The Role of Finance in the Strategic-Planning and Decision-Making Process

The fundamental success of a strategy depends on three critical factors: a firm’s alignment with the external environment, a realistic internal view of its core competencies and sustainable competitive advantages, and careful implementation and monitoring.[1] This article discusses the role of finance in strategic planning, decision making, formulation, implementation, and monitoring.

[powerpress: http://gsbm-med.pepperdine.edu/gbr/audio/winter2010/PedroKono_article.mp3]

Any person, corporation, or nation should know who or where they are, where they want to be, and how to get there.[2] The strategic-planning process utilizes analytical models that provide a realistic picture of the individual, corporation, or nation at its “consciously incompetent” level, creating the necessary motivation for the development of a strategic plan.[3] The process requires five distinct steps outlined below and the selected strategy must be sufficiently robust to enable the firm to perform activities differently from its rivals or to perform similar activities in a more efficient manner.[4]

A good strategic plan includes metrics that translate the vision and mission into specific end points.[5] This is critical because strategic planning is ultimately about resource allocation and would not be relevant if resources were unlimited. This article aims to explain how finance, financial goals, and financial performance can play a more integral role in the strategic planning and decision-making process, particularly in the implementation and monitoring stage.

The Strategic-Planning and Decision-Making Process

1. Vision Statement

The creation of a broad statement about the company’s values, purpose, and future direction is the first step in the strategic-planning process.[6] The vision statement must express the company’s core ideologies—what it stands for and why it exists—and its vision for the future, that is, what it aspires to be, achieve, or create.[7]

2. Mission Statement

An effective mission statement conveys eight key components about the firm: target customers and markets; main products and services; geographic domain; core technologies; commitment to survival, growth, and profitability; philosophy; self-concept; and desired public image.[8] The finance component is represented by the company’s commitment to survival, growth, and profitability.[9] The company’s long-term financial goals represent its commitment to a strategy that is innovative, updated, unique, value-driven, and superior to those of competitors.[10]

3. Analysis

This third step is an analysis of the firm’s business trends, external opportunities, internal resources, and core competencies. For external analysis, firms often utilize Porter’s five forces model of industry competition,[11] which identifies the company’s level of rivalry with existing competitors, the threat of substitute products, the potential for new entrants, the bargaining power of suppliers, and the bargaining power of customers.[12]

For internal analysis, companies can apply the industry evolution model, which identifies takeoff (technology, product quality, and product performance features), rapid growth (driving costs down and pursuing product innovation), early maturity and slowing growth (cost reduction, value services, and aggressive tactics to maintain or gain market share), market saturation (elimination of marginal products and continuous improvement of value-chain activities), and stagnation or decline (redirection to fastest-growing market segments and efforts to be a low-cost industry leader).[13]

Another method, value-chain analysis clarifies a firm’s value-creation process based on its primary and secondary activities.[14] This becomes a more insightful analytical tool when used in conjunction with activity-based costing and benchmarking tools that help the firm determine its major costs, resource strengths, and competencies, as well as identify areas where productivity can be improved and where re-engineering may produce a greater economic impact.[15]

SWOT (strengths, weaknesses, opportunities, and threats) is a classic model of internal and external analysis providing management information to set priorities and fully utilize the firm’s competencies and capabilities to exploit external opportunities,[16] determine the critical weaknesses that need to be corrected, and counter existing threats.[17]

4. Strategy Formulation

To formulate a long-term strategy, Porter’s generic strategies model [18] is useful as it helps the firm aim for one of the following competitive advantages: a) low-cost leadership (product is a commodity, buyers are price-sensitive, and there are few opportunities for differentiation); b) differentiation (buyers’ needs and preferences are diverse and there are opportunities for product differentiation); c) best-cost provider (buyers expect superior value at a lower price); d) focused low-cost (market niches with specific tastes and needs); or e) focused differentiation (market niches with unique preferences and needs).[19]

5. Strategy Implementation and Management

In the last ten years, the balanced scorecard (BSC)[20] has become one of the most effective management instruments for implementing and monitoring strategy execution as it helps to align strategy with expected performance and it stresses the importance of establishing financial goals for employees, functional areas, and business units. The BSC ensures that the strategy is translated into objectives, operational actions, and financial goals and focuses on four key dimensions: financial factors, employee learning and growth, customer satisfaction, and internal business processes.[21]

The Role of Finance

Financial metrics have long been the standard for assessing a firm’s performance. The BSC supports the role of finance in establishing and monitoring specific and measurable financial strategic goals on a coordinated, integrated basis, thus enabling the firm to operate efficiently and effectively. Financial goals and metrics are established based on benchmarking the “best-in-industry” and include:

1. Free Cash Flow

This is a measure of the firm’s financial soundness and shows how efficiently its financial resources are being utilized to generate additional cash for future investments.[22] It represents the net cash available after deducting the investments and working capital increases from the firm’s operating cash flow. Companies should utilize this metric when they anticipate substantial capital expenditures in the near future or follow-through for implemented projects.

2. Economic Value-Added

This is the bottom-line contribution on a risk-adjusted basis and helps management to make effective, timely decisions to expand businesses that increase the firm’s economic value and to implement corrective actions in those that are destroying its value.[23] It is determined by deducting the operating capital cost from the net income. Companies set economic value-added goals to effectively assess their businesses’ value contributions and improve the resource allocation process.

3. Asset Management

This calls for the efficient management of current assets (cash, receivables, inventory) and current liabilities (payables, accruals) turnovers and the enhanced management of its working capital and cash conversion cycle. Companies must utilize this practice when their operating performance falls behind industry benchmarks or benchmarked companies.

4. Financing Decisions and Capital Structure

Here, financing is limited to the optimal capital structure (debt ratio or leverage), which is the level that minimizes the firm’s cost of capital. This optimal capital structure determines the firm’s reserve borrowing capacity (short- and long-term) and the risk of potential financial distress.[24] Companies establish this structure when their cost of capital rises above that of direct competitors and there is a lack of new investments.

5. Profitability Ratios

This is a measure of the operational efficiency of a firm. Profitability ratios also indicate inefficient areas that require corrective actions by management; they measure profit relationships with sales, total assets, and net worth. Companies must set profitability ratio goals when they need to operate more effectively and pursue improvements in their value-chain activities.

6. Growth Indices

Growth indices evaluate sales and market share growth and determine the acceptable trade-off of growth with respect to reductions in cash flows, profit margins, and returns on investment. Growth usually drains cash and reserve borrowing funds, and sometimes, aggressive asset management is required to ensure sufficient cash and limited borrowing.[25] Companies must set growth index goals when growth rates have lagged behind the industry norms or when they have high operating leverage.

7. Risk Assessment and Management

A firm must address its key uncertainties by identifying, measuring, and controlling its existing risks in corporate governance and regulatory compliance, the likelihood of their occurrence, and their economic impact. Then, a process must be implemented to mitigate the causes and effects of those risks.[26] Companies must make these assessments when they anticipate greater uncertainty in their business or when there is a need to enhance their risk culture.

8. Tax Optimization

Many functional areas and business units need to manage the level of tax liability undertaken in conducting business and to understand that mitigating risk also reduces expected taxes.[27] Moreover, new initiatives, acquisitions, and product development projects must be weighed against their tax implications and net after-tax contribution to the firm’s value. In general, performance must, whenever possible, be measured on an after-tax basis. Global companies must adopt this measure when operating in different tax environments, where they are able to take advantage of inconsistencies in tax regulations.

Conclusion

The introduction of the balanced scorecard emphasized financial performance as one of the key indicators of a firm’s success and helped to link strategic goals to performance and provide timely, useful information to facilitate strategic and operational control decisions. This has led to the role of finance in the strategic planning process becoming more relevant than ever.

Empirical studies have shown that a vast majority of corporate strategies fail during execution. The above financial metrics help firms implement and monitor their strategies with specific, industry-related, and measurable financial goals, strengthening the organization’s capabilities with hard-to-imitate and non-substitutable competencies. They create sustainable competitive advantages that maximize a firm’s value, the main objective of all stakeholders.


[1] M.E. Porter, “What is Strategy?” Harvard Business Review, 74, no. 6 (1996). [purchase required]

[2] D. Abell, Defining the Business: The Starting Point of Strategic Planning, (New Jersey: Prentice-Hall, 1980).

[3] J.S. Bruner, The Process of Education: A Landmark in Education Theory, (hyperlink no longer accessible). (Boston: Harvard University Press, 1977).

[4] J.A. Pearce and R.B. Robinson, Formulation, Implementation, and Control of Competitive Strategy, (New York: Irwin McGraw-Hill, 2000).

[5] C.S. Clark and S.E. Krentz, “Avoiding the Pitfalls of Strategic Planning,” Healthcare Financial Management, 60, no. 11 (2004): 63–68.

[6] T. Jick and M. Peiperl, Managing Change: Cases and Concepts, (New York: Irwin/McGraw-Hill, 2003).

[7] J.C. Collins and J.I. Porras, “Building Your Company’s Vision,” Harvard Business Review, 74, no. 5 (1996). [purchase required]

[8] Pearce and Robinson.

[9] J.A. Pearce and F. David, “Corporate Mission Statement: The Bottom Line,” The Academy of Management Executive, 1, no. 2 (1987): 109–116. [purchase required]

[10] R.K. Johnson, “Strategy, Success, a Dynamic Economy, and the 21st Century Manager,” The Business Review, 5, no. 2 (2006).

[11] M.E. Porter, “How Competitive Forces Shape Strategy,” Harvard Business Review, 57, no. 2 (1979).

[12] Ibid.

[13] A.A. Thompson, A.J. Strickland, and J.E. Gamble, Crafting and Executing Strategy, (New York: McGraw-Hill/Irwin, 2009).

[14] Pearce and Robinson.

[15] Thompson, Strickland, and Gamble.

[16] B. Jovanovic and G.M. MacDonald, “The Life Cycle of a Competitive Industry,” The Journal of Political Economy, 102, no. 2 (1994: 322–347).

[17] C.A. Lai and J.C. Rivera, Jr., “Using a Strategic Planning Tool as a Framework for Case Analysis,” Journal of College Science Teaching, 36, no. 2 (2006): 26–31.

[18] M.E. Porter, Competitive Advantage: Techniques for Analyzing Industries and Competitors, (New York: The Free Press, 1980).

[19] Thompson, Strickland, and Gamble.

[20] R.S. Kaplan and D.P. Norton, “Using the Balanced Scorecard as a Strategic Management System,” (hyperlink no longer accessible). Harvard Business Review, 74, no. 1 (1996).

[21] Ibid.

[22] Peter Grant, “How Financial Targets Determine Your Strategy,” Global Finance, 11, no. 3 (1997): 30–34

[23] Ibid.

[24] Sidney L. Barton and Paul J. Gordon, “Corporate Strategy: Useful Perspective for the Study of Capital Structure?” The Academy of Management Review, 12, no. 1 (1987): 67–75.

[25] B.T. Gale and B. Branch, “Cash Flow Analysis: More Important Than Ever,” Harvard Business Review, July–August (1981).

[26] H.D. Pforsich, B.K.P. Kramer, and G.R. Just, “Establishing an Effective Internal Audit Department,” Strategic Finance, 87, no. 10 (2006): 22–29.

[27] Q. Lawrence, “Hedging in Perspective,” Corporate Finance, 115, no. 36 (1994).

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House of Cards by William D. Cohan

House of Cards: A Tale of Hubris and Wretched Excess on Wall Street

By William D. Cohan
Doubleday, 2009

[powerpress http://gsbm-med.pepperdine.edu/gbr/audio/fall2009/tkrause.mp3]

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4 stars: Thought-provoking and intellectually stimulating materialHouse of Cards describes in rich detail the rapid and complex series of events that led to the downfall of Bear Stearns in March 2008. Its real appeal, however, derives from its thorough analysis of the history of the firm since its inception as an upstart brokerage firm in 1923 and a riveting account of the demise of Bear Stearns Asset Management (BSAM) in 2007. This failure foreshadowed many of the issues that would eventually cripple the firm, and the author asserts that its departure from several historical operating practices led to its ultimate sale to JPMorgan Chase at $10 per share, down from over $170 just a year earlier.

The first of three sections, “Ten Days in March,” is an account of the last days of Bear Stearns as an independent entity. It is a fairly gripping account of executives embroiled in a crisis of confidence that overtook world financial markets and their participants.

The second section, titled, “Why It Happened: Eighty-Five Years,” explains the roots of the firm and its founding and operating principles, and it sets the groundwork for why several departures from these founding principles eventually led the firm astray.

Ultimately, however, it is the final section of the book, “The End of the Second Gilded Age,” that provides the real lessons for financial and operating business managers. According to Cohan, for many years the firm had maintained the policy: “If you make money, you can run your business any way you want to.” Additionally, the author asserts that CEO Jimmy Cayne, with his retail brokerage background, had only passing knowledge of Bear Stearns’ businesses outside of fixed-income and clearing.

A lack of leadership and a “siloed” business mentality allowed this independence to go unchecked as Cayne was absent in the midst of several crises (he was playing in world-class bridge tournaments). Ultimately, according to Cohan, Cayne admitted his failure to diversify the firm’s businesses. In my opinion, this was due to his reliance on an attitude of “if it ain’t broke, don’t fix it,” while not being directly involved enough to know what was actually broken.

In a partnership environment, this policy served the firm extremely well. As long as a significant portion of the partners’ wealth was on the line, risk management was thoroughly enforced. The partners (led by former CEO “Ace” Greenberg and before that the legendary Cy Lewis) enforced a consistent policy of cutting losses quickly. But once the company went public, incentives shifted to make many of the firm’s traders, especially the principals at BSAM, take on as much risk as possible in the hopes of extraordinary returns.

My interpretation of the author’s conclusions is that the firm’s history of independent business units, a pronounced lack of risk management oversight, and a disdain for risk management personnel (“[expletive-deleted] accountants,” in Cayne’s words) who might thwart efforts to make as much money as possible, hurtled the firm into a financial abyss.

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Examining the Role of Short-Term Correlation in Portfolio Diversification

From the third quarter of 2008 to the present, the financial markets have “gone to one,” meaning that all investment options have become highly correlated. They have all gone down (with the notable exceptions of cash and government bonds). The benefit of holding uncorrelated assets is that they should not all move in lock step, so that while one goes down, hopefully another will increase. The question that this article attempts to answer is whether the long-term correlations that sales and marketing materials often quote are similar in the short term as well.




Image: Geopaul





Typically, correlation between investment assets and asset classes is calculated over extended time periods, such as 5, 10, or 15 years. But what is of greater concern to the investor is what the correlation will be next month. The use of a low 15-year correlation might obscure more recent data due to the length of time over which the correlation was calculated. Could it be, for example, that the last 12 months would show a much higher correlation between assets than the figure contained in the marketing literature?

This article looks at the near-term issues regarding correlation. Using two series of random numbers (180 observations to simulate 15 years of monthly returns) and running a short (100-trial) Monte Carlo simulation (a process that repeats the same trial), these uncorrelated random series showed significant 36-, 24-, and 12-month correlations. This suggests that investors should also consider short-term correlations between assets when attempting to diversify their portfolios. In addition, correlations should be rebalanced as often as asset allocations because investment strategies, personnel, and so forth change over time.

What is Correlation?

Most investors have the singular goal of maximizing investment return given a certain level of risk tolerance. Modern portfolio theory holds that returns are maximized in the long run when they are held in a diversified portfolio. A statistical measure of diversification is “correlation,” which is measured on a scale that runs from -1.0 to +1.0. A correlation coefficient of -1.0 or +1.0 is considered perfect correlation, knowing how one series of data moves provides perfect information on how the second series will move.

A negative correlation coefficient signifies that the two series move in opposite directions, for example, as one series increases, the other decreases. This is also known as an inverse correlation. A positive or direct correlation indicates that the series move together, as one increases, the other also increases. It is rare that one comes across perfect correlation, that is, a correlation coefficient of exactly -1.0 or +1.0.

The plus or minus sign indicates whether the relationship is direct or inverse, whereas the calculated value indicates the strength of the relationship. As the correlation coefficient moves from zero toward +1.0, there is an increasingly direct statistical relationship. Conversely, as the correlation coefficient moves from zero to -1.0, there is an increasingly inverse statistical relationship. In addition, a correlation of -0.7, then, is exactly as significant as a correlation of +0.7. A correlation coefficient of zero indicates that there is no statistical relationship between the two series of numbers, the series behave randomly with respect to one another. This is also called “non-correlation,” or, sometimes, the two series are said to be “uncorrelated.”

One important point about correlation is that it does not represent causality. For instance, in school-age children, shoe size is a great predictor of reading ability, not because shoe size has anything to do with reading, but because it is a proxy for age, older children tend to read better.

Correlation and Investing

Some investors believe that they make only three investment decisions: asset allocation, manager selection, and vehicle choice. Asset allocation is important because it is widely held that diversification is a cornerstone of investing theory. Diversification follows the logic of not putting all of your eggs in one basket. If an investor invests in a single stock, then the portfolio will do as well or as poorly as that single stock. If the investors select two stocks, they would appear to have achieved some level of diversification, but this is only at a company level. If both companies are engaged in the same industry, like Pepsi and Coca-Cola, or American Airlines and Delta, or Ford and GM, then the stock price movements that affect an industry segment will affect both stocks, that is, 100 percent of their portfolio. So, the investors might want also to diversify along company, industry, or geographical lines.

Diversification is usually quantified by correlation, that is, the degree to which the movement of one investment or asset class allows for inferences about how another investment or asset class will move. This is not indicative of causality, but simply a statistical relationship that may include causality and that can also occur simply by chance. A portfolio is not diversified if all of its holdings are correlated with one another, meaning that if one holding moves a certain way, we can predict how the other holdings will move. Brokers of commodity-based products (whether futures contracts or hard-asset ownership), infrastructure investments, and real estate funds often cite “uncorrelated with existing asset classes” as a major selling point of their products:

Issues with Non-Correlation as an Investment Strategy

Asset classes are too broad

Individual products within an asset class are not created equal; there are a wide variety of investment choices within any class. For instance, within the “hedge fund” asset class (assuming one considers hedge funds an asset class) there are over 8,000 investment choices. Treating the returns of the asset class as representative of the returns of the underlying components could be erroneous. The same is true of U.S. equities as a whole, or even when dealing with subcategories, such as Small Cap Growth, Small Cap Value, Large Cap Growth, Large Cap Value, and so forth. To be useful, non-correlation should focus on product-level asset holdings.

Not all portfolios are alike

Portfolio compositions usually differ among investors in terms of asset allocation and individual investment choices. To claim that a particular product will not be correlated with the portfolio does not give appropriate credit to the diversity of investments and the particular holdings. To be relevant, correlation should be calculated based on the returns of specific portfolio holdings, not generic asset-class returns.

Different types of non-correlation

Third, there can be different types of non-correlation. One type of non-correlation is the one people ordinarily think of when they define non-correlation, when one variable changes, the other variable will behave randomly. Another type of non-correlation operates very differently. Two series can have a low overall level of correlation even if they are 100-percent positively correlated half of the time (i.e., they have a correlation of +1.0 for half of the observations) and 100 percent negatively correlated the other half of the time (i.e., they have a correlation of -1.0 for half of the observations). In this situation, the variables clearly have some kind of relationship to one another, although the overall correlation coefficient might indicate otherwise.

Perhaps what makes correlation so interesting is that similar situations can lead to quite different results. Consider the following small series:

Observation X Y
1 1 9
2 2 8
3 3 7
4 4 6
5 5 5
6 4 4
7 3 3
8 1 1

The overall correlation is -.096, which is not even remotely statistically significant. But within that overall insignificant correlation are two sub-series (observations 1 to 4 and observations 5 to 8). The correlation of observations 1 to 4 is -1.0, and the correlation of observations 5 to 8 is +1.0, which are perfect correlations.

Now consider another small series:

Observation X Y
1 4 5
2 3 6
3 2 7
4 6 3
5 7 4
6 8 4

The correlation of observations 1 to 3 is -1.0, and the correlation of observations 4 to 6 is +1.0, as in the last series. However, the overall correlation is .72, which is on the border of statistical significance at the .10 level.

In the first case, we had an overall correlation coefficient that indicated there was absolutely no statistical relationship between the two series. However, embedded within that series were two shorter series that had extreme levels of correlation (one positive and one negative). In the second case, the observations were similarly arranged so that the first half of the series had a correlation coefficient of -1.0, and the second half of the series had a correlation coefficient of +1.0, yet the overall result was a nearly statistically significant correlation of .72.

Even when it operates as we think it does, do we want it?

If two asset classes (or individual investments) are truly uncorrelated, then when the first asset class increases, the other class may increase, decrease, or remain unchanged. There is no existing statistical relationship that allows us to infer how one class will behave based on the behavior of the other, but is this random effect desirable? We can couch the issue in the following terms: When the first asset class increases, we would like the other class to increase. However, since it is behaving randomly, there is only a one-in-three likelihood that it will do so (with the three possibilities being for it to increase, decrease, and remain unchanged). Similarly, when the first asset class decreases, we would like the other class to increase, though, again, there is a one-in-three chance this will happen. Better odds can be achieved by betting “black” at a roulette table.




Photo: Daniel Haller





The Experiment

This article examines whether uncorrelated (in the long term) series of numbers (representing investment returns) are also uncorrelated in the short term. While most investment professionals will not be surprised that uncorrelated asset classes (or investments) may have short-term correlations, the question is whether the frequency and duration of the short-term correlations are what might be expected.

This study was exploratory in nature because we could not find empirical research that quantifies the type of short-term correlation that would be considered “normal.” Since we have no basis on which to a priori establish whether the short-term series are abnormal, we will quantify and present the results and establish the literature.

We began with two series of 180 random numbers representing 15 years of monthly returns. Correlations were calculated for the last 36, 24, and 12 months of the series, since these timeframes were representative of the effect that will be introduced into the portfolio. In other words, the relevant correlation is the most recent one, not the one that was evident 15 years ago. A hundred iterations of this experiment were performed.

Exhibit 1: Frequency of observed correlations resulting from 100 trials

Each trial had 180 monthly observations (15 years). During the 100 trials, the overall correlation was .20 one time, and less than .20 the other 99 times.

The correlation for each trial was recalculated over the last 36, 24, and 12 months, and the correlations for these shorter periods are indicated:

Correlation (+/-) 0.2 0.3 0.4 0.5
Overall 1
Last 36 months 27 9 2
Last 24 months 33 18 6 2
Last 12 months 61 39 21 10

The Results

The test revealed that, overall, the two series were uncorrelated. In the 100 trials, the overall correlation of .20 was only obtained once. When we reviewed the correlations of the last 36, 24, and 12 months, some startling results were evident. In the last 36 months of each trial, the correlation was 0.2 or more 27 percent (27/100) of the time, 0.3 or more 9 percent of the time, and 0.4 or more 2 percent of the time.

For the last 24 months, a correlation of 0.2 or more occurred 33 percent of the time, a correlation of 0.3 or more resulted 18 percent of the time, a correlation of 0.4 or more was obtained 6 percent of the time, and a correlation of 0.5 or more occurred 2 percent of the time.

The last 12 months, however, may be the most relevant period because this timeframe is the most likely to impact a portfolio. A correlation of 0.2 or more occurred 61 percent of the time, a correlation of 0.3 or more was evident 39 percent of the time, a correlation of 0.4 or more occurred 21 percent of the time, and a correlation of 0.5 or more was found 10 percent of the time. An investor adding an investment and expecting it to be uncorrelated (based on 15 years worth of data) could very well be surprised at the resultant effect.

Conclusion: Do Your Short-Term Correlation Home-Work

Our findings suggest that if an investor is adding an investment to his or her portfolio with the goal of aiding diversification, he or she should parse the long-term correlation into shorter-term metrics. The nearer and the shorter the timeframe, the greater the likelihood that the investment will move from uncorrelated to correlated. As the correlation that will be added to the portfolio is more reflective of the 180th month than the first month of the series, the additional calculation of a near-term 36-, 24-, and 12-month correlation could prove useful. Perhaps there is an investment that can be added to the portfolio that, over the long term, will provide uncorrelated returns and, therefore, aid in diversification. However, if the return stream is presently correlated to the portfolio, the investor should wait a couple of periods before adding the investment, thereby mitigating the short-term effects of correlation.

Review Investments Periodically for Correlation Shifts

An additional implication from this study concerns investments that are already in the portfolio. Once an investment is added, there is usually no further attention devoted to the correlation. This study suggests, however, that the correlations of the existing investments should also be reviewed periodically. Manager changes, style drift, and so forth may mean that the original correlation that made the investment attractive is no longer accurate.

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1998 Volume 1 Issue 3

1998 Volume 1 Issue 2

1998 Volume 1 Issue 1