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    • br Experimental design materials and methods Asset allocatio

    2018-10-29


    Experimental design, materials and methods Asset allocation aims at selecting a portfolio over available assets in an investment universe according to specific choice criteria under uncertainty. More precisely, we must decide how much of each asset should be purchased in the selected portfolio. The portfolio is denoted by , where is the fraction of the given capital invested in asset . Let denote the price of asset at time , observed for m+1 time periods, i.e., . The linear return of asset at time iswhere . Denoting by the value of the benchmark (e.g., the Market Index) at time , the benchmark linear returns arewhere . The portfolio linear return at time is All the datasets listed in the following Table 1 contain linear return values for each of the N assets contained in the market, together with the linear returns of the benchmark index, computed as described above. Datasets 1–5 consist of weekly linear returns computed on daily price data, adjusted for dividends and stock splits, obtained from Thomson Reuters Datastream. The selected benchmark is the market index. Stocks with less than ten years of observations were disregarded, thus obtaining a reasonable tradeoff between the number of assets (N) and of observations ( T ). Furthermore, when necessary, the assets prices are filtered to check and to correct inaccurate data. Data cleaning is indeed an important issue for similar data (see, e.g., [4] for references on this GSK1070916 cost widespread problem). Dataset 6 is derived from the Fama and French 49 Industry portfolios, available from the Fama & French Data Library, which contains daily returns from July 1926 to July 2015. Since there are many data missing, especially before July 1969, we choose a subsample of periods where all the daily returns of the 49 industries are available, namely from July 1969 to July 2015. Furthermore, to standardize the frequencies of all data sets we extract weekly returns by cumulating daily returns in groups of five as follows: In addition to the returns datasets, we also make available the composition (weights) and the out-of-sample returns of the portfolios obtained, for all datasets and for several in-sample periods, with the models listed in Table 2 and fully described in the companion paper [2]. For each dataset and for each model, we compute the solutions using a rolling in-sample window of 52 returns observations. We initially set the in-sample window on the first 52 time periods, we select the portfolio by solving the model, and we evaluate the performance of the selected portfolio on the following 12 (out-of-sample) periods. Next, we update the in-sample window, with the inclusion of the previous 12 out-of-sample periods and the exclusion of the first 12 periods of the previous in-sample window. We then rebalance the portfolio by solving the model again, and repeat until the end of the dataset (see Fig. 1). Following the notation of Table 1, the data provided with this article are organized as in Fig. 2 and labeled as follows:
    Acknowledgments The third author wishes to thank the partial support received from the Spanish Ministry of Science and Technology through grant number MTM2013-46962-C2-1-P.
    Data
    Experimental design, materials and methods The data presented in this article is basis for the statistical analysis in terms of confidence bounds – as discussed in [1] – for the annual energy savings, cost savings and payback periods of EEMs when replaced for SMs. To acquire the necessary data, some industries based at Pakistan were surveyed and data on existing standard efficiency motors were noted while personal visits. We describe in next section, the acquired data, its processing using different formulas and its descriptive statistical analysis which is required for the construction of confidence bounds. To gather the best possible sample of existing standard efficiency motors, we surveyed the industries where majority of the motors were installed. Surveyed industries included: Pakistan Steel Mill (PSM), Karachi; Thermal Power Station, Jamshoro; Regional Control Centre, Jamshoro; and, Water Works/Pumps, Hyderabad. The data was obtained in the year 2011 while personal visits to the surveyed industries. Some photos of motors at survey sites are given in Figs. 1–3 according to their applications. The important electrical parameters related to the installed standard efficiency motors were noted on the motor data and energy consumption form, a sample in Fig. 4. For reference, a completely filled form for 2HP motor can be found as Fig. 1 of [1]. The comprehensive data measured and noted in these forms for all 20 sample SMs is gathered in Table 1. The values in Table 1 can be used to calculate power (in kW), annual energy consumption (in kWh/year) and annual operational cost (in Rs./year) for the 20 sample SMs.