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Dynamic policy games feature a wide range of equilibria. This paper provides a methodology for obtaining robust predictions. We focus on a model of sovereign debt, although our methodology applies to other settings, such as models of monetary policy or capital taxation. Our main result is a characterization of distributions over outcomes that are consistent with a subgame perfect equilibrium conditional on the observed history. We illustrate our main result by computing—conditional on an observed history—bounds across all equilibria on the maximum probability of a crisis: means, variances, and covariances over debt prices. [ABSTRACT FROM AUTHOR]

We present a unified duality approach to Bayesian persuasion. The optimal dual variable, interpreted as a price function on the state space, is shown to be a supergradient of the concave closure of the objective function at the prior belief. Strong duality holds when the objective function is Lipschitz continuous. When the objective depends on the posterior belief through a set of moments, the price function induces prices for posterior moments that solve the corresponding dual problem. Thus, our general approach unifies known results for one‐dimensional moment persuasion, while yielding new results for the multidimensional case. In particular, we provide a condition for the optimality of convex‐partitional signals, derive structural properties of solutions, and characterize the optimal persuasion scheme when the state is two‐dimensional and the objective is quadratic. [ABSTRACT FROM AUTHOR]

Evidence suggests that consumers do not perfectly optimize, contrary to a critical assumption of classical consumer theory. We propose a model in which consumer types can vary in both their preferences and their choice behavior. Given data on demand and the distribution of prices, we identify the set of possible values of the consumer surplus based on minimal rationality conditions: every type of consumer must be no worse off than if they either always bought the good or never did. We develop a procedure to narrow the set of surplus values using richer data sets and provide bounds on counterfactual demands. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between a technology shock and employment, considering price, wage rigidities, and heterogeneous agents. To explore this relationship, we utilized a Dynamic Stochastic General Equilibrium (DSGE) model, incorporating households with varying savings rates. For empirical validation, we conducted a Structural Vector Autoregression (SVAR) analysis using data from two economies with distinct savings patterns—the United States and China. This approach allowed us to assess the impact of technology shocks on employment dynamics across different savings environments. Under these conditions, we observe that the effect of technology on aggregate employment is initially positive. Still, it gradually decreases in the mid-term, eventually switching to a negative impact before slowly recovering to equilibrium. The reason for this phenomenon depends on (i) the magnitude of fluctuations in price and wage, precisely, which variable's fluctuations have a greater magnitude, and (ii) which effect, between income effect and substitute effect, is preferred by restricted and unrestricted households. Due to (i), real wages change, and because of (ii), households make different labor supply decisions, leading to fluctuations in employment in response to technology shocks. [ABSTRACT FROM AUTHOR]

A number of studies have shown that, in hedonic models, the structural attributes of real property have a greater influence on price than external attributes related to location and the immediate neighbourhood. This makes it necessary to include detailed information about structural attributes when predicting prices using regression models and machine learning algorithms and makes it difficult to study the influence of external attributes. In our study of asking prices on the primary residential market in Warsaw (Poland), we used a methodology we developed to determine price indices aggregated to micro-markets, which we further treated as a dependent variable. The analysed database consisted of 10,135 records relating to 2444 residential developments existing as offers on the market at the end of each quarter in the period 2017–2021. Based on these data, aggregated price level indices were determined for 503 micro-markets in which primary market offers were documented. Using the analysed example, we showed that it is possible to predict the value of aggregated price indices based only on aggregated external attributes—location and neighbourhood. Depending on the model, we obtained an R2 value of 75.8% to 82.9% for the prediction in the set of control observations excluded from building the model. [ABSTRACT FROM AUTHOR]

Widespread attention has been paid to the continuous rounds of rising agricultural product prices in China since 2003. During this period, rising prices were affected by input costs and international market prices; therefore, the degree of benefits obtained by farmers from the rise in agricultural product prices has been questioned. However, limited studies have examined the effect and relationship between rising agricultural product prices and farmers' income. The purpose of the study was to provide empirical evidence on how the rise in agricultural product prices affects farmers' income and to understand the role of land and labor costs in this context using provincial data from 2003 to 2020. The findings reveal that there is a threshold effect on the impact of the rise in agricultural product prices on farmers' income. A moderate rise in agricultural product prices can increase farmers' income, but excessive price increases have an insignificant effect. The causes behind the price rise significantly influence farmers' income. An increase attributed to higher land and labor costs can improve farmers' income, whereas a rise driven by international market prices or the cost of agricultural production inputs is unlikely to benefit farmers. [ABSTRACT FROM AUTHOR]

The land price reflects the supply and demand relationship in the land market and plays an important role in regulating land use. Improving land auction rules is of great significance for avoiding abnormal fluctuations in the land market and promoting the sustainable use of land resources. To regulate the abnormal fluctuations in the state-owned land use rights' auction prices, Chinese local governments have implemented a "sealed one-time bidding, average price wins" rule. However, limited theoretical and empirical research that assesses its policy impact exists. This study examines the policy motivations behind this rule, constructing three game models; namely, static complete information, static incomplete information, and multiperiod repeated games. By deducing bidding strategies and equilibrium results, hypotheses are formulated. A baseline difference-in-differences (DID) and a dynamic policy effect model are designed, and the Python crawler is used to obtain 1182 microland auction samples in Suzhou. This study evaluates the impact of the one-time bidding rule on the starting prices, transaction prices, and premium rates. The empirical results underwent multiple robustness tests, eliminating potential endogeneity issues and biases. The results show that while the policy is effective in restraining the premium rate, indicating the bidding intensity in single-land auctions, it proves challenging to curb the long-term rise in land prices through continuous bidding auctions. Moreover, the policy may stimulate local governments to increase auction starting prices. [ABSTRACT FROM AUTHOR]

This paper studies the implementation of a system consisting of an electrolyzer connected to a bioelectricity generator set that operates using vinasse biogas. Data on total ethanol production across all regions of Brazil were collected, and two ethanol plants in São Paulo state were selected for analysis (São Jose da Estiva and São Manoel). The feasibility of implementing this system at these facilities was evaluated using energy analysis for biohydrogen production. The process's energy efficiency was assessed, allowing for the construction of a Sankey Diagram for both plants. Additionally, the costs of producing bioelectricity and biohydrogen via electrolysis using vinasse biogas from Brazil's sugar/alcohol industry were determined, factoring in the payback period, equivalent utilization period, and annual interest rate. The study demonstrates the efficiency and cost-effectiveness of producing biohydrogen using vinasse biogas. The cost of producing bioelectricity at the two plants decreases significantly over the 25-year system implementation period, starting at around 0.09–0.08 USD/kWh in the first year and dropping to a minimum of 0.027–0.039 USD/kWh by the end of the period. The initial cost of producing biohydrogen ranges from 0.24 to 0.25 USD/kWh at São Jose da Estiva and from 0.30 to 0.32 USD/kWh at São Manoel in the first year. However, prices drop by nearly half in the second year, reaching 0.05 to 0.06 USD/kWh at São Jose da Estiva and 0.06 to 0.07 USD/kWh at São Manoel. The payback period for the biohydrogen and bioelectricity production system increases, with São Jose da Estiva generating income by 1st year for a 4% to 12% interest rate. For São Manoel, income is expected by the 1.5 year for a 4% to 12% interest rate. Both plants recover their investment within the first 2 years of operation. By the 10th year, São Jose da Estiva's income is projected to be between 3,888,501.96 USD (4%) and 3,678,407.29 USD (12%), while São Manoel's income is expected to range from 3,123,824.69 USD (4%) to 2,932,050.63 USD (12%), demonstrating that the system is viable for sugar and ethanol plants. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Economics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper aims at extracting a common factor from major carbon pricing which is interpreted as an effective carbon pricing or a shadow pricing. For this purpose, we use a dynamic factor model for which the unobserved common factors and idiosyncratic noises are potentially nonstationary processes. The two‐step Kalman smoother procedure is used to estimate the model. We found (i) that the extracted common factor (i.e., the effective carbon pricing) exhibits very low values which range from 2.05 to 16.1 USD per ton, and more importantly, (ii) this factor does not cointegrate with the market prices. This last result highlights a total lack of integration of carbon markets. [ABSTRACT FROM AUTHOR]

In this study, we examine the influence of a preopening call market (CM) session on the subsequent continuous double auction (CDA) trading phase within a combined market structure (CMDA). We propose that the introduction of a CM phase before the CDA could potentially mitigate price volatility during the CDA by facilitating the disclosure of agents' private information through the preopening pricing phase. Our findings reveal a positive relationship between the preopening price and the prices traded during the subsequent phase, alongside a decreased deviation of prices from their fundamental value when high‐quality information is available. These results suggest that a well‐defined preopening phase has the potential to improve market efficiency. [ABSTRACT FROM AUTHOR]

We use an artificial neural network for finance in two directions: to estimate prices and Greeks based on the geometric Brownian motion and the constant elasticity of variance model for European options, and to construct a local volatility surface. To show the efficiency and successful usage of the network, we compare prices and Greeks obtained by a solution formula and by the artificial neural network when there is a solution formula is known. Then, we calculate Dupire's equations to construct a local volatility surface by the network. [ABSTRACT FROM AUTHOR]

Altcoins are alternative types of coins under cryptocurrency, apart from traditional Bitcoins, for which predicting the price movement presents a multifaceted challenge deeply rooted in the volatile nature of the cryptocurrency market. This study compares and analyzes different Machine Learning (ML) and Deep Learning (DL) models for price movement prediction through diverse data sources like Bitcoin prices, social media sentiments, and news sentiments, apart from different socio-economic factors specific to USA geography due to its maturity on use of Altcoins, with temporal scope spanning from 2016 to 2022 collating over 77 M tweets and news items. Ethereum, Binance, XRP, Cardano, Monero, Tron, Stellar, and Litecoin, were considered for experimentation across widely used algorithms like Gradient Boosting, Naive Bayes, Decision Trees, Neural Networks, and the like, with different day-length lags ranging up to 4 days. Highly relevant features were selected using Random Forest selection method and highly correlated features have been removed before the modeling. Accuracy for price movement prediction models varied from 71.03% for Ethereum to 66.14% for Stellar, which were better by 15–20% as compared to percentage benchmarking done by literature to be ranging around 50 s and 60 s. For the model validation, sensitivity analysis involving day-wise lag analysis, and different data splits (based on size and months) were considered, which was stable for the high performing models. Further, an interesting result was observed during the study. In order of priority, Bitcoin prices, social media sentiments, and news sentiments significantly impact altcoin price movement. This implies that by studying the Bitcoin price movement and market sentiments, investors can make wise decisions towards altcoin investments. This study holds significance for researchers and practitioners to understand the impact in the trading market of cryptocurrency and help an investor diversify their portfolio. The findings will be helpful for Algo Trading Platforms, Financial Advisors, Trading Experts, Industry Experts, Researchers, and Scholars. [ABSTRACT FROM AUTHOR]

The symmetric trading algorithm (STA) and asymmetric trading algorithm (ATA) are proposed, and a step-by-step pseudo code of proposed algorithms are presented. Utilizing a real dataset, we examine the profitability of these trading algorithms on stablecoin markets. In the STA, the buying and selling prices are chosen equidistant from the expected asset price, while in the ATA, the choice of the selection of buying and selling prices is flexible. The profitability of the algorithms is computed and it is demonstrated that as the volatility of the considered market is increasing, the average and maximum profits of the both algorithms are raising in general. It is also shown that although the profitability of both algorithms is closer for relatively low volatile price series, the ATA outperforms the STA when the considered market is undervalued (or overvalued), whereas the STA is more profitable when the price series is oscillating near the expected price level. Furthermore, it is observed that although it is critical to determine the optimal profit margin (OPM) to maximize the profit of the trading algorithms, there is not an obvious relation between the volatility of the price series and the OPM values. This problem is solved by machine learning methods, and the naive Bayes classifier is used to classify the OPM values as small, medium or large. It is noted that proposed trading algorithms can be applied to all practical stock and cryptocurrency exchange markets, as they only need two assets with an expected price ratio. [ABSTRACT FROM AUTHOR]

American-type financial instruments are often priced with specific Monte Carlo techniques whose efficiency critically depends on the dimensionality of the problem and the available computational power. Our work proposes a novel approach for pricing Bermudan swaptions, well-known interest rate derivatives, using supervised learning algorithms. In particular, we link the price of a Bermudan swaption to its natural hedges, which include the underlying European swaptions, and other relevant financial quantities through supervised learning non-parametric regressions. We explore several algorithms, ranging from linear models to decision tree-based models and neural networks and compare their predictive performances. Our results indicate that all supervised learning algorithms are reliable and fast, with ridge regressor, neural networks, and gradient-boosted regression trees performing the best for the pricing problem. Furthermore, using feature importance techniques, we identify the most important driving factors of a Bermudan swaption price, confirming that the maximum underlying European swaption value is the dominant feature. [ABSTRACT FROM AUTHOR]

In this paper we develop a closed-form spread option pricing formula based on Gauss-Hermite quadrature (GHQ) and show that the proposed method is a competitive method for the Black-Scholes model and is best-suited for the jump-diffusion model. The GHQ method turns the integral of spread option pricing formula into a summation of call option pricing formulas with adjusted parameters, and therefore the final formula remains in closed-form which ensures its computational advantage. Under the basic Black-Scholes model, the proposed GHQ formula provides equally nice accuracy compared to the best-performing LDZ formula in the literature. But for the extended jump-diffusion model, the LDZ formula sees a significant loss of accuracy due to the multi-layered summation, whereas the GHQ formula is still able to achieve very high accuracy at only slightly increased computing costs. Various closed-form formulas are tested in our numerical analysis which demonstrates that the proposed GHQ formula is the most recommended for pricing spread options under the jump-diffusion model. [ABSTRACT FROM AUTHOR]

The influence of non-linear terms and non-white noise terms on stochastic differential equation model for time evolution of prices of the market is investigated with aim to analyse the effect generated on exponent of the long-tail distribution of the probability density of the returns and Hurst index. In particular, whether the model proposed is adequate as a possible mathematical model for description of the market either if it satisfies to the stylized facts obeyed by the financial markets as the long-tail distribution of the returns, which must obey to the inverse cubic law observed. [ABSTRACT FROM AUTHOR]

Purpose: Firms need competitiveness in terms of quality, price, and delivery to satisfy public sector customers. The purpose of this study is to investigate the effect of innovation capability on this competitiveness in a market-oriented firm and to determine the role of supply chain integration and marketing-technical integration in enhancing innovation capability. Design/methodology/approach: A theoretical model was developed to test hypotheses. Data was collected from 199 top-level managers serving pharmaceutical companies. Partial least squares-structural equation modelling was used for datas analysis. Findings: The results highlight the vital role of innovation capability in the development of firm competitiveness. Significant results for all relationships were obtained. The effect of market orientation on supply-chain integration was stronger than its effect on marketing-technical integration. Partial multi-mediation of supply chain integration and marketing-technical integration was found in the relation between market orientation and innovation capability. Practical implications: The study helps managers develop an efficient strategy by using firms' innovation capability to supply products according to public sector customer needs. Originality/value: The study is based on a real-time practical problem faced by firms, as the majority of them fail to sell their products to public sector customers. To the best of the authors' knowledge, the current study is one of the first studies that discusses the role of innovation capability at the marketing-supply chain management interface to help a firm become an attractive supplier for public sector customers. [ABSTRACT FROM AUTHOR]

We study a mechanism that gives students the option of paying higher tuition to attend their preferred schools. This seat‐purchasing mechanism is neither strategyproof nor stable. Our paper combines administrative and survey data to estimate students' preferences and conducts welfare analysis. We find that changing from a deferred acceptance mechanism to the cadet‐optimal stable mechanism reduces students' welfare but that adopting the observed seat‐purchasing mechanism alleviates this welfare loss. Moreover, students from affluent communities prefer to pay higher tuition to stay at preferred schools, while those from less affluent communities are more likely be priced out to lower‐quality schools. [ABSTRACT FROM AUTHOR]

In the case of commercial surrogacy, a portion of the monetary transfer between the intended parent(s) and the surrogate is given to the latter to compensate for her contribution. Based on a study of the practices of the 10 most important intermediation agencies for surrogacy in the Unites States, the paper studies the amount, form, legal status and use of such compensations, and shows that it cannot be equated with real payment for work. Surrogacy work can thus be interpreted as a form of 'contested labour', i.e. an activity that has all the characteristics of work, but actually takes place in a sector where socio-economic institutions prevent the activity from being recognized as such. [ABSTRACT FROM AUTHOR]

We consider direct mechanisms to sell heterogeneous objects when buyers have private additive valuations and nonunit demand. We completely characterize the class of strategy-proof and agent sovereign mechanisms that satisfy a local side-flatness condition. Further, we introduce a notion of "continuity up to utility" and show that any such mechanism allocating all objects at all profiles is continuous and anonymous only if it is efficient. We find that the only mechanism satisfying these properties is equivalent to operating simultaneous second-price auctions for each object—as was done by the New Zealand government in allocating license rights to the use of radio spectrum in 1990. Finally, we present a complete characterization of simultaneous second-price auctions with object-specific reserve prices in terms of these properties and a weak nonbossiness restriction. [ABSTRACT FROM AUTHOR]

Hotelling's model—which is commonly referred to as the "linear city" model—is perhaps the most widely-used model of competition in differentiated products. However, pure-strategy Nash equilibria in prices do not exist unless the firms are located either sufficiently far apart from each other or at the same place. In fact, pure-strategy Nash equilibrium prices exist in only about 15 percent of all possible locations of firms in the linear city. This paper applies the undercut-proof equilibrium concept to solve for equilibrium prices for all possible locations of firms in the linear city. [ABSTRACT FROM AUTHOR]

DSGE models based on New Keynesian principles, which have been extended to allow for banking, the zero lower bound on interest rates (ZLB), and varying price duration, can account well for recent macroeconomic behavior across a variety of economies. These models find that active fiscal policy can contribute to macroeconomic stability and welfare by reducing the frequency of hitting the ZLB. Fiscal policy can also share the stabilisation role with monetary policy, whose effectiveness under the ZLB is much reduced. [ABSTRACT FROM AUTHOR]

This paper investigates the long-debated economic relationship between prices and interest rates through two macroeconomic relationships, the Gibson paradox and the Fisher effect. We use recent advances in applied econometrics and quarterly frequency data for the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and E7 (Brazil, China, India, Indonesia, Mexico, Russia, and Turkey) countries. We also use the Schmelzing (2019) new data on advanced economy inflation rates and interest rates to investigate the Fisherian link in eight advanced economies (France, Germany, Holland, Italy, Japan, Spain, the United Kingdom, and the United States) across eight centuries. The overall conclusion of the paper demonstrates poor evidence of the Gibson paradox and the Fisher effect across developed and emerging economies. [ABSTRACT FROM AUTHOR]

Before regulations were enacted to prevent such practices, information leaked through selective disclosure was incorporated into markets prior to the public release of news. "News days" did not deliver news to markets; now they do. We provide novel evidence of changes in returns and turnover behavior around the enactment of regulations barring selective disclosure practices in the United States and the EU. We conversely document lack of such changes in Australia and Japan, which did not implement similar measures. We conclude that selective disclosure resolves Roll's R 2 puzzle. [ABSTRACT FROM AUTHOR]

Problem definition: When a firm (buyer) outsources the production of a new product/component to a supplier subject to random yield, a major challenge is that the supplier's yield is usually private information. In practice, yield information is often shared via nonbinding communication—for example, a supplier self-assessment report. We examine whether such communication can be truthful and credible. Methodology/results: We analyze a cheap-talk game in which, given a simple contract that specifies the prices for each unit ordered and for each effective unit delivered, the supplier first communicates its yield level, and then the buyer determines an order quantity. We prove that truthful communication can emerge in equilibrium. To do so, we first show that if knowing the supplier's type, the buyer will either inflate or reduce the order quantity to cope with a lower yield, depending on the product's market potential. Under asymmetric information, the supplier will truthfully communicate its type if (i) the buyer with a high market potential intends to inflate the order quantity for a lower yield, but the buyer with a low market potential prefers to do the reverse; and (ii) the supplier is uncertain about the product's market potential, which is the buyer's private information, and anticipates that a hard-to-make product is more likely to have a higher market potential. Managerial implications: Truthful cheap-talk communication can emerge in equilibrium when the product's market size and yield are negatively correlated. Truthful communication always benefits the buyer and consumers and may benefit the supplier if the product has sufficient market potential and the supplier's production cost is not too high. Moreover, the buyer can be better off paying more for the input quantity (although part of the output is defective) or paying a higher wholesale rate if the adjustment in payment terms enhances communication credibility. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0089. [ABSTRACT FROM AUTHOR]

This study explores the effects of temporal reframing of price (TRP), a strategy presenting the total cost of a product or service in smaller, segmented amounts (e.g., $1 per day rather than $365 in total), and its potential to intensify consumers' feelings of being misled, thereby negatively impacting their attitudes toward the product. While previous research has underscored the benefits of TRP, such as lowering perceived costs and enhancing perceived benefits through heuristic information processing, the current study, informed by theories of heuristic versus systematic processing and attribution theory, investigates whether TRP leads to increased feelings of manipulation by deviating from conventional category‐specific temporal frames of pricing. Through two experiments, we find that such deviations may prompt consumers to view TRP as manipulative, subsequently heightening feelings of being misled, especially at higher price levels, whereas lower price levels could diminish the scrutiny applied to marketers' intentions. Additionally, this study demonstrates that feelings of being misled mediate the relationship between the presentation of price information and product attitude, highlighting the potential risks of employing TRP strategies. The paper discusses optimal situations for implementing TRP, aiming to navigate its strategic use effectively. [ABSTRACT FROM AUTHOR]

Repeat, cross-sectional, nationally representative data from the International Food Information Council (IFIC) 2019, 2020, and 2021 Food and Health Surveys were used to examine trends in consumer interest in sustainable diets. Between 2019 and 2021, the importance of purchasing food and drinks based on healthfulness, being antibiotic-free, hormone/steroid free, or locally sourced declined among Americans. In contrast, the importance of environmental sustainability and price in purchasing decisions remained consistent; however, low-income households reported slight declines in the impact of price. This knowledge could be used to encourage expanding sustainable options in the food system and enhance equity in access. [ABSTRACT FROM AUTHOR]

Purpose: Data science industry is a multidisciplinary field that deals with a large amount of data and derives useful information for taking routine and strategic business decisions. The purpose of this article is to examine the relationship between pricing models, engagement models, and firm performance (FP). This study also aims at uncovering the most effective pricing model and engagement model for improving FP. Design/methodology/approach: Indian data scientists were the respondents of the study. A total of 213 responses were carefully chosen. The data were analyzed using structural equations on Statistical Package for Social Sciences-Analysis of Moment Structures (SPSS-AMOS) version 25 software. Findings: The findings of the study suggested the positive and significant impact of pricing models and engagement models on FP. Value-based pricing strategies have the maximum impact on FP. On the other hand, managed services have a higher influence on FP. Originality/value: By developing a multi-faceted framework, this study is a novel contribution to the field of business strategy, especially for the data science industry. [ABSTRACT FROM AUTHOR]

The rising prices of N fertilizer led to exploring cost‐saving efforts, such as intercropping cool‐ or warm‐season legumes serving as alternative sources of N for managing fall‐stockpiled tall fescue [Schedonorus arundinaceus (Schreb.) Dumort.; TF]. We aimed to evaluate red clover (Trifolium pratense L.; RC) and sunn hemp (Crotalaria juncea L.; SH) mixed with TF as alternative sources of N for stockpiling TF to increase productivity and animal performance. The experiment was conducted in Crossville, TN, in 2020 and 2021 and consisted of TF pastures mixed with RC (TRC) or SH (TSH), and TF fertilized with urea (TU). The experiment was divided into two periods: the pre‐grazing period (stockpiling) (April–October) and the grazing period (October–December). After the stockpiling period, Black Angus beef (Bos taurus) steers were used for the grazing period. The study evaluated the botanical composition, herbage mass (HM), nutritive value, steer average daily gain (ADG), and net returns (NR). The TRC pastures had a greater proportion of legumes compared to TSH plots in May, October, November, and December of both years. There were no differences among treatments for the total HM and nutritive value in 2020; however, in 2021, TU had greater HM at the beginning of the grazing period and greater average crude protein values compared to the other treatments. In both years, there were no differences among treatments for ADG or NR. Therefore, producers can make the same profit considering the beef steer price and the cost of conventional and alternative N sources. Core Ideas: Legumes have the potential to suppress weeds when mixed with grasses.Nitrogen is the most important nutrient that contributes to forage production.Stockpiling forages contributes to the extension of grazing season.Grass and legume mixtures can increase the nutritive value and, consequently, improve animal performance. [ABSTRACT FROM AUTHOR]

Collusion is deemed unlikely in wholesale markets where upstream suppliers and intermediate buyers privately negotiate discounts off list prices and sales quotas are unfeasible. However, many wholesale markets include both small and large buyers who compete in the retail market. We study the role of publicly announced list prices in this wholesale-retail setting, whether suppliers collude or compete. When suppliers collude, public announcements of list prices extend the possibility of collusion from small to large buyers (the multi-buyer contact effect). When suppliers compete, these announcements provide them with commitment to negotiate better terms with large buyers (the commitment effect). [ABSTRACT FROM AUTHOR]

Throughout the recent pandemic, governments used digital video technologies to facilitate social distancing during political meetings. In addition to enabling social distancing, a theoretical advantage of virtual political governance is that it has the potential to mitigate the hierarchical administrative relationship between capitals and regions and the differences in real estate prices and wealth that often follows from such hierarchical structures. However, hardly any governments are currently planning a long‐term transition to work‐from‐home digital governance. On the contrary, several countries are doubling down on the centralization model of government by building new capitals in new locations. This article proposes that in a time of digital alternatives, physically centralized "capitals" could be considered examples of bad equilibrium. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine the asymmetric S-curve between the trade balances of Pakistan and China at the commodity level using disaggregated data. Design/methodology/approach: This study focuses on Pakistan and China bilateral trade based on commodity-level data. This study delves into the S-curve phenomena by examining time series data from 1980 to 2023 across 32 three-digit industries/commodities. Findings: The findings show significant evidence in favor of the "asymmetric S-curve" in 27 out of the 32 industries studied. This study confirms that the devaluation of home currency is not a viable solution always to improve trade balance. Research limitations/implications: This study considers 32 three-digit industries limiting the generalizability of findings. Due to data unavailability, the authors fail to consider other industries. In the absence of quarterly data on industry-level trade between Pakistan and China, annual data from 1980 to 2023 were used in generating the cross-correlation functions. Previous literature frequently resorted to the general consumer price index with its inherent aggregation issues, whereas this study has opted for commodity price indices to overcome the shortcomings in the estimation of S-curves at the commodity level. Practical implications: The findings have practical relevance in guiding policy decisions regarding commodity trade, whereas the industry-wise analysis enriches the understanding of the short-term effects of currency depreciation on trade balance dynamics. Originality/value: The S-curve hypothesis predicts a negative cross-correlation between a country's current exchange rate and its past trade balance and a positive cross-correlation between the current exchange rate and its future trade balance. Previous empirical S-curve studies had the limitation of assuming symmetry in cross-correlation with both current and future trade balance values. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to the relationship between liquidity fluctuation trends and stocks' expected returns. Design/methodology/approach: The authors use the multi-fractal detrended fluctuation analysis to study the liquidity non-linear multi-fractal characteristics of stock market and predict the future volatility trend of market liquidity with the help of potential entropy dimensional model and also consider the non-linear volatility trend of liquidity, construct a fractal momentum strategy under different sorting periods and holding periods, to test the liquidity momentum effects of Chinese stock market and, finally, compare with the traditional price momentum strategy. Findings: The result suggests that: Chinese stock market liquidity has obvious non-linear multi-fractal characteristics; the trend entropy dimensional model can accurately predict the future volatility trend of market liquidity; significant liquidity trend momentum effects persist in the Chinese stock market, while price momentum effects exist only in the short term; a significant liquidity premium exists in the Chinese equity market; the fractal momentum strategy constructed in this paper achieves higher returns and less risk than the price momentum strategy. Originality/value: Based on the above analysis, this paper will further construct a momentum strategy incorporating liquidity trends, i.e. fractal momentum strategy (LMP strategy), with the realistic background that Chinese stock market liquidity has non-linear multi-fractal characteristics – the main contribution of this paper is to overcome the shortcomings of existing momentum strategies, which seldom combine the trend of future liquidity fluctuations with momentum strategies, and to incorporate the real characteristics of the market. [ABSTRACT FROM AUTHOR]

We estimate the implicit market price of soil erosion using quarterly data of 2824 agricultural farms traded in Uruguay between 2000 and 2014. A unique feature of our estimation is that we allow for possible spatial spillovers. We find evidence of a negative and statistically significant association between erosion and land values. On average, an additional loss of 1% of the original topsoil due to erosion is associated with a direct (own) decrease of.24% in the per‐hectare price of agricultural land (P‐value:.012, 95% CI: −.0042, −.0005). In 2023 dollars, this is equivalent to a decrease of USD 8.7 in the average price per hectare, or USD 1130 in the price of the average farm. In terms of tons of soil, the average value is $.24 a ton. Finally, considering the 50 km radius of our spatial model, the value of losing 1% of topsoil is $15.8 million. The value of our estimates is sensitive to our measure of erosion and our specification of the spatial‐temporal weighting matrix, but the statistical association is robust. [ABSTRACT FROM AUTHOR]

Purpose: This study seeks to explore the strategies purchasing and supply managers can employ to navigate the challenges presented by low-probability-high-impact (LPHI) disruptions. The core aim is to create a process framework that provides a systematic, step-by-step method to help purchasing and supply managers effectively deal with the chaos triggered by LPHI events. Design/methodology/approach: The study draws on qualitative data collected from eight firms operating within different industries (healthcare, fishing, food retail and manufacturing), where two firms represented each industry. The data underwent a thorough analytical process involving open coding, axial coding and aggregation of categories, resulting in the identification and formulation of overarching themes. Findings: The analysis unveiled five primary challenges purchasing and supply management (PSM) encountered during the COVID-19 pandemic. These include supply shortages, supplier opportunism, the imperative to build a new supply base, price volatility and the need to make critical decisions based on limited information. It also identified contingent factors that influenced the magnitude of these challenges and approaches applied to address them. Additionally, it identified five responses to the challenges and two contingent factors that affected the responses. Originality/value: This study extends the existing body of knowledge in purchasing and supply management by developing a process framework tailored to assist purchasing and supply managers in effectively addressing LPHI disruptions. To the best of our knowledge, this is one of the first studies to offer a structured, step-by-step approach that guides PSM professionals in navigating the chaos likely to be caused by such events. [ABSTRACT FROM AUTHOR]

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This paper examines the impact of retail e-commerce on pricing behavior and welfare. Using Japanese data, we find that e-commerce lowered relative inflation rates for goods sold intensively online. We use long time series and historical catalog sales as an instrument for e-commerce sales intensity. The entry of e-commerce firms raised the rate of intercity price convergence in physical stores for goods sold intensively online, but not for other goods, which suggests e-commerce enhances price arbitrage. We estimate that e-commerce lowered variety-adjusted prices on average by 0. 9 % between 1996 and 2014, and more in cities with highly educated populations. [ABSTRACT FROM AUTHOR]

We collect data on prices, travel costs, and farmer decisions to quantify market access for chemical fertilizer and its impact on agricultural productivity in 1,180 villages in Northern Tanzania. Villages at the bottom of the travel cost-adjusted input price distribution face 40%–55% less favorable prices than those at the top. A standard deviation increase in village-level remoteness is associated with 20%–25% lower input adoption. A spatial model of input adoption conservatively estimates that total trade costs are 4 times pecuniary travel costs. Counterfactuals suggest that halving travel costs would more than double adoption and reduce the adoption-remoteness gradient by 63%. [ABSTRACT FROM AUTHOR]

This paper studies the impacts of the 2018 U.S. tariff surges on export prices and adjustments of sales across different markets of Chinese exporters. The finding that U.S. tariffs did not affect the free-on-board price of Chinese exports is robust to controlling for firm-related fixed effects. While firms' exports to the United States dropped significantly, exports to the EU increased moderately and domestic sales or exports to other foreign markets were barely affected. Finally, by surveying managers of exporting firms, we shed light on potential impediments to firms' adjustments of export prices and sales. [ABSTRACT FROM AUTHOR]

Border closure by the Buhari's administration has as one of it its major objectives the increase in domestic rice production as a major means of attaining food security in Nigeria. Therefore border closure was perceived as means of preventing smuggling of foreign rice and protection of domestic rice industries that will make rice available and affordable for Nigerians. However, after the border closure, here was a leap in rice production but there was hike in the price of rice, which made it unaffordable by most Nigerians. The objectives of the study were to; identify the objectives of border closure in Nigeria, analyse the interface between border closure and food security, examine the challenges experienced at the border during border closure, find out whether border closure led to guaranteed domestic rice production, examine the impediments to rice production in Nigeria, and proffer solutions to the challenges confronting domestic rice production in Nigeria. 391 questionnaires were shared among rice farmers and Custom Officers in Ogun and Kebbi states, while 21 respondents were interviewed and percentage tool was adopted in data analysis. The study discovered that rice farmers are confronted with high cost of production such as high price of chemicals, tractors, competition with foreign rice, insecurity, inability to access the Anchor Borrower's Program. The study recommended among others that there should be subsidization of farm inputs, setting up of Agrochemical industries, provision of incentives and adequate security for rice farmers by the government. [ABSTRACT FROM AUTHOR]

Data trading platforms play a crucial role in facilitating data circulation and promoting the sustainable allocation of data resources. Establishing a transparent, fair, and efficient pricing mechanism is key to ensuring the long-term stability and development of such platforms. However, these platforms face challenges in pricing due to the small sample problem, as traditional machine learning methods typically rely on large amounts of data. To address this issue, this paper proposes a data resource pricing model that combines WGAN-GP data augmentation and the Reptile algorithm. Data augmentation generates related datasets to increase sample size, enhancing the renewability of data resources, while meta-learning transfers knowledge across tasks, improving the model's ability to quickly adapt to new tasks and efficiently utilize resources. Validation using actual trading data from the data trading platform shows that the proposed model accurately predicts data resource prices under small-sample conditions, outperforming other models. This study addresses the limitations of existing pricing methods in small-sample scenarios, providing a sustainable pricing solution for small-sample data resources and improving the accuracy and long-term stability of data pricing in the market. [ABSTRACT FROM AUTHOR]

The formal recycling of municipal solid waste plays a crucial role in establishing a resource-saving and environmentally friendly society. In China, policy-makers have implemented various financial incentives to promote the growth of the formal recycling sector. However, informal recycling still dominates the market, resulting in relatively low rates of formal recycling. To shed light on the mechanisms and effects of policies, the price competition of recyclers is introduced, and the two-way interaction between residents and recyclers is considered. A multi-agent-based simulation model including residents, formal recyclers, and informal recyclers is constructed to explore the differential effects of subsidy policies on different participants. The results show significant variations in the effectiveness of single-agent subsidy policies. Notably, the subsidy policy for formal recyclers outperforms the negative subsidy policy for informal recyclers. The former promotes the spontaneous regularization of more than 60% of residents while improving the profit level of formal recyclers. Furthermore, a multi-agent mixed policy for residents and formal and informal recyclers is the optimal subsidy strategy for waste management, enabling more than 60% of residents to choose formal recycling while causing the profits of formal recyclers to exceed those of informal recyclers by 1.5 times. Moreover, the price competition of recyclers is a vicious competition mode that blindly pursues the recovery amount at the expense of economic profits. The results not only clarify the differential effects of diverse subsidy policies but also furnish valuable insights for the government to design more targeted waste recycling policies. [ABSTRACT FROM AUTHOR]

This paper proposes to investigate tourism performance by employing fuzzy-set Qualitative Comparative Analysis. International tourist destinations are examined to identify the different paths leading to high levels of tourism arrivals and tourism receipts. The findings demonstrate that tourism performance is complex and requires specific combinations of conditions to succeed. The research identifies two types of destinations that rely on different factors to achieve success: Type 1 focuses on price competitiveness, while Type 2 emphasises the quality of the experience. Furthermore, it shows that a peculiar destination performs well in terms of receipts, relying on both price competitiveness and quality of the experience despite a lack of attractions. Type 1 and Type 2 destinations have good predictive validity in a pandemic context. [ABSTRACT FROM AUTHOR]

As the principal emitter of carbon dioxide, China institutes dual carbon goals and fosters carbon pilot markets. This paper explores the mechanism of carbon's price jump, both considering the deployments of policy instruments and inherent financial characteristics unique to each pilot market within China. The results indicate that market transaction information exerts the most profound influence on instances of price jumps, whereas the implementation of uniform national policies also yields a noteworthy effect. However, the impact of pilot-specific policies varies across different regions and highlights its heterogeneity. Consequently, these findings could provide sound evidence to facilitate the involvement of traders and furnish decision-making departments to produce more accurate and efficient policies. [ABSTRACT FROM AUTHOR]

This study examines the short- and medium-run dependence structures across carbon emissions prices, commodity futures, equity prices, and geopolitical uncertainty in the case of the BRICS countries. Previous studies have focused on the co-movement between commodity futures and equity prices, with a few attempts at capturing the economic uncertainty in that relationship but failed to look at the broader context of both emission prices and geopolitical uncertainty. This study employs the spillover index developed by Diebold and Yilmaz and data spaning from January 3, 2008, to October 31, 2021 to identify the dependence structure across ETS, commodity futures, equity prices, and geopolitical uncertainty (GPR). Our key findings suggest that the contribution of the MOEX Russia Index to the natural gas prices return is the highest. As the ETS is concerned, the GPR of China plays the most crucial role compared to other BRICS countries. Comparing energy commodities (e.g. ETS and natural gas), the net pairwise spillover effects of oil to metal commodities are more evident. Among the GPRs of BRICS countries, China, India, Russia, and Brazil have a positive net pairwise directional connectedness transmitted to South Africa. For stakholders working in commodity trading, the insights obtained from this research is essential for developing strategies that lessen the effects of geopolitical risks and help in promoting more resilient and stable commodity markets. [ABSTRACT FROM AUTHOR]

This article studies the pricing of VIX futures and options by directly modeling the dynamics of VIX, based on realized semivariances computed from high‐frequency data of VIX. We derive the closed‐form pricing formula for both the VIX futures and options. The empirical results show that the new model provides superior pricing performance compared with the model based on conventional unsigned realized variance and the classic Heston‐Nandi GARCH model, both in sample and out of sample. Our study confirms that the decomposition of realized variance into upside and downside components helps to improve the pricing performance for VIX futures and options. [ABSTRACT FROM AUTHOR]

We explore the impact of management team ability on firm‐level accounting comparability. Through using a sample of Chinese listed firms from 2009 to 2022, the paper documents that accounting comparability has an inverse U‐shaped association with management team ability where a certain level of managerial ability leads to the highest levels of accounting comparability. Further analysis shows that high‐ability managers have different incentives on accounting comparability when compared to low‐ability managers, especially when they suffer financing constraints, possess more proprietary information, experience weak market environment, bear excessive price risk and risk premium and work in the politically connected firms. [ABSTRACT FROM AUTHOR]

Conventional pairs trading strategies (PTS) exploit the mean-reverting nature of stock pairs with stationary value processes. This paper elevates PTS by integrating trend-stationary value processes, thereby enhancing profitability and expanding trading opportunities. Our asymptotic analysis reveals that the value process, adjusted for the derived slant asymptote, adheres to a stationary distribution. By capitalizing on price deviations and value trends, the strategy profits by longing undervalued or shorting overvalued processes based on their respective upward or downward slopes. Positions are strategically closed when they revert to the asymptote, thus securing profits and avoiding counterproductive trades against prevailing trends. In this context, conventional stationary-based PTS can be considered a specific instance of our broader approach when the asymptote is non-trended. To refine trade selection, we evaluate the mean-reversion velocity, monitoring the frequency at which the portfolio's value crosses the slant asymptote to exclude high-risk pairs. Empirical evidence underscores our method's superiority over conventional stationary PTS, delivering higher average returns per trade, improved Sharpe ratios, and increased trading opportunities, even amidst the financial uncertainties of the COVID era. [ABSTRACT FROM AUTHOR]

Critiquing—where users propose directional preferences to attribute values—has historically been a highly popular method for conversational recommendation. However, with the growing size of catalogs and item attributes, it becomes increasingly difficult and time-consuming to express all of one's constraints and preferences in the form of critiquing. It is found to be even more confusing in case of critiquing failures: when the system returns no matching items in response to user critiques. To this end, it would seem important to combine a critiquing-based conversational system with a personalized recommendation component to capture implicit user preferences and thus reduce the user's burden of providing explicit critiques. To examine the impact of such personalization on critiquing, this article reports on a user study with 228 participants to understand user critiquing behavior for two different recommendation algorithms: (i) non-personalized, that recommends any item consistent with the user critiques; and (ii) personalized, which leverages a user's past preferences on top of user critiques. In the study, we ask users to find a restaurant that they think is the most suitable to a given scenario by critiquing the recommended restaurants at each round of the conversation on the dimensions of price, cuisine, category, and distance. We observe that the non-personalized recommender leads to more critiquing interactions, more severe critiquing failures, overall more time for users to express their preferences, and longer dialogs to find their item of interest. We also observe that non-personalized users were less satisfied with the system's performance. They find its recommendations less relevant, more unexpected, and somewhat equally diverse and surprising than those of personalized ones. The results of our user study highlight an imperative for further research on the integration of the two complementary components of personalization and critiquing to achieve the best overall user experience in future critiquing-based conversational recommender systems. [ABSTRACT FROM AUTHOR]