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Financial Leverage and its Determinants in Companies Producing Electricity from Wind Resources in Latvia

100%

Bobinaite V.

Economics and Business

2015

tom 27

nr 1

29-39

The paper aims to analyse the development of the financial leverage and its determinants in companies producing electricity from wind resources in Latvia during 2005-2012. The financial ratio technique is used to compute the financial leverage in the companies and the regression analysis method is employed to determine the relationships between variables. The results of the analysis revealed that wind electricity generating companies use substantial share of debt and the financial leverage is increasing. Statistically significant relationships were found between the financial leverage and profitability of companies, their growth opportunities, collateral value of assets, size of the company and an effective tax rate. Results will be used to construct weighted average cost of capital (WACC) for the economic assessment of investment into wind electricity sector in Latvia.

Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia

63%

Bobinaite V. , Zuters J.

Economics and Business

2016

tom 29

nr 1

12-26

The paper aims at modelling the electricity generator’s expectations about price development in the Latvian day-ahead electricity market. Correlation and sensitivity analysis methods are used to identify the key determinants of electricity price expectations. A neural network approach is employed to model electricity price expectations. The research results demonstrate that electricity price expectations depend on the historical electricity prices. The price a day ago is the key determinant of price expectations and the importance of the lagged prices reduces as the time backwards lengthens. Nine models of electricity price expectations are prepared for different natural seasons and types of the day. The forecast accuracy of models varies from high to low, since errors are 7.02 % to 59.23 %. The forecasting power of models for weekends is reduced; therefore, additional determinants of electricity price expectations should be considered in the models and advanced input selection algorithms should be applied in future research. Electricity price expectations affect the generator’s loss through the production decisions, which are made considering the expected (forecasted) prices. The models allow making the production decision at a sufficient level of accuracy.