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1 – 10 of over 1000Massimo Garbuio, Dan Lovallo and John Horn
Mergers & acquisitions (M&A) are an important element of any company's growth plan. However, the actual performance of most M&A activity fails to live up to the expectations of…
Abstract
Mergers & acquisitions (M&A) are an important element of any company's growth plan. However, the actual performance of most M&A activity fails to live up to the expectations of the acquirers. The psychological biases that affect decision-making have been posited as a source of this disappointing performance. The broad strokes in which these biases have been offered up as explanation for M&A failure don't offer much insight into the specific causes, and therefore the actions business leaders can take to mitigate their impact. We review a 4-step M&A process, identify the different biases that affect the different stages, and then offer practical debiasing techniques targeted at that particular stage of the decision-making process. This targeted debiasing can help business leaders find practical solutions to this vexing problem. Finally, we review two biases that motivate decision makers to avoid pursuing M&A deals at all – to the detriment of achieving their growth targets.
Olav Torp, Ingemund Jordanger, Ole Jonny Klakegg and Yvonne C.B. Bjerke
The purpose of the paper is 1) to address the importance of contingency at the right level when defining project control baseline, including cost reserves / “room to manoeuvre”…
Abstract
Purpose
The purpose of the paper is 1) to address the importance of contingency at the right level when defining project control baseline, including cost reserves / “room to manoeuvre” and 2) present proactive uncertainty management as a regime to ensure cost effective management of project reserves and contribute to project success.
Design/Methodology/Approach
The paper is a combination of literature study and quantitative research on how contingency develops during the lifetime of a case project. The investigation into the case project includes document study into quantitative material from the case project. The combination of empirical material and theory makes the discussion robust.
Findings
Unrealistic low cost uncertainty will lead to unrealistic low contingency. The case study from a Norwegian mega project shows a contingency of 15 per cent in addition to expected costs. The case study shows that by continuous opportunity management and risk reduction, the needs for management reserves are systematically reduced and the contingency is controlled.
Research Limitations/Implications
This research is limited to one case study. A higher number of cases are necessary to generalise the findings. However, the authors would claim that the systematic mapping of need for management reserve towards the project contingency, and a continuous uncertainty management system will help to obtain cost effective management. The findings from the case study could be applied on similar cases.
Practical Implications
The case study shows a way of setting contingencies and managing contingencies through systematic uncertainty management.
Originality/Value
Improved management of project provisions will increase the value of future projects.
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The success of multinational enterprises (MNEs) is at least as much a function of management ability and behavior as it is of industry characteristics or environmental factors…
Abstract
The success of multinational enterprises (MNEs) is at least as much a function of management ability and behavior as it is of industry characteristics or environmental factors. MNE managers, like all managers, display human limitations, e.g., overconfidence that affect judgment. Yet IB researchers still tend to ignore management in their research, treating the firm as a black box. To the extent that the top management team is considered, rational behavior in the classical economic sense is assumed, yet, clearly, managers behave according to different rules than those assumed in much of the IB literature. Further, managers are not part of a herd, but unique. The result of such a lacuna is that theory fails to predict actual behavior and does not allow best guidance for policy options. The paper summarizes research on behavioral decision making and calls for its application in future research in international business.
I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov…
Abstract
I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.
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John F. Kros and William J. Rowe
Business schools are tasked with matching curriculum to techniques that industry practitioners rely on for profitability. Forecasting is a significant part of what many firms use…
Abstract
Business schools are tasked with matching curriculum to techniques that industry practitioners rely on for profitability. Forecasting is a significant part of what many firms use to try to predict budgets and to provide guidance as to the direction the business is headed. This chapter focuses on forecasting and how well business schools match the requirements of industry professionals. Considering its importance to achieving successful business outcomes, forecasting is increasingly becoming a more complex endeavor. Firms must be able to forecast accurately to gain an understanding of the direction the business is taking and to prevent potential setbacks before they occur. Our results suggest that, although techniques vary, in large part business schools are introducing students to the forecasting tools that graduates will need to be successful in an industry setting. The balance of our chapter explores the forecasting tools used by business schools and firms, and the challenge of aligning the software learning curve between business school curriculum and industry expectations.
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Increasing availability of the financial data has opened new opportunities for quantitative modeling. It has also exposed limitations of the existing frameworks, such as low…
Abstract
Increasing availability of the financial data has opened new opportunities for quantitative modeling. It has also exposed limitations of the existing frameworks, such as low accuracy of the simplified analytical models and insufficient interpretability and stability of the adaptive data-driven algorithms. I make the case that boosting (a novel, ensemble learning technique) can serve as a simple and robust framework for combining the best features of the analytical and data-driven models. Boosting-based frameworks for typical financial and econometric applications are outlined. The implementation of a standard boosting procedure is illustrated in the context of the problem of symbolic volatility forecasting for IBM stock time series. It is shown that the boosted collection of the generalized autoregressive conditional heteroskedastic (GARCH)-type models is systematically more accurate than both the best single model in the collection and the widely used GARCH(1,1) model.