{"title":"Moving beyond Beyond Budgeting: A case study of the dynamic interrelationships between budgets and forecasts","authors":"P. N. Bukh, Amalie Ringgaard, Niels Sandalgaard","doi":"10.2139/ssrn.4846931","DOIUrl":"https://doi.org/10.2139/ssrn.4846931","url":null,"abstract":"","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":" 71","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141680024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The convergence of ensemble deep learning and machine learning has become a critical strategy for tackling intricate challenges across diverse fields such as healthcare, finance, and autonomous systems. Ensemble approaches, which combine the strengths of multiple models, are known for enhancing predictive accuracy, robustness, and generalizability. This paper investigates the applications of ensemble techniques, emphasizing their role in improving diagnostic precision in medical imaging, advancing fraud detection mechanisms in financial services, and refining decision-making in autonomous vehicles. Recent advancements in ensemble methods, including stacking, boosting, and bagging, have shown to outperform single models in various contexts. However, several challenges accompany the opportunities offered by ensemble learning, such as high computational demands, issues with model interpretability, and the potential for overfitting. This study explores ways to address these challenges, including the creation of more efficient algorithms and the incorporation of explainable AI (XAI) frameworks to enhance transparency and user trust. Furthermore, we discuss the future impact of cutting-edge technologies like quantum computing and federated learning on the evolution of ensemble techniques. The future of ensemble deep learning and machine learning is set to be shaped by the proliferation of big data, advancements in computational hardware, and the need for real-time, scalable solutions. This paper provides an extensive review of the current state of ensemble learning, identifies significant challenges, and suggests future research directions to fully harness the potential of these techniques in addressing real-world problems.
{"title":"Ensemble Deep Learning and Machine Learning: Applications, Opportunities, Challenges, and Future Directions","authors":"N. Rane, Saurabh Choudhary, Jayesh Rane","doi":"10.2139/ssrn.4849885","DOIUrl":"https://doi.org/10.2139/ssrn.4849885","url":null,"abstract":"The convergence of ensemble deep learning and machine learning has become a critical strategy for tackling intricate challenges across diverse fields such as healthcare, finance, and autonomous systems. Ensemble approaches, which combine the strengths of multiple models, are known for enhancing predictive accuracy, robustness, and generalizability. This paper investigates the applications of ensemble techniques, emphasizing their role in improving diagnostic precision in medical imaging, advancing fraud detection mechanisms in financial services, and refining decision-making in autonomous vehicles. Recent advancements in ensemble methods, including stacking, boosting, and bagging, have shown to outperform single models in various contexts. However, several challenges accompany the opportunities offered by ensemble learning, such as high computational demands, issues with model interpretability, and the potential for overfitting. This study explores ways to address these challenges, including the creation of more efficient algorithms and the incorporation of explainable AI (XAI) frameworks to enhance transparency and user trust. Furthermore, we discuss the future impact of cutting-edge technologies like quantum computing and federated learning on the evolution of ensemble techniques. The future of ensemble deep learning and machine learning is set to be shaped by the proliferation of big data, advancements in computational hardware, and the need for real-time, scalable solutions. This paper provides an extensive review of the current state of ensemble learning, identifies significant challenges, and suggests future research directions to fully harness the potential of these techniques in addressing real-world problems.","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141679167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research documents changes in employment and wages in the Netherlands for different types of workers. We compare 2017 to 2023 using regression-adjusted wages to make sure changes in composition of the workforce do not influence our estimates. The research period has been characterised by high labour demand, negative supply shocks, high levels of inflation and economic lockdowns, all of which have contributed to substantial labour-market dynamics. Our findings suggest that employment has been growing by 2 percent in the period 2017–2023, of which 1.8 percent has been due to additional workers finding employment. Women have experienced the largest increase in employment, while the employment of men on temporary contracts has slightly fallen. Wages have been rising for workers at the bottom of the wage distribution. From the median of the wage distribution onwards real gross hourly wages have been fallen. The most likely explanation for rising wages at the bottom is the stepwise increase in minimum wages enforced by new labour-market legislation.
{"title":"Wages and Employment in the Netherlands, 2017-2023","authors":"Iris Klinker, B. ter Weel","doi":"10.2139/ssrn.4856928","DOIUrl":"https://doi.org/10.2139/ssrn.4856928","url":null,"abstract":"This research documents changes in employment and wages in the Netherlands for different types of workers. We compare 2017 to 2023 using regression-adjusted wages to make sure changes in composition of the workforce do not influence our estimates. The research period has been characterised by high labour demand, negative supply shocks, high levels of inflation and economic lockdowns, all of which have contributed to substantial labour-market dynamics. Our findings suggest that employment has been growing by 2 percent in the period 2017–2023, of which 1.8 percent has been due to additional workers finding employment. Women have experienced the largest increase in employment, while the employment of men on temporary contracts has slightly fallen. Wages have been rising for workers at the bottom of the wage distribution. From the median of the wage distribution onwards real gross hourly wages have been fallen. The most likely explanation for rising wages at the bottom is the stepwise increase in minimum wages enforced by new labour-market legislation.","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":" 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141678094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper studies how a federal procurement regulation, known as the Truth in Negotiations Act (TINA), affects the competitiveness and execution of government contracts. TINA stipulates how contracting officials (COs) can ensure reasonable prices. Following TINA, for contracts above a certain size threshold, COs can no longer rely solely on their own judgment that a price is reasonable. Instead, they must either require suppliers to provide accounting data supporting their proposed prices or expect multiple bids. Using a regression discontinuity design, I find that above‐threshold contracts experience greater competition (i.e., more bids), improved performance (i.e., less frequent renegotiations and cost overruns), and reduced use of the harder‐to‐monitor cost‐plus pricing, compared to below‐threshold contracts. These findings suggest that TINA's requirements enhance competition and oversight for above‐threshold contracts.
{"title":"Show Your Hand: The Impacts of Fair Pricing Requirements in Procurement Contracting","authors":"Brad Nathan","doi":"10.2139/ssrn.4849021","DOIUrl":"https://doi.org/10.2139/ssrn.4849021","url":null,"abstract":"This paper studies how a federal procurement regulation, known as the Truth in Negotiations Act (TINA), affects the competitiveness and execution of government contracts. TINA stipulates how contracting officials (COs) can ensure reasonable prices. Following TINA, for contracts above a certain size threshold, COs can no longer rely solely on their own judgment that a price is reasonable. Instead, they must either require suppliers to provide accounting data supporting their proposed prices or expect multiple bids. Using a regression discontinuity design, I find that above‐threshold contracts experience greater competition (i.e., more bids), improved performance (i.e., less frequent renegotiations and cost overruns), and reduced use of the harder‐to‐monitor cost‐plus pricing, compared to below‐threshold contracts. These findings suggest that TINA's requirements enhance competition and oversight for above‐threshold contracts.","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141681279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Donelson, Christian M. Hutzler, Brian R. Monsen, Christopher G. Yust
Critics assert that securities class actions are economically burdensome and yield minimal recoveries, whereas proponents claim they deter wrongdoing. We examine key events in the recent Goldman Sachs Supreme Court case to test the net effect of securities litigation risk on shareholder value. We find that investors view securities class actions as value‐increasing. However, the strength of this effect varies based on external monitoring. Investors view securities class actions as more value‐enhancing when institutional ownership is low. We also use this setting to examine the effect of securities litigation risk on mandatory disclosure because the Goldman Sachs case focuses on mandatory disclosure properties. Using a difference‐in‐differences design, we find firm risk factor disclosures become shorter and less similar to industry peers, and they contain more uncertain and weak terms. Overall, our results show nuanced effects of securities litigation risk on shareholder value and firm disclosure.
{"title":"The Effect of Securities Litigation Risk on Firm Value and Disclosure","authors":"D. Donelson, Christian M. Hutzler, Brian R. Monsen, Christopher G. Yust","doi":"10.2139/ssrn.4748971","DOIUrl":"https://doi.org/10.2139/ssrn.4748971","url":null,"abstract":"Critics assert that securities class actions are economically burdensome and yield minimal recoveries, whereas proponents claim they deter wrongdoing. We examine key events in the recent Goldman Sachs Supreme Court case to test the net effect of securities litigation risk on shareholder value. We find that investors view securities class actions as value‐increasing. However, the strength of this effect varies based on external monitoring. Investors view securities class actions as more value‐enhancing when institutional ownership is low. We also use this setting to examine the effect of securities litigation risk on mandatory disclosure because the Goldman Sachs case focuses on mandatory disclosure properties. Using a difference‐in‐differences design, we find firm risk factor disclosures become shorter and less similar to industry peers, and they contain more uncertain and weak terms. Overall, our results show nuanced effects of securities litigation risk on shareholder value and firm disclosure.","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":"38 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sam Williams, Anthony Glass, Madeleine Matos, Tom Elder, David Arnett
{"title":"The UK productivity puzzle: A survey of the literature and expert views","authors":"Sam Williams, Anthony Glass, Madeleine Matos, Tom Elder, David Arnett","doi":"10.2139/ssrn.4708301","DOIUrl":"https://doi.org/10.2139/ssrn.4708301","url":null,"abstract":"","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":"28 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141684370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Cricelli, R. Mauriello, Serena Strazzullo, Mark Camilleri
Industry 4.0 technologies present new opportunities for the sustainable development of companies in the agrifood industry. The extant literature on this topic suggests that innovative technologies can support agrifood companies in addressing environmental, economic and social sustainability issues. While the environmental and economic benefits of technological innovations in the agrifood industry have been widely investigated, few studies sought to explore the impact of the adoption of Industry 4.0 technologies on long‐standing social issues. This research addresses this knowledge gap, the data were gathered from 116 Italian agrifood companies that utilized Industry 4.0 technologies. The findings from structural equations modelling partial least squares (SEM‐PLS) show that adopting Industry 4.0 technologies helps agrifood companies to improve human resources management, supply chain management and stakeholder relationships. Finally, this contribution puts forward implications for practitioners, as it raises awareness on the benefits of using technological innovations to promote social sustainability outcomes.
{"title":"Assessing the impact of Industry 4.0 technologies on the social sustainability of agrifood companies","authors":"L. Cricelli, R. Mauriello, Serena Strazzullo, Mark Camilleri","doi":"10.2139/ssrn.4874469","DOIUrl":"https://doi.org/10.2139/ssrn.4874469","url":null,"abstract":"Industry 4.0 technologies present new opportunities for the sustainable development of companies in the agrifood industry. The extant literature on this topic suggests that innovative technologies can support agrifood companies in addressing environmental, economic and social sustainability issues. While the environmental and economic benefits of technological innovations in the agrifood industry have been widely investigated, few studies sought to explore the impact of the adoption of Industry 4.0 technologies on long‐standing social issues. This research addresses this knowledge gap, the data were gathered from 116 Italian agrifood companies that utilized Industry 4.0 technologies. The findings from structural equations modelling partial least squares (SEM‐PLS) show that adopting Industry 4.0 technologies helps agrifood companies to improve human resources management, supply chain management and stakeholder relationships. Finally, this contribution puts forward implications for practitioners, as it raises awareness on the benefits of using technological innovations to promote social sustainability outcomes.","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":"42 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anishka Chauhan, Pratham Mayur, Y. Gokarakonda, Pooriya Jamie, Naman Mehrotra
This paper presents price prediction models using Machine Learning algorithms augmented with Superforecasters predictions, aimed at enhancing investment decisions. Five Machine Learning models are built, including Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU, and a model built using LSTM and GRU algorithms. The models are evaluated using the Mean Absolute Error to determine their predictive accuracy. Additionally, the paper suggests incorporating human intelligence by identifying Superforecasters and tracking their predictions to anticipate unpredictable shifts or changes in stock prices . The predictions made by these users can further enhance the accuracy of stock price predictions when combined with Machine Learning and Natural Language Processing techniques. Predicting the price of any commodity can be a significant task but predicting the price of a stock in the stock market deals with much more uncertainty. Recognising the limited knowledge and exposure to stocks among certain investors, this paper proposes price prediction models using Machine Learning algorithms. In this work, five Machine learning models are built using Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU and the last one is built using LSTM and GRU algorithms. Later these models are assessed using MAE scores to find which model is predicting with the highest accuracy. In addition to this, this paper also suggests the use of human intelligence to closely predict the shift in price patterns in the stock market The main goal is to identify Superforecasters and track their predictions to anticipate unpredictable shifts or changes in stock prices. By leveraging the combined power of Machine Learning and the Human Intelligence, predictive accuracy can be significantly increased.
{"title":"Indian Stock Market Prediction using Augmented Financial Intelligence ML","authors":"Anishka Chauhan, Pratham Mayur, Y. Gokarakonda, Pooriya Jamie, Naman Mehrotra","doi":"10.2139/ssrn.4697853","DOIUrl":"https://doi.org/10.2139/ssrn.4697853","url":null,"abstract":"This paper presents price prediction models using Machine Learning algorithms augmented with Superforecasters predictions, aimed at enhancing investment decisions. Five Machine Learning models are built, including Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU, and a model built using LSTM and GRU algorithms. The models are evaluated using the Mean Absolute Error to determine their predictive accuracy. Additionally, the paper suggests incorporating human intelligence by identifying Superforecasters and tracking their predictions to anticipate unpredictable shifts or changes in stock prices . The predictions made by these users can further enhance the accuracy of stock price predictions when combined with Machine Learning and Natural Language Processing techniques. Predicting the price of any commodity can be a significant task but predicting the price of a stock in the stock market deals with much more uncertainty. Recognising the limited knowledge and exposure to stocks among certain investors, this paper proposes price prediction models using Machine Learning algorithms. In this work, five Machine learning models are built using Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU and the last one is built using LSTM and GRU algorithms. Later these models are assessed using MAE scores to find which model is predicting with the highest accuracy. In addition to this, this paper also suggests the use of human intelligence to closely predict the shift in price patterns in the stock market The main goal is to identify Superforecasters and track their predictions to anticipate unpredictable shifts or changes in stock prices. By leveraging the combined power of Machine Learning and the Human Intelligence, predictive accuracy can be significantly increased.","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":"14 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141685828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In light of the increasingly dire consequences of climate change, fostering environmental literacy within the educational system has become an imperative. By measuring student competency, we can ensure future generations are empowered to become responsible stewards of the environment and active participants in tackling this global challenge. Thus, this study aims to assess the science students’ competency in factors that affect climate, the effects of changing climate, and how to adapt accordingly. The subjects of this study were thirty-one (31) science students, third-year and fourth-year students, composed of 8 males and 23 females who have taken the subject Environmental Science. Results show that students have a higher level of knowledge in competencies 1 and 2, namely: (1) Relate species extinction to the failure of the populations of organisms to adapt to abrupt changes in the environment, and (2) Explain how different factors affect the climate of an area. However, the present investigation yielded no statistically significant differences in climate change competency based on sex or year level, suggesting a potentially homogenous knowledge base regarding climate change across the studied demographic.
{"title":"Climate Change Competency Assessment: Focus on Lower Order Thinking Skills (LOTS)","authors":"Perzeus Lhey D. Villahermosa, July M. Villaren","doi":"10.2139/ssrn.4793998","DOIUrl":"https://doi.org/10.2139/ssrn.4793998","url":null,"abstract":"In light of the increasingly dire consequences of climate change, fostering environmental literacy within the educational system has become an imperative. By measuring student competency, we can ensure future generations are empowered to become responsible stewards of the environment and active participants in tackling this global challenge. Thus, this study aims to assess the science students’ competency in factors that affect climate, the effects of changing climate, and how to adapt accordingly. The subjects of this study were thirty-one (31) science students, third-year and fourth-year students, composed of 8 males and 23 females who have taken the subject Environmental Science. Results show that students have a higher level of knowledge in competencies 1 and 2, namely: (1) Relate species extinction to the failure of the populations of organisms to adapt to abrupt changes in the environment, and (2) Explain how different factors affect the climate of an area. However, the present investigation yielded no statistically significant differences in climate change competency based on sex or year level, suggesting a potentially homogenous knowledge base regarding climate change across the studied demographic.","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141685095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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