The diploma thesis analyzes the development of agricultural land prices examines the development of agricultural land in the Hodonín district in 2015-2018. Three databases of comparative samples are created for the thesis - according to the source (realized sales, execution sales and offer sales), which are subsequently analyzed and examined. The result is our own findings and conclusions, including the price index, which are compared and confronted with public aggregated data.

Skillnaden mellan det marknadsförda priset och försäljningspriset är något media under de senaste åren och än idag gärna riktar fokus mot. Fastighetsmäklare påstås använda sig av lockpriser vid marknadsföring av objekt till salu. År 2015 nådde man rekordsiffror för antalet anmälningar till Fastighetsmäklarinspektionen (FMI). Enligt fastighetsmäklarna själva handlar det om att dem gör ett bra arbete. Fastighetsmäklarna kan inte påverka hur den enskilda budgivaren agerar vid en budgivning. I den här studien tittade jag därför på hur budgivningen påverkar skillnaden mellan utgångspris och slutpris. Sex olika kommuner i Stockholm undersöktes, vardera med 156 observationer från år 2011-2015. Med hjälp av multipla regressionsanalyser försökte jag se ett samband mellan prisskillnaden och budgivningen i de olika kommunerna. De faktorer jag valde att titta på inom budgivningen var "kontraktsdatum", "antal bud" och "antal budgivare". Statistik framställdes även för att se den genomsnittliga skillnaden mellan utgångspris och slutpris i respektive kommun. Detta för att kunna diskutera förekomsten av lockpriser. Studien visade att ett svagt samband råder mellan prisskillnaden och valda faktorer. Slutsatsen som drogs var att antal bud och antal budgivare inte på egen hand utgör en anledning till skillnaden mellan utgångspris och slutpris. Dessa faktorer kan istället tillsammans med andra faktorer förklara slutpriset bra. I samtliga kommuner var skillnaden mellan utgångspris och slutpris dessutom för liten för att kunna klassas som lockpris. / The difference between the marketed price and the selling price is something the media has been focusing on during the last few years and the focus is still the same today. Real estate agents are said to use too low prices in order to attract customers when marketing their listings. The amount of reports to FMI (the authority that supervise real estate agents in Sweden) reached record levels during 2015. According to the real estate agents themselves, this is due to the good work they do. The real estate agents cannot influence how the individual bidder will act during bidding. I therefore looked at how bidding affects the difference between the listed price and the selling price in this study. Six different municipalities in Stockholm are examined, each with 156 observations from year 2011-2015. I tried to see the relationship between the difference in price and bidding in each of the municipalities with the help of multiple regression models. The factors I selected within bidding was the "contract date", "amount of bids" and "amount of bidders". Statistics were also generated to see the average difference between the listed price and the selling price in each municipality. This was done to discuss the occurrences of too low prices. The study shows a weak relationship between the difference in price and the chosen factors. The conclusion was that the amount of bids and the amount of bidders did not single-handedly cause the difference between the listing price and selling price. These factors could instead, alongside with other factors, account for the selling price. The difference between the listing price and selling price in all of the municipalities was too insignificant to be classified as "price to catch customers".

This study has conducted an OLS-regression to examine the relationship between gasoline and crude oil prices in the Swedish petroleum market, focusing on potential asymmetry, where gasoline prices respond more quickly to increases in crude oil prices compared to decreases. Additionally, we examine the impact of individual petroleum stations' competitiveness on this asymmetry, applying the distance to the nearest station as a measure of competitiveness. To explore this relationship, we utilise two datasets: one comprises unique user-generated data for individual gas stations spanning the period from 2019 to 2022, while the other includes recommended prices covering the period from 2001 to 2020. The findings provide some evidence supporting the existence of asymmetry, indicating the presence of inefficiencies within the market. However, no evidence suggesting larger asymmetry concerning individual competitiveness was discovered. Overall, this research offers novel insights into the dynamics of the Swedish fuel market in recent years.

An investigation into whether the colour red has more impact or leads to common perceptions about the price or value of retail products in advertising. / ABSTRACT Colour is often an important non verbal cue in advertising. Much research has been dedicated to the creative aspects of advertising generally and to factors affecting consumer response in relation to marketing, advertising and pricing. This study looks at the colour red and investigates whether use predominant use of red in sale promotions print advertising is justified on the basis of its ability to impact cognitively or affectively. The study comprises of two experiments, one for awareness and one for price perception and purchase intent. In each experiment, red is compared to other colours in order to establish any significant differences. The second experiment goes further to examine whether the intensity of colour, verbal cues or demographic differences have an impact on the results. The literature review begins with an examination of price theory and the role of sales promotions in organizations. It considers advertising response models with focus on the persuasive hierarchy AIDA model. The impact of colour is then considered along with consumer psychology and behavior as well as theories relating to demographic and cultural responses to colour in advertising. Argument from the sources is then put forward to suggest that research into the effects of colour in advertising is underexplored and that the role of colour in affecting response is complex and is over oversimplifies by advertising practitioners. The research results are presented revealing few significant differences between red and alternative colours for awareness, price perception or purchase intent. The result for awareness is blurred by research limitations, but red does not emerge as a candidate for exacting higher levels of awareness than a number of other colours. In the second experiment, red is found to be inferior to blue in affecting purchase intent. Red at 50% saturation is shown to have a more positive impact on purchase intent that a red hue at full saturation. Demographic splits do not show conclusive results, but it is suggested that a larger sample size would induce a better price perception of red for the black community than for other racial groupings. Discussion and recommendations follow. In this study, red shows no qualities to justify its predominant use in price promotion advertising and more benefit might be obtained for the brand by differentiating through use of alternative colours that may stand out in a sea of red over traditional sale periods. The principle recommendations are, firstly, the need to acquire a deeper understanding of the effect of colour in advertising. In the more complex, competitive global marketplace competition for customer attention is high and the margin of error for irrelevant advertising appeals are low. Secondly, the argument is made for colour usage to be built around long term branding concerns rather than short term requirements for advertising response.

The inference of causal relationships between stock markets constitutes a major research topic in the field of financial time series analysis. A successful reconstruction of the underlying causality structure represents an important step towards the overall aim of improving stock market price forecasting. In this thesis, I utilise the concept of Granger-causality for the identification of causal relationships. One major challenge is the possible presence of latent variables that affect the measured components. An instantaneous interaction can arise in the inferred network of stock market relationships either spuriously due to the existence of a latent confounder or truly as a result of hidden agreements between market players. I investigate the implications of such a scenario; proposing a new method that allows for the first time to distinguish between instantaneous interactions caused by a latent confounder and those resulting from hidden agreements. Another challenge is the implicit assumption of existing Granger-causality analysis techniques that the interactions have a time delay either equal to or a multiple of the observed data. Two sub-cases of this scenario are discussed: (i) when the collected data is simultaneously recorded, (ii) when the collected data is non-simultaneously recorded. I propose two modified approaches based on time series shifting that provide correct inferences of the complete causal interaction structure. To investigate the performance of the above mentioned method improvements in predictions, I present a modified version of the building block model for modelling stock prices allowing causality structure between stock prices to be modelled. To assess the forecasting ability of the extended model, I compare predictions resulting from network reconstruction methods developed throughout this thesis to predictions made based on standard correlation analysis using stock market data. The findings show that predictions based on the developed methods provide more accurate forecasts than predictions resulting from correlation analysis.

This report investigates the effects a demand based tariff would have on private electricity consumers and how it could contribute to a sustainable electrical grid. The fuse tariff that is currently used charges customers for their electricity usage and does not fully reflect the electrical grid load and thereby not the cost for the distribution system operators. A demand based tariff, that charges customers for their power peaks and promotes a change of behaviour better reflects the grid load and could lead to a more even grid distribution and a sustainable development. In light of this issue, two demand based tariffs have been constructed for the energy company Upplands Energi’s private customers’ current electricity consumption. One of the tariffs consists of a higher fixed cost and a lower variable cost and the other consists of a lower fixed cost and a higher variable cost, both leading to the same revenue för Upplands Energi. The two different demand based tariffs have been simulated and analysed, where the one with the lowest fixed cost is recommended since it gives customers higher economic incentives to change their electricity usage behaviour. It is essential that electricity retailers communicate the information of how the demand based tariff works to the customers in order to make them change their electricity behaviour to attain a sustainable grid.

<p>This dissertation presents the construction procedure of &ldquo;high-frequency&rdquo; daily measure of changes in housing valuations, and analyzes its return dynamics, as well as investigates its relationship to capital markets. The dissertation consists of three chapters. The first chapter introduces the house price index methodologies and housing transaction data, and reviews the related literature. The second chapter shows the construction and modeling of daily house price indexes and highlights the informational advantage of the daily indexes. The final chapter provides detailed empirical and theoretical investigations of housing index return volatilities. </p><p>Chapter 2 discusses the relationship of the housing market with the other markets, such as consumer good market and financial markets. Different housing price indexes and their construction methodologies are introduced, with emphases on the repeat sales model and S&P/Case Shiller Home Price Index. A detailed description of the housing transaction data I use in the dissertation is also provided in this chapter.</p><p>Chapter 3 is co-authored with Professor Tim Bollerslev and Professor Andrew Patton. We construct daily house price indexes for ten major U.S. metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat-sales method that closely mimics the procedure used in the construction of the popular monthly Case-Shiller house price indexes. Our new daily house price indexes exhibit dynamic features similar to those of other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity. The correlations across house price index returns are low at the daily frequency, but rise monotonically with the return horizon, and are commensurate with existing empirical evidence for existing monthly and quarterly house price series. Timely and accurate measures of house prices are important in a variety of applications, and are particularly valuable during times of turbulence, such as the recent housing crisis. To quantify the informational advantage of our daily index, we show that a relatively simple multivariate time series model for the daily house price index returns, explicitly allowing for commonalities across cities and GARCH effects, produces forecasts of monthly house price changes that are superior to various alternative forecast procedures based on lower frequency data.</p><p>Chapter 4 investigates the properties of housing index return volatilities. Similar to stock market volatility, housing volatilities are found to respond asymmetrically to negative and positive returns. A direct test of volatility on changes in loan-to-value ratio suggests that the observed volatility asymmetry does not stem from changes in degree of housing financial leverage, but could result from the risk premium carried by housing volatility, which is supported by a consumption-based asset pricing model with housing. Moreover, housing and stock volatilities are found to be positively correlated from a set of predictive regressions based on realized variances of housing and stock markets, in which higher (lower) volatility in one market will be followed by higher (lower) volatility in the other. Finally, housing and stock cross-sectional return dispersions are shown to contain useful information in predicting both within-market and cross-market realized volatilities.</p> / Dissertation

This paper investigates both the information contents of recommendations disseminated by foreign security firms and the interaction of foreign security firms’ trading activities with their recommendations in Taiwan’s stock market. Using event study, correlation test, and regression analysis, we find negative average abcdrmal returns(AARs) and average cumulative abcdrmal returns(CARs) for negative and neutral foreign analysts’ recommendations levels and recommendation changes in the pre-recommendation period. AARs and CARs for positive recommendations in pre-recommendation period are positive, but reverse to negative three days after the event day. Our results also show that correlation coefficients of recommendations (both in recommendation levels and recommendation changes) and holding period returns are significantly positive in the pre-recommendation period, but insignificantly negative in the post-recommendation period. In the regression analyses, we find that price momentum factor is significantly related to foreign analysts’ recommendation, but the incremental contribution of this factor to foreign analysts’ recommendations are marginal and not significant. We also find that foreign security firms respond more rigorously to stocks receiving recommendation above buy recommendations and stocks being downgraded. These results show that foreign security firms are more conservative toward trading stocks in Taiwan’s stock market. They only buy stocks above buy recommendations (in a delay pattern), but immediately sell downgraded stocks.

<p>Prior knowledge about the possible characteristics of demand and supply is vital in the planning and operation of economically sustainable isolated rural power systems. System modelling and simulation is one of the tools that can be used in planning and assessing the performance of these systems. This thesis is presenting a Monte Carlo simulation methodology for modelling, simulating and analysing the performance of isolated rural electricity markets applicable in developing countries. The definitions of possible power system operators managing these markets are introduced based on different economic objectives of operating the systems. The two system operators considered in the thesis are: altruistic and profit maximising operators. The concept used to define types of isolated rural electricity markets is combining the definitions of operators and the possible combinations of power supply options (purely thermal or hybrid system). It is anticipated that the rural electricity markets under consideration comprise of uncertainties in demand and supply (both demand and generation are modelled as random variables from assumed or estimated probability distributions).</p><p>Demand is price sensitive and modelled as a product of two random variables, relative demand and peak demand. The price sensitivity of demand is shown by representing peak demand using an economic price-demand function. The parameters (price sensitivity and demand factor) of this function are modelled as random variables which reflect the randomness of consumers’ preferences.</p><p>The simulation algorithm is based on the theory of correlated sampling, in order to compare the performance of systems under different operators. The thesis introduces the concept of nested Monte Carlo simulation to be able manage the simulation of different operators subjected to the same market conditions. The performance of electricity markets is assessed by analysing three parameters (tariffs, profit and reliability), which are random variables presented using probability distributions in form of duration curves.</p><p>The methodology is tested on a theoretical case study system using load data obtained from a rural community in Africa.  The case study illustrates how to use the model, preparation of the input variables and how to use the output to estimate and assess the possible performance of isolated rural power systems under different power system operators. It is anticipated that the proposed methodology can be used by researchers, planners and academia as a tool for planning, estimating and assessing the performance of rural power systems in isolated areas of developing countries</p>

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