Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model

In this paper we investigate the ability of a number of different ordered probit models to predict ratings based on firm-specific data on business and financial risks. We investigate models based on momentum, drift and ageing and compare them against alternatives that take into account the initial rating of the firm and its previous actual rating. Using data on US bond issuing firms rated by Fitch over the years 2000 to 2007 we compare the performance of these models in predicting the rating in-sample and out-of-sample using root mean squared errors, Diebold-Mariano tests of forecast performance and contingency tables. We conclude that initial and previous states have a substantial influence on rating prediction.

Designing a Demand Forecasting Process in the Fast Moving Consumer Goods Context

The purpose of this thesis was to study the design of demand forecasting processes. A literature review in the field of forecasting was conducted, including general forecasting process design, forecasting methods and techniques, the role of human judgment in forecasting and forecasting performance measurement. The purpose of the literature review was to identify the important design choices that an organization aiming to design or re-design their demand forecasting process would have to make. In the empirical part of the study, these choices and the existing knowledge behind them was assessed in a case study where a demand forecasting process was re-designed for a company in the fast moving consumer goods business. The new target process is described, as well as the reasoning behind the design choices made during the re-design process. As a result, the most important design choices are highlighted, as well as their immediate effect on other processes directly tied to the demand forecasting process. Additionally, some new insights on the organizational aspects of demand forecasting processes are explored. The preliminary results indicate that in this case the new process did improve forecasting accuracy, although organizational issues related to the process proved to be more challenging than anticipated.

Net working capital estimation model in project business

The objective of this Master’s thesis is to develop a model which estimates net working capital (NWC) monthly in a year period. The study is conducted by a constructive research which uses a case study. The estimation model is designed in the need of one case company which operates in project business. Net working capital components should be linked together by an automatic model and estimated individually, including advanced components of NWC for example POC receivables. Net working capital estimation model of this study contains three parts: output template, input template and calculation model. The output template gets estimate values automatically from the input template and the calculation model. Into the input template estimate values of more stable NWC components are inputted manually. The calculate model gets estimate values for major affecting components automatically from the systems of a company by using a historical data and made plans. As a precondition for the functionality of the estimation calculation is that sales are estimated in one year period because the sales are linked to all NWC components.

Development of project cash flow forecasting process

Process management refers to improving the key functions of a company. The main functions of the case company - project management, procurement, finance, and human resource - use their own separate systems. The case company is in the process of changing its software. Different functions will use the same system in the future. This software change causes changes in some of the company’s processes. Project cash flow forecasting process is one of the changing processes. Cash flow forecasting ensures the sufficiency of money and prepares for possible changes in the future. This will help to ensure the company’s viability. The purpose of the research is to describe a new project cash flow forecasting process. In addition, the aim is to analyze the impacts of the process change, with regard to the project control department’s workload and resources through the process measurement, and how the impacts take the department’s future operations into account. The research is based on process management. Processes, their descriptions, and the way the process management uses the information, are discussed in the theory part of this research. The theory part is based on literature and articles. Project cash flow and forecasting-related benefits are also discussed. After this, the project cash flow forecasting as-is and to-be processes are described by utilizing information, obtained from the theoretical part, as well as the know-how of the project control department’s personnel. Written descriptions and cross-functional flowcharts are used for descriptions. Process measurement is based on interviews with the personnel – mainly cost controllers and department managers. The process change and the integration of two processes will allow work time for other things, for example, analysis of costs. In addition to the quality of the cash flow information will improve compared to the as-is process. Analyzing the department’s other main processes, department’s roles, and their responsibilities should be checked and redesigned. This way, there will be an opportunity to achieve the best possible efficiency and cost savings.

A Project Portfolio Tool for Forecasting Small Projects Profitability

The case company in this study is a large industrial engineering company whose business is largely based on delivering a wide-range of engineering projects. The aim of this study is to create and develop a fairly simple Excel-based tool for the sales department. The tool’s main function is to estimate and visualize the profitability of various small projects. The study also aims to find out other possible and more long-term solutions for tackling the problem in the future. The study is highly constructive and descriptive as it focuses on the development task and in the creation of a new operating model. The developed tool focuses on estimating the profitability of the small orders of the selected project portfolio currently on the bidding-phase (prospects) and will help the case company in the monthly reporting of sales figures. The tool will analyse the profitability of a certain project by calculating its fixed and variable costs, then further the gross margin and operating profit. The bidding phase of small project is a phase that has not been covered fully by the existing tools within the case company. The project portfolio tool can be taken into use immediately within the case company and it will provide fairly accurate estimate of the profitability figures of the recently sold small projects.

Forecasting the Yield Curve of Government Bonds: A Comparative Study

For the past 20 years, researchers have applied the Kalman filter to the modeling and forecasting the term structure of interest rates. Despite its impressive performance in in-sample fitting yield curves, little research has focused on the out-of-sample forecast of yield curves using the Kalman filter. The goal of this thesis is to develop a unified dynamic model based on Diebold and Li (2006) and Nelson and Siegel’s (1987) three-factor model, and estimate this dynamic model using the Kalman filter. We compare both in-sample and out-of-sample performance of our dynamic methods with various other models in the literature. We find that our dynamic model dominates existing models in medium- and long-horizon yield curve predictions. However, the dynamic model should be used with caution when forecasting short maturity yields

Innovative approach to building systems integration problems: Using systems theory, technological forecasting and scenario planning

Building services are worth about 2% GDP and are essential for the effective and efficient operations of the building. It is increasingly recognised that the value of a building is related to the way it supports the client organisation’s ongoing business operations. Building services are central to the functional performance of buildings and provide the necessary conditions for health, well-being, safety and security of the occupants. They frequently comprise several technologically distinct sub-systems and their design and construction requires the involvement of numerous disciplines and trades. Designers and contractors working on the same project are frequently employed by different companies. Materials and equipment is supplied by a diverse range of manufacturers. Facilities managers are responsible for operation of the building service in use. The coordination between these participants is crucially important to achieve optimum performance, but too often is neglected. This leaves room for serious faults. The need for effective integration is important. Modern technology offers increasing opportunities for integrated personal-control systems for lighting, ventilation and security as well as interoperability between systems. Opportunities for a new mode of systems integration are provided by the emergence of PFI/PPP procurements frameworks. This paper attempts to establish how systems integration can be achieved in the process of designing, constructing and operating building services. The essence of the paper therefore is to envisage the emergent organisational responses to the realisation of building services as an interactive systems network.

Forecasting UK real estate cycle phases with leading indicators: a probit approach

This paper examines the significance of widely used leading indicators of the UK economy for predicting the cyclical pattern of commercial real estate performance. The analysis uses monthly capital value data for UK industrials, offices and retail from the Investment Property Databank (IPD). Prospective economic indicators are drawn from three sources namely, the series used by the US Conference Board to construct their UK leading indicator and the series deployed by two private organisations, Lombard Street Research and NTC Research, to predict UK economic activity. We first identify turning points in the capital value series adopting techniques employed in the classical business cycle literature. We then estimate probit models using the leading economic indicators as independent variables and forecast the probability of different phases of capital values, that is, periods of declining and rising capital values. The forecast performance of the models is tested and found to be satisfactory. The predictability of lasting directional changes in property performance represents a useful tool for real estate investment decision-making.

Data uncertainty in real estate forecasting

The rapid expansion of the TMT sector in the late 1990s and more recent growing regulatory and corporate focus on business continuity and security have raised the profile of data centres. Data centres offer a unique blend of occupational, physical and technological characteristics compared to conventional real estate assets. Limited trading and heterogeneity of data centres also causes higher levels of appraisal uncertainty. In practice, the application of conventional discounted cash flow approaches requires information about a wide range of inputs that is difficult to derive from limited market signals or estimate analytically. This paper outlines an approach that uses pricing signals from similar traded cash flows is proposed. Based upon ‘the law of one price’, the method draws upon the premise that two identical future cash flows must have the same value now. Given the difficulties of estimating exit values, an alternative is that the expected cash flows of data centre are analysed over the life cycle of the building, with corporate bond yields used to provide a proxy for the appropriate discount rates for lease income. Since liabilities are quite diverse, a number of proxies are suggested as discount and capitalisation rates including indexed-linked, fixed interest and zero-coupon bonds. Although there are rarely assets that have identical cash flows and some approximation is necessary, the level of appraiser subjectivity is dramatically reduced.

Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States

Many macroeconomic series, such as U.S. real output growth, are sampled quarterly, although potentially useful predictors are often observed at a higher frequency. We look at whether a mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth. The MIDAS specification used in the comparison uses a novel way of including an autoregressive term. We find that the use of monthly data on the current quarter leads to significant improvement in forecasting current and next quarter output growth, and that MIDAS is an effective way to exploit monthly data compared with alternative methods.

A monthly indicator of Brazilian GDP. The Brazilian business cycle and growth cycle

This paper constructs an indicator of Brazilian GDP at the monthly ftequency. The peculiar instability and abrupt changes of regimes in the dynamic behavior of the Brazilian business cycle were explicitly modeled within nonlinear ftameworks. In particular, a Markov switching dynarnic factor model was used to combine several macroeconomic variables that display simultaneous comovements with aggregate economic activity. The model generates as output a monthly indicator of the Brazilian GDP and real time probabilities of the current phase of the Brazilian business cycle. The monthly indicator shows a remarkable historical conformity with cyclical movements of GDP. In addition, the estimated filtered probabilities predict ali recessions in sample and out-of-sample. The ability of the indicator in linear forecasting growth rates of GDP is also examined. The estimated indicator displays a better in-sample and out-of-sample predictive performance in forecasting growth rates of real GDP, compared to a linear autoregressive model for GDP. These results suggest that the estimated monthly indicator can be used to forecast GDP and to monitor the state of the Brazilian economy in real time.

Service needs forecasting: an approach for the automotive industry using analogies with medical ER management models.

During the past years, the industry has shifted position and moved towards “the luxury universe” whose customers are demanding, treating individuals as unique and valued customer for the business, offering vehicles produced with the state of the art technologies and implementing the highest finishing standards. Due to the competitive level in the market, car makers enable processes which equalizes customer services to E.R. management, being dealt with the maximum urgency that allows the comparison between both, car workshops and emergency rooms, where workshop bays or ramps will be equal to emergency boxes and skilled technicians are equivalent to the health care specialist, who will carry out tests and checks prior to afford any final operation, keeping the “patient” under control before it is back to normal utilization. This paper establishes a valid model for the automotive industry to estimate customer service demand forecasting under variable demand conditions using analogies with patient demand models used for the medical ER.

SERVICE NEEDS FORECASTING: An Approach for the Motor Industry Using Analogies with Medical ER Management Models.

During the past years, the industry has shifted position and moved towards “the luxury universe” whose customers are demanding, treating individuals as unique and valued customer for the business, offering vehicles produced with the state of the art technologies and implementing the highest finishing standards. Due to the competitive level in the market, motor makers enable processes which equalizes customer services to E.R. management, being dealt with the maximum urgency that allows the comparison between both, car workshops and emergency rooms, where workshop bays or ramps will be equal to emergency boxes and skilled technicians are equivalent to the health care specialist, who will carry out tests and checks prior to afford any final operation, keeping the “patient” under control before it is back to normal utilization. This paper ratify a valid model for the automotive industry to estimate customer service demand forecasting under variable demand conditions using analogies with patient demand models used for the medical ER