The volatility of steel scrap pricing makes formulating accurate scrap price forecasts difficult if not entirely impossible. While literature abounds regarding price forecasts for oil, electricity, and other commodities, no reliable scrap price forecasting models exist. The purpose of this quantitative futuristic study was (1) to assess factors that influence scrap prices and (2) based on this information, to develop an effective scrap price forecast model to help steel managers make effective purchasing decisions. The theoretical foundation for the study was provided by probability theory, which has traditionally informed futures research. Probability theory draws conclusions of a dataset based on statistical, logical consequence but without attempting to identify physical causation. Secondary data from the American Metal Market were subjected to time series techniques and auto-regressive moving averages. The research led to the development of two key indices---the West Texas Index and the Advanced Decliner Index---that should improve the reliability of this scrap price forecast model. The literary, business, and social change implications of this work include a unique price forecasting technique for scrap material; a globally more competitive, profitable, and sustainable steel industry in America; and consequently, increased employment opportunities in this industrial sector so vital for the health of the entire American economy and society.
| Identifer | oai:union.ndltd.org:CHENGCHI/U0003479231 |
| Creators | Hardin, Kristin. |
| Publisher | Walden University. |
| Source Sets | National Chengchi University Libraries |
| Detected Language | English |
| Type | text |
| Rights | Copyright © nccu library on behalf of the copyright holders |
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