Industrial Electricity Usage and Stock Returns

The growth rate of industrial electricity usage predicts future stock returns up to 1 year with an R 2 of 9%. High industrial electricity usage today predicts low stock returns in the future, consistent with a countercyclical risk premium. Industrial electricity usage tracks the output of the most cyclical sectors. Our findings bridge a gap between the asset pricing literature and the business cycle literature, which uses industrial electricity usage to gauge production and output in real time. Industrial electricity growth compares favorably with traditional financial variables, and it outperforms Cooper and Priestley’s output gap measure in real time

The Consumer Production Journey: Marketing to Consumers as Co-Producers in the Sharing Economy

New digital technologies not only support consumers in better fulfilling their own consumption needs, but also enable them to create greater value for other consumers. These new consumer co- production activities, collectively referred to as the sharing economy, require firms to rethink their role in the marketing value creation process. In particular, firms need to find new ways to create value for consumers who are also becoming producers. To address this challenge, we propose a two-layered conceptual framework of consumer co-production networks and the individual consumer production journeys therein. These concepts expand the traditional production model and consumer journey, respectively, explicitly taking into account consumer co-production activities in the value creation process. Within this framework, we draw on institutional design theory and household production theory to analyze how marketing functions can support consumers’ co-production activities. We conclude with a discussion of managerial and consumer welfare implications, and of new opportunities for further research

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

Natural Disasters and Financial Markets

The first essay (chapter 2) examines the impact of major U.S. natural disasters on the stock returns and volatilities of firms based in disaster areas. We find that a small proportion of catastrophes (between six and eight percent) have a significant impact on returns, after controlling for false discoveries. The meaningful shocks are distributed over a relatively long period of time with the uttermost effects being felt in the two or three months following disasters. Furthermore, we observe that the second moments of returns of affected firms more than double when hurricanes, floods, winter storms and episodes of extreme temperature occur. The second essay (chapter 3) studies the effect of major floods on new municipal bond issues marketed by U.S. counties. The results show that bonds sold in the midst of floods exhibit yields about seven percent higher than bonds sold at other times, which is a net loss of almost $100,000 in terms of proceeds on a$10 million debt issue. Consistent with a behavioral explanation based on the availability bias, the abnormal yields rapidly decay over time and are limited to first-time disaster counties. The evidence for an increase in credit risk is mixed and the results do not support lower market liquidity stories. Selection bias, underpricing activities and issuance costs are examined and are unlikely to materially affect the conclusions. The final essay (chapter 4) focuses on the consequences of disasters on investor risk preferences. We infer the impact of major catastrophes on the risk-taking behavior of investors from a database of U.S. municipal bond transactions. As the effect of disasters is mostly regional, we exploit the geographic segmentation of the municipal bond market to estimate a measure of regional risk aversion using a conventional consumption capital asset pricing model. The findings strongly support the assumption that natural disasters cause a statistically and economically significant increase in financial risk aversion at the local level