Exploring the concept of coopetition: A typology for the strategic moves of the Finnish forest industry

The Finnish forest industry is sustainable, well-established, internationally oriented and successful. For companies in such a basic industry, an industry that features mainly bulk products and relatively small domestic markets, sustainability in business may hinge on long-term strategic choices. It is proposed in this paper that it is not only strong domestic competition, but also—and especially—coopetition traditions (simultaneous cooperation and coopetition) that are the crucial factors contributing to the success of the industry. This article contributes to the literature by providing industry-level tools, namely a coopetition framework and combined coopetition typology. The findings indicate that coopetition traditions have been present throughout the activities of the Finnish forest industry, enabling sustainability of the industry to a significant extent. Managerial and theoretical implications are also discussed.     

Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and CI benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research.     

Quantiles as optimal point forecasts

Loss functions play a central role in the theory and practice of forecasting. If the loss function is quadratic, the mean of the predictive distribution is the unique optimal point predictor. If the loss is symmetric piecewise linear, any median is an optimal point forecast. Quantiles arise as optimal point forecasts under a general class of economically relevant loss functions, which nests the asymmetric piecewise linear loss, and which we refer to as generalized piecewise linear (GPL). The level of the quantile depends on a generic asymmetry parameter which reflects the possibly distinct costs of underprediction and overprediction. Conversely, a loss function for which quantiles are optimal point forecasts is necessarily GPL. We review characterizations of this type in the work of Thomson, Saerens and Komunjer, and relate to proper scoring rules, incentive-compatible compensation schemes and quantile regression. In the empirical part of the paper, the relevance of decision theoretic guidance in the transition from a predictive distribution to a point forecast is illustrated using the Bank of England’s density forecasts of United Kingdom inflation rates, and probabilistic predictions of wind energy resources in the Pacific Northwest.     

Are VIX futures prices predictable? An empirical investigation

This paper investigates whether volatility futures prices per se can be forecasted by studying the fast-growing VIX futures market. To this end, alternative model specifications are employed. Point and interval out-of-sample forecasts are constructed and evaluated under various statistical metrics. Next, the economic significance of the forecasts obtained is also assessed by performing trading strategies. Only weak evidence of statistically predictable patterns in the evolution of volatility futures prices is found. No trading strategy yields economically significant profits. Hence, the hypothesis that the VIX volatility futures market is informationally efficient cannot be rejected.     

Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models

We extend Diebold and Li’s dynamic Nelson-Siegel three-factor model to a broader empirical prospective by including the evaluation of the state space approach and by using nine different ratings for corporate bonds. We find that the dynamic Nelson-Siegel factor AR(1) model outperforms other competitors on the out-of-sample forecast accuracy, especially on the investment-grade bonds for the short-term forecast horizon and on the high-yield bonds for the long-term forecast horizon. The dynamic Nelson-Siegel factor state space model, however, becomes appealing on the high-yield bonds in the short-term forecast horizon, where the factor dynamics are more likely time-varying and parameter instability is more probable in the model specification.     

New evidence on the efficiency of Italian airports: A bootstrapped DEA analysis

A bootstrapped DEA procedure is used to estimate technical efficiency of 18 Italian airports during the period 2000-2004. Departing from previous studies, we separate the efficiency related to ability to manage airside activities (operational) from that related to the management of all business activities (financial). In general, Italian airports operate at poor levels of efficiency, with slightly better performance in terms of their financial activities. In the current study, selected intrinsic and environmental characteristics are considered as possible drivers of Italian airport performance. In particular, we found that: (i) the airport dimension does not allows for operational efficiency advantages, (ii) on the other hand, the airport dimension allows for financial efficiency advantages for the case of hubs and disadvantages for the case of the smallest airports (iii) the type(s) of concession agreement(s) might be considered as important source of technical efficiency differentials for those airports running marginal commercial activities; (iv) the introduction of a dual-till price cap regulation might create incentives which lead to the increase of financial efficiency at the detriment of the operational performance. Lastly, the development of a second hub (Milano Malpensa), has negatively affected the performance of the country’s national hub (Roma Fiumicino).     

Making unilateral trade liberalisation beneficial to the poor

In this paper, we investigate the impacts of unilateral import liberalisation by a representative South Asian developing economy, Nepal, and demonstrate those conditions required to make the impacts ‘pro-poor.’ Applying the Computable General Equilibrium model to Social Accounting Matrix data, we conclude that import liberalisation is growth-enhancing but that, unfortunately, the rich benefit more than do the poor. We envisage a restructured but plausible model economy that requires a transformational period of ten years, and simulate unilateral trade liberalisation but, in the context of a dynamic model. We conclude that improvement in efficiency parameters, reorganisation of investment patterns, along with reallocation of factors of production by both household group and activity type are required to make growth accrued by import liberalisation ‘pro-poor’ in developing economies such as that of Nepal.     

Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model is to utilize the concept of forecast combination, which has proven to be an effective methodology in the forecasting literature. In the proposed system we attempted to follow a principled approach, and make use of some of the guidelines and concepts that are known in the forecasting literature to lead to superior performance. For example, we considered various previous comparison studies and time series competitions as guidance in determining which individual forecasting models to test (for possible inclusion in the forecast combination system). The final model ended up consisting of neural networks, Gaussian process regression, and linear models, combined by simple average. We also paid extra attention to the seasonality aspect, decomposing the seasonality into weekly (which is the strongest one), day of the month, and month of the year seasonality.     

Role thinking: Standing in other people’s shoes to forecast decisions in conflicts

When forecasting decisions in conflict situations, experts are often advised to figuratively stand in the other person’s shoes. We refer to this as “role thinking”, because, in practice, the advice is to think about how other protagonists will view the situation in order to predict their decisions. We tested the effect of role thinking on forecast accuracy. We obtained 101 role-thinking forecasts of the decisions that would be made in nine diverse conflicts from 27 Naval postgraduate students (experts) and 107 role-thinking forecasts from 103 second-year organizational behavior students (novices). The accuracy of the novices’ forecasts was 33% and that of the experts’ was 31%; both were little different from chance (guessing), which was 28%. The small improvement in accuracy from role-thinking strengthens the finding from earlier research that it is not sufficient to think hard about a situation in order to predict the decisions which groups of people will make when they are in conflict. Instead, it is useful to ask groups of role players to simulate the situation. When groups of novice participants adopted the roles of protagonists in the aforementioned nine conflicts and interacted with each other, their group decisions predicted the actual decisions with an accuracy of 60%.