Asset Portfolio Management — Using MarketBuilder to Inform Key Decisions
Quantifying portfolio risk/return by analyzing correlations, synergies, intraportfolio competition, and common market prices
Companies build, buy, and otherwise acquire collections of assets, termed a "portfolio." Whether generators in the Florida Reliability Coordinating Council (FRCC) or gas wells in the Haynesville Shale Field, businesses invariably acquire and manage a mix of assets. As energy companies acquire and dynamically manage their asset portfolios, it is common for companies to periodically evaluate their assets in order to decide which ones to sell and which to keep; they also evaluate potential assets to acquire. The objective of this evaluation is to reduce risk and maximize earnings, i.e., referred to here as portfolio management.
The complex and extensive analysis required for portfolio management typically includes many factors for consideration, but it is essential that it include a fundamental market-based approach in order to facilitate analysis of future market behavior and prices. A fundamental market-based approach helps you to project price using inputs of key fundamental factors for which you typically have data and/or a good intuitive understanding, such as production costs and commodity availability.
Alternatively, in nonfundamental approaches, future commodity price is usually the input; future price, typically the most important factor in evaluating your portfolio, is more difficult to understand than the key fundamental factors. With projected price, you are able to determine the profitability of each asset over a forward time horizon, enabling you to make more informed decisions about your portfolio components.
MarketBuilder was designed to help companies perform fundamental market analysis and project market prices. It has many differentiating capabilities that facilitate analyzing markets globally or regionally and altering selected factors to help customers examine the combined effects of potential market changes on the market behavior and prices.
Specifically, MarketBuilder models the supply chains for the energy commodities pertinent to each of your portfolio assets. In MarketBuilder, each component of the supply chains is treated as an independent agent, competing against the others, as they do in real-world markets, helping users to analyze market fundamentals and project prices, and simulating the real market behavior under a variety of potential conditions.
MarketBuilder helps you analyze these key aspects in your portfolio optimization:
- Synergy among assets. MarketBuilder can help you understand the extent that your assets complement each other or compete with each other:
- Positive synergy (e.g., scale economies in a large-enough portfolio that reduces cost for the assets)
- Negative synergy (e.g., competition — if you put two or three assets in one place, they can overload that place and drive prices down)
- Correlation and hedging. MarketBuilder helps you analyze your portfolio correlation, enabling you to "self-hedge" through portfolio decisions rather than merely acquiring financial instruments to hedge; it helps you understand:
- Positive correlation — assets whose profitabilities move up and down together.
- Independence — assets whose profitabilities move independently, one not affecting the other
- Negative correlation — assets whose profitabilities move counter to each other. When one goes up, the other goes down, and vice versa. These can be natural portfolio hedges, much less expensive than buying financial instruments.
- Integration — across supply chains
- Vertical integration (a portfolio that extends up and down the supply chain)
- Horizontal integration (a portfolio that is very wide, invoking economies of scope)
- Economies of scale (e.g., consolidating spare parts inventory over a large number of plants)
- Risk — Return efficiency. MarketPoint uses Markowitz as a conceptual guide as shown in the figure. MarketBuilder helps you to produce the key inputs necessary for plotting each prospective portfolio component and analyzing whether it lies on the "frontier" — the lowest level of uncertainty for each given level of expected value and equivalently highest expected value for each given level of uncertainty.