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where AAR1 = Annual account return for year 1 for each account in the composite. Note: This formula includes only accounts that were fully invested for the entire year. Accounts that were invested for a portion of a given year are not included in that year, but would be included in the following year (assuming they stayed in the composite for the full year). Accounts with large cash in ows and/or large cash out ows during a given year might experience additional dispersion compared to accounts that did not experience large in ows/out ows because the timing of the asset in ow/out ow might impact returns. In this case, the analyst might elect to exclude these accounts from the calculation. Step 2: Calculate the standard deviation of accounts for the period. The formula for this statistic will be covered in detail in the next chapter. Step 3: Determine how many of the composite accounts have returns within +/ one standard deviation of the average account return and take that number as a percentage of the total number of accounts. This gives

We explain why this is so and provide real-life examples Finally, we highlight another essential danger associated with the prevalence of theoretical ideology within the markets and the economy, namely the fact that we may succumb to the charms of a mathematical gadget and abide by it without actually understanding what it means This can cause undue dislocations in the markets, as players act according to a guide that they can t read right We can t have wildly misunderstood, in uential theoretical devices running loose throughout the nancial landscape The analysis focuses on perhaps the most prominent of such misread tools, the famous volatility index (VIX)..

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[1] Whitehead, A N Science and the Modern World, Free Press, New York, 1967, p 58 [2] von Laszewski, G and Wagstrom, P (2004) Gestalt of the Grid, Performance Evaluation and Characterization of Parallel and Distributed Computing Tools, Series on Parallel and Distributed Computing, pp 149 187; Wiley, (to be published); http://wwwmcsanlgov/ gregor/papers/vonLaszewski gestaltpdf [3] Smarr, L and Catlett, C (1992) Metacomputing Communications of the ACM, 35(6), 44 52 [4] Foster, I (2002) The Grid: A New Infrastructure for 21st Century Science Physics Today, 55(22), 42; http://wwwaiporg/pt/vol-55/iss-2/p42html [5] von Laszewski, G, Su, M-H, Insley, J A, Foster, I, Bresnahan, J, Kesselman, C, Thiebaux, M, Rivers, M L, Wang, S, Tieman, B, and McNulty, I (1999) Real-Time Analysis, Visualization, and Steering of Microtomography Experiments at Photon Sources.

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Jorgensen T and Wallace SW (2000) Improving project cost estimation by taking into account managerial exibility European Journal of Operational Research 127: 239 251 Kahneman D, Slovic P and Tversky A (1986) Judgement under Uncertainty: Heuristics and Biases Cambridge University Press: Cambridge, UK Kamburowski J (1995) On the minimum cost project schedule Omega 23(4): 463 465 Kaming PF, Holt GD, Kometa ST and Olomolaiye PO (1998) Severity diagnosis of productivity problems a reliability analysis International Journal of Project Management 16: 107 113 Keefer DL and Verdini WA (1993) Better estimation of PERT activity time parameters Management Science 39: 1086 1091 Keeney RL and Winterfeldt DV (1989) On the uses of expert judgement on complex technical problems IEEE Transactions of Engineering Management 36(2): 83 86 Kerzner H (2000) Applied Project Management Best Practices On Implementation John Wiley: New York.

Ninth SIAM Conference on Parallel Processing for Scienti c Computing, San Antonio, TX, March 22-24; http://wwwmcsanlgov/ gregor/papers/vonLaszewski siamCmt99pdf [6] Oram, A, ed (2001) Peer-To-Peer, O Reiley [7] Foster, I, Kesselman, C, and Tuecke, S (2002) The Anatomy of the Grid: Enabling Scalable Virtual Organizations International Journal of High Performance Computing Applications, 15(3), 200 222, Fall 2001; http://wwwglobusorg/research/ papers/anatomypdf [8] von Laszewski, G, Foster, I, Gawor, J, Smith, W, and Tuecke, S (2000) CoG Kits: A Bridge Between Commodity Distributed Computing and High-Performance Grids ACM Java Grande 2000 Conference, San Francisco, CA, June 3-5 , pp 97 106; http://wwwmcsanlgov/ gregor/papers/vonLaszewski cog- nalpdf [9] von Laszewski, G, Amin, K, Hategan, M, and Zaluzec, N J (2004) GridAnt: A Client-Controllable Grid Work ow System.

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37th Hawai i International Conference on System Science, Island of Hawaii, Big Island, January 5-8; http://wwwmcsanlgov/ gregor/papers/vonLaszewski gridant-hicspdf [10] von Laszewski, G (1999) A Loosely Coupled Metacomputer: Cooperating Job Submissions Across Multiple Supercomputing Sites Concurrency, Experience, and Practice, 11(5), 933 948; http://wwwmcsanlgov/ gregor/papers/vonLaszewski CooperatingJobsps [11] Unicore, Web Page, http://wwwunicorede/ Accessed 4/22/2004 [12] Grimshaw, A S and Wulf, W A (1997) The Legion Vision of a Worldwide Virtual Computer Communications of the ACM, 40(1), 39 45; http://legion virginiaedu/copy-cacmhtml [13] Condor: High Throughput Computing, Web Page, http://wwwcswiscedu/condor/ Accessed 4/22/2004 [14] The Global Grid Forum Web Page, Web Page, http://wwwgridforumorg Accessed 4/22/2004 [15] Portable Batch System, Web Page, Veridian Systems, http://wwwopenpbsorg/ Accessed 4/22/2004..

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