This paper discusses the factors that affect the share price index of Taiwan solar power industry, crude oil prices and the size of the solar manufacturers in Taiwan and Taiwan's market index into the consideration. In addition, considering whether the policies implemented by our government would change the solar industry in Taiwan¡¦s stocks structural .Using the correlation coefficient, the unit root test, Chow test, cointegration test , vector error correction model, impulse response and forecast error variance decomposition to explore their relationship respectively. The study period starts from January 3,2002 until December 30,2011, a total of 2450 daily data for empirical analysis. By Chow test , we find that there is no structural change of solar stock index after the implementation of the domestic policies. Three international crude oil prices and the total share capital of solar manufacturers in Taiwan and the Taiwan solar power industry stocks index has co-integration relationship, means the three international crude oil prices and solar companies total share capital of solar stock index has a long-run equilibrium relationship. By the error correction model of West Texas crude oil price of Brent crude oil prices, the total share capital of the solar companies in Taiwan and Taiwan solar stock index mutual interaction, and the relationship between changes in Taiwan's solar stock price index and Brent crude oil price, West Texas crude oil prices and the total manufacturers of solar energy manufacturers in Taiwan¡¦s share capital are positive, besides, I also found a positive relationship in the impulse response.

Abstract Japan is the second largest economic power in the world. It has a great deal of FDI outflows but few FDI inflows. Therefore, Japan is in the serious situation of ¡§FDI balance of payments deficit.¡¨ In terms of inward FDI stocks as a percentage of GDP and gross fixed capital formation, Japan is the lowest place of G-7. The purpose of this research is focusing on discussing the shortage of FDI inflows and causing factors which lower the desires of investments in Japan by using the simplest way which is based on the actual situation and the limit of the information in Japan. This paper takes the quarterly data of Japan from 1978 to 2005 and four variables (wage index, real exchange rate, trade and FDI inflows). In this research, the unit root test is used to check if the data have the stationarity or not, and then it uses vector autoregression model (VAR) to proceed impulse response function and forecast error variance decomposition. According to the result of these two approaches, we can figure out the influences of four variables for each other, and then find out the causing factors which lead Japan to have less FDI inflows. The calculation shows that the reason which leads Japanese wages to increase gradually results not only from real exchange rate, trade and FDI inflows, but also from Japanese labor system (lifetime employment system and payment according to working seniority) and the labor quantities. The causality runs from real exchange rate to trade is greater than vice versa. Trade has a positive impact from the real exchange rate which means that the depreciation can accelerate trade. However, the main factor of hindering FDI inflows is Japanese high wages rather than real exchange rate or trade. Therefore, in order to get rid of the depression which was caused by the bubble economy in 1990s, Japanese government not only opens up the restrictions in policy but also takes the control of the prime costs into the most important consideration.

No description available.

In this master thesis, the impact of forward-looking macroeconomic information under IFRS 9 is studied using fictional data from a Swedish mortgage loan portfolio. The study employs a time series analysis approach and employs vector autoregression models to model expected credit loss parameters with multiple incorporated macroeconomic parameters. The models are analyzed using impulse response functions to study the impact of macroeconomic shocks and the results show that the unemployment rate, USD/SEK exchange rate and 3-month interest rates have a significant impact on expected credit losses. / I detta examensarbete studeras effekterna av framåtblickande makroekonomisk information enligt IFRS 9 med fiktiv data baserad på en svensk bolåneportfölj. Studien använder sig av tidsserieanalys och vektorautoregressionsmodeller för att modellera förväntade kreditförlust-parametrar med flera inkorporerade makroekonomiska parametrar. Modellerna analyseras med hjälp av impulsresponsfunktioner för att studera effekterna av makroekonomiska chocker. Resultaten visar att arbetslöshet, USD/SEK växelkurs och 3-månaders räntor har en signifikant inverkan på förväntade kreditförluster.

Seminal papers of Solow (1957) and Swan (1956) stimulated debate among economists on the role of technical change in productivity improvements and for that matter economic growth. The consensus is that technological change accounts for a significant proportion of gross national product (GNP) growth in industrialised economies. In the case of Australia, the aggregate productivity performance was poor in the 1970s and 1980s, but picked up very strongly by the 1990s, and was above the OECD average growth level for the first time in its productivity growth history. However, this high productivity growth rate could not be sustained and Australia started to experience a slowdown in productivity growth since 2000. This study empirically measures the performance of productivity in Australia’s economy for the period 1950-2005, using an econometric approach. Time-series data are used to develop econometric models that capture the dynamic interactions between GDP, fixed capital, labour units, human capital, foreign direct investment (FDI) and information and communication technology (ICT). The Johansen (1988) cointegration techniques are used to establish a long-run steady-state relation between or among economic time series. The econometric analysis pays careful attention to the time-series properties of the data by conducting unit root and conintegration tests for the variables in the system. / This study finds that Australia experienced productivity growth in the 1950s, a slow down in the mid 1960s, a very strong productivity growth in the mid 1990s and another slowdown from 2000 onwards. The study finds evidence that human capital, FDI and ICT are very strong determinants of long-run GDP and productivity growth in Australia. The study finds that the three, four and the five factor models are likely to give better measures of productivity performance in Australia as these models recognise human capital, FDI and ICT and include them as separate factors in the production function, This study finds evidence that the previous studies on the Australia’s productivity puzzle have made a very significant omission by not considering human capital, FDI and ICT as additional exogenous variables and by excluding them from the production function for productivity analysis.

<p>Volvo in Arvika produces wheel loaders, and the production is based on forecasts. When a machine is ordered, the customer can choose what type of equipment he or she wants, and these equipments are also made forecasts on. This is made by giving each equipment an estimated procentual usage that shows how many of the machines that will use this option. Today two people are working with the forecasts, planer A in Eskilstuna and planer B in Arvika. Planer A makes a forecast based on the historical outcome and planer B then makes adjustments of this based on how many options that are ordered. Volvo in Arvika is having problems with the accuracy of the forecasts and because of this they have got too much in stock. But how big are the forecast deviations, what is the cause of it, in what or which places does the process lack? What can be made to make more accurate forecasts, how can you get a more affective process with less work made? To answer these questions we surveyed the process and analyzed it to find strong and week spots. We found that Planer A has a lack of information about how the forecast influence the stock in Arvika, that she gets pour feedback from production, that Planer B is the only one with knowledge about the forecast work in Arvika. We also made a benchmarking with the factory in Braås to see how they differ. Just like in Arvika there are two people working with the forecasts, but in Braås both of them are located close to the production and they share the options equal. They are also able to fill in for each other if someone would be sick.</p><p>To see how much the forecast differ from market demand, we have analyzed forecast data from nine different options for eight months. It turned out that the automatic calculated forecasts are a bit high and that planner B lower them.</p><p>Our conclusion is that the forecasts should be made only in Arvika, and not as it is today when half of it is made in Eskilstuna. There should also be documents and routines on how the work shall be done. This is to make it easier for people that will do the same job in the future.</p> / <p>Volvo Wheel Loaders (WLO) i Arvika tillverkar hjullastare och gör detta mot prognos. Till hjullastarna finns olika utrustningar som kunden kan välja mellan och även dessa gör Volvo prognoser på. Detta görs genom att de uppskattar hur många procent av maskinerna som kommer använda varje utrustning och lägger in det i ett program. Idag arbetar två personer med prognoserna, planerare A på huvudkontoret i Eskilstuna och planerare B på plats i Arvika. Planerare A gör först prognosen med avseende på historiskt utfall, sedan justerar planerare B dessa gentemot bl.a. orderingång. WLO har problem med träffsäkerheten i sina utrustningsprognoser och detta har medfört höga lagernivåer och därmed bundet kapital. Hur stora är prognosavvikelserna, vad är det som gör att prognoserna blir fel, på vilket eller vilka ställen i processen är det som bristerna uppstår? Vad kan de göra för att få bättre prognoser, hur kan man effektivisera processen så att det blir mindre arbete? För att svara på dessa frågor började vi med att kartlägga prognostiseringsprocessen och sedan analysera den för att få fram svagheter och styrkor. Här fann vi t ex att planerare A inte har någon kunskap om hur prognoserna påverkar lagret i Arvika och att hon får för dålig feedback från produktion, att planerare B är ensam kunnig om prognosarbetet vilket leder till problem när han är sjuk eller borta av andra skäl.</p><p>Sedan gjorde vi även en processjämförelse med Volvo Braås för att se hur de skiljer sig åt. I Braås är det två personer som tar fram prognoserna och de arbetar med hälften av utrustningsnumren var. Båda sitter nära produktion och är även väl insatta i varandras arbete om någon av dem skulle vara borta.</p><p>För att få fram hur prognoserna avviker från utfallet har vi gått igenom prognoshistorik för nio olika typer av utrustningar och sedan gjort beräkningar på det materialet. Det visade sig att prognoserna som automatiskt beräknas ofta ligger för högt och att planerare B sänker dessa.</p><p>Vi har kommit fram till att allt arbetet med prognoserna borde ske på plats i Arvika och inte som i nuläget när hälften görs i Eskilstuna. Man bör även införa rutiner på hur arbetet med prognoserna ska gå till och göra dokument på detta så att det är lättare för personer som ska ta över eller måste sätta sig in hur det fungerar.</p>

Volvo in Arvika produces wheel loaders, and the production is based on forecasts. When a machine is ordered, the customer can choose what type of equipment he or she wants, and these equipments are also made forecasts on. This is made by giving each equipment an estimated procentual usage that shows how many of the machines that will use this option. Today two people are working with the forecasts, planer A in Eskilstuna and planer B in Arvika. Planer A makes a forecast based on the historical outcome and planer B then makes adjustments of this based on how many options that are ordered. Volvo in Arvika is having problems with the accuracy of the forecasts and because of this they have got too much in stock. But how big are the forecast deviations, what is the cause of it, in what or which places does the process lack? What can be made to make more accurate forecasts, how can you get a more affective process with less work made? To answer these questions we surveyed the process and analyzed it to find strong and week spots. We found that Planer A has a lack of information about how the forecast influence the stock in Arvika, that she gets pour feedback from production, that Planer B is the only one with knowledge about the forecast work in Arvika. We also made a benchmarking with the factory in Braås to see how they differ. Just like in Arvika there are two people working with the forecasts, but in Braås both of them are located close to the production and they share the options equal. They are also able to fill in for each other if someone would be sick. To see how much the forecast differ from market demand, we have analyzed forecast data from nine different options for eight months. It turned out that the automatic calculated forecasts are a bit high and that planner B lower them. Our conclusion is that the forecasts should be made only in Arvika, and not as it is today when half of it is made in Eskilstuna. There should also be documents and routines on how the work shall be done. This is to make it easier for people that will do the same job in the future. / Volvo Wheel Loaders (WLO) i Arvika tillverkar hjullastare och gör detta mot prognos. Till hjullastarna finns olika utrustningar som kunden kan välja mellan och även dessa gör Volvo prognoser på. Detta görs genom att de uppskattar hur många procent av maskinerna som kommer använda varje utrustning och lägger in det i ett program. Idag arbetar två personer med prognoserna, planerare A på huvudkontoret i Eskilstuna och planerare B på plats i Arvika. Planerare A gör först prognosen med avseende på historiskt utfall, sedan justerar planerare B dessa gentemot bl.a. orderingång. WLO har problem med träffsäkerheten i sina utrustningsprognoser och detta har medfört höga lagernivåer och därmed bundet kapital. Hur stora är prognosavvikelserna, vad är det som gör att prognoserna blir fel, på vilket eller vilka ställen i processen är det som bristerna uppstår? Vad kan de göra för att få bättre prognoser, hur kan man effektivisera processen så att det blir mindre arbete? För att svara på dessa frågor började vi med att kartlägga prognostiseringsprocessen och sedan analysera den för att få fram svagheter och styrkor. Här fann vi t ex att planerare A inte har någon kunskap om hur prognoserna påverkar lagret i Arvika och att hon får för dålig feedback från produktion, att planerare B är ensam kunnig om prognosarbetet vilket leder till problem när han är sjuk eller borta av andra skäl. Sedan gjorde vi även en processjämförelse med Volvo Braås för att se hur de skiljer sig åt. I Braås är det två personer som tar fram prognoserna och de arbetar med hälften av utrustningsnumren var. Båda sitter nära produktion och är även väl insatta i varandras arbete om någon av dem skulle vara borta. För att få fram hur prognoserna avviker från utfallet har vi gått igenom prognoshistorik för nio olika typer av utrustningar och sedan gjort beräkningar på det materialet. Det visade sig att prognoserna som automatiskt beräknas ofta ligger för högt och att planerare B sänker dessa. Vi har kommit fram till att allt arbetet med prognoserna borde ske på plats i Arvika och inte som i nuläget när hälften görs i Eskilstuna. Man bör även införa rutiner på hur arbetet med prognoserna ska gå till och göra dokument på detta så att det är lättare för personer som ska ta över eller måste sätta sig in hur det fungerar.

Prognoser publicerade av aktieanalytiker har en betydande påverkan på kapitalmarknaden och investeringsbeslut. Genom att förmedla information inom kapitalmarknaden spelar aktieanalytiker en vital roll. Därför syftar denna studie till att undersöka aktieanalytikers förmåga att prognostisera finansiella nyckeltal från företags resultaträkning. Studien syftar även till att undersöka under vilka förhållanden aktieanalytikers träffsäkerhet påverkas. Vi undersöker aktieanalytikers träffsäkerhet genom att mäta prognosfel i konsensusestimat över perioden 2000 – 2017 för 93 företag listade på Nasdaq OMX Stockholm Large Cap. Vi finner ett positivt samband mellan marknadsvärde och ökad träffsäkerhet i aktieanalytikers prognoser för mindre företag. Vi finner även att ett högt antal analytiker som ingår i konsensus positivt påverkar prognosens träffsäkerhet. Resultaten visar även att träffsäkerheten i aktieanalytikers prognoser varierar beroende på antal analytiker i konsensus inom olika sektorer. / In this study, we investigate the accuracy of stock analyst estimates. We measure forecasting accuracy by forecasting error for key financial figures from the firm’s income statements. This study also measures firm specific factors effect on analyst forecasting accuracy. Our study includes 93 companies listed on Nasdaq OMX Stockholm Large Cap between the years 2000 – 2017. Our findings conclude that a positive correlation exists between market value and forecasting accuracy for smaller firms. We also find that analyst coverage is positively related to forecasting accuracy. Our findings also show that forecasting accuracy varies with analyst coverage within different sectors.

本論文分為兩部分，第一部份探討日經225和摩根台指成分股調整的價格變動效果。第二部份則是探討分析師對於股票被納入日經225和摩根台指的盈餘預測反應和絕對預測誤差。 / Two essays are comprised in this dissertation to examine the effects of changes in price and the analyst responses of earnings forecasts to stocks Indices adjustments. Stock markets vary in nature from one country to another and the characteristic of stock index adjustments also alter significantly. The analytical results can provide better information for investors and management to make better decisions. In the first essay, we examine price effects associated with changes in the composition of the Nikkei 225 Index and MSCI Taiwan Index. The analytical results show the price effects on stocks experiencing adjustments in the Nikkei 225 Index are consistent with the price pressure hypothesis. The price effects of composite stocks changed for the MSCI Taiwan Index are consistent with the downward sloping demand curve hypothesis. Based on classifying the characteristics of composite stocks into three categories, we find that large-scale added stocks dominate the price trend of the whole added sample in the Nikkei 225 Index. Also, added stocks with upwards revision earnings forecasts make more abnormal returns than the added stocks with downwards revision earnings forecasts in the Nikkei 225 Index during the post-announcement period. The electronic stocks earn larger abnormal returns than non-electronic stocks in the MSCI Taiwan Index. That can enable investors to profit by buying electronic stocks and added stocks with upwards revision earnings forecasts. The price reactions for the composite stocks in the Nikkei 225 Index and MSCI Taiwan Index around the Internet bubble burst have significantly difference. In the second essay, we study the earnings forecast changes and absolute forecast errors made by analysts of the Nikkei 225 Index and MSCI Taiwan Index. Depending on the properties of brokerage firms that analysts work for, we divide them into local analysts and foreign analysts to separate who are more accurate than one the other. The results show that in comparison with the matching firms in Japan, the magnitudes of mean forecast revisions and absolute forecast errors are smaller made by analysts focusing on firms newly added to the Nikkei 225 Index. For firms newly added to the MSCI Taiwan Index, the magnitude of changes in analysts EPS forecasts do not differ clearly from those of their peer groups. Absolute forecast errors made by analysts focusing on firms newly added to the MSCI Taiwan Index are smaller than those made by analysts focusing on the matching firms. This phenomenon demonstrates firms that are newly added to the Nikkei 225 Index and MSCI Taiwan index exhibit significantly improved performance. In terms of the relative accuracy of local and foreign analysts, the results display that the forecasts of foreign analysts are less accurate than those of local analysts in Japan and the forecasts of foreign analysts are more accurate than those of local analysts in Taiwan.

Made available in DSpace on 2010-04-20T20:51:42Z (GMT). No. of bitstreams: 3 142184.pdf.jpg: 20410 bytes, checksum: 720b476fe32b25d220b0dde4d663ee25 (MD5) 142184.pdf: 373613 bytes, checksum: 1b6743be6830c2ae7ab8245255b9ad6b (MD5) 142184.pdf.txt: 120505 bytes, checksum: be4e63d920365eb874f914450f641b26 (MD5) Previous issue date: 2005-10-17T00:00:00Z / A presente dissertação analisa o erro de projeção dos analistas de investimentos do sell side, definido como a diferença entre o consenso das projeções dos analistas e o resultado reportado pela empresa. O tamanho do erro de projeção é uma medida da qualidade das projeções dos analistas de um determinado mercado de capitais. Uma vasta literatura acadêmica mostra que uma melhora na qualidade das projeções dos analistas, medida através de uma diminuição do tamanho do erro de projeção, está relacionada com a redução da assimetria de informação e com um aumento do valor de mercado das empresas. São testadas duas regressões, nas quais características das empresas, como setor, tamanho, endividamento e variabilidade do lucro, e características do ambiente de informação da empresa, como listagem de ADR, número de analistas que acompanham a empresa e convergência das projeções, são testadas contra duas métricas do erro de projeção, acurácia e viés. Nossas hipóteses são que existem fatores que influenciam de maneira significativa o tamanho do erro de projeção (acurácia) e o viés das projeções (viés). Estas hipóteses foram confirmadas, isto é, nossas regressões apresentaram pelo menos um fator que se mostrou significativo estatisticamente para influenciar o tamanho do erro de projeção (hipóteses H1 e H2) ou o seu viés (hipótese H3). Entretanto, os resultados mostram que vários fatores que se mostram significativos em testes conduzidos em mercados desenvolvidos – tais como tamanho, endividamento e variabilidade do lucro – não se mostraram significativos no mercado brasileiro. Por outro lado, os fatores relacionados com o resultado do ano projetado ou do ano anterior se mostraram fortemente significativos. Acreditamos que os resultados podem ser explicados de três maneiras: 1) ou a capacidade de adicionar valor dos analistas em relação a modelos estatísticos de projeção é muito pequena, devido à sua falta de habilidade; ou 2) a instabilidade macroeconômica é tão grande domina todos os outros fatores que poderiam influenciar o tamanho do erro de projeção; ou 3) os resultados das empresas nos mercados desenvolvidos são tão administrados, isto é, tão estáveis, que permitem que fatores mais sutis como o tamanho, o nível de endividamento e a variabilidade do lucro se tornem significativos. Esta dissertação não permite distinguir qual das explicações é a correta. Uma de suas limitações é não incluir variáveis referentes à habilidade e experiência dos analistas e, também, variáveis relacionadas a fatores como governança corporativa e disclosure de informações. Em uma linha de pesquisa muito extensa nos países desenvolvidos, mas praticamente inexistente no Brasil, esperamos que estudos futuros supram estas lacunas e nos permitam entender melhor a questão da qualidade das projeções de resultados no contexto brasileiro. / The current dissertation analyses the forecast error of the sell side analysts in the Brazilian context, defined as the difference between the forecast consensus and the company earnings effectively reported. The size of the forecast error is used as a proxy for the quality of the forecast produced by the analysts of a specific stock market. A vast academic literature shows that an improvement in the quality of the forecasts produced by the analysts, measured by a decrease in the size of the forecast error, is related with a decrease in the asymmetry of information in such market and by an increase in the market value of its companies. Two regressions are tested, in which company characteristics, such as sector, size, leverage and variability of earnings, and characteristics of the company’s information environment, such as ADR listing, number of analysts following and forecast convergence, are tested against two metrics of forecast error, accuracy and bias. Our hypotheses are that there are factors that impact significatively both the size of the forecast error (accuracy) and the bias presented by the projections (bias). The hypotheses are confirmed, that is, the regressions present at least one factor that impacts significantly either the size of the forecast error (hypotheses H1 and H2) or the bias (hypothesis H3). However, the results show that many factors that are significant in tests conducted in developed markets – such as size, leverage and earnings variability – are not significant in the Brazilian context. On the other hand, factors related to the company results in the fiscal year being forecast and in the previous year result to be strongly significant. We believe that these results can be explained in three ways: 1) either forecasts produced by Brazilian analysts add very little value over statistical models, probably because of lack of ability; or 2) the macroeconomic instability in Brazil is so great that its influence on the companies’ results dominates all other factors that could impact the size of the forecast error; or 3) the earnings management of the companies in the developed markets is so widespread, leading to such a stability of earnings, that it allows for more subtle factors such as size and leverage become significant. This study does not allow us to distinguish which one is the correct explanation. One of its limitations is not to include variables related to the ability and experience of the analysts, as well as variables related to governance and disclosure. In a body of research that is very extensive in developed countries, but practically inexistent in Brazil, we hope that future research fills these gaps and allow us to better understand the issue of the quality of earnings forecast in the Brazilian context.