Quantitative market and sales forecast accuracy analysis

The main objective of this master’s thesis was to quantitatively study the reliability of market and sales forecasts of a certain company by measuring bias, precision and accuracy of these forecasts by comparing forecasts against actual values. Secondly, the differences of bias, precision and accuracy between markets were explained by various macroeconomic variables and market characteristics. Accuracy and precision of the forecasts seems to vary significantly depending on the market that is being forecasted, the variable that is being forecasted, the estimation period, the length of the estimated period, the forecast horizon and the granularity of the data. High inflation, low income level and high year-on-year market volatility seems to be related with higher annual market forecast uncertainty and high year-on-year sales volatility with higher sales forecast uncertainty. When quarterly market size is forecasted, correlation between macroeconomic variables and forecast errors reduces. Uncertainty of the sales forecasts cannot be explained with macroeconomic variables. Longer forecasts are more uncertain, shorter estimated period leads to higher uncertainty, and usually more recent market forecasts are less uncertain. Sales forecasts seem to be more uncertain than market forecasts, because they incorporate both market size and market share risks. When lead time is more than one year, forecast risk seems to grow as a function of root forecast horizon. When lead time is less than year, sequential error terms are typically correlated, and therefore forecast errors are trending or mean-reverting. The bias of forecasts seems to change in cycles, and therefore the future forecasts cannot be systematically adjusted with it. The MASE cannot be used to measure whether the forecast can anticipate year-on-year volatility. Instead, we constructed a new relative accuracy measure to cope with this particular situation.

Asymptotic analysis of stock price densities and implied volatilities in mixed stochastic models

In this paper, we obtain sharp asymptotic formulas with error estimates for the Mellin con- volution of functions de ned on (0;1), and use these formulas to characterize the asymptotic behavior of marginal distribution densities of stock price processes in mixed stochastic models. Special examples of mixed models are jump-di usion models and stochastic volatility models with jumps. We apply our general results to the Heston model with double exponential jumps, and make a detailed analysis of the asymptotic behavior of the stock price density, the call option pricing function, and the implied volatility in this model. We also obtain similar results for the Heston model with jumps distributed according to the NIG law.

Caracterização espectroscópica multivariada do potencial antioxidante de vinhos

In this work the antioxidant capacity of red wine samples was characterized by conventional spectroscopic and chromatographic methodologies, regarding chemical parameters like color, total polyphenolic and resveratrol content, and antioxidant activity. Additionally, multivariate calibration models were developed to predict the antioxidant activity, using partial least square regression and the spectral data registered between 400 and 800 nm. Even when a close correlation between the evaluated parameters has been expected many inconsistencies were observed, probably on account of the low selectivity of the conventional methodologies. Models developed from mean-centered spectra and using 4 latent variables allowed high prevision capacity of the antioxidant activity, permitting relative errors lower than 3%.

Determinação simultânea de ácido kójico e hidroquinona por espectrofotometria visível e calibração multivariada

A multivariate spectrophotometric method was developed for analysis of kojic acid/hydroquinone associations in skin whitening cosmetics. The method is based on the reaction between kojic acid and Fe3+ and on the reduction of Fe3+ by hydroquinone and further complexation of Fe2+ with 1,10-phenanthroline. The multivariate model was developed by Partial Least Squares Regression (PLSR), using 25 synthetic mixtures and mean-centered spectral data (350-380 nm). The use of 3 (kojic acid) and 2 (hydroquinone) latent variables permits the observation of mean errors of about 5% in the external validation phase.

Modelling mean above and below ground litter production based on yield tables

Selective papers of the workshop on "Development of models and forest soil surveys for monitoring of soil carbon", Koli, Finland, April 5-9 2006.

Least squares regression with errors in both variables: case studies

Analytical curves are normally obtained from discrete data by least squares regression. The least squares regression of data involving significant error in both x and y values should not be implemented by ordinary least squares (OLS). In this work, the use of orthogonal distance regression (ODR) is discussed as an alternative approach in order to take into account the error in the x variable. Four examples are presented to illustrate deviation between the results from both regression methods. The examples studied show that, in some situations, ODR coefficients must substitute for those of OLS, and, in other situations, the difference is not significant.

A comparison of electricity spot prices simulation using ARMA-GARCH and mean-reverting models

The aim of this work is to compare two families of mathematical models for their respective capability to capture the statistical properties of real electricity spot market time series. The first model family is ARMA-GARCH models and the second model family is mean-reverting Ornstein-Uhlenbeck models. These two models have been applied to two price series of Nordic Nord Pool spot market for electricity namely to the System prices and to the DenmarkW prices. The parameters of both models were calibrated from the real time series. After carrying out simulation with optimal models from both families we conclude that neither ARMA-GARCH models, nor conventional mean-reverting Ornstein-Uhlenbeck models, even when calibrated optimally with real electricity spot market price or return series, capture the statistical characteristics of the real series. But in the case of less spiky behavior (System prices), the mean-reverting Ornstein-Uhlenbeck model could be seen to partially succeeded in this task.

Stellar population synthesis: the effect of improved Asymptotic Giant Branch models and of interstellar dust on mass-to-light–color relations

Teoreettisen populaatiosynteesin avulla voidaan mallintaa tähtijoukkojen ja galaksien fotometrisiä ominaisuuksia yhdistämällä yksittäisten tähtien tuottama säteily, joka saadaan teoreettisista tähtien kehitysmalleista. Valitsemalla sopiva massajakauma syntyville tähdille voidaan muodostaa yksinkertainen tähtipopulaatio, joka koostuu saman ikäisistä ja kemialliselta koostumukseltaan yhtenäisistä tähdistä. Monimutkaisempia tähtipopulaatioita voidaan muodostaa konvoloimalla yksinkertaisten tähtipopulaatioiden luminositeetti jonkin valitun tähtienmuodostushistorian kanssa sekä yhdistämällä näin muodostettuja populaatioita. Tässä työssä tarkastellaan asymptoottisen jättiläishaaran (AGB) tähtien uusien, tarkentuneiden evoluutiomallien vaikutusta populaatiosynteesin tuloksiin niin yksinkertaisten tähtipopulaatioiden kuin galaksien mallinnukseen soveltuvien monimutkaisempien tähtipopulaatioiden kohdalla. Työn päätarkoitus on tuottaa uudistuneisiin malleihin perustuvat populaation massa-luminositeetti -suhteen ja värin väliset relaatiot (MLC-relaatiot). MLC-relaatioita voidaan käyttää populaation massan määrittämiseen sen fotometristen ominaisuuksien (väri, luminositeetti) perusteella. Lisäksi tutkitaan tähtienvälisen pölyn vaikutusta yksinkertaisen spiraaligalaksimallin MLC-relaatioihin. Työssä käytetyt tähtien kehitysmallit perustuvat julkaisuun Marigo et al. (Astronomy & Astrophysics 482, 2008). Havaitaan, että AGB-tähtien vaikutus populaation integroituun luminositeettiin on pieni näkyvillä aallonpituuksilla, mutta merkittävä lähi-infrapuna-alueella. Vaikutus MLC-relaatioihin on vastaavasti merkittävä tarkkailtaessa luminositeettia lähi-infrapunassa sekä käytettäessä värejä, joissa yhdistetään optisia ja lähi-infrapunan kaistoja. Todetaan, että MLC-relaatioiden käyttö lähi-infrapunassa edellyttää tarkentuneen AGB-vaiheen sisällyttämistä populaatiosynteesin malleihin. Tähtienvälisen pölyn vaikutus MLC-relaatioihin todetaan riippuvan käytetystä kaistasta ja väristä, mutta vaikutuksen havaitaan olevan suurin optisen ja lähi-infrapunan väriyhdistelmillä.

Investment risks in Russia: A Finnish perspective

Purpose of this study is to analyze the effect of Russia's economic environment changes in the total return indexes of Finnish companies. The research data consisted of Finnish publicly listed companies, which have made physical investments to Russia, and operating in the area. The study used six different variables to model the Russian operating environment. The data consists of total return indexes of Finnish companies. From those we calculated the monthly mean interval between timeline of 1 January 2000 to 31 December 2009. Sample period is divided into two different parts. Variables impact on companies' total return indices is tested by regression analysis. By F-test we tested significance of model and squared coefficient correlation told us how much model explains from changes. Goodness of the β-coefficient is tested in the model by t-test. The research results shows that the Russian operating environment, or changes in which the active Finnish companies in total return indices. On partial sample periods results were not so significant.

Heritability at family mean level

Based on a polygenic system of a diploid species, without epistasis, and a population in Hardy-Weinberg equilibrium, without inbreeding and under linkage equilibrium, it can be shown that: (1) the narrow sense heritability at half-sib family level is equal to the square of the correlation coefficient between family mean and the additive genetic value of its common parent; (2) the narrow sense heritability at full-sib family level is equal to the square of the correlation coefficient between family mean and the mean of the additive genetic values of its parents; (3) the narrow sense heritability at Sn family level is exactly equal to the square of the correlation coefficient between family mean and the additive genetic value of its parent only in absence of dominance or when allele frequencies are equal; and (4) the broad sense heritability at full-sib or Sn family level can be used to analyze selection efficiency, since the progeny genotypic mean is, in general, a good indicator of parents, or Sn-1 plant superiority with respect to the frequency of favorable genes.

DYNAMICS AND PREDICTION OF DIAMETRIC STRUCTURE IN TWO ATLANTIC FOREST FRAGMENTS IN NORTHEASTERN BRAZIL

ABSTRACT Monitoring analyses aim to understand the processes that drive changes in forest structure and, along with prediction studies, may assist in the management planning and conservation of forest remnants. The objective of this study was to analyze the forest dynamics in two Atlantic rainforest fragments in Pernambuco, Brazil, and to predict their future forest diameter structure using the Markov chain model. We used continuous forest inventory data from three surveys in two forest fragments of 87 ha (F1) and 388 ha (F2). We calculated the annual rates of mortality and recruitment, the mean annual increment, and the basal area for each of the 3-year periods. Data from the first and second surveys were used to project the third inventory measurements, which were compared to the observed values in the permanent plots using chi-squared tests (a = 0.05). In F1, a decrease in the number of individuals was observed due to mortality rates being higher than recruitment rates; however, there was an increase in the basal area. In this fragment, the fit to the Markov model was adequate. In F2, there was an increase in both the basal area and the number of individuals during the 6-year period due to the recruitment rate exceeding the mortality rate. For this fragment, the fit of the model was unacceptable. Hence, for the studied fragments, the demographic rates influenced the stem density more than the floristic composition. Yet, even with these intense dynamics, both fragments showed active growth.

Method of numerical correction of errors occasioned by delay of records during the monitoring of environmental variables of interest for animal production

Thermal and air conditions inside animal facilities change during the day due to the influence of the external environment. For statistical and geostatistical analyses to be representative, a large number of points spatially distributed in the facility area must be monitored. This work suggests that the time variation of environmental variables of interest for animal production, monitored within animal facility, can be modeled accurately from discrete-time records. The aim of this study was to develop a numerical method to correct the temporal variations of these environmental variables, transforming the data so that such observations are independent of the time spent during the measurement. The proposed method approached values recorded with time delays to those expected at the exact moment of interest, if the data were measured simultaneously at the moment at all points distributed spatially. The correction model for numerical environmental variables was validated for environmental air temperature parameter, and the values corrected by the method did not differ by Tukey's test at 5% significance of real values recorded by data loggers.

Monotonic trend and change points in southern Brazil precipitation

Scientific evidence on climate changes at global level has gained increasing interest in the scientific community in general. The impacts of climate change as well as anthropogenic actions may cause errors in hydro-agricultural projects existent in the watershed under study. This study aimed to identify the presence or absence of trend in total annual precipitation series of the watershed of the Mirim Lagoon, state of Rio Grande do Sul-RS / Brazil / Uruguay (Brazilian side) as well as to detect the period in which they occurred. For that, it was analyzed the precipitation data belonging to 14 weather stations. To detect the existence of monotonic trend and change points, it was used the nonparametric tests of Mann-Kendall and Mann-Whitney, the "t" test of Student for two samples of unpaired data (parametric), as well as the technique of progressive mean. The Weather Station 3152014 (Pelotas) presented changes in the trend in the series of annual precipitation in the period from 1953 to 2007. The methodologies that use subdivided series were more efficient in detecting change in trend when compared with the Mann-Kendall test, which uses the complete series (from 1921 to 2007).