The end of the corporate life cycle

This paper asks how takeover and failure hazards change as listed firms get older. The hypothesis is that they increase because firms gradually run out of growth opportunities. We find the opposite. Both takeover and failure hazard drop significantly with age. The decline in takeover hazard can be explained with Loderer, Stulz, and Waelchli’s (2013) “buggy whip makers” hypothesis: Because old firms are comparatively well-managed and are affected by limited agency problems, on average, they offer little value added potential to acquirers. Failure hazard drops because to learning. The results are robust to various alternative interpretations and cannot be explained by unobserved heterogeneity. While hazards decline with age, they do not go to zero. This explains why, eventually, all listed firms disappear

The end of the corporate life cycle

This paper asks how takeover and failure hazards change as listed firms get older. The hypothesis is that they increase because firms gradually run out of growth opportunities. We find the opposite. Both takeover and failure hazard drop significantly with age. The decline in takeover hazard can be explained with Loderer, Stulz, and Waelchli’s (2013) “buggy whip makers” hypothesis: Because old firms are comparatively well-managed and are affected by limited agency problems, on average, they offer little value added potential to acquirers. Failure hazard drops because to learning. The results are robust to various alternative interpretations and cannot be explained by unobserved heterogeneity. While hazards decline with age, they do not go to zero. This explains why, eventually, all listed firms disappear

The end of the corporate life cycle

This paper asks how takeover and failure hazards change as listed firms get older. The hypothesis is that they increase because firms gradually run out of growth opportunities. We find the opposite. Both takeover and failure hazard drop significantly with age. The decline in takeover hazard can be explained with Loderer, Stulz, and Waelchli’s (2013) “buggy whip makers” hypothesis: Because old firms are comparatively well-managed and are affected by limited agency problems, on average, they offer little value added potential to acquirers. Failure hazard drops because to learning. The results are robust to various alternative interpretations and cannot be explained by unobserved heterogeneity. While hazards decline with age, they do not go to zero. This explains why, eventually, all listed firms disappear

Limited managerial attention and corporate aging

As firms have more assets in place, more of management’s limited attention is focused on managing assets in place rather than developing new growth options. Consequently, as firms grow older, they have fewer growth options and a lower ability to generate new growth options. This simple theory predicts that Tobin’s q falls with age. Further, competition in the product market is expected to slow down the decrease in Tobin’s q because it forces firms to look for alternative sources of rents. Similarly, greater competition in the labor market reduces the decrease in Tobin’s q with age because old firms are in a better position to hire employees that can help with innovation. In contrast, competition in the market for corporate control should accelerate the decline because it forces management to focus more on managing assets in place whose performance is more directly observable than on developing growth options where results may not be observable for some time. We find strong support for these predictions in tests using exogenous variation in competition

Reanalysis of the Hamburg Storm Surge of 1962

In February 1962, Hamburg experienced its most catastrophic storm surge event of the 20th century. This paper analyses the event using the Twentieth Century Reanalysis (20CR) dataset. Responsible for the major flood was a strong low pressure system centred over Scandinavia that was associated with strong north-westerly winds towards the German North Sea coast – the ideal storm surge situation for the Elbe estuary. A comparison of the 20CR dataset with observational data proves the applicability of the reanalysis data for this extreme event.

Governing individual learning in the transition phase of software maintenance offshoring: a dynamic perspective

Prior studies suggest that clients need to actively govern knowledge transfer to vendor staff in offshore outsourcing. In this paper, we analyze longitudinal data from four software maintenance offshore out-sourcing projects to explore why governance may be needed for knowledge transfer and how governance and the individual learning of vendor engineers inter-act over time. Our results suggest that self-control is central to learning, but may be hampered by low levels of trust and expertise at the outset of projects. For these foundations to develop, clients initially need to exert high amounts of formal and clan controls to enforce learning activities against barriers to knowledge sharing. Once learning activities occur, trust and expertise increase and control portfolios may show greater emphases on self-control.

Learning software-maintenance tasks in offshoring projects: a cognitive-load perspective

Individual learning is central to the success of the transition phase in software mainte-nance offshoring projects. However, little is known on how learning activities, such as on-the-job training and formal presentations, are effectively combined during the tran-sition phase. In this study, we present and test propositions derived from cognitive load theory. The results of a multiple-case study suggest that learning effectiveness was highest when learning tasks such as authentic maintenance requests were used. Con-sistent with cognitive load theory, learning tasks were most effective when they imposed moderate cognitive load. Our data indicate that cognitive load was influenced by the expertise of the onsite coordinator, by intrinsic task complexity, by the degree of specifi-cation of tasks, and by supportive information. Cultural and semantic distances may in-fluence learning by inhibiting supportive information, specification, and the assignment of learning tasks.

Portfolio-optimization models for small investors

Since 2010, the client base of online-trading service providers has grown significantly. Such companies enable small investors to access the stock market at advantageous rates. Because small investors buy and sell stocks in moderate amounts, they should consider fixed transaction costs, integral transaction units, and dividends when selecting their portfolio. In this paper, we consider the small investor’s problem of investing capital in stocks in a way that maximizes the expected portfolio return and guarantees that the portfolio risk does not exceed a prescribed risk level. Portfolio-optimization models known from the literature are in general designed for institutional investors and do not consider the specific constraints of small investors. We therefore extend four well-known portfolio-optimization models to make them applicable for small investors. We consider one nonlinear model that uses variance as a risk measure and three linear models that use the mean absolute deviation from the portfolio return, the maximum loss, and the conditional value-at-risk as risk measures. We extend all models to consider piecewise-constant transaction costs, integral transaction units, and dividends. In an out-of-sample experiment based on Swiss stock-market data and the cost structure of the online-trading service provider Swissquote, we apply both the basic models and the extended models; the former represent the perspective of an institutional investor, and the latter the perspective of a small investor. The basic models compute portfolios that yield on average a slightly higher return than the portfolios computed with the extended models. However, all generated portfolios yield on average a higher return than the Swiss performance index. There are considerable differences between the four risk measures with respect to the mean realized portfolio return and the standard deviation of the realized portfolio return.

A continuous-time MILP model for short-term scheduling of make-and-pack production processes

In process industries, make-and-pack production is used to produce food and beverages, chemicals, and metal products, among others. This type of production process allows the fabrication of a wide range of products in relatively small amounts using the same equipment. In this article, we consider a real-world production process (cf. Honkomp et al. 2000. The curse of reality – why process scheduling optimization problems are diffcult in practice. Computers & Chemical Engineering, 24, 323–328.) comprising sequence-dependent changeover times, multipurpose storage units with limited capacities, quarantine times, batch splitting, partial equipment connectivity, and transfer times. The planning problem consists of computing a production schedule such that a given demand of packed products is fulfilled, all technological constraints are satisfied, and the production makespan is minimised. None of the models in the literature covers all of the technological constraints that occur in such make-and-pack production processes. To close this gap, we develop an efficient mixed-integer linear programming model that is based on a continuous time domain and general-precedence variables. We propose novel types of symmetry-breaking constraints and a preprocessing procedure to improve the model performance. In an experimental analysis, we show that small- and moderate-sized instances can be solved to optimality within short CPU times.