ELECTRICITY USAGE AND ASSET PRICING OVER THE BUSINESS CYCLE

The well-known difficulty in finding the predictors capable of explaining the Italian stock returns is at the basis of this thesis. The morphology of the Italian Stock Exchange is characterised by the presence of numerous small capitalisation stocks. This fact prevents the widely spread asset pricing models and predictors, both financial and real business cycle, to operate as expected. So, this thesis fills the abovementioned gaps. The first part of the thesis (Pirogova and Roma, (2020)) investigates the performance of size- and value-based strategies in the Italian Stock Market in the period 2000 - 2018. Previous research (Beltratti and Di Tria (2002)) argued the impossibility to define properly value-sorted portfolios due to the inaccuracy of book-to-market ratios available for Italian listed stocks. Using more accurate data, I implement portfolios sorting based on value and growth stocks, in order to assess the relevance of the value factor in the Italian Stock Market. I find that the CAPM, the capital asset pricing model, fails to explain the cross section of returns on the different strategies while the Fama and French (1993) three-factor model provides a better fit. The results show that all three factors are significant in explaining Italian stock returns during the sample period. Unlike previous studies, which either found no value effect at all (Barontini (1997); Aleati et al., (2000)) or no clear-cut results when testing the book-to-market variable (Bruni et al. (2006); Rossi (2012)), I find that the value factor is statistically significant, and the associated risk premium is of a considerable size. Pursuing the aim of finding new real business cycle predictors of the Italian stock returns, the second part of the study concentrates on the industrial electricity usage variable following the work of Zhi Da et al. (2017). The reason for using industrial electricity usage for this matter lies in the difficulty in storing energy. Therefore, the logic suggests that the changes in energy consumption can be used to track industrial production in real time. Real business cycle variables, like production, co-move with stock market returns. Zhi Da et al. (2017) show that industrial energy usage performs optimally in the prediction of US stock returns. However, despite the previous encouraging results, a deeper understanding of the industrial technologies used in the production process suggests that the matter is not so simple. The reason for this can be found in the concept of energy efficiency of the equipment that plants use. A comparable measure of energy efficiency is the intensity of energy consumption which is the ratio of the total final energy consumption (in GJ) and the value added of production at constant price. Another possible efficiency measure is the specific energy consumption per unit of the product. Moreover, the energy efficiency is closely linked to the analysis of the carbon footprint (emissions of greenhouse gases (GHG)) that each firm leaves during its production process, with special attention paid to the emissions of CO2. So, the task of this part of work is to check whether the industrial electricity usage variable is capable of predicting future Italian stock returns, either alone or after the correction using one or more energy efficiency measures. The theoretical basis of this study could be found in the production-based model by Burnside, Eichenbaum, Rebelo (1995). The fixed-coefficient energy-production relationship proposed by the authors was modified to vary throughout the sample period based on available energy intensity measures. The study concentrates on three energy-intensive Italian industrial sectors, Construction & Materials, Chemicals and Basic Resources. The relative time-series of the prices were downloaded from the website www.investing.com, the time-series of the electricity consumption of the subsectors of Concrete, Chemicals, Steel and Non-ferrous metals were kindly provided by Terna s.p.a., all energy-efficiency measures were downloaded from Odyssee Mure website. The main statistical method is the ordinary least squares (OLS). The third part of this study applies the same procedure of the second part of the thesis to the Swedish data. The only difference is that the data relative to the industrial electricity consumption come from the Statistics Sweden and there is no subdivision in Steel and Non-ferrous Metals of the Basic Resources electricity consumption time-series. The rest of the data come from the same sources as for Italy. This chapter’s goal is to enrich the Italian dataset and to confirm the obtained results. I find that the electricity consumption influences the stock returns through the impact on the productivity which then influences the financial values such as the book-to-market ratio and the price-earnings ratio. The relative tests showed that these ratios are explained by the electricity consumption together with the energy efficiency variables. The results of the tests on industrial electricity consumption growth rates referred to Italian and Swedish energy-intensive industrial sectors and their role in asset pricing are encouraging. The industrial electricity consumption variable corrected by the energy efficiency measures does influence the industrial stock returns and does so with significative predictor power. The sign of the regression coefficients of the energy efficiency measures remains the same for each Italian industrial sector no matter whether the month-over-month or the year-over-year data is used. This means that the correcting impact of these intensities is present, and it is stable and strong. The same result is true for most of the Swedish data.