Search results

1 – 10 of over 2000
Open Access
Article
Publication date: 26 September 2023

Kahuina Miller and Andrea Clayton

This study provides empirical evidence on the impact of the Panama Canal expansion (PCE) on the economies of Latin American and Caribbean (LAC) countries, particularly in light of…

Abstract

Purpose

This study provides empirical evidence on the impact of the Panama Canal expansion (PCE) on the economies of Latin American and Caribbean (LAC) countries, particularly in light of the emergence of larger container ships such as neo-Panamax and post-Panamax vessels.

Design/methodology/approach

This study uses the Bayesian structural time Series (BSTS) model to evaluate the economic effects of the PCE on 21 countries within the LAC region. It utilized the World Bank's gross domestic product (GDP) figures between 2000 and 2019 as the primary variable, alongside the human development index (HDI) (X1), container throughput (TEU) (X2) and unemployment rates (UNEMPL) (X3) covariates. This allowed a precise and robust approach to analyzing time series data while accounting for uncertainties and allowing the inclusion of various components and external factors.

Findings

The findings revealed that the PCE has a positive and statistically significant impact on most countries within the Caribbean Transshipment Triangle, ranging from 9.2% in Belize to 46% in Cuba. This suggests that the causal effect of the PCE on regional economies was not confined to any specific type of economy or geographical location within the LAC region. Where the growth rates were statistically insignificant, primarily in some Latin American countries, it coincided with countries that are primarily driven by exports and service industries, where bulk and oil tanker vessels are likely to be the main carriers for exports rather than container vessels.

Originality/value

The practical implications of this research are crucial for various stakeholders in the maritime industry and economic planning. The factors influencing economic growth resulting from investing in maritime activities are vital for decision-makers to create policies that lead to positive outcomes and sustainable development in regions and countries with flourishing maritime industries. The methodology and findings have significant implications for governments, managers, professionals, policy-makers and investors.

Details

Marine Economics and Management, vol. 6 no. 2
Type: Research Article
ISSN: 2516-158X

Keywords

Open Access
Article
Publication date: 31 January 2022

Zameelah Khan Jaffur, Boopen Seetanah, Verena Tandrayen-Ragoobur, Sheereen Fauzel, Viraiyan Teeroovengadum and Sonalisingh Ramsohok

This study aims at evaluating the effect of the COVID-19 pandemic on the export trade system for Mauritius during the first half of 2020 (January 2020–June 2020).

6584

Abstract

Purpose

This study aims at evaluating the effect of the COVID-19 pandemic on the export trade system for Mauritius during the first half of 2020 (January 2020–June 2020).

Design/methodology/approach

An initial analysis of the monthly export time series data proves that on the whole, the series have diverged from their actual trends after the outbreak of the COVID-19 pandemic: observed values are less than those predicted by the selected optimal forecast models. The authors subsequently employ the Bayesian structural time series (BSTS) framework for causal analysis to estimate the impact of the COVID-19 pandemic on the island's export system.

Findings

Overall, the findings show that the COVID-19 pandemic has a statistically significant and negative impact on the Mauritian export trade system, with the five main export trading partners and sectors the most affected. Despite that the impact in some cases is not apparent for the period of study, the results indicate that total exports will surely be affected by the pandemic in the long run. Nevertheless, this depends on the measures taken both locally and globally to mitigate the spread of the pandemic.

Originality/value

This study thus contributes to the growing literature on the economic impacts of the COVID-19 pandemic by focussing on a small island economy.

Details

International Trade, Politics and Development, vol. 6 no. 1
Type: Research Article
ISSN: 2586-3932

Keywords

Book part
Publication date: 6 April 2023

Scott M. Mourtgos and Ian T. Adams

Purpose – We investigate the impact of overlapping crises of COVID-19 and the George Floyd protests on one major US police department, focusing on staffing and officer proactivity…

Abstract

Purpose – We investigate the impact of overlapping crises of COVID-19 and the George Floyd protests on one major US police department, focusing on staffing and officer proactivity.

Methodology/Approach – The study investigates the impact of the two crises on operational capacity. Using Bayesian interrupted time-series analysis, the authors investigate if officer proactivity levels were adversely impacted in the short and long terms.

Findings – A statewide stay-at-home order (SAHO) was associated with a sharp decline in proactive contacts, but that effect dissipated quickly. However, the Floyd protests were associated with a sharp decline in proactivity, which persisted throughout the study period.

Originality/Value – The findings of this study contribute to ongoing research agendas that seek to understand the impact of dual, overlapping crises on US police departments and the communities they serve. The authors demonstrate a methodological approach capable of disentangling both crises’ effects on police activity levels.

Details

Crime and Social Control in Pandemic Times
Type: Book
ISBN: 978-1-80382-279-2

Keywords

Article
Publication date: 31 January 2018

Deniz A. Appelbaum, Alex Kogan and Miklos A. Vasarhelyi

There is an increasing recognition in the public audit profession that the emergence of big data as well as the growing use of business analytics by audit clients has brought new…

1775

Abstract

There is an increasing recognition in the public audit profession that the emergence of big data as well as the growing use of business analytics by audit clients has brought new opportunities and challenges. That is, should more complex business analytics beyond the customary analytical procedures be used in the engagement and if so, where? Which techniques appear to be most promising? This paper starts the process of addressing these questions by examining extant external audit research. 301 papers are identified that discuss some use of analytical procedures in the public audit engagement. These papers are then categorized by technique, engagement phase, and other attributes to facilitate understanding. This analysis of the literature is categorized into an External Audit Analytics (EAA) framework, the objective of which is to identify gaps, to provide motivation for new research, and to classify and outline the main topics addressed in this literature. Specifically, this synthesis organizes audit research, thereby offering guidelines regarding possible future research about approaches for more complex and data driven analytics in the engagement.

Details

Journal of Accounting Literature, vol. 40 no. 1
Type: Research Article
ISSN: 0737-4607

Keywords

Open Access
Article
Publication date: 24 March 2020

Marco Botta and Luca Vittorio Angelo Colombo

It is widely believed that deviating from the “one share-one vote” principle leads to corporate inefficiencies. To measure the market appraisal of this potential inefficiency…

Abstract

Purpose

It is widely believed that deviating from the “one share-one vote” principle leads to corporate inefficiencies. To measure the market appraisal of this potential inefficiency, this study aims to analyse the market reaction to a change from the “one head-one vote” to the “one share-one vote” mechanism by means of a quasi-natural experiment: a 2015 Italian reform forcing all listed cooperative banks to transform into joint-stock companies.

Design/methodology/approach

To investigate the market reaction around the regulatory change, this study uses both a traditional event study and a novel methodology based on the synthetic control method as well as on Bayesian statistical techniques.

Findings

This study estimates the market valuation of the effects of the governance change around the event date being equal to a cumulative average increase in market value of about 14 per cent using an event study methodology, and of about 13 per cent using Bayesian techniques.

Originality/value

This study provides evidence on the fact that the voting mechanism significantly affects the market values of companies. The study also introduces a novel statistical technique that can be extremely useful in analysing single-firm event studies.

Details

Managerial Finance, vol. 46 no. 7
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 28 November 2022

Prateek Kumar Tripathi, Chandra Kant Singh, Rakesh Singh and Arun Kumar Deshmukh

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this…

Abstract

Purpose

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

Design/methodology/approach

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Findings

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

Practical implications

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Originality/value

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

Book part
Publication date: 1 June 2022

Monica Billio, Roberto Casarin and Fausto Corradin

This chapter studies the effects of the COVID-19 pandemic on the economic structure of the US and EU economies by measuring its impact on some reference macro-economic variables…

Abstract

This chapter studies the effects of the COVID-19 pandemic on the economic structure of the US and EU economies by measuring its impact on some reference macro-economic variables. We use a factor model approach on a set of variables available at different frequencies (daily, weekly, monthly, and quarterly) and provide evidence of instability in the primary factors driving the economy. A sequential analysis of the factors allows us to evaluate the model's forecasting performance and extract some instability measures based on the factor model's eigenvalues. Finally, we show how to use COVID-related variables, such as policy, economic, and health indicators, to compute conditional forecasts with factor models, and perform a scenario analysis on the variables of interest to understand economic instability.

Details

The Economics of COVID-19
Type: Book
ISBN: 978-1-80071-694-0

Keywords

Article
Publication date: 15 May 2017

Jerry H. Ratcliffe, Amber Perenzin and Evan T. Sorg

The purpose of this paper is to evaluate the violence-reduction effects following an FBI-led gang takedown in South Central Los Angeles.

Abstract

Purpose

The purpose of this paper is to evaluate the violence-reduction effects following an FBI-led gang takedown in South Central Los Angeles.

Design/methodology/approach

The time series impact of the intervention was estimated using a Bayesian diffusion-regression state-space model designed to infer a causal effect of an intervention using data from a similar (non-targeted) gang area as a control.

Findings

A statistically significant 22 percent reduction in violent crime was observed, a reduction that lasted at least nine months after the interdiction.

Research limitations/implications

The research method does make assumptions about the equivalency of the control area, though statistical checks are employed to confirm the control area crime rate trended similarly to the target area prior to the intervention.

Practical implications

The paper demonstrates a minimum nine-month benefit to a gang takedown in the target area, suggesting that relatively long-term benefits from focused law enforcement activity are possible.

Social implications

Longer-term crime reduction beyond just the day of the intervention can aid communities struggling with high crime and rampant gang activity.

Originality/value

Few FBI-led gang task force interventions have been studied for their crime reduction benefit at the neighborhood level. This study adds to that limited literature. It also introduces a methodology that can incorporate crime rates from a control area into the analysis, and overcome some limitations imposed by ARIMA modeling.

Details

Policing: An International Journal of Police Strategies & Management, vol. 40 no. 2
Type: Research Article
ISSN: 1363-951X

Keywords

Article
Publication date: 19 July 2022

Harish Kundra, Sudhir Sharma, P. Nancy and Dasari Kalyani

Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it…

Abstract

Purpose

Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model.

Design/methodology/approach

In this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm.

Findings

The proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively.

Originality/value

In this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.

Details

Kybernetes, vol. 52 no. 11
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 27 November 2023

Mehir Baidya and Bipasha Maity

Managers engage in marketing efforts to boost sales and in setting marketing budgets based on current or historical sales. Past studies have overlooked the reciprocal relationship…

Abstract

Purpose

Managers engage in marketing efforts to boost sales and in setting marketing budgets based on current or historical sales. Past studies have overlooked the reciprocal relationship between marketing spending and sales. This study aims to examine the nature of the relationship between sales and marketing expenses in the B2B market.

Design/methodology/approach

Five hypotheses on the relationship between sales and marketing expenditures were framed. A total of 30 of India’s dyeing firms provided data on revenues, sales (in units) and marketing expenditures over time. The structural vector auto-regressive model and the vector error correction model were fitted to the data.

Findings

The results show that marketing expenses and sales are related bidirectionally in a sequential way. Furthermore, sales drive the long-term equilibrium relationship to a greater extent than marketing expenditures.

Practical implications

The findings of this study should assist managers in predicting sales and marketing budgets simultaneously and devising precise marketing strategies and tactics.

Originality/value

Using econometric models in data-driven research is not a frequent practice in marketing. This study adds value to the body of marketing literature by advancing the theory of the relationship between sales and marketing spending using real-world data and econometric models in the B2B sector.

Details

Journal of Business & Industrial Marketing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0885-8624

Keywords

1 – 10 of over 2000