Search

PurposeThe purpose of this paper is to propose two hybrid forecasting models which integrate available ones. A hybrid contaminated normal distribution (CND) model accurately reflects the non‐normal features of monthly S&P 500 index returns, and a hybrid GARCH model captures a serial correlation with respect to volatility. The hybrid GARCH model potentially enables financial institutions to evaluate long‐term investment risks in the S&P 500 index more accurately than current models.Design/methodology/approachThe probability distribution of an expected investment outcome is generated with a Monte Carlo simulation. A taller peak and fatter tails (kurtosis), which the probability distribution of monthly S&P 500 index returns contains, is produced by integrating a CND model and a bootstrapping model. The serial correlation of volatilities is simulated by applying a GARCH model.FindingsThe hybrid CND model can simulate the non‐normality of monthly S&P 500 index returns, while avoiding the influence of discrete observations. The hybrid GARCH model, by contrast, can simulate the serial correlation of S&P 500 index volatilities, while generating fatter tails. Long‐term investment risks in the S&P 500 index are affected by the serial correlation of volatilities, not the non‐normality of returns.Research limitations/implicationsThe hybrid models are applied only to the S&P 500 index. Cross‐sectional correlations among different asset groups are not examined.Originality/valueThe proposed hybrid models are unique because they combine available ones with a decision tree algorithm. In addition, the paper clearly explains the strengths and weaknesses of existing forecasting models.

PurposeThis paper seeks to propose a hybrid performance measurement framework integrating available frameworks and mathematical models. The hybrid framework potentially allows decision makers to move from intuitive decisions to analysis‐based decisions by using a complete hierarchy of objectives, mathematical equations and a simulation of increased capabilities. To illustrate the utility of the proposed framework, this paper aims to apply the framework to a hypothetical decision‐making scenario in a computer manufacturing company.Design/methodology/approachIn the proposed framework, a developed hierarchy is verified with correlation and regression analyses. Mathematical equations relating performance indicators are defined with a multiple linear regression model. An expected final outcome and uncertainty are evaluated with a Monte Carlo simulation.FindingsAn organization can find consistent performance indicators based on correlation and regression analyses. In addition, based on a forecast final outcome, the organization can make proactive decisions about up‐front investments in its capabilities.Research limitations/implicationsA hierarchy of objectives developed in this paper is not comprehensive. A scenario used for simulating a future outcome is hypothetical.Originality/valueAlthough some studies illustrate mathematical equations relating objectives, the studies are limited to parts of a hierarchy and there are few practical directions. This paper proposes mathematical equations that represent vertical relationships among objectives in a hierarchy, while evaluating the importance of a performance measurement system in a big picture. Moreover, this paper explains a decision‐making procedure based on a forecast outcome.