Analysis of Earnings, Stock Prices and Bond Yields: The Fed Model Approach

Prediction of the stock market valuation is a common interest to all market participants. Theoretically sound market valuation can be achieved by discounting future earnings of equities to present. Competing valuation models seek to find variables that affect the equity market valuation in a way that the market valuation can be explained and also variables that could be used to predict market valuation. In this paper we test the contemporaneous relationship between stock prices, forward looking earnings and long-term government bond yields. We test this so-called Fed model in a long- and short-term time series analysis. In order to test the dynamics of the relationship, we use the cointegration framework. The data used in this study spans over four decades of various market conditions between 1964-2007, using data from United States. The empirical results of our analysis do not give support for the Fed model. We are able to show that the long-term government bonds do not play statistically significant role in this relationship. The effect of forward earnings yield on the stock market prices is significant and thus we suggest the use of standard valuation ratios when trying to predict the future paths of equity prices. Also, changes in the long-term government bond yields do not have significant short-term impact on stock prices.

Analysis of outliers in electricity spot prices with exampleof New England and New Zealand markets

Electricity spot prices have always been a demanding data set for time series analysis, mostly because of the non-storability of electricity. This feature, making electric power unlike the other commodities, causes outstanding price spikes. Moreover, the last several years in financial world seem to show that ’spiky’ behaviour of time series is no longer an exception, but rather a regular phenomenon. The purpose of this paper is to seek patterns and relations within electricity price outliers and verify how they affect the overall statistics of the data. For the study techniques like classical Box-Jenkins approach, series DFT smoothing and GARCH models are used. The results obtained for two geographically different price series show that patterns in outliers’ occurrence are not straightforward. Additionally, there seems to be no rule that would predict the appearance of a spike from volatility, while the reverse effect is quite prominent. It is concluded that spikes cannot be predicted based only on the price series; probably some geographical and meteorological variables need to be included in modeling.

Comparing service contract prices of outsourcing partner

The main objective of this Master’s thesis is to find out which one of the two pricing models is the most cost-effective. In this thesis there are two companies that have made an outsourcing contract, in which they have a possibility to choose between two different pricing models. The first model is so called FTE (Full Time Employee) -based. The total cost will be based on the amount of outsourced person-workyears. The second pricing model is the transaction-based, in which the price will be formed according to the amount of transactions. Changing the pricing model from FTE-based to the transaction-based will also incur other costs. It is very important that these other costs are also taken into consideration, so that it is possible to determine the total costs of the pricing models. These other costs are direct costs, indirect costs and performance related costs of outsourcing. Activity based-costing (ABC) was used in order to find out the trues indirect costs of the outsourced processes. Performance related costs are related to quality, so Pareto-analysis was used to analyse the costs. Based on all of that, a framework for service related cost analysis was developed. Quality costs were almost impossible to quantify, so quality had to be taken into consideration in a qualitative way. Furthermore, considering only the indirect and direct costs in a quantitative way and quality costs in a qualitative way, it was possible to find a conditional solution for the research question.

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

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

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kuv., 14 x 22 cm

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Modelling Long-Run Relationship between Spot and Future Prices of Different Commodities

The purpose of this study is to investigate whether there exists any kind of relationship between the spot and future prices of the different commodities or not. Commodities like cocoa, coffee, crude oil, gold, natural gas and silver are considered from January 3, 2000 to December 31, 2012. For this purpose, ADF test and KPSS test are used in testing the stationarity whereas Johansen Cointegration test is used in testing the long-run relationship. Johansen co-integration test exhibits that there at least 5 co-integrating pairs out of 6 except crude oil. Moreover, the result of Granger Causality supports the fact that if two or more than two time series tend to be co-integrated there exists either uni-directional or bi-directional relationship. However, our results reveled that although there exists the co-integration between the variable, one might not granger causes another .VAR model is also used to measure the proportion of effects. These findings will help the derivative market and arbitragers in developing the strategies to gain the maximum profit in the financial market.

Forecasting financial weather - can we foresee market sentiment? Quantitative analysis of nodal prices in the New Zealand Electricity Spot Market

Time series of hourly electricity spot prices have peculiar properties. Electricity is by its nature difficult to store and has to be available on demand. There are many reasons for wanting to understand correlations in price movements, e.g. risk management purposes. The entire analysis carried out in this thesis has been applied to the New Zealand nodal electricity prices: offer prices (from 29 May 2002 to 31 March 2009) and final prices (from 1 January 1999 to 31 March 2009). In this paper, such natural factors as location of the node and generation type in the node that effects the correlation between nodal prices have been reviewed. It was noticed that the geographical factor affects the correlation between nodes more than others. Therefore, the visualisation of correlated nodes was done. However, for the offer prices the clear separation of correlated and not correlated nodes was not obtained. Finally, it was concluded that location factor most strongly affects correlation of electricity nodal prices; problems in visualisation probably associated with power losses when the power is transmitted over long distance.

Bacterial community structure and petroleum hydrocarbon degradation in the Baltic Sea

The Baltic Sea is unique by its biological, geochemical and physical features. The number of species of larger organisms is small and the species composition is distinctive. On the contrary microbial communities are diverse. Because of the low salinity levels, bacterial communities differ from the ones in the oceans. Knowing the structure of these communities better and how they response to different environmental conditions helps us to estimate how different factors affect the balance and function of the Baltic Sea ecosystem. Bacteria are the key players when it comes to natural biogeochemical processes and human-induced phenomena like eutrophication, oil spills or disposal of other harmful substances to the sea ecosystem. In this thesis, bacterial community structure in the sea surface microlayer and subsurface water of the Archipelago Sea were compared. In addition, the effect of diatom derived polyunsaturated aldehydes on bacterial community structure was studied by a mesocosm experiment. Diesel, crude oil and polycyclic aromatic hydrocarbon degradation capacity of the Baltic Sea bacteria was studied in smaller scale microcosm experiments. In diesel oil experiments bacteria from water phase of the Archipelago Sea was studied. Sediment and iron manganese concretions collected from the Gulf of Finland were used in the crude oil and polycyclic aromatic hydrocarbon experiments. The amount of polycyclic aromatic hydrocarbon degradation genes was measured in all of the oil degradation experiments. The results show how differences in bacterial community structure can be seen in the sea surface when compared to the subsurface waters. The mesocosm experiment demonstrated how diatom-bacteria interactions depend on other factors than diatom derived polyunsaturated aldehydes, which do not seem to have an effect on the bacterial community structure as has been suggested in earlier studies. The dominant bacterial groups in the diesel microcosms differed in samples taken from a pristine site when compared to a site with previous oil exposure in the Archipelago Sea area. Results of the study with sediment and iron-manganese concretions indicate that there are diverse bacterial communities, typical to each bottom type, inhabiting the bottoms of the Gulf of Finland capable to degrade oil and polycyclic aromatic hydrocarbon compounds.