I en tid då samhället alltmer elektrifieras, står elnätsföretagen inför en växande utmaning att garantera att elsystemet kan hantera de ökande kraven. I detta sammanhang kan långtidsprognosverktyg spela en avgörande roll för att optimera det befintliga samt det framtida elnätet. Genom att använda sådana verktyg kan elnätsföretag förbättra sina planer för nätutveckling, vilket är viktigt för att främja en effektiv och hållbar utveckling av elnätet. Genom att följa välgrundade riktlinjer och bästa praxis kan man säkerställa tillgänglighet, pålitlighet och kostnadseffektivitet i elförsörjningen. Syftet med examensarbetet är att utforska vad som krävs för att hantera ett effektivt prognosverktyg och uppfylla föreskrift 7 § enligt EiFS 2024:1 från Energimarknadsinspektionen. Uppdraget kom som ett resultat av detta krav, där det anges att distributionsnätsföretaget ska rapportera sin prognos för överföringskapacitet i megawatt för att hantera kundernas energiförbrukning och produktion. Examensarbetet fokuserar på att utveckla metoder och använda programvaran AFS för att uppnå dessa mål samt att analysera nuvarande och framtida belastningar och parametrar som påverkar prognoserna En litteraturstudie har utförts kring de olika prognosverktygen och metoderna som finns ute på marknaden. Parallellt har fem olika elnätsföretag intervjuats för att undersöka vad för prognosverktyg de har utvecklat och hur de arbetar för att uppnå nätutvecklingsplanen. Utöver litteraturstudien och intervjuerna har prognosverktyget Aiolos Forecast Studio (AFS) analyserats för att undersöka dess potential vid långtidsprognostisering. Resultatet från litteraturstudien och intervjuerna visade att det krävs mer än bara prognosverktyg för att utföra långtidsprognoser. Litteraturstudien visade att det finns olika verktyg samt metoder för att utföra dessa prognoser, men att det beror på företagets specifika krav eller behov för vad som passar dem.  Intervjuerna avslöjade att många använder eller har använt Excel för prognosarbete, men att man behövde utveckla andra verktyg för att kunna hantera större mängder data då det kan överstiga Excels kapacitet. Med huvudverktyget som beräknar prognoserna, framkom det att det krävs en hel del förarbete. Samt att ett kompletterande verktyg som kan applicera prognoserna ute på elnätet är att föredra. Analysen av Aiolos Forecast Studio gav blandade resultat. Då uppdraget kom från Jämtkraft Elnät AB, är den bearbetade datan från deras lokalnät. AFS är ett givande verktyg för korttidsprognoser men går även att använda inför framtidsscenarion. De olika framtidsscenarion som arbetades med var om solelproduktionen skulle ha en ökad toppeffekt från år 2023 med 18 MW till 62 MW för 2030 och 150 MW för 2035. Det utfördes skalningar i programmet för att få de nya önskade toppeffekterna. Resultatet visade att de prognostiserade toppeffekterna för år 2030 samt 2035 låg på 14% och 20% ifrån det önskade toppvärdet. Detta beror på att när man arbetar med en regressionsmodell så kommer prognosen att skala relativt till det historiska utfallsdata, det vill säga 18 MW. Därefter analyserades skalningarna för ett årsperiod, inklusive en vecka med låg förbrukning och en vecka med hög förbrukning. Detta gav en tydlig inblick i hur solelproduktionen påverkade belastningen under olika förhållanden och med olika skalningar. Under lågförbrukningsveckan, som inträffade i juni, visade resultaten tydligt att vid en skalning på 150 MW skiftade belastningen från förbrukning till produktion under vissa timmar, med tydliga tecken på den så kallade ankkurvan. Under högförbrukningsveckan, som ägde rum i december, var resultaten knappt märkbara. Detta var förväntat, med tanke på att den globala strålningen ligger på lägre nivåer under vintermånaderna i Jämtlands län, där Jämtkraft AB är placerad. Sammanfattningsvis tyder resultaten på att den mest effektiva metoden verkar vara att skapa ett anpassat verktyg, där AFS kan vara till nytta för potentiella elnätsföretag genom att analysera historiska data och upptäcka avvikelser. / Electricity grid companies are facing a growing challenge to ensure that the electrical system can handle increasing demands in a time when society is becoming more electrified. In this context, long-term forecasting tools can play a crucial role in optimizing both the existing and future electricity grid. By using such tools, electricity grid companies can improve their network development plans, which is crucial for promoting an efficient and sustainable development of the electrical grid. By following well-established guidelines and best practices, it is possible to ensure accessibility, reliability, and cost-effectiveness in electricity supply. The purpose of the thesis is to explore what is required to manage an effective forecasting tool and meet the requirements of Regulation 7 § according to EiFS 2024:1 from the Swedish Energimarknadsinspektionen. The assignment arose as a result of this requirement, which states that distribution network companies must report their forecast for transmission capacity in megawatts to manage customers' energy consumption and production. The thesis focuses on developing methods and using the AFS software to achieve these goals, as well as analyzing current and future loads and parameters that affect the forecasts. A literature review has been conducted on various forecasting tools and methods available on the market. Simultaneously, five different electricity grid companies have been interviewed to investigate what forecasting tools they have developed and how they work to achieve the nätutvecklingsplanen. In addition to the literature review and interviews, the forecasting tool Aiolos Forecast Studio (AFS) has been analyzed to examine its potential for long-term forecasting. The findings from the literature review and interviews indicated that it takes more than just a single forecasting tool to conduct long-term forecasts. The literature review highlighted the presence of various tools and methods for performing these forecasts, but their suitability depends on the specific requirements or needs of the company. The interviews revealed that many individuals use or have used Excel for forecasting work, but additional tools needed to be developed to handle larger amounts of data as it might exceed Excel's capacity. Regarding the primary forecasting tool, it became evident that substantial preparatory work is required. Additionally, having a supplementary tool capable of applying the forecasts directly to the electricity grid is preferable. The analysis of AFS yielded mixed results. Since the assignment came from Jämtkraft Elnät AB, the data worked on was from their local grid. AFS is a valuable tool for short-term forecasts but can also be used for future scenarios. The different future scenarios worked on involved whether solar power production would have an increased peak capacity from 18 MW in 2023 to 62 MW in 2030 and 150 MW in 2035. Scalings were performed in the program to achieve the new desired peak capacities. The results showed that the forecasted peak capacities for 2030 and 2035 were 14% and 20% off from the desired peak value. This is because when working with a regression model, the forecast will scale relative to the historical outcome data, 18 MW. Following that, the scalings were analyzed for a year-long period, including a week of low consumption and a week of high consumption. This provided a clear insight into how solar power production influenced the load under different conditions and with different scalings. During the low-consumption week, occurring in June, the results clearly showed that at a scaling of 150 MW, the load shifted from consumption to production during certain hours, with clear signs of the so-called duck curve. During the high-consumption week, which took place in December, the results were barely noticeable. This was expected, considering that global radiation levels are lower during the winter months in Jämtlands län, where Jämtkraft AB is located. In summary, the results suggest that the most effective approach appears to be creating a customized tool, where AFS can be beneficial for potential grid companies by analyzing historical data and detecting anomalies.

We study the benefits of resource flexibility, considering two different flexibility structures. First, we want to understand the impact of the firm's pricing strategy on its resource investment decision, considering a partially flexible resource. Secondly, we study the benefits of a flexible resource strategic approach, considering a resource flexibility structure that has not been studied in the previous literature. First, we study the capacity investment decision faced by a firm that offers two products/services and that is a price-setter for both products/services. The products offered by the firm are of varying levels (complexities), such that the resources that can be used to produce the higher level product can also be used to produce the lower level one. Although the firm needs to make its capacity investment decision under high demand uncertainty, it can utilize this limited (downward) resource flexibility, in addition to pricing, to more effectively match its supply with demand. Sample applications include a service company, whose technicians are of different capabilities, such that a higher level technician can perform all tasks performed by a lower level technician; a firm that owns a main plant, satisfying both end-product and intermediate-product demand, and a subsidiary, satisfying the intermediate-product demand only. We formulate this decision problem as a two-stage stochastic programming problem with recourse, and characterize the structural properties of the firm's optimal resource investment strategy when resource flexibility and pricing flexibility are considered in the investment decision. We show that the firm's optimal resource investment strategy follows a threshold policy. This structure allows us to understand the impact of coordinated decision-making, when the resource flexibility is taken into account in the investment decision, on the firm's optimal investment strategy, and establish the conditions under which the firm invests in the flexible resource. We also study the impact of demand correlation on the firm's optimal resource investment strategy, and show that it may be optimal for the firm to invest in both flexible and dedicated resources when product demand patterns are perfectly positively correlated. Our results offer managerial principles and insights on the firm's optimal resource investment strategy as well as extend the newsvendor problem with pricing, by allowing for multiple resources (suppliers), multiple products, and resource pooling. Secondly, we study the benefits of a delayed decision making strategy under demand uncertainty, considering a system that satisfies two demand streams with two capacitated and flexible resources. Resource flexibility allows the firm to delay its resource allocation decision to a time when partial information on demands is obtained and demand uncertainty is reduced. We characterize the structure of the firm's optimal delayed resource allocation strategy. This characterization allows us to study how the revenue benefits of the delayed resource allocation strategy depend on demand and capacity parameters, and the length of the selling season. Our study shows that the revenue benefits of this strategy can be significant, especially when demand rates of the different types are close, while resource capacities are much different. Based on our analysis, we provide guidelines on the utilization of such strategies. Finally, we incorporate the uncertainty in demand parameters into our models and study the effectiveness of several delayed capacity allocation mechanisms that utilize the resource flexibility. In particular, we consider that demand forecasts are uncertain at the start of the selling season and are updated using a Bayesian framework as early demand figures are observed. We propose several heuristic capacity allocation policies that are easy to implement as well as a heuristic procedure that relies on a stochastic dynamic programming formulation and perform a numerical study. Our study determines the conditions under which each policy is effective. / Ph. D.

Not too long ago, commercial supersonic aircraft flights were part of the air transportation system. In the 1970's we had the Russian-built Tupolev Tu-144 and the BAC/Aerospatiale Concorde, the latest being tin operation for 27 years. The work documented in this dissertation focused on the viability of bringing back supersonic aircraft as a transportation mode. Throughout three years, Virginia Tech and a team from NASA have been combining efforts to develop a model capable of predicting future air travel demand for supersonic vehicles. The model can predict future supersonic commercial services and allows aircraft designers from NASA to optimize aircraft performance and characteristics by maximizing the potential air travel demand. The final product of this study was the development of the Low-Boom Supersonic Aircraft Model (LBSAM). The development progress took three years to be completed, and during each year, a version of the model with the preliminary predictions was made available to NASA. Each of the three versions of the model predicts future supersonic commercial services. What differentiates each version is the data, method, and aircraft type/design implemented; the latest version of the model is more realistic and provides a higher number of functionalities. The first version of the model predicted the possible supersonic commercial service for three aircraft types: each with two variations. An 18-seat, 40-seat, and 60-seat low-boom and non-low-boom aircraft were analyzed. The second version of the model analyzed a 20-seat and 40-seat low-boom, non-low-boom aircraft with restrictions and non-low-boom aircraft without restrictions. The latest version of the model tries to estimate potential demand for a 43-seat and a 52-seat supersonic low-boom aircraft design. The low-boom concept refers to the implementation of technology that reduces the loudness of a sonic boom. A non-low-boom concept refers to an aircraft flying faster than Mach 1 with the technology's implementation that reduces the loudness of a sonic boom. The final results suggest that for a 52-seat LBSA, the potential worldwide demand is as follows. • 33.4 million seats worldwide. Assuming an overland range of 3,200 nm., an overland Mach 1.7, and an overland fuel scale factor of 0.98. • 772 aircraft needed worldwide. Assuming an overland range of 2,800 nm., an overland Mach 1.7, and an overland fuel scale factor of 0.90. • 1,032 one-way OD pairs where LBSA can operate. Assuming an overland range of 2,800 nm., an overland Mach 1.7, and an overland fuel scale factor of 0.90. The LBSAM is mainly driven by the cost per passenger mile values calculated for each one-way Origin-Destination (OD) pair. Additional uncertainties in the model include the market share and annual aircraft utilization. The market share refers to the percent of the demand that will switch from current subsonic commercial services to commercial supersonic services. During the three-year work, we considered a market share of 50% and 100%. Aircraft utilization refers to the number of hours that the airline will be able to use the aircraft. The majority of the projections were based on a 3,500-hour aircraft utilization. / Doctor of Philosophy / Not too long ago, commercial supersonic aircraft flights were part of the air transportation system. An aircraft flying faster than the speed of sound is known as an aircraft flying at supersonic speed. Current commercial aircraft fly at subsonic speed. Subsonic speed refers to aircraft flying at a speed lower than the speed of sound. In the 1970's we had the Russian-built Tupolev Tu-144 and the BAC/Aerospatiale Concorde, the latest being tin operation for 27 years. The work documented in this dissertation focused on the viability of bringing back supersonic aircraft as a transportation mode. Throughout three years, Virginia Tech and a team from NASA have been combining efforts to develop a model capable of predicting future air travel demand for supersonic vehicles. The model can predict future supersonic commercial services and allows aircraft designers from NASA to optimize aircraft performance and characteristics by maximizing the potential air travel demand. The purpose of this dissertation effort is to provide a better understanding of what could be the potential commercial demand for supersonic flight in the near future. We consider all the benefits and characteristics of supersonic flight and studied in detail what percentage of the travelers might be willing to migrate from the current subsonic market to the supersonic market. We estimated this ratio by studying the spending behavior of passengers in the current market. How much more are passengers willing to pay to save time? We can infer how much travelers value their time by comparing direct flights versus flights with an intermediate stop. The results show that a demand of 33.4 million seats could be reached by the year 2040. The supersonic market would consist of more than one thousand one-way origin-destination pairs worldwide, and more than seven hundred supersonic aircraft are expected to satisfy the forecast demand.

No / This paper proposes that the changes which occur in composition of the bed load during the transport of mixed-grain-size sediments are largely controlled by the distributions of critical entrainment shear stress for the various size fractions. This hypothesis is examined for a unimodal sediment mixture by calculating these distributions with a discrete particle model and using them in a probabilistic calculation of bed-load composition. The estimates of bed-load composition compare favorably with observations of fractional transport rates made in a laboratory flume for the same sediment, suggesting that the hypothesis is reasonable. The analysis provides additional insight, in terms of grain mechanics, into the processes that determine bed-load composition. These insights strongly suggest that better prediction methods will result from taking account of the variation of threshold within size fractions, something that most previous studies have neglected.

This study goal was to first determine the baseline Global Forecast System (GFS) skill in forecasting borderline (non-bomb:0.75-0.95, bomb: 1.-1.25) bomb events, and second to determine if machine learning (ML) techniques as a post-processor can improve the forecasts. This was accomplished by using the Tempest Extreme cyclone tracking software and ERA5 analysis to develop a case list during the period of October to March for the years 2008-2021. Based on the case list, GFS 24-hour forecasts of atmospheric base state variables in 10-degree by 10-degree cyclone center subdomains was compressed using S-mode Principal Component Analysis. A genetic algorithm was then used to determine the best predictors. These predictors were then used to train a logistic regression as a baseline ML skill and a Support Vector Machine (SVM) model. Both the logistic regression and SVM provided an improved bias over the GFS baseline skill, but only the logistic regression improved skill.

Yes / This is a pioneering effort to test the comparative performance of two competing models for out-of-sample forecasting the term structure of volatility of crude oil price changes employing both symmetric and asymmetric evaluation criteria. Under symmetric error statistics, our empirical model using the estimated growth factor of volatility through time is overall superior, and it beats in most cases the benchmark model of the square-root-of-time for holding periods between one and 250 days. Under asymmetric error statistics, if over-prediction (under-prediction) of volatility is undesirable, the empirical (benchmark) model is consistently superior. Relative performance of the empirical model is much higher for holding periods up to fifty days.

The U.S. Coast Guard maintains a network structure to connect its nation-wide assets. This paper analyzes and models four highly aggregate traces of the traffic to/from the Coast Guard Data Network ship-shore nodes, so that the models may be used to predict future system demand. These internet traces (polled at 5â 40â intervals) are shown to adhere to a Gaussian distribution upon detrending, which imposes limits to the exponential distribution of higher time-resolution traces. Wavelet estimation of the Hurst-parameter is shown to outperform estimation by another common method (Sample-Variances). The First Differences method of detrending proved problematic to this analysis and is shown to decorrelate AR(1) processes where 0.65< phi1 <1.35 and correlate AR(1) processes with phi1 <-0.25. The Hannan-Rissanen method for estimating (phi,theta) is employed to analyze this series and a one-step ahead forecast is generated. / Master of Science

Consistently high returns in private equity has lead to a steady increase in the global totalassets under management during the past few decades. Therefore, the relevancy of investing in private equity is obvious. As an investment class, private equity is much younger thanits public counterpart, which is a big part of the reason why the amount of financial researchand modeling on it is quite confined. Nevertheless, the need for forecasting capabilities for anyinvested party in private equity is still great, and the authors of this thesis set out to delivera model which accurately forecasts expected net asset value development of a private equityportfolio and present a confidence interval for it. Furthermore, the thesis serves to present suchresults conditional on macroeconomic scenarios. The scope of the study includes private equityfunds of various investment classes, namely, small-cap and mid-cap buyout, large-cap buyoutand venture capital and growth equity.To achieve an accurate model, the study is based on data from a credible source and threeseparate models are derived and tested against each other. The three models consist of one using a simple historical mean approach, another is based on theory presented by Takahashi andAlexander (2002) (the TA-model), and the third model (the modified TA-model) comes fromresearch by Buchner, Kaserer and Wagner (2009). The TA-model and the modified TA-modelhave at least one parameter which needs to be optimized. This was done using a conditional leastsquare method, utilizing MATLAB’s tool for solving nonlinear optimization problems, fmincon.Subsequent to the derivation of each model, a statistical test (a p-value test) was completed.This resulted in the TA-model being proved to be the best in forecasting net asset value development of private equity funds (which by extension means it is also the best at projectingthe same for an entire private equity portfolio) and was therefore implemented in further areas. By sorting the data on vintage year of the fund, data sets corresponding to pre-definedmacroeconomic periods could be attained. The TA-model was then fitted on these data setswhich produced meaningful results in regards to net asset value development, conditional onthree different macroeconomic scenarios, early-, mid-, and late market cycle. Next, Monte Carlosimulations were performed by stochastically simulating the distributions of funds in the various investment classes, resulting in confidence intervals of potential outcomes. Ultimately, theresults were applied to a mock portfolio designed by the authors to represent reality in fair way.The results of the study allow for two important conclusions to be drawn. Firstly, the authors areconfident that the thesis delivers a model which forecasts net asset value development of privateequity investments within certain confidence intervals in a good way, thereby fulfilling the aimof the study as accurately as possible, given the scope and limitations of the study. Secondly,the investigation provides solid evidence that the net asset value development of a private equityfund is dependent on what market cycle is prevailing at the time of fund commencement, andhow the development varies between such scenarios. Finally, using insights gained during theinvestigation process, the authors identify some potential areas for future studies.

Submitted by Sara Ribeiro (sara.ribeiro@ucb.br) on 2018-06-18T18:53:22Z No. of bitstreams: 1 RafaeldeMoraisVargasDissertacao2018.pdf: 2179808 bytes, checksum: e2993cd35f13b4bd6411d626aefa0043 (MD5) / Approved for entry into archive by Sara Ribeiro (sara.ribeiro@ucb.br) on 2018-06-18T18:54:14Z (GMT) No. of bitstreams: 1 RafaeldeMoraisVargasDissertacao2018.pdf: 2179808 bytes, checksum: e2993cd35f13b4bd6411d626aefa0043 (MD5) / Made available in DSpace on 2018-06-18T18:54:14Z (GMT). No. of bitstreams: 1 RafaeldeMoraisVargasDissertacao2018.pdf: 2179808 bytes, checksum: e2993cd35f13b4bd6411d626aefa0043 (MD5) Previous issue date: 2018-02-27 / Given the importance of market risk measures, such as value at risk (VaR), in this paper, we compare traditionally accepted volatility forecast models, in particular, the GARCH family models, with more recent models such as HAR-RV and GAS in terms of the accuracy of their VaR forecasts. For this purpose, we use intraday prices, at the 5-minute frequency, of the S&P 500 index and the General Electric stocks, for the period from January 4, 2010 to December 30, 2013. Based on the tick loss function and the Diebold-Mariano test, we did not find difference in the predictive performance of the HAR-RV and GAS models in comparison with the Exponential GARCH (EGARCH) model, considering daily VaR forecasts at the 1% and 5% significance levels for the return series of the S&P 500 index. Regarding the return series of General Electric, the 1% VaR forecasts obtained from the HAR-RV models, assuming a t-Student distribution for the daily returns, are more accurate than the forecasts of the EGARCH model. In the case of the 5% VaR forecasts, all variations of the HAR-RV model perform better than the EGARCH. Our empirical study provides evidence of the good performance of HAR-RV models in forecasting value at risk. / Dada a importância de medidas de risco de mercado, como o valor em risco (VaR), nesse trabalho, comparamos modelos de previsão de volatilidade tradicionalmente mais aceitos, em particular, os modelos da família GARCH, com modelos mais recentes, como o HAR-RV e o GAS, em termos da acurácia de suas previsões de VaR. Para isso, usamos preços intradiários, na frequência de 5 minutos, do índice S&P 500 e das ações da General Electric, para o período de 4 de janeiro de 2010 a 30 de dezembro de 2013. Com base na função perda tick e no teste de Diebold-Mariano, não encontramos diferença no desempenho preditivo dos modelos HAR-RV e GAS em relação ao modelo Exponential GARCH (EGARCH), considerando as previsões de VaR diário a 1% e 5% de significância para a série de retornos do índice S&P 500. Já com relação à série de retornos da General Electric, as previsões de VaR a 1% obtidas a partir dos modelos HAR-RV, assumindo uma distribuição t-Student para os retornos diários, mostram-se mais acuradas do que as previsões do modelo EGARCH. No caso das previsões de VaR a 5%, todas as variações do modelo HAR-RV apresentam desempenho superior ao EGARCH. Nosso estudo empírico traz evidências do bom desempenho dos modelos HAR-RV na previsão de valor em risco.

本論文以公司規模大小、公司成長率、盈餘變異程度、盈餘持續率、負債比率、系統風險、以及產業別為控制變數，探討管理當局持股比率與管理當局自願性（強制性）盈餘預測準確度、盈餘管理程度之關係。並進一步探討管理當局持股比率與七個控制變數之交互作用對管理當局自願性（強制性）盈餘預測準礁度及盈餘管理程度之影響。 　　本實證研究結果發現： 　　1、自願性盈餘預測方面： 　　(1)管理當局持股比率越高且盈餘變異程度越大之公司，盈餘預測誤差越高，盈餘預測準確度越低。 　　(2)管理當局持股比率越高且負債比率越高之公司，盈餘預測誤差越高，盈餘預測準確度越低。 　　(3)產業別會影響其預測準確度，而產業中以鋼鐵業之盈餘預測準確度，顯著較高。 　　(4)公司成長率越高、盈餘持續率越高，其盈餘管理程度越高。 　　(5)產業中以電子業有顯著較高之盈餘管理程度。 　　2、強制性盈餘預測力面： 　　(1)管理當局持股比率與盈餘預測準確度成正相關。 　　(2)公司規模與盈餘預測準確度成負相關。 　　(3)盈餘持續率與盈餘預測準確度成負相關。 　　(4)產業別確實與強制性盈餘預測準確度有關，其中以電子業之盈餘預測準確度顯著較低。 　　(5)管理當局持股比率越高之紡織業其盈餘預測準確度顯著較低。 　　(6)強制性盈餘預測並無顯著的盈餘管理情況產生。 　　3、綜合結論： 　　(1)自願性之盈餘預測準確度高於強制性之盈餘預測準確度。 　　(2)自願性之盈餘管理程度高於強制性之盈餘管理程度。 / This research hypothesizes that the level of managerial ownership that controlling for earnings growth、earnings variability、earnings persistence、company risk、 debt、industry、and size has effect on both the magnitude of forecast precise of voluntary(compelling) forecast and the magnitude of discretionary accounting accrual adjustment. 　　In addition，this study examines that there are interaction of ownership effects on both the magnitude of forecast precise of voluntary (compelling) forecast and the magnitude of discretionary accounting accrual adjustment. 　　The empirical results show as follow: 　　1、Voluntary forecast aspect: 　　(1) Managerial ownership is negatively associated with the magnitude of forecast precise. 　　(2) Managerial ownership of is positively associated with the magnitude of discretionary accounting accrual adjustment. 　　2、Compelling forecast aspect: 　　(1) Managerial ownership is positively associated with the magnitude of forecast precise. 　　(2) Managerial ownership is not associated with the magnitude of discretionary accounting accrual adjustment. 　　3、Conclusion explication: 　　(1) The magnitude of forecast precise of voluntary forecast is more than that of compelling forecast. 　　(2) The magnitude of discretionary accounting accrual adjustment of voluntary forecast is more than that of compelling forecast. 　　(3) Industry variable indeed affects both the magnitude of forecast precise and the magnitude of discretionary accounting accrual adjustment.