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1 – 10 of over 4000Seyed Mojtaba Taghavi, Vahidreza Ghezavati, Hadi Mohammadi Bidhandi and Seyed Mohammad Javad Mirzapour Al-e-Hashem
This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of…
Abstract
Purpose
This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of disruptions. The authors use conditional value at risk (CVaR) as a risk measure in optimizing the combined objective function of the total expected value and CVaR cost. A sustainable supply chain can create significant competitive advantages for companies through social justice, human rights and environmental progress. To control disruptions, the authors applied (proactive and reactive) resilient strategies. In this study, the authors combine resilience and social responsibility issues that lead to synergy in supply chain activities.
Design/methodology/approach
The present paper proposes a risk-averse two-stage mixed-integer stochastic programming model for sustainable and resilient SS,OA&PS problem under supply disruptions. In this decision-making process, determining the primary supplier portfolio according to the minimum sustainable-resilient score establishes the first-stage decisions. The recourse or second-stage decisions are: determining the amount of order allocation and scheduling of parts by each supplier, determining the reactive risk management strategies, determining the amount of order allocation and scheduling by each of reaction strategies and determining the number of products and scheduling of products on the planning time horizon. Uncertain parameters of this study are the start time of disruption, remaining capacity rate of suppliers and lead times associated with each reactive strategy.
Findings
In this paper, several numerical examples along with different sensitivity analyses (on risk parameters, minimum sustainable-resilience score of suppliers and shortage costs) were presented to evaluate the applicability of the proposed model. The results showed that the two-stage risk-averse stochastic mixed-integer programming model for designing the SS,OA&PS problem by considering economic and social aspects and resilience strategies is an effective and flexible tool and leads to optimal decisions with the least cost. In addition, the managerial insights obtained from this study are extracted and stated in Section 4.6.
Originality/value
This work proposes a risk-averse stochastic programming approach for a new multi-product sustainable and resilient SS,OA&PS problem. The planning horizon includes three periods before the disruption, during the disruption period and the recovery period. Other contributions of this work are: selecting the main supply portfolio based on the minimum score of sustainable-resilient criteria of suppliers, allocating and scheduling suppliers orders before and after disruptions, considering the balance constraint in receiving parts and using proactive and reactive risk management strategies simultaneously. Also, the scheduling of reactive strategies in different investment modes is applied to this problem.
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Our result of this paper aims to indicate that the beta pricing formula could be applied in a long-term model setting as well.
Abstract
Purpose
Our result of this paper aims to indicate that the beta pricing formula could be applied in a long-term model setting as well.
Design/methodology/approach
In this paper, we show that the capital asset pricing model can be derived from a three-period general equilibrium model.
Findings
We show that our extended model yields a Pareto efficient outcome.
Practical implications
The capital asset pricing model (CAPM) model can be used for pricing long-lived assets.
Social implications
Long-term modelling and sustainability can be modelled in our setting.
Originality/value
Our results were only known for two periods. The extension to 3 periods opens up a large scope of applicational possibilities in asset pricing, behavioural analysis and long-term efficiency.
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Guoli Wang and Chenxin Ma
Motivated by the wide application of procurement strategies in retailing, this paper aims to examine the effect of procurement strategies on decisions and profits and strategic…
Abstract
Purpose
Motivated by the wide application of procurement strategies in retailing, this paper aims to examine the effect of procurement strategies on decisions and profits and strategic inventory (SI) is considered.
Design/methodology/approach
The game-theoretic models are developed under a two-period fresh product supply chain (FSC), and consist of the mode of purchasing products only in the first period without SI (Scenario S), the mode of purchasing products in every period without SI (Scenario T) and the mode of purchasing products in every period with SI (Scenario TS).
Findings
Conducting the calculating and comparing, some major findings can be concluded. In general, two-period purchasing strategies (Scenarios T and TS) promote a higher freshness-keeping effort than the single buying strategy (Scenario S). Regarding the pricing strategy, SI and Scenario S can both contribute to obtaining a lower wholesale price, the retailer's pricing is relatively complicated and hinges on the consumer's sensitivity to freshness-keeping effort and the holding cost. Besides, comparing the sales quantity and the profit, the authors find that Scenario TS stimulates more demands and brings more profits for the manufacturer. However, Scenario TS is not the optimal selection for the reason that SI sometimes hurts the retailer and even the whole supply chain. Whereas, when the holding cost is in a certain range, Scenario TS will lead to a win-win situation.
Originality/value
The main findings of this study can give the enterprises some advice on the procurement strategies of fresh products and the decisions of pricing and the freshness-keeping effort.
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Yadong Liu, Nathee Naktnasukanjn, Anukul Tamprasirt and Tanarat Rattanadamrongaksorn
Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related…
Abstract
Purpose
Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.
Design/methodology/approach
This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.
Findings
The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.
Originality/value
The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.
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Shaghayegh Abolmakarem, Farshid Abdi, Kaveh Khalili-Damghani and Hosein Didehkhani
This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long…
Abstract
Purpose
This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM).
Design/methodology/approach
First, data are gathered and divided into two parts, namely, “past data” and “real data.” In the second stage, the wavelet transform is proposed to decompose the stock closing price time series into a set of coefficients. The derived coefficients are taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the third stage, the mean-variance portfolio optimization problem (MVPOP) has iteratively been run using the “past,” “future” and “real” data sets. The epsilon-constraint method is adapted to generate the Pareto front for all three runes of MVPOP.
Findings
The real daily stock closing price time series of six stocks from the FTSE 100 between January 1, 2000, and December 30, 2020, is used to check the applicability and efficacy of the proposed approach. The comparisons of “future,” “past” and “real” Pareto fronts showed that the “future” Pareto front is closer to the “real” Pareto front. This demonstrates the efficacy and applicability of proposed approach.
Originality/value
Most of the classic Markowitz-based portfolio optimization models used past information to estimate the associated parameters of the stocks. This study revealed that the prediction of the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio.
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Marko Kureljusic and Erik Karger
Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current…
Abstract
Purpose
Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.
Design/methodology/approach
The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.
Findings
The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.
Research limitations/implications
Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.
Practical implications
Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.
Originality/value
To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.
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Grant Samkin, Dessalegn Getie Mihret and Tesfaye Lemma
We develop a conceptual framework as a basis for thinking about the impact of extractive industries and emancipatory potential of alternative accounts. We then review selected…
Abstract
Purpose
We develop a conceptual framework as a basis for thinking about the impact of extractive industries and emancipatory potential of alternative accounts. We then review selected alternative accounts literature on some contemporary issues surrounding the extractive industries and identify opportunities for accounting, auditing, and accountability research. We also provide an overview of the other contributions in this special issue.
Design/methodology/approach
Drawing on alternative accounts from the popular and social media as well as the alternative accounting literature, this primarily discursive paper provides a contemporary literature review of identified issues within the extractive industries highlighting potential areas for future research. The eight papers that make up the special issue are located within a conceptual framework is employed to illustrate each paper’s contribution to the field.
Findings
While accounting has a rich literature covering some of the issues detailed in this paper, this has not necessarily translated to the extractive industries. Few studies in accounting have got “down and dirty” so to speak and engaged directly with those impacted by companies operating in the extractive industries. Those that have, have focused on specific areas such as the Niger Delta. Although prior studies in the social governance literature have tended to focus on disclosure issues, it is questionable whether this work, while informative, has resulted in any meaningful environmental, social or governance (ESG) changes on the part of the extractive industries.
Research limitations/implications
The extensive extractive industries literature both from within and outside the accounting discipline makes a comprehensive review impractical. Drawing on both the accounting literature and other disciplines, this paper identifies areas that warrant further investigation through alternative accounts.
Originality/value
This paper and other contributions to this special issue provide a basis and an agenda for accounting scholars seeking to undertake interdisciplinary research into the extractive industries.
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Shahin Rajaei Qazlue, Ahmad Mehrabian, Kaveh Khalili-Damghani and Mohammad Amirkhan
Because of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this…
Abstract
Purpose
Because of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this paper is to develop a data envelopment analysis (DEA) model that is fully compatible with the wheat production process so that managers and farmers can use it to evaluate the efficiency of wheat farms for strategic decisions.
Design/methodology/approach
A dynamic multi-stage network DEA model is developed to evaluate the efficiency of wheat production farms in short-term (two-year) and long-term (eight-year) periods.
Findings
The results of this study show that because of the lack of long-term planning and excessive reliance on rain, most of the investigated regions have no stability in efficiency, and the efficiency of the regions changes in a zigzag manner over time. Among studied regions, only the Hashtrood region has high and stable efficiency, and other regions can follow the example of this region's cultivation method.
Originality/value
To the best of the authors’ knowledge, this study is the first one that uses the dynamic multi-stage network DEA considering every other year cultivation method and direct–indirect inputs in the agricultural section.
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Julien Dhima and Catherine Bruneau
This study aims to demonstrate and measure the impact of liquidity shocks on a bank’s solvency, especially when the bank does not hold sufficient liquid assets.
Abstract
Purpose
This study aims to demonstrate and measure the impact of liquidity shocks on a bank’s solvency, especially when the bank does not hold sufficient liquid assets.
Design/methodology/approach
The proposed model is an extension of Merton’s (1974) model. It assesses the bank’s probability of default over one or two (short) periods relative to liquidity shocks. The shock scenarios are materialised by different net demands for the withdrawal of funds (NDWF) and may lead the bank to sell illiquid assets at a depreciated value. We consider the possibility of second-round effects at the beginning of the second period by introducing the probability of their occurrence. This probability depends on the proportion of illiquid assets put up for sale following the initial shock in different dependency scenarios.
Findings
We observe a positive relationship between the initial NDWF and the bank’s probability of default (particularly over the second period, which is conditional on the second-round effects). However, this relationship is not linear, and a significant proportion of liquid assets makes it possible to attenuate or even eliminate the effects of shock scenarios on bank solvency.
Practical implications
The proposed model enables banks to determine the necessary level of liquid assets, allowing them to resist (i.e. remain solvent) different liquidity shock scenarios for both periods (including eventual second-round effects) under the assumptions considered. Therefore, it can contribute to complementing or improving current internal liquidity adequacy assessment processes (ILAAPs).
Originality/value
The proposed microprudential approach consists of measuring the impact of liquidity risk on a bank’s solvency, complementing the current prudential framework in which these two topics are treated separately. It also complements the existing literature, in which the impact of liquidity risk on solvency risk has not been sufficiently studied. Finally, our model allows banks to manage liquidity using a solvency approach.
Details
Keywords
- Liquidity shock scenarios
- Bank solvency
- Probability of default (over one and two periods)
- Net demand for the withdrawal of funds (NDWF)
- Liquid and illiquid assets
- Second-round effects
- Probability of the occurrence of second-round effects
- Internal liquidity adequacy assessment process (ILAAP)
- C30
- G01
- G21
- G33
Hongyu Hou, Feng Wu and Xin Huang
The development of the digital age has made data and information more transparent, enhancing the strategic perspectives of both buyers (strategic waiting) and sellers (price…
Abstract
Purpose
The development of the digital age has made data and information more transparent, enhancing the strategic perspectives of both buyers (strategic waiting) and sellers (price fluctuations) in their decision-making. This research investigates the optimal dynamic pricing strategy of the content product developer in relation to their consideration of consumer fairness concerns to elucidate the impact of consumer fairness concerns on the dynamic pricing strategy of the developer.
Design/methodology/approach
This paper assumes that monopolistic content developers implement a dynamic pricing strategy for the content product. Through constructing a two-period dynamic pricing game model, this research investigates the optimal decisions of the content developer, contingent upon their consideration or disregard of consumer fairness concerns. In the extension section, the authors additionally account for the influence of myopic consumers on these optimal decisions.
Findings
Our findings reveal that the degree of consumer fairness concerns significantly influences the developer’s optimal dynamic pricing decision. When a developer offers content products with lower depth, there is a propensity for the developer to refrain from incorporating consumer fairness concerns into a dynamic pricing strategy. Conversely, in cases where the developer offers a high-depth content product, consumer fairness concerns benefit the developer. Furthermore, our analysis reveals a consistent benefit for the developer from the inclusion of myopic consumers.
Originality/value
Few studies have delved into the conjoined influence of consumer fairness concerns and strategic behavior on dynamic pricing strategy. Our findings indicate that consumer fairness concerns can enhance the efficiency of the value chain for content products under specific conditions. This paper not only enriches the existing literature on dynamic pricing by incorporating consumer fairness concerns theoretically but also offers practical insights. The outcomes of this research can guide content product developers in devising optimal dynamic pricing strategies.
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