Organizational learning and market orientation: interface and effects on performance

This paper discusses the relationship between market orientation and organizational learning and, in particular, the former's contribution to the generation of double-loop learning. Although prior contributions on this topic have been controversial, the authors, departing from the principles of RBV, consider market orientation a resource capable of generating higher order organizational learning and, in this way, capable of additionally reinforcing firms' sustainable competitive advantage. The empirical study provides evidence on the existing relationship between a firm's learning and market orientation degree and the organization's economic and non-economic results. Findings indicate that learning orientation stimulates the market-oriented behavior and that it also positively affects the establishment of long-term relationships with strategic clients. Contrary to prior research a significant and positive effect on business performance is only contrasted in the case of market orientation.     

Zombie Board: Board Tenure and Firm Performance

We show that board tenure exhibits an inverted U‐shaped relation with firm value and accounting performance. The quality of corporate decisions, such as M&A, financial reporting quality, and CEO compensation, also has a quadratic relation with board tenure. Our results are consistent with the interpretation that directors’ on‐the‐job learning improves firm value up to a threshold, at which point entrenchment dominates and firm performance suffers. To address endogeneity concerns, we use a sample of firms in which an outside director suffered a sudden death, and find that sudden deaths that move board tenure away from (toward) the empirically observed optimum level in the cross‐section are associated with negative (positive) announcement returns. The quality of corporate decisions also follows an inverted U‐shaped pattern in a sample of firms affected by the death of a director.     

Machine learning in individual claims reserving

Machine learning techniques make it feasible to calculate claims reserves on individual claims data. This paper illustrates how these techniques can be used by providing an explicit example in individual claims reserving.     

Asking ‘Why’ in AI: Explainability of intelligent systems – perspectives and challenges

Recent rapid progress in machine learning (ML), particularly so‐called ‘deep learning’, has led to a resurgence in interest in explainability of artificial intelligence (AI) systems, reviving an area of research dating back to the 1970s. The aim of this article is to view current issues concerning ML‐based AI systems from the perspective of classical AI, showing that the fundamental problems are far from new, and arguing that elements of that earlier work offer routes to making progress towards explainable AI today.     

Learning minimum variance discrete hedging directly from the market

Option hedging is a critical risk management problem in finance. In the Black–Scholes model, it has been recognized that computing a hedging position from the sensitivity of the calibrated model option value function is inadequate in minimizing variance of the option hedge risk, as it fails to capture the model parameter dependence on the underlying price (see e.g. Coleman et al., J. Risk, 2001, 5(6), 63–89; Hull and White, J. Bank. Finance, 2017, 82, 180–190). In this paper, we demonstrate that this issue can exist generally when determining hedging position from the sensitivity of the option function, either calibrated from a parametric model from current option prices or estimated nonparametricaly from historical option prices. Consequently, the sensitivity of the estimated model option function typically does not minimize variance of the hedge risk, even instantaneously. We propose a data-driven approach to directly learn a hedging function from the market data by minimizing variance of the local hedge risk. Using the S&P 500 index daily option data for more than a decade ending in August 2015, we show that the proposed method outperforms the parametric minimum variance hedging method proposed in Hull and White [J. Bank. Finance, 2017, 82, 180–190], as well as minimum variance hedging corrective techniques based on stochastic volatility or local volatility models. Furthermore, we show that the proposed approach achieves significant gain over the implied BS delta hedging for weekly and monthly hedging.     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.     

An explainable machine learning approach to improve take-off time predictions

Accurate aircraft trajectory predictions are necessary to compute exact traffic demand figures, which are crucial for an efficient and effective air traffic flow and capacity management. At present, the uncertainty of the take-off time is one of the major contributions to the loss of trajectory predictability. In the EUROCONTROL Maastricht Upper Area Control Centre, the predicted take-off time for each individual flight relies on the information received from the Enhanced Traffic Flow Management System. However, aircraft do not always take-off at the times reported by this system due to several factors, which effects and interactions are too complex to be expressed with hard-coded rules. Previous work proposed a machine learning model that, based on historical data, was able to predict the take-off time of individual flights from a set of input features that effectively captures some of these elements. The model demonstrated to reduce by 30% the take-off time prediction errors of the current system one hour before the time that flight is scheduled to depart from the parking position. This paper presents an extension of the model, which overcomes this look-ahead time constraint and allows to improve take-off time predictions as early as the initial flight plan is received. In addition, a subset of the original set of input features has been meticulously selected to facilitate the implementation of the solution in an operational air traffic flow and capacity management system, while minimising the loss of predictive power. Finally, the importance and interactions of the input features are thoroughly analysed with additive feature attribution methods.     

Overcoming localization of knowledge — the role of professional service firms

The literature on organizational learning asserts that external learning is often limited geographically and technologically. We scrutinize to what extent organizations acquire external knowledge by accessing external knowledge repositories. We argue that professional service firms (PSFs) grant access to nonlocalized knowledge repositories and thereby not only facilitate external learning but also help to overcome localization. Focusing on patent law firms, we test our predictions using a unique dataset of 544,820 pairs of European patent applications. Analyzing patterns of knowledge flows captured in patent citations, we find that accessing a PSF's repository facilitates the acquisition of external knowledge. As the effect is more pronounced for knowledge that is distant to a focal organization, we conclude that having access to a knowledge repository compensates for localization disadvantages. Copyright © 2013 John Wiley & Sons, Ltd.     

Creating financial statements using FRx within Microsoft Dynamics GP: Learning vs. teaching

This project requires you to create financial statements using FRx within Microsoft Dynamics GP, an enterprise system. The project emphasizes the learner-centered paradigm rather than a teacher-centered educational paradigm. Researchers have found great importance in the learner-centered approach in educating students, especially in information systems (Landry, Saulnier, Wagner, & Longenecker, 2008; Saulnier, Landry, & Wagner, 2008). Major differences exist between the two approaches; for example, the professor gives information and evaluates, and emphasizes the right answer in a teacher-centered approach. The professor coaches and facilitates, and emphasizes that students generate good questions and learn from mistakes in a learner-centered approach. This project utilizes the learner-centered approach, an effective approach for you to use in today’s environment.     

Adaptive learning and distributional dynamics in an incomplete markets model

Recent research shows that several DSGE models provide a closer fit to the data under adaptive learning. This paper extends this research by introducing adaptive learning in the model of Krusell and Smith (1998) with uninsurable idiosyncratic risks and aggregate uncertainty. A first contribution of this paper establishes that the equilibrium of this framework is stable under least-squares learning. The second contribution consists of showing that bounded rationality enhances the ability of this model to match the distribution of income in the US. Learning increases significantly the Gini coefficients because of the opposite effects on consumption of the capital-rich and of the capital-poor agent. The third contribution is an empirical exercise that shows that learning can account for increases in the income Gini coefficient of up to 25% in a period of 28 years. Overall, these findings suggest that adaptive learning has important distributional repercussions in this class of models.