Sample Business Plan commodicast

_ orrow's Futures, Today 125 Lincoln Avenue Suite 400 Santa Fe, New Mexico 87501 505-989-3558 www.CommodiCast.com THIS BUSINESS PLAN IS CONFIDENTIAL AND CONTAINS PROPRIETARY INFORMATION AND TRADE SECRETS OF COMMODICAST, INC. NEITHER THIS BUSINESS PLAN NOR ANY OF THE INFORMATION CONTAINED HEREIN MAY BE REPRODUCED OR DISCLOSED WITHOUT PRIOR WRITTEN CONSENT OF COMMODICAST, INC. NO SALE OF SECURITIES. THIS IS NOT A SOLICITATION OR OFFER TO BUY OR SELL SECURITIES. SUCH SALE OR REPRESENTATION SHALL BE MADE ONLY B Y A D UL Y AUTHORIZED REPRESENTATIVE COMMODICAST. ANY OF DISCLAIMER. CERTAIN STATEMENTS CONTAINED HEREIN ARE "FORWARD-LOOKING" STATEMENTS (AS SUCH TERM IS DEFINED IN THE PRIVATE SECURITIES LITIGATION REFORM ACT OF 1995). BECAUSE SUCH STATEMENTS INCLUDE RISKS AND UNCERTAINTIES, ACTUAL RESULTS MAY DIFFER MA TERIALL Y FROM THOSE EXPRESSED OR IMPLIED BY SUCH FORWARD-LOOKING STATEMENTS. FACTORS THAT COULD CA USE ACTUAL RESULTS TO DIFFER MA TERIALL Y FROM THOSE EXPRESSED INCLUDE, BUT ARE NOT LIMITED TO RISK OF MARKET ACCEPTANCE, RISK OF TECHNOLOGY OBSOLESCENCE, DELAYS IN DEVELOPMENT, RELIANCE ON PARTNERS, COMPETITIVE RISKS, FAILURE TO SECURE THE REQUIRED FUNDS AND PERSONNEL AND OTHER RISKS AND UNCERTAINTIES. COMMODICAST IS UNDER NO OBLIGATION TO UPDATE THE FOR WARD-LOOKING STATEMENTS. VISION To become the industry standard commodity price forecasting firm for producers, users and speculators within targeted commodity markets. 2 EXECUTIVE Su MMARY .The Company .............................. Commodity producers, users, and speculators have long sought to successfully forecast short and longterm commodity price changes. The ability to identify future commodity prices has obvious benefits to all stakeholders in commodity markets. CommodiCast, Inc. utilizes highly accurate commodity price forecasts to develop mechanical trading systems for commodity speculators. Additionally, CommodiCast offers highly specialized commodity price forecasts that have been optimized for producers and users of a given commodity to assist in hedging their risk. CommodiCast has differentiated itself by demonstrating significantly higher forecast accuracy over existing commercial forecasters in the solid wood commodity market. Highly accurate price forecasts for many lumber spot prices (i.e. 2x4 vs. 2x8) will allow producers to optimize the effects of trade-offs in production. Currently, CommodiCast has successfully developed multiple price-forecast models for the solid wood commodity market with significant improvement in accuracy. Error reduction has been 70% and greater. CommodiCast's proprietary intellectual property employs sophisticated forecasting techniques and mathematical models to predict future prices of a select number of commodity products. Advanced Nonlinear and Adaptive Neural Network Techniques including CNLS Network (developed by CommodiCast Co-Founder, Roger D. Jones, Ph.D.), Adaptive Clustering, Kohonen Networks, Genetic Algorithms, Simulated Annealing, Intelligent Agent Based Learning are utilized to develop commodity price forecast models for short, intermediate, and long time frames. Compap.¥_S_trategg ....... __ .... ___ - ................................ Over the next five years, CommodiCast has a goal to be the leader in providing highly accurate commodity price forecasts. The Company has developed a three-part strategy to become this market's leader: 1. Continue to develop proprietaryhighly accurate forecast modelsfortargetedcommodities. 2. Partner with leading commodity producers and purchasers to continue to evolve our forecasting technology. 3. Utilize our commodity price forecast models to develop successful mechanical tradingsystems for the commoditiesfutures market. Partnering with leading commodity producers to develop commodity price forecast models allows CommodiCast the ability to develop a deep understanding of that commodity's production process as well as significant market and economic factors that drive a commodity market. A thorough understanding of production and market factors allows us to optimize our forecast models for a given commodity. Once a model has been optimized for an underlying commodity, a mechanical trading system for the commodity futures market can be developed. In turn, the development of a mechanical trading system for a given commodity has provided learning that helps to improve the specific commodity's forecast model. This iterative process provides a mechanism for continuous improvement of a model's performance. ,c.omm_ o-c_a_sts Roots ....... ....................................... CommodiCast is a spin-off of two leading complexity science companies, Complexica, Inc. and the Bios Group, Inc. Complexica TM, founded in early 1999 by Dr. Roger D. Jones and Professor John L. Casti, is an advanced analytics company that partners with clients to design, develop, and build adaptive, intelligent software systems and strategic business simulators. The roots of Complexica lie in the science and technologies of Los Alamos National Laboratory and the Santa Fe Institute. Complexica has extensive experience in the application of advanced data mining techniques to business problems. Much of this experience has been developed in the financial services industry, dealing with problems such as loan acquisition, customer behavior, fraud detection, loss rate prediction, and profitability. In addition, Complexica's business modeling expertise has been applied to strategic problems such as enterprise risk management and organizational design. Bios Group, Inc. was founded as a joint venture between the Center for Business Innovation of Ernst & Young (now known as Cap Gemini Ernst & Young) and Dr. StuartKauffman, a co-founder of the Santa Fe Institute and author of several books on complexity science. Bios Group pioneered the use of complexity science to solve complex business problems, and is now the world leader in adapting the techniques of this emerging science to large-scale commercial applications. Bios Group counts over 30 Fortune 500 companies as clients for its services, which include applications in automated markets, supply chain management, dynamic scheduling, marketing diffusion, operational risk, and project portfolio management. Bios Group's resources include 2 Nobel Laureates and over 50 Ph.D.'s in mathematics, physics, biology, chemistry, computer science, and economics, as well as highly skilled software developers and experienced business managers with a track record of success. CommodiCast's proprietary intellectual property employs sophisticated forecasting techniques and mathematical models to predict future prices of a select number of commodity products. The technology utilizes advanced statistical techniques, including Regression Techniques such as Partial Least Squares and Sliced Inverse Regression. We also utilize clustering techniques such as Logistic Regression, Principal Component Analysis, CART, and Fisher Discriminants. Advanced Nonlinear and Adaptive Neural Network Techniques including CNLS Network, Adaptive Clustering, Kohonen Networks, Genetic Algorithms, Simulated Annealing, Intelligent Agent Based Learning are also utilized to develop commodity price forecast models for short, intermediate, and long time frames. Roger D. Jones, Ph.D., Chairman of the successfully applied the above time-series services industry including loan acquisition fraud detection, authorization of credit card consumer profiles. Board, CommodiCast, Inc., and CEO, Complexica has forecasting techniques to several areas in the financial scores, consumer behavior scores, loss rate predictions, purchases, forecast of losses in credit card accounts and These techniques have recently been applied to forecasting commodity lumber prices with exceptional results. Currently, CommodiCast has successfully developed multiple price-forecast models for the solid wood commodity market with significant improvement in accuracy. Error reduction has been 70% and greater. An example is given in the figure below. Here, two models were developed from two separate modeling techniques. The models were trained on data from before October 1998. Prices were forecast from October 31, 1998 to June 1999. Actual prices are in red (solid). Two CommodiCast forecasts are in green (dotted). The forecast from the industry-wide forecast standard is in black (dashed). 440 420 400 380 360 340 320 300 - ActualPrice -'_"_ _" ._ Commercial Forecast _ "'" ' Coi,iinodiCast Forecasts _ Jan99 , Feb99 , M ar99 , Apr99 , M ay99 , Jun99 The market for the Company's product is segregated into two distinct segments. The first market segment represents the opportunity for generating revenues from producers and users (hedge market) of commodities by customizing our models and licensing them on an annual license basis. The second segment represents revenues generated from trading commodities (speculative market). The Company estimates that the market for these two segments exceeds $40 billion annually. 5 Hedge Market - First Market Segment It is estimated that the market size for the commodity hedge market is $600,000,000. The revenue model for the hedge market will be to license our proprietary models to targeted commodity producers and users on an annual basis. While the size of this market is a small fraction of the larger speculative commodity market, the hedge market plays an essential role in CommodiCast's strategy for at least two reasons. First, having hedge client allows CommodiCast the ability to develop a deep understanding of the market factors that influence that commodity's price. The knowledge gained allows us to optimize our model for that commodity for both hedge and speculative purposes. Secondly, our revenue model for the hedge market is an annual service license. This annual service license model will provide consistent revenue stream. Hedge Market - Target Marketplace The Chart below identifies the Company's five-year market goals, as assessed by the management of the Company. Target Market Pricing Contract enewalGoal R Hedge Market - Client Universe This table illustrates the anticipated number of clients anticipated by CommodiCast, through a focused sales and marketing effort, and includes the sales run rate. Year 1 2 3 4 5 Footnotes (a) (b) (c) A "Client '" engagement is calculated as a $150,000 annual service license. This license would be for one family of commodities forecasted. Client renewal rate was estimated at 75% Sales Run Rate includes all sales booked within the calendar year and not the revenues realized for that year. /In estimated sales cycle of 6 to 12 months was utilized. Bias Group, Inc and Cap Gemini Ernst and Young account management team will be used to augment CommodiCast Sales efforts. Business plan calls for the development of a 5 account director CommodiCast sales team to be phased in over the first 4 years. Approximately 400 clients, representing the top 20 Producers/Users for eachof the top 20 commodities, l0 productsper customer $150,000, whichis the annuallicensingfee 75.00% NewClients(a) 5 11 13 16 20 TotalClients(b) 7 15 21 26 32 SalesRunRate(c) $975,000 $2,212,500 $3,187,500 $3,862,500 $4,800,000 Speculative Futures - Second Market Segment For CommodiCast And CTAs International Traders Research, Inc., La Jolla, CA., has estimated the size of the domestic commodity managed futures market at $40 billion annually. CommodiCast will develop mechanical trading systems for Commodity Trading Advisors (CTAs). Typically, developers of mechanical trading systems receive 6 a 1% annual management fee for assets that are traded within the system. Additionally, it is customary to receive 20% of the net profits in the trading account as an incentive fee. In 1999, the mean Annual Rate of Return (ARR) across all CTAs was 2% and the top 10% of CTAs realized an ARR of 44%. On average, the top 10% of CTAs managed over $400 million on an annual basis. Although paper-trading results do not guarantee real results, we have accomplished ARR between 45% and 255% with early versions of our mechanical trading systems for Cotton, Lumber, and Crude Oil in paper trading activities. If the above results can be duplicated in actual trading, CommodiCast will be positioned to aggressively pursue a significant market position in the managed futures market. Our goal is to be in the top 10%of CTAs by 2005. Speculative Futures - Target Marketplace The following table represents the managed futures market, as estimated by the management of CommodiCast. $40,000,000,000 $61,245,691 2.01% $439,693,796 44.00% 20.03% Sources: International Traders Research, Total funds managedby CTAs 1999 Average EquityAccount Size Managed Weighted AverageAnnual Rate of Return Average Equityof Top 40 CTAs Average Annual Rate of ReturnTop 10% of CTAs Annual Rate of Return for 41st Ranked CTA Inc., La Jolla, CA. Speculative Market - Funds Managed For CTAs The table below sets forth the funds managed by CommodiCast and the resulting sales run rate. The table is for purposes of illustration only. CommodiCast Shareof Mat.Fee(b) $75,000 $200,000 $600,000 $1,000,000 $5,000,000 CommodiCast CommodiCast Incentive Shareof Fee(d) IncentiveFee 20% 20% 20% 20% 20% $367,500 $980,000 $2,940,000 $4,900,000 $24,500,000 Year 1 2 3 4 5 Funds Mana_oed (a) $7,500,000 $20,000,000 $60,000,000 $100,000,000 $500,000,000 Projected ARR(c) 25% 25% 25% 25% 25% SalesRun Rate(e) $442,500 $1,180,000 $3,540,000 $5,900,000 $29,500,000 Footnotes (a) Pace of Funds Managed places CommodiCast in top 10% of CTAs in our 5thyear of operation. (b) Typical management fee of CTAs is 2_ I% is used in this example. (d) Projected ARR (Annual Rate of Return) is calculated net all fees. CommodiCast current performance is double the projected ARR used in this example. (d) Typical Incentive Fee is 20% of annual net profits in an account. Incentive fees are as high as 50% have been successfully negotiated with the best performing CTAs. (e) Sales Run Rate includes all sales booked within the calendar year and not the revenues realized for that year. 7 Speculative Futures - CommodiCast Traded Fund(s) trading systems In addition to the market identified above, CommodiCast plans to utilize the mechanical we developed to actively trade a CommodiCast Company speculative fund. Year 1 2 3 4 5 Footnotes (a) (b) Company Funds Traded (a) $100,000 $200,000 $400,000 $3,403,289 $7,578,466 Prolected ARR (b) 25% 25% 25% 25% 25% Annual Revenues $25,000 $50,000 $100,000 $850,822 $1,894,616 In year one and two, $100,000 will be utUized for the CommodiCast Company Trading fimd. In year 3, 4 and 5 a portion of the cash reserves will be utilized to trade in thefund. CommodiCast current performance is double the projected ARR (Annual Rate of Return) used in this example. Currently, commodity information service providers focus their analysis on particular commodity markets and do not emphasize predicting future trends in the market. As an example, Standard & Poor's Platt's, the specialist commodity market reporting company focuses on providing content and analysis of retrospective data for petroleum commodities. Price forecasting is a small portion of their overall business. Additionally, there seems to be little linkage between organizations that predict commodity prices and Commodity Trading Advisors (CTAs) who trade Managed Futures Accounts for clients. Differentiators for CommodiCast vs. the Competition in the marketplace for the Company, due to the unique nature of The following points are differentiators the forecasting techniques 1. CommodiCast uses nonlinear time-series forecasting techniques developed by Roger D. Jones, Ph.D. and his team of scientists at Los Alamos National Laboratory. These methods were used to forecast flooding in Venice Lagoon, control chemical processes, optimal vehicle control during panic stops, identification of fraud in IRS tax filings, forecast of losses in credit card accounts, and several other applications. These techniques have recently been applied to forecasting commodity lumber prices with exceptional results. CommodiCast has exclusive use of this technology for commodity forecasting. Dr. Jones serves on the CommodiCast Board of directors. 2. For producers and users of a commodity, we will provide accurate commodity price predictions that are specific (i.e., West Texas Intermediate Crude), time bound (i.e., April 2000), and Geographically specific (i.e., to account for regional variations in pricing). 3. Will partner with producer customers to fully understand the production cycle for each commodity. This deeper understanding will allow for the development for highly accurate commodity specific models. 8 4. CommodiCastwill provide the needed tools to allow commodity producersto forecast demand, adjust productionschedules, and hedge marketfinancial risks. s. lt,.'_,_g_ ............... e _ The Company currently licenses commodity modds to commodity producers and users directly and through the sales efforts of our parent organizations, Complexica, Inc and The Bios Group, Inc. The Bios Group has strategic relationships with leading companies including Cap Gemini Ernst & Young, Proctor & Gamble, and NASDAQ. The Bios Group account management team represents CommodiCast to targeted commodity producers and users. We arccurrently in negotiations with Cap Gemini Ernst & Young (CGEY) to have CGEY market CommodiCast's commodity forecast technology for producers ands users of commodities. CGEY owns a minority stake in the Bios Group, Inc and therefore, an interest in CommodiCast. CGEY account directors and partners have introduced our services to key customers and engagements are being discussed. CommodiCast also has strategic relationships with commodity traders and brokers. For example, MERIT, of Vienna, Austria is the largest commodities broker in Austria and has entered into a strategic relationship with CommodiCast to jointing develop and represent mechanical trading systems for the European market. To date, the Company has been funded through one round of private equity financing. Proceeds will be used to develop commodity-forecast models, cover initial sales and marketing efforts, to develop a corporate website and provide working capital. During the next five years, it is Management's intent to either position the Company for an initial public offering or to grow, position and ready the Company to be acquired by a strategic buyer. 9 The Company's management team consists of the following team leaders: Kelly D. Myers, CEO and Co-Founder. Mr. Myers has over 16 years of business experience in developing, negotiating and implementing strategic alliances as well as new business entities within healthcare and information technology fields. Kelly's management experience ranges from a Fortune 100 company to high information technology startups. CommodiCast represents the third information technology startup where he has held a senior management role. In his last position as VP, Strategic Development for MEDai, Inc., Kelly expanded the business model from a healthcare information technology company that serviced healthcare providers to a business-to-business data-mining firm by identifying three new business applications of the company's core technology. Also, Kelly led the development and implementation of the first application of its technology outside the healthcare market that transferred intellectual property to financial markets. The project resulted in a stock prediction model for a risk neutral hedge portfolio that achieved an annualized return of approximately 70% in simulation; the model is currentlyutilized for investing a $2 million portfolio. Roger D. Jones, Ph.D., Chairman of the Board and Co-Founder. Dr. Jonesearneda Ph.D. in physics from Dartmouth College in 1979. His early interests were in biological physics and the basis of learning, later interests were in plasma physics and fusion. He worked for ten years in the Laser Fusion Program at Los Alamos National Laboratory in New Mexico. Dr. Jones founded the Center for Adaptive Systems Applications (CASA) to service this bank and other clients in the financial sector. Dr. Jones founded Complexica to continue developing state-of-the-art analytics to help businesses maintain a competitive advantage in complex business environments. Dr. Jones is currently the CEO and Chief Scientist for Complexica. Board Otblrectors -:-- - - _ _ Associated with Messrs. Myers and Jones, the board of directors consists of the following individuals. Robert MacDonald, President, The Bios Group. Robert MacDonald joined Bios in February 1997 as President and the first employee, having been seduced by the science of Stu Kauffman and the backing of Ernst & Young. Bob has over twenty years of venture company experience as a general partner of two national venture capital firms and president of four venture capital-backed companies, including two that he took to public ownership. John Casti, Ph.D. Over the past few years, Dr. Casti has written a numerous articles and seven technical monographs and textbooks on mathematical modeling. In addition, he is the editor of the journals Applied Mathematics: Computation (Elsevier, New York) and Complexity (Wiley, New York). In 1989 his text/reference work Alternate Realities: Mathematical Models of Nature and Man (Wiley, 1989) was awarded a prize by the Association of American Publishers in a competition among all l0 scholarly books published in mathematics and the natural sciences. In 1992, Dr. Casti also published Reality Rules (Wiley, New York), a two-volume text on mathematical modeling. morate stra eio plannei"- -f-+ " ........ + .... Richard L. Triska. Corporate Strategic Planner. Mr. Triska was the Managing Director of Technology for the investment banking firm of Harris Webb & Garrison, Inc. In the strategic planning area, he has functioned as Chief Operating Officer for a $30 million revenue integrations services firm (INTERNET, VoIP and Videoconferencing), with active participation in P&L, marketing, corporate alliances, business development, strategic planning, financing options and operational matters. This experience provided a solid working knowledge base in the technology area. He was an Associate of Houston Venture Partners and assisted in strategic planning, marketing, investment analysis and transactions for such partnership. In addition, he was a Senior Credit Analyst and member of the Corporate Finance Department of American General Corporation, and shared transactional responsibilities for the private placement and venture capital portfolios aggregating approximately $1.5 billion. Mr. Triska received an MBA. From The University of Houston and a BA from The University of Texas at Austin 11 Financial Summary. ..... CommodiCast Financial Projections Five Year Income Statement (AII Amounts In Thousands) Revenues Licenses and Maintenance Total Revenue Operating Expenses Sales And Marketing Production And Development General And Administration Total Operating Expenses Operating Income Investment Income Income Before Taxes Income Taxes Netlncome $(216) 200 700 300 1,200 (241) 25 (216) 350 1,150 400 1,900 464 50 514 206 $309 800 1,400 650 2,850 566 100 666 266 $400 1,000 1,900 1,000 3,900 4,801 663 5,464 2,185 $3,278 1,000 2,000 1,000 4,000 26,397 1,634 28,030 11,212 $16,818 Year I $959 959 Year 2 $2,364 2,364 Year 3 $3,416 3,416 Year 4 $8,701 8,701 Year 5 $30,397 30,397 12 CommodiCast Financial Projections Five Year Balance Sheets (AII Amounts In Thousands) Year I Cash Accounts Receivable CommodiCast Trading Fund Other Current Assets Total Current Assets Property And Equipment Accumulated Depreciation Other Long-Term Assets Total Assets $680 464 100 0 1,244 60 7 0 $1,311 Year 2 $805 845 100 0 1,750 95 19 0 $1,764 Year 3 $1,103 938 3,403 0 5,443 143 35 0 $2,218 Year 4 $3,009 2,663 7,578 0 13,250 338 88 0 $6,097 Year 5 $15,297 9,024 25,746 0 50,066 538 181 0 $25,038 Accounts Payable Accrued Compensation Other Accrued Expenses Total Current Liabilities Common Stock Paid-In Capital Retained Earnings Total liabilities And Equity $155 30 0 185 0 1,342 (216) $1,311 $282 48 0 329 0 1,342 93 $1,764 $313 71 0 384 0 1,342 492 $2,218 $888 98 0 985 0 1,342 3,770 $6,097 $3,008 100 0 3,108 0 1,342 20,589 $25,038 00 !215 CommodiCast Business Plan 13