examines the accuracy of commodity selection and aspects of data availability. The selected global price composite for East Java is a composite of seven global commodities which include food (wheat, soybeans, corn and CPO) and non-food (iron, gold and oil). These are two aspects determining the relative weight, namely (I) the import portion of the total input based on the Input-Output table and (ii) the commodity weight of derivatives in the East Java Consumer Price Index (IHK) basket. Furthermore, with OLS regression, the composite of East Java global commodity prices affects the core-traded inflation movement in East Java. Thus, the composite of global commodity prices in East Java can be used as an indicator of East Java inflation projections, especially core-traded inflation. This study, in general, will also examine the effect of the exchange rate impact on the movement of core inflation, especially traded groups in East Java. Based on the regression results it is known that the impact of the exchange rate movement on core traded inflation in East Java is more significant than the effect of world commodity price movements.

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15

DZIKEVIČIUS, Audrius. "FORMATION OF AN INTEGRATED STOCK PRICE FORECAST MODEL IN LITHUANIA." Business, Management and Education 14, no. 2 (December 29, 2016): 292–307. http://dx.doi.org/10.3846/bme.2016.337.

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Technical and fundamental analyses are widely used to forecast stock prices due to lack of knowledge of other modern models and methods such as Residual Income Model, ANN-APGARCH, Support Vector Machine, Probabilistic Neural Network and Genetic Fuzzy Systems. Although stock price forecast models integrating both technical and fundamental analyses are currently used widely, their integration is not justified comprehensively enough. This paper discusses theoretical one-factor and multi-factor stock price forecast models already applied by investors at a global level and determines possibility to create and apply practically a stock price forecast model which integrates fundamental and technical analysis with the reference to the Lithuanian stock market. The research is aimed to determine the relationship between stock prices of the 14 Lithuanian companies listed in the Main List by the Nasdaq OMX Baltic and various fundamental variables. Based on correlation and regression analysis results and application of c-Squared Test, ANOVA method, a general stock price forecast model is generated. This paper discusses practical implications how the developed model can be used to forecast stock prices by individual investors and suggests additional check measures.

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16

Luis Méndez García de Paredes, José, Ronald Sebastián Angola Cárdenas, and Dayana Lisseth Sánchez Garcés. "Unit price information on the reference price formation." Journal of Product & Brand Management 22, no. 5/6 (August 19, 2013): 413–25. http://dx.doi.org/10.1108/jpbm-05-2013-0307.

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17

BAVIERA, R., M. PASQUINI, J. RABOANARY, and M. SERVA. "MOVING AVERAGES AND PRICE DYNAMICS." International Journal of Theoretical and Applied Finance 05, no. 06 (September 2002): 575–83. http://dx.doi.org/10.1142/s0219024902001560.

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We introduce a stochastic price model where, together with a random component, a moving average of logarithmic prices contributes to the price formation. The future price is linearly influenced by the difference between the moving average and the current price, together with a noise component. Our model is tested against financial datasets, showing an extremely good agreement with them.

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18

Flåm, S. D., and O. Godal. "Market clearing and price formation." Journal of Economic Dynamics and Control 32, no. 3 (March 2008): 956–77. http://dx.doi.org/10.1016/j.jedc.2007.04.006.

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19

Kračn, Davorin. "Price Formation and Inflation Multiplier." Eastern European Economics 37, no. 4 (July 1999): 50–69. http://dx.doi.org/10.1080/00128775.1999.11648695.

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20

Nagel, Kai, Martin Shubik, Maya Paczuski, and Per Bak. "Spatial competition and price formation." Physica A: Statistical Mechanics and its Applications 287, no. 3-4 (December 2000): 546–62. http://dx.doi.org/10.1016/s0378-4371(00)00392-7.

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21

Gjerstad, Steven, and John Dickhaut. "Price Formation in Double Auctions." Games and Economic Behavior 22, no. 1 (January 1998): 1–29. http://dx.doi.org/10.1006/game.1997.0576.

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22

Burger, Martin, Jan-Frederik Pietschmann, and Marie-Therese Wolfram. "Data assimilation in price formation." Inverse Problems 36, no. 6 (June 1, 2020): 064003. http://dx.doi.org/10.1088/1361-6420/ab6d5a.

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23

Lord, M. J. "Price formation in commodity markets." Journal of Applied Econometrics 6, no. 3 (July 1991): 239–54. http://dx.doi.org/10.1002/jae.3950060303.

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24

Beckert, J. "Where do prices come from? Sociological approaches to price formation." Socio-Economic Review 9, no. 4 (April 27, 2011): 757–86. http://dx.doi.org/10.1093/ser/mwr012.

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25

Irz, Xavier, Jyrki Niemi, and Liu Xing. "Determinants of food price inflation in Finland." Suomen Maataloustieteellisen Seuran Tiedote, no. 28 (January 31, 2012): 1–7. http://dx.doi.org/10.33354/smst.75469.

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The agricultural commodity crisis of 2006-8 and the recent evolution of commodity markets have reignited anxieties in Finland over fast-rising food prices and food security. Although the impact of farm commodity price shocks on the final consumer is mitigated by a large degree of processing as well as the complex structure of the food chain, little is known about the strength of the linkages between food markets and input markets. Using monthly series of price indices from 1995 to 2010, we estimate a vector error-correction (VEC) model in a co-integration framework in order to investigate the short-term and long-term dynamics of food price formation. The results indicate that a statistically significant long-run equilibrium relationship exists between the prices of food and those of the main variable inputs consumed by the food chain, namely agricultural commodities, labour, and energy. When judged by the magnitude of long-run pass-through rates, farm prices represent the main determinant of food prices, followed by wages in food retail and the price of energy. However, highly volatile energy prices are also important in explaining food price variability. The parsimonious VEC model suggests that the dynamics of food price formation is dominated by a relatively quick process of adjustment to the long-run equilibrium, the half life of the transitional dynamics being six to eight months following a shock.

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26

Brünner, Tobias. "Price formation in call auctions with insider information." Studies in Economics and Finance 36, no. 3 (July 26, 2019): 408–26. http://dx.doi.org/10.1108/sef-02-2018-0066.

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Purpose This study aims to investigate – theoretically and empirically – if call auctions incorporate asymmetric information into prices. Design/methodology/approach First, this study introduces a new model of price formation in a call auction with insider information. In this call auction model, insider trading gives rise to an asymmetric information component of transaction costs. Next, this study estimates the model using 20 stocks from Euronext Paris and investigates if the asymmetric information component is present. Findings The theoretical analysis reveals that call auctions incorporate asymmetric information into prices. The empirical analysis finds strong evidence for the asymmetric information component. Testable implications provide further support for the model. Practical implications Call auctions have recently been proposed as an alternative to continuous limit order book markets to overcome problems associated with high-frequency trading. However, it is still an open question whether call auctions efficiently aggregate asymmetric information. The findings of this study imply that call auctions facilitate price discovery and, therefore, are a viable alternative to continuous limit order book markets. Originality/value There is no generally accepted measure of trading costs for call auctions. Therefore, the measure introduced in this study is of great value to anyone who wants to quantify trading costs in call auctions, understand the determinants of trading costs in call auctions or compare trading costs and their components between continuous markets and call auctions. This study also contributes to the literature devoted to estimating the probability of information-based trading.

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27

Pozdnyakov, Yuri, Nataliya Chukhray, Nataliya Hryniv, and Taisia Nakonechna. "Management of tangible assets using a modified market value price formation model." Problems and Perspectives in Management 19, no. 2 (April 22, 2021): 28–39. http://dx.doi.org/10.21511/ppm.19(2).2021.03.

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The paper deals with the economic measurements of the market value of enterprise assets, which are of great importance for their effective management. The use of more accurate economic measurements is an integral part of an optimal strategy to manage business assets. Therefore, reduction of evaluation results uncertainty is a necessary condition for effective management. To achieve mentioned goals, the paper aims to determine the mathematical base for the assets valuation methodology of value/depreciation that change over time, which can be applied to its dynamic objective quantitative analysis. The basic hypothesis suggests that all tangible assets, characterized by removable depreciation, are inclined to a negative periodic depreciation during short inter-service periods when remedial repair works are carried out to eliminate depreciation. The methodical approaches concerning a mathematical description of assets value/depreciation dynamics are considered. It is shown that both traditional, progressive and regressive value/depreciation dynamics models change over time. They do not correspond to the actual state since they do not take into account increased objects value and negative periodic depreciation. To evaluate value/depreciation change over time more precisely, a new kind of mathematical model is proposed, which equations take into account the opposite signs of periodic depreciation during operational service periods and non-operational inter-service periods. It is proved that the actual indicators of fair market value and periodic depreciation of enterprise assets can be determined with higher reliability based on a new mathematical model. AcknowledgmentsComments from the editor and anonymous referees have been gratefully acknowledged. The authors are grateful to the Ministry of Education and Science of Ukraine for financial support, which made it possible to carry out this study within the state budget topic “Value estimation and assessing technology readiness for transfer from universities to the business environment” (2019–2021).

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28

Fiedor, Paweł, and Artur Hołda. "The Effects of Bankruptcy on the Predictability of Price Formation Processes on Warsaw’s Stock Market." e-Finanse 12, no. 1 (March 1, 2016): 32–42. http://dx.doi.org/10.1515/fiqf-2016-0134.

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AbstractIn this study we investigate how bankruptcy affects the market behaviour of prices of stocks on Warsaw’s Stock Exchange. As the behaviour of prices can be seen in a myriad of ways, we investigate a particular aspect of this behaviour, namely the predictability of these price formation processes. We approximate their predictability as the structural complexity of logarithmic returns. This method of analysing predictability of price formation processes using information theory follows closely the mathematical definition of predictability, and is equal to the degree to which redundancy is present in the time series describing stock returns. We use Shannon’s entropy rate (approximating Kolmogorov-Sinai entropy) to measure this redundancy, and estimate it using the Lempel-Ziv algorithm, computing it with a running window approach over the entire price history of 50 companies listed on the Warsaw market which have gone bankrupt in the last few years. This enables us not only to compare the differences between predictability of price formation processes before and after their filing for bankruptcy, but also to compare the changes in predictability over time, as well as divided into different categories of companies and bankruptcies. There exists a large body of research analysing the efficiency of the whole market and the predictability of price changes en large, but only a few detailed studies analysing the influence of external stimulion the efficiency of price formation processes. This study fills this gap in the knowledge of financial markets, and their response to extreme external events.

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29

Obrosova, Natalia K. "EQUILIBRIUM PRICE BIFURCATION IN WALRAS PRICE FORMATION MODEL WITH DELAYS." IFAC Proceedings Volumes 38, no. 1 (2005): 18–23. http://dx.doi.org/10.3182/20050703-6-cz-1902.02239.

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30

VOLONTYR, Ludmila, Nadiya POTAPOVA, and Oksana ZELINSKA. "ECONOMETRIC MODELING IN FORMATION OF OPTIMAL PRICE FOR IMPLEMENTATION OF AGRICULTURAL PRODUCTS." "EСONOMY. FINANСES. MANAGEMENT: Topical issues of science and practical activity", no. 5 (45) (May 2019): 83–93. http://dx.doi.org/10.37128/2411-4413-2019-5-9.

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Ukraine is a predominantly agricultural country, and this branch has been recently demonstrating relatively high efficiency. Vegetable growing is a specific branch of crop production, which includes a large set of vegetables grown according to different technologies, with different shelf life of vegetable products, their different cost and production efficiency. The analysis of the situation on the vegetable market of Ukraine showed that there is a certain correlation between production volumes, sales and products sales prices. The price market environment on the vegetable market in recent years is largely determined by the ratio of supply and demand on the market. Thus, sales volumes increase when the supply on the market is the highest and the price level on the market is the lowest. The absence of permanent wholesale distribution channels also leads to an increase in the hidden market for vegetable products. According to experts of the Ukrainian Agrarian Confederation, the hidden market for fruit and vegetables is about $ 14 billion, or about 60% of the total turnover of vegetable products in Ukraine. Due to the moratorium on the sale of agricultural land, businesses are not able to buy land on their own and develop their business in the long-term prospects. Today, government support in the vegetable sector is limited to preferential lending and to individual funding programs, most often in collaboration with international donors. Much of the support for agro-industrial farms goes to grain and pulse plant producers, which significantly limits the opportunity for developing crop producers with higher marginality. The conditions in which the agrarian sector operates have a high degree of changeable uncertainty, and this circumstance requires agricultural producers to find ways to obtain reliable information about the state of the agricultural market, organizational and functional links between the subjects of the agricultural market, prices for agricultural products. etc. The purpose of this study is to: analyze the price of vegetable sales in Ukraine; substantiation of the use of the AGMEMOD partial equilibrium model for forecasting vegetable production in Ukraine; establish dependence of demand and supply of vegetable production on their sales price; determine the point of equilibrium of supply and demand and calculate of the optimal selling price of vegetables in Ukraine; justify the optimal costs for vegetable production; analyze of the price of selling vegetables in Ukraine and determine the optimal price according to supply and demand, as well as the optimal cost of vegetable production. Now, there are 12 key vegetable crops in Ukraine. These are potatoes, cucumbers, tomatoes, cabbage, beets, carrots, onions, garlic, peppers, zucchini, eggplants and pumpkin. Of these 12 cultures, 9 showed an increase in the period 2010-2016, even without taking into account the uncontrolled Crimea and Donbass. This increase has been driven by two crucial factors: - yield increase. This was made possible due to improving the quality of the seed and natural technological progress in the processing and the use of crop protecting agents. - increase in export demand for products. The demand, for example, for Ukrainian carrots and onions has increased, and therefore the opportunities for their cultivation have become greater. Price is a complex economic category, practically the only element of marketing that enables an enterprise to earn real income. Without proper economic justification of the price level, the normal functioning of economic entities and entire sectors of the economy is impossible, which in turn has a significant impact on the material well-being of the population. The level of market price depends on the value of other marketing elements, as well as on the level of competition on the market and the general state of the economy. As a rule, other marketing elements also change (for example, with increasing product differentiation in order to maximize price or at least the difference between price and cost). The price formation strategy allows determining the price level and marginal prices for individual product groups. The price formation should always be carried out taking into account the nomenclature and quality of products, their usefulness, importance and purchasing power of consumers and prices of the competitors. The strategy of price formation management is a set of measures to maintain conditional prices while actually regulating them in accordance with the variety and characteristics of demand, competition in the market. The AGMEMOD model is an example of the partial equilibrium (PE) models used in agriculture. The main advantages of partial equilibrium models are: the simplicity of the implemented algorithms, the operation of which is quite easily traced; relative availability of necessary data; the calculations are amenable to adequate economic interpretation, making it possible to quickly analyze the consequences of making a decision in the agricultural sector. However, partial equilibrium models are not without their disadvantages. In particular, they do not permit to assess macroeconomic effects such as changes in national income or employment levels, the effects that may be obtained from the redistribution of resources (labor, capital, etc.) into more efficient sectors. For national researchers, it is advisable to use these models, because they have a module of Ukraine, but it is necessary to supplement the program with statistics on vegetables. The demand is a function of price changes in the current period, and the supply is a function of price changes in previous periods. Econometric models of supply and demand dependence of vegetable production on the price of their sale are constructed. The equilibrium of the system is observed at the price of 6558 UAH. for 1 ton of vegetables under the given conditions of consumption, the demand is equal to supply and is 9321 thousand tons. Econometric models of price dependence on material costs, labor costs and depreciation have been constructed. By the first model, it can be determined that the content of unaccounted factors is estimated at 99.82 UAH. per hectare; with an increase in material costs by 1 hectare by 1 UAH, selling price increases by 0.9 UAH. per ton. Based on the Fisher's ratio test, the model is adequate, the relationship between the indicators is tight. The relationship between the indicators of the second model is weak, the calculated correlation coefficient can be trusted, but in general, the adequacy of the model conclusion cannot be made. The model shows that with an increase in labor costs by 1 UAH per hectare, the price increases by 10.03 UAH per ton. The third model based on the Fisher's ratio test is adequate, the relationship between the indicators is average. With the increase in depreciation costs per hectare by 1 UAH, the selling price will increase by 12.42 UAH per ton. The value of the linear correlation coefficient other than zero is statistically significant. Based on the calculated models, we will determine the optimal cost per hectare: material – 7144.2 UAH, labor costs – 689.4 UAH, depreciation costs – 543.4 UAH.

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31

Tarasovych, Lyudmyla. "Price policy in the marketing system of agricultural enterprises." Management Theory and Studies for Rural Business and Infrastructure Development 36, no. 3 (October 14, 2014): 672–78. http://dx.doi.org/10.15544/mts.2014.064.

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The aim of the research is to explain the peculiarities and mechanisms of price formation the system of agricultural enterprises’ marketing. Research methods have scientific and analytical comparison, induction and deduction, analysis and synthesis. Place, role, and peculiarities of price policy formation in the system of marketing of agricultural enterprises are explained. Theoretically proven that effective functioning of agricultural enterprises is determined by pricing policy formation which is efficient and corresponds to the market. The pricing policy is considered as a system of actions directed on determining prices for products and services, and on formation of pricing strategy and tactics, and also on changing the level of prices depending on the competitive position of an enterprise on the market in order to hold the desired marketing chares in strategic perspective. The key ideas of complementary connection of pricing policy of agricultural enterprises with other directions of their marketing activity are described.

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32

BHANUMURTHY, N. R., PAMI DUA, and LOKENDRA KUMAWAT. "WEATHER SHOCKS AND AGRICULTURAL COMMODITY PRICES IN INDIA." Climate Change Economics 04, no. 03 (August 2013): 1350011. http://dx.doi.org/10.1142/s2010007813500115.

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We analyze the impact of weather shocks on price formation in spot and futures market for food in India where until the recent introduction of commodity futures markets in 2005, the transmission of these shocks to short-term (spot) price movements was unclear. Hitherto, the price discovery mechanism was weak and end price was expected to be different (mostly higher unless some product prices were administered) from the market-clearing price. In addition, this weak mechanism was expected to result in higher price volatility. The introduction of a futures market is expected to reduce risk, a major component in agricultural production as well as in price formation. Though the commodity futures market in India is nascent, we model transmission of weather shocks to futures and spot prices using monthly data. Based on cointegration analysis, our results suggest strong long-run co-movement between futures prices and spot prices for commodities traded in futures markets. Changes in rainfall affect both futures and spot prices with different lags. However, rainfall shocks generate larger responses from futures prices than from spot prices. Although there could be other factors that affect futures prices, after controlling for fuel prices, our results clearly show the transmission mechanism of weather shocks from futures to spot prices. We also explore the changes in responsiveness of prices of major agricultural commodities to rainfall with introduction of futures contracts to facilitate the pass-through of various types of shocks to agricultural commodity prices. Using smooth transition regression, we find that the bivariate relationships between rainfall and prices of rice, wheat and pulses show some nonlinearity with the structural change happening after the introduction of futures market. These relations are found to be much stronger in the post-structural change period that broadly coincides with the introduction of futures market.

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Golovanova, S. "Incentive Domestic Gas Price Regulation in Russia: Comparative Analysis of Alternative “Price Cap” Indicators." Voprosy Ekonomiki, no. 8 (August 20, 2014): 106–21. http://dx.doi.org/10.32609/0042-8736-2014-8-106-121.

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Introduction of the price regulatory mechanism based on the principle of equal profitability of domestic and export gas sales is announced in Russia. In this article the “price cap” mechanism is considered as a form of incentive regulation; the experience of using the external market price as an indicator of the “fair” price level in antitrust enforcement is presented. Analyzing the existing wholesale gas price formation mechanisms around the world we identify alternative indicators of the external market price for gas and compare levels and volatilities of the “base” internal gas prices calculated on their basis with the use of retrospective data.

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Llop, Maria. "Measuring the influence of energy prices in the price formation mechanism." Energy Policy 117 (June 2018): 39–48. http://dx.doi.org/10.1016/j.enpol.2018.02.040.

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HOMMES, CARS, JOEP SONNEMANS, JAN TUINSTRA, and HENK VAN DE VELDEN. "LEARNING IN COBWEB EXPERIMENTS." Macroeconomic Dynamics 11, S1 (May 7, 2007): 8–33. http://dx.doi.org/10.1017/s1365100507060208.

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Different theories of expectation formation and learning usually yield different outcomes for realized market prices in dynamic models. The purpose of this paper is to investigate expectation formation and learning in a controlled experimental environment. Subjects are asked to predict the next period's aggregate price in a dynamic commodity market model with feedback from individual expectations. Subjects have no information about underlying market equilibrium equations, but can learn by observing past price realizations and predictions. We conduct a stable, an unstable, and a strongly unstable treatment. In the stable treatment, rational expectations (RE) yield a good description of observed aggregate price fluctuations: prices remain close to the RE steady state. In the unstable treatments, prices exhibit large fluctuations around the RE steady state. Although the sample mean of realized prices is close to the RE steady state, the amplitude of the price fluctuations as measured by the variance is significantly larger than the amplitude under RE, implying persistent excess volatility. However, agents' forecasts are boundedly rational in the sense that fluctuations in aggregate prices are unpredictable and exhibit no forecastable structure that could easily be exploited.

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Lin, Haiyan. "Network Consumers’ Reference Price Formation Analysis." Open Journal of Business and Management 06, no. 03 (2018): 696–706. http://dx.doi.org/10.4236/ojbm.2018.63053.

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King, David A., and J. A. Sinden. "Price Formation in Farm Land Markets." Land Economics 70, no. 1 (February 1994): 38. http://dx.doi.org/10.2307/3146439.

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Kadoya, Toshihisa, Tetsuo Sasaki, Akihiko Yokoyama, and Satoru Ihara. "Understanding Price Formation in Electricity Markets." IEEJ Transactions on Power and Energy 126, no. 3 (2006): 327–35. http://dx.doi.org/10.1541/ieejpes.126.327.

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39

Cao, Charles, Yong Chen, William N. Goetzmann, and Bing Liang. "Hedge Funds and Stock Price Formation." Financial Analysts Journal 74, no. 3 (July 1, 2018): 54–68. http://dx.doi.org/10.2469/faj.v74.n3.4.

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40

Deriabin, A. A. "Let's Start by Correcting Price Formation." Problems in Economics 32, no. 9 (January 1990): 24–36. http://dx.doi.org/10.2753/pet1061-1991320924.

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Wan, Li. "Factor Analysis of Rice Price Formation." Journal of Rural Problems 52, no. 4 (2016): 217–22. http://dx.doi.org/10.7310/arfe.52.217.

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van Dijk, J. C., and D. D. van der Stelt-Scheele. "Price formation in tourism industry branches." Annals of Tourism Research 20, no. 4 (January 1993): 716–28. http://dx.doi.org/10.1016/0160-7383(93)90093-i.

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43

Kardasz, Stanley W., and Kenneth Stollery. "Price formation in Canadian manufacturing industries." Applied Economics 20, no. 4 (April 1988): 473–83. http://dx.doi.org/10.1080/00036848800000058.

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Ciaian, Pavel, Miroslava Rajcaniova, and d’Artis Kancs. "The economics of BitCoin price formation." Applied Economics 48, no. 19 (November 13, 2015): 1799–815. http://dx.doi.org/10.1080/00036846.2015.1109038.

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Tada, Minoru. "Econometric analysis of expected price formation." Agricultural Economics 5, no. 1 (January 1991): 59–73. http://dx.doi.org/10.1111/j.1574-0862.1991.tb00135.x.

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Theissen, Erik. "Trader Anonymity, Price Formation and Liquidity *." Review of Finance 7, no. 1 (April 1, 2003): 1–26. http://dx.doi.org/10.1023/a:1022579423978.

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Al-Zand, D. A. "Food Price Formation - An International Prospective." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 33 (April 1985): 107. http://dx.doi.org/10.1111/j.1744-7976.1985.tb03244.x.

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Feldman, Todd, and Gabriele Lepori. "Asset price formation and behavioral biases." Review of Behavioral Finance 8, no. 2 (November 14, 2016): 137–55. http://dx.doi.org/10.1108/rbf-05-2015-0020.

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Purpose The purpose of this paper is to examine the debate on whether psychology affects asset prices using agent-based modeling. Design/methodology/approach The authors set up three simulation regimes where the first regime contains fundamental investors who invest based on the mean-variance framework. The second regime includes purely irrational investors who invest based on behavioral biases. The third regime combines the two types of investors. The authors test whether the return properties from regime 3 converge to that of regime 1 or 2. Findings Results suggest that the type of irrationality affects return properties in different ways. Irrational investors who are introspective in their irrationality, only examining their performance and deficiencies, do not have much of a systematic effect on stock returns when combined with rational investors. However, irrational investors that aggregate information in an irrational manner have a systematic effect when combined with rational investors. Research limitations/implications Research implication of using simulation analysis is that the results need to be verified via other methods such as empirical and/or experimental analysis. Practical implications Practical implications of the research is that policy makers can look for factors that investors use to aggregate to better understand the movement of financial prices and ignore other factors. Social implications Social implication is that mass psychology impacts financial prices. Originality/value No other paper has used agent-based/behavioral analysis to better understand how different types of behavior may impact financial prices in different ways.

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Caporale, Guglielmo Maria, and Alessandro Girardi. "Price formation on the EuroMTS platform." Applied Economics Letters 18, no. 3 (February 2011): 229–33. http://dx.doi.org/10.1080/13504850903559567.

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Cason, Timothy N., and Daniel Friedman. "Price Formation in Single Call Markets." Econometrica 65, no. 2 (March 1997): 311. http://dx.doi.org/10.2307/2171895.

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