One of the fundamental premises of financial reporting by business enterprises is that it should provide users with information that will assist them in predicting the amounts, timing and uncertainty of future cash flows of the enterprise. The requirement for alternative income measurements was partially justified by an assumed correspondence between the new information and the cash flow prediction objective. The existence of that correspondence, however, has not been precisely verified by the research to date. The overall objective of this research was to contribute additional evidence to address conflicts in the prior research findings, and additionally, to consider possible industry and firm-size effects on the ability to predict cash flow from alternative incomes. A data base was compiled from COMPUSTAT tapes (historical cost), the Parker model restatement procedures (general price-level) and the Easman data base that used the Falkenstein-Weil restatement model (replacement cost). One conclusion was that the alternative income measurements produce different cash flow forecast errors. Overall, historical cost net income produced the lowest forecast errors for two approximations of cash flow. The inclusion of monetary gains/losses and holding gains/losses in net income did not improve predictions, and in one case worsened them. Another conclusion was that a multiple linear regression model produced significantly lower forecast errors for both cash flow definitions. The simple linear and exponential regression prediction models did not produce different forecast errors. Finally, both an industry effect and a firm-size effect were identified in the prediction of working capital from operations. When net income plus depreciation was the object of prediction, an industry effect was identified but not a firm-size effect. The overall impact of these findings is that the alternative income measurements should be justified on some basis other than facilitating cash flow prediction. In fact, a random-walk cash flow prediction model performed better than any prediction based on net income. Financial accounting standards in the area of alternative income measurements should consider possible industry and firm-size differences. The choice of cash flow definition is apparently critical because different conclusions were obtained. / Ph. D.

An investment in a private equity is any investment made in a financial asset that is not publicly traded. As such these assets are very difficult to value and also give rise to great difficulty when it comes to quantifying risk. In a typical private equity investment the investor commits a prespecified amount of capital to a fund, this capital will be called upon as needed by the fund and eventually capital will be returned to the investor by the fund as it starts to turn a profit. In this way a private equity investment can be boiled down to consist of two cash flows, the contributions to the fund and distributions from the fund to the investor. These cash flows are usually made within a prespecified time frame but at unspecified intervals and amounts. As an investor in a fund, carrying too little liquid assets when contributions are called upon will cause trouble, but carrying significantly more than needed is also not desirable as it represents a loss in potential revenue from having less capital in more profitable investments. The goal of this thesis was to attempt to find a way to reliably model these cash flows and to find a way to represent the results in a meaningful way for the benefit of the investor by constructing value at risk like risk measures for the necessary liquid capital to carry at a given time in case contributions are called upon. It was found that the distributions could be modelled very well with the chosen stochastic processes, both as it related to predicting the average path of the cash flows and as it relates to modelling the variability of them. Contrary to this it was found that the contributions could not be modelled very well. The reason for this was found to be an observed lag in the speed of contributions at the start of the funds lifetime, this lag was not taken into account when constructing the stochastic model and hence it produced simulated cash flows not in line with those used in the calibration. / En investering i private equity är en investering i en tillgång som inte är börsnoterade. På grund av detta är sådana tillgångar väldigt svåra att värdera och medför även store svårigheter när det kommer till att kvantifiera risk. I en typisk private equity investering so ingår en investerare i ett löfte att under en viss förbestämd tidsperiod bidra med en fixt mängd kapital till en private equity fond. Detta kapital kommer att gradvis kallas på av fonden vid behov för att sedan mot slutet av fondens livstid ge utdelning när private equity fonden börjar göra en vinst. På detta viset kan en private equity investering brytas ner i två kassaflöden, kontributioner in i fonden, och distributioner ut ur fonden. Dessa kassaflöden sker under en förbestämd tidsperiod men ej förbestämda belopp. Som en investerare i denna typen av fond är därför en risk att bära för lite likvid kapital när kontributioner blir kallade på men även oattraktivt att bäre på för mycket de detta representerar förlorar potentiell avkastning. Målet i denna uppsatts är att hitta ett sätt att på att tillförlitligt vis modellera dessa kassaflöden och representera resultaten på ett meningsfullt sätt från perspektivet av en investerare. För att uppnå detta skapades value-at-risk liknande mått för mängden likvid kapital som krävs under en tidsperiod för att säkra sig mot påkallade kontributioner. Slutsatsen blev att distributioner kunde modelleras väl, både när det kom till att efterlikna den genomsnittliga vägen av kassaflöden och även för att modellera risken. I kontrast till detta så kunde inte kontributioner modelleras mot tillräckligt hög säkerhet för att användes i det ämnade syftena. Anledningen till detta var en eftersläpning i hastigheten som kontributioner kallades med som inte tågs i beaktande av den tillämpade matematiska modellen.

CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior / This study aims to develop a planning system on multiple spatial and temporal scales, and apply it to the Brazilian electric sector and Cearà State, Jaguaribe Metropolitan System. For realization of this proposal, we have been considered some temporal scales: short-term (up to 1 month), short term (up to one year) and medium to long term (1-10 years and 10-30 years, respectively). To obtain estimates of the flow of short-term rainfall forecasts from atmospheric models for later entry in the hydrological rainfall-runoff model are used. To short term scale were considered stochastic and statistical models, as the Periodic Autoregressive type (PAR), Periodic Autoregressive with exogenous variables (PARx) and K-nearest neighbor models, and the use of global atmospheric models as input to hydrological rainfall-runoff model Soil Moisture Accounting Procedure (SMAP). For the range of the medium term were considered auto regressive models (AR) and Fourier and wavelets. We used data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) as input in hydrological rainfall-runoff model for long-term scale. For the weather forecast, as the rain threshold adopted in the construction of the contingency table increases, the quality of the forecasts decreases, except for the adjustment index. Thus, the system of numerical prediction proves efficient in detecting the occurrence of rainfall of less intensity, with most satisfactory results in the North and Northeast regions of Brazil. On seasonal scale the models feature up better than the climatology. Likewise, in the range of medium-term models based on Fourier series and wavelets have better likelihood than the weather. In multi-scale, there are differences in the future shown by the projections of the CMIP5 models that were analyzed for RCP8.5 and RCP4.5 the XXI century scenarios, but in the North sector of the National Interconnected System (SIN), most models indicate negative trend, diverging only in magnitude. / O presente trabalho visa elaborar um sistema de planejamento em mÃltiplas escalas temporais e espaciais e aplicÃ-lo ao setor elÃtrico brasileiro e ao sistema Jaguaribe-Metropolitano do Estado do CearÃ. Para realizaÃÃo desta proposta, foram consideradas algumas escalas temporais: curtÃssimo prazo (atà um mÃs), curto prazo (atà um ano) e mÃdio e longo prazo (1 a 10 anos e atà 30 anos, respectivamente). Para obtenÃÃo das previsÃes de vazÃes de curtÃssimo prazo sÃo utilizadas as previsÃes de precipitaÃÃo a partir de modelos atmosfÃricos, para posterior entrada no modelo hidrolÃgico chuva-vazÃo. Para escala de curto prazo foram considerados modelos estocÃsticos e estatÃsticos, como do tipo PeriÃdico Autorregressivo (PAR), PeriÃdico Autorregressivo com variÃveis exÃgenas (PARx) e K-vizinhos, e o uso de modelos atmosfÃricos globais como entrada do modelo hidrolÃgico chuva-vazÃo Soil Moisture Accounting Procedure (SMAP). Na escala de mÃdio prazo foram considerados modelos autorregressivos (AR) e as transformadas de Fourier e ondeletas. Para escala de longo prazo foram utilizados dados provenientes do Coupled Model Intercomparison Project Phase 5 (CMIP5) como dados de entrada no modelo hidrolÃgico chuva-vazÃo. Quanto à previsÃo de tempo, à medida que o limiar de chuva adotado na construÃÃo da tabela de contingÃncia aumenta, a qualidade das previsÃes diminui, exceto para o Ãndice acerto. Dessa forma, o sistema de previsÃo numÃrica mostra-se eficiente em detectar a ocorrÃncia de chuvas de menor intensidade, apresentando resultados mais satisfatÃrios nas regiÃes Norte e Nordeste do Brasil. Na escala sazonal, os modelos apresentam-se melhor que a climatologia. Da mesma forma, na escala de mÃdio prazo, os modelos baseados na sÃrie de Fourier e ondeletas apresentam melhor verossimilhanÃa do que a climatologia. Na escala plurianual, hà divergÃncias quanto ao futuro mostrado pelas projeÃÃes dos modelos do CMIP5 que foram analisados para os cenÃrios RCP8.5 e RCP4.5 do sÃculo XXI, porÃm no setor Norte do Sistema Interligado Nacional (SIN), a maioria dos modelos sinaliza tendÃncia negativa, divergindo apenas em magnitude.

Short-term traffic flow forecasting is a critical function in advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). Accurate forecasting results are useful to indicate future traffic conditions and assist traffic managers in seeking solutions to congestion problems on urban freeways and surface streets. There is new research interest in short-term traffic flow forecasting due to recent developments in ITS technologies. Previous research involves technologies in multiple areas, and a significant number of forecasting methods exist in literature. However, forecasting reliability is not properly addressed in existing studies. Most forecasting methods only focus on the expected value of traffic flow, assuming constant variance when perform forecasting. This method does not consider the volatility nature of traffic flow data. This paper demonstrated that the variance part of traffic flow data is not constant, and dependency exists. A volatility model studies the dependency among the variance part of traffic flow data and provides a prediction range to indicate the reliability of traffic flow forecasting. We proposed an ARIMA-GARCH (Autoregressive Integrated Moving Average- AutoRegressive Conditional Heteroskedasticity) model to study the volatile nature of traffic flow data. Another problem of existing studies is that most methods have limited forecasting abilities when there is missing data in historical or current traffic flow data. We developed a General Regression Neural Network(GRNN) based multivariate forecasting method to deal with this issue. This method uses upstream information to predict traffic flow at the studied site. The study results indicate that the ARIMA-GARCH model outperforms other methods in non-missing data situations, while the GRNN model performs better in missing data situations.

Intelligent transportation systems (ITS) have garnered considerable attention for providing efficient traffic management solutions. Traffic flow forecasting is a crucial component of it which serves as the foundation for various state-of-the-art deep learning approaches. Initially, researchers recognized that significant temporal changes from traffic flow data for modelling. However, as researchers delved deeper into the underlying correlations within traffic flow data, they discovered that spatial information from the road network also plays a crucial role in accurate forecasting. Consequently, deep learning methods that incorporate Spatio-temporal representation have been employed to address traffic flow forecasting. Although recent solutions to this problem are impressive, it is essential to discuss the reasoning behind the architecture of the model. The expression of each feature relies on selecting appropriate models for feature extraction and designing architectures that minimize information loss during modeling. In this thesis, the work focuses on graph-based Spatio-temporal feature engineering. The experiments are divided into two parts: 1). explores the efficient architecture for expressing spatial-temporal information by considering both different sequential modelling approaches. 2). Based on the result obtained, the second experiment focuses on multi- scale modelling to capture informative Spatio-temporal feature. We propose a model that incorporates sequential modeling and captures multi-scale Spatiotemporal semantics by employing residual connections in different hierarchy. We validate our model using three datasets, each containing varying information for extraction. Taking into account the dataset characteristics and the model structure, our model outperforms the baselines and state-of-the-art models. The experimental results indicate that the performance of sequential modeling and multi-scale semantics, combined with thoughtful model design, significantly contribute to the overall forecasting performance. Furthermore, our work serves as inspiration for expressive data mining methods that rely on appropriate feature extraction models and architecture design, taking into consideration the information content within the dataset.

Made available in DSpace on 2015-05-14T12:09:27Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 7820449 bytes, checksum: c6acd880295b25b51f289018a1415c70 (MD5) Previous issue date: 2012-08-17 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The Brazilian energetic system is mainly based on hydropower, which is highly dependent on the watershed water availability. In order to minimize the risk of failure, which affects the uptake from the water bodies, this system is interconnected. During the dry season, there is less volume stored in the reservoirs, which leads to a lower production of energy. Before the flood period, in order to attend the multiple uses of the water resources, it is necessary to keep an operational volume in the reservoir, which also decreases the water level and has impacts on power generation. In order to make the flood control, the electricity sector forecasts the availability of a waiting volume in the reservoirs, which are capable of receiving parts of the inflows to prevent, at prefixed risk, damage at downstream. It is in this scenario that the problem highlighted in this dissertation arises, the forecasting of inflow to a reservoir, in order to have a judicious allocation of these void spaces in the reservoirs for the flood control. Thus, the main objective of this study is to analyze the use of the wavelet transform to forecast daily inflows in Sobradinho reservoir (Bahia State) seven days ahead, by a wavelet-ANN hybrid system, with the following specific objectives: (a) eliminate the noise present in the observed inflow time series by wavelet analysis, (b) define the optimal level for decomposition of the signals, (c) determine the appropriate mother-wavelet for this type of forecasting with ANN, and (d) carry out simulations with the proposed wavelet-ANN hybrid system and compare the results with the predictions made without the application of wavelet transform. It was used the daily data for the period from January 1931 to December 2010. From the obtained results, it was observed that the wavelet-ANN hybrid system performed better forecasting for seven days ahead than the system using the ANN with the raw data, and the approximation A3 from the discrete mother-wavelet Meyer obtained the best result (R2 = 0.9977; Nash = 0.9954 and RMS = 96.4523 m³/s), whereas the prediction using RNA with raw data obtained the following results: R2 = 0.9481; Nash = 0.8971 and RMS = 456.7712 m³/s, i.e., the RMS decreased almost 80%, while the Nash and R2 coefficients have increased more than 5% and 10%, respectively, when compared with the forecasts using the raw data. / O sistema energético brasileiro é fortemente baseado na energia hidroelétrica, a qual é altamente dependente da disponibilidade hídrica das bacias hidrográficas. A fim de minimizar os riscos de falha que afetam o aporte de água aos mananciais, esse sistema é interligado, pois, nas épocas secas, tem-se um menor volume armazenado nos reservatórios, o que leva a uma menor produção de energia, e antes das épocas de cheias, para atender ao uso múltiplo dos recursos hídricos, deve-se deixar um volume operacional no reservatório, o que também diminui o seu nível de água e tem impactos na geração de energia. Para efetuar o controle de cheias, o setor elétrico prevê a disponibilidade de um volume de espera nos reservatórios, capazes de absorver parcelas das afluências, para evitar, com um risco prefixado, que sejam causados danos a jusante. É nesse cenário que surge o problema destacado no presente trabalho, a previsão da vazão afluente a um reservatório, a fim de se ter uma alocação criteriosa desses espaços vazios nos reservatórios para o controle de cheias. Diante do exposto, o objetivo geral deste trabalho é analisar o uso da transformada wavelet para realizar previsões das vazões diárias afluentes ao reservatório de Sobradinho BA com sete dias de antecedência, por meio de um sistema híbrido wavelet-RNA, sendo os objetivos específicos: (a) eliminar os ruídos presentes nas séries históricas de vazão através da análise wavelet; (b) definir o nível ótimo de decomposição dos sinais; (c) determinar a wavelet-mãe adequada para este tipo de previsão com RNAs; e (d) realizar simulações com o sistema híbrido wavelet-RNA proposto e comparar os resultados com as previsões realizadas sem a aplicação da transformada wavelet. Utilizou-se dados de médias diárias de vazões naturais do período de janeiro de 1931 a dezembro de 2010. Diante dos resultados apresentados, observa-se que o sistema híbrido wavelet-RNA proposto obteve resultados melhores de previsão para sete dias de antecedência que o sistema utilizando a RNA com os dados brutos, sendo a aproximação A3 da waveletmãe Meyer Discreta a que obteve o melhor resultado na validação (R2 = 0,9977; Nash = 0,9954 e RMS = 96,4523 m³/s), enquanto que a previsão utilizando os dados brutos forneceu os seguintes resultados: R2 = 0,9481; Nash = 0,8971 e RMS = 456,7712 m³/s; i.e., o RMS diminuiu quase 80%, enquanto que os coeficientes R2 e Nash tiveram um aumento maior que 5% e 10%, respectivamente, em relação às previsões com os dados brutos.

In Statement No. 33, "Financial Reporting and Changing Prices," the FASB requires that some large companies disclose their historical cost/constant dollar and current cost information in the published financial statements. One of the purposes of these disclosures is to help users of the financial statements in assessing future cash flows. This study was directed toward the examination of the effects of the different levels of disclosures on cash flow projections.

Submitted by Vinícius Bisogni (vibisogni@uol.com.br) on 2014-08-26T22:48:46Z No. of bitstreams: 1 Dissertacao_Vinicius de Araujo Bisogni.pdf: 2688252 bytes, checksum: 608dee9763c8c1a015d3b54b3692c5b0 (MD5) / Approved for entry into archive by JOANA MARTORINI (joana.martorini@fgv.br) on 2014-08-27T11:45:32Z (GMT) No. of bitstreams: 1 Dissertacao_Vinicius de Araujo Bisogni.pdf: 2688252 bytes, checksum: 608dee9763c8c1a015d3b54b3692c5b0 (MD5) / Made available in DSpace on 2014-08-27T12:01:56Z (GMT). No. of bitstreams: 1 Dissertacao_Vinicius de Araujo Bisogni.pdf: 2688252 bytes, checksum: 608dee9763c8c1a015d3b54b3692c5b0 (MD5) Previous issue date: 2014-07-30 / This paper aims to compare different methods of forecasting cash needs in overnight, to ensure that the liquidity of a particular financial product - in this case, the Call Deposits (Demand Deposits Account, in foreign currency) - are sufficient to cover the liquidity risks of a financial institution and, in other hand, optimize the profit provided from the remaining balance that exceeds the outputs of the models. Here, the Cash Flow model of Schmaltz (2009), which segregates the model in different components (deterministic and stochastic), is applied to determine the cash needs and, through the Monte Carlo method for predicting different cash flows, is stipulated an average value of balance to be used in overnight. As a contrast, the deterministic model of Ringbom et al (2004) is used to provide the "Profit-Maximizing Reserve Ratio" to finally compare both of them historically, between Jan/2009 and Dec/2013, in order to conclude which of models of cash reserve shows to be more satisfying. The database used replicate balances and withdraws of a commercial bank, to this specific financial product, and it is also used for parameters estimation. / Este trabalho tem como objetivo comparar diferentes métodos de previsão de necessidades de caixa no overnight, que assegurem que a liquidez de um produto financeiro específico – neste caso, o Call Deposits (Depósito à Vista, em moeda estrangeira) – seja suficiente para cobrir os riscos de liquidez de uma instituição financeira e, em contrapartida, otimizem o lucro oriundo do saldo restante que ultrapasse o valor de saída destes modelos. Para isso, o modelo de Fluxo de Caixa de Schmaltz (2009), que segrega os diferentes componentes do caixa (determinísticos e estocásticos), será utilizado para determinar as necessidades de caixa e, através do método de Monte Carlo para a previsão de diferentes caminhos, chegamos a um valor médio de saldo para ser utilizado no overnight. Como comparativo, será utilizado o modelo determinístico de Ringbom et al (2004), que oferece a 'Taxa de Reserva de Maximização de Lucro', para, enfim, compará-los historicamente, entre Jan/2009 e Dez/2013, a fim de concluirmos qual dos modelos de reservas de caixa se mostra mais satisfatório. A base de dados utilizada replica os saldos e saques de um banco comercial, para o produto financeiro em questão, e, também, é utilizada para a estimação dos parâmetros.

The thesis deals with the individual aspects of liquidity and solvency management in the context of financial risk management and working capital components. The main accent is put on the foreign exchange risk management and the cash management tools on the group level: netting and cash pooling. These tools are supported by the cash forecasting system and the actual cash flows evaluation. The methods of liquidity and solvency management are demonstrated on the example of a Shared Service Center organisation.

The climate adaptation of our cities and the optimization of our technical systems with regards to weather sets high demands on the availability and the processing of weather data. The possibility to forecast disturbances of influent flow rate to wastewater treatment plants allow control systems counteract these disturbances before they have a harmful effect on the treatment processes. These forecasts can be made by different models A neural network models complex patterns between different data sets through a multi-layered structure containing a large amount of transformation functions. The aim of this project was to examine how the complex neural network performed compared with a simpler linear regression model when forecasting wastewater flow using high resolution X-band rain radar data. The study also investigated to what extent X-band rain radar data contributes to the performance of the model. The performance was evaluated at rain flow periods only. Wastewater flow data were provided by Avedøre wastewater treatment plant in Copenhagen operated by BIOFOS. The X-band rain radar data was provided by HOFOR. The neural network was developed by Informetics on the TensorFlow platform. This project concluded that the neural network and the linear regression model performed equally well at predicting when a rain flow period began. The neural network was more accurate at predicting the flow rate while the linear regression was better at approximating the accumulated flow over an entire rain flow period. Using additional rain data up to 30 km within the radar station location in comparison with using data only from within the catchment indicated a 20 to 30-minutes improvement of possible lead time. A conceivable lead time when forecasting the sewer flow to Avedøre wastewater treatment plant was estimated to be around 4 hours. / Det föreligger höga krav på tillgänglighet och bearbetning av väderdata för att kunna optimera tekniska system i förhållande till väder och klimat. Att kunna förutsäga ändrat inkommande flöde till avloppsreningsverk möjliggör för kontrollsystem att kunna motverka negativa konsekvenser på reningsprocesserna på grund av det ändrade flödet. X-band radardata kan användas för att prognoser av flöden med hjälp av olika modeller.Ett neuralt nätverk, reproducerar komplexa mönster mellan olika dataset genom en struktur med flera lager och en mängd överföringsfunktioner.  Målsättningen med det här projektet var att utvärdera hur ett komplext neuralt nätverk presterar jämfört med en enklare regressionsmodell i att förutsäga avloppsflöde med hjälp av högupplöst X-band radardata. I projektet undersöktes också hur tillgång av olika radardata kunde bidra till modellens prestanda. Modellerna utvärderades endast under regnflödesperioder. Data över avloppsflödet som användes i projektet kom från Avedøre avloppsreningsverk i Köpenhamn. Reningsverket drivs av BIOFOS. Radardata kom från HOFOR. Det neurala nätverket som användes har utvecklats av Informetics på plattformen Tensorflow. Slutsatser som kunde dras i projektet var att det neurala nätverket och den linjär regressionsmodellen var lika bra på att förutsäga när en regnflödesperiod startade. Det neurala nätverket kunde förutsäga det momentana flödet bättre än regressionsmodellen, medan det omvända gällde för att uppskatta den totala flödesvolymen under en hel regnflödesperiod. Genom att använda ytterligare regndata, upp till 30 kilometer från radarstationen, jämfört med att endast använda data från avrinningsområdet kunde en 20–30 minuters förbättring av den möjliga prognostiden påvisas. En tänkbar prognostiden för att förutsäga avloppsflödet till Avedøre avloppsreningsverk visades ligga omkring 4 timmar.