Thesis (Ph. D.)--Brigham Young University. Dept. of Electrical and Computer Engineering, 2006. / Includes bibliographical references (p. 111-118).

Thesis (M.L.S.)--University of Michigan, 1941.

Thesis (M.Phil.) - University of Queensland, 2007. / Includes bibliography.

Magister Commercii - MCom / The equity market has a long memory of indexing. The market portfolio is a capweighted index that weights stocks based on the market capitalisation of the stocks constituting the index and has been upheld by modern portfolio theory as the optimal portfolio, generating the highest return for given risk. Justification for the meanvariance efficiency of the market portfolio stems from the assumed efficiency of stock markets. However, Siegel (2006) states that, because of speculative trading in the market, which induces noise in stock prices, the prices of stocks deviate from their intrinsic value. The subsequent reversal of overweighting of overvalued stocks and underweighting of undervalued stocks to their intrinsic values by capitalisation weighting results in a return drag. Recent observations of portfolios constructed based on weighting methodologies other than capitalisation weighting have resulted in portfolios that generate excess riskadjusted returns over and above that of the market portfolio; casting doubt on the assumed efficiency of the market. One such weighting methodologies is fundamental indexation, under which stocks are weighted by their fundamental metrics of size. The concept was introduced by Arnott, Hsu and Moore (2005). Chen, Chen and Bassett (2007) also introduced the concept of smoothed cap weights (SCW) as a more reliable estimate of the intrinsic value of a stock. This research study applies the concept of fundamental indexation and SCW to investigate the relative performance of fundamental indices of different concentrations (top 50 and mid-100 stocks) against cap-weighted portfolios on the Taiwanese equity market. The research period runs from January 2001 to June 2014, using the TEJ database as the data source. The TAIEX is employed as the market proxy. The research also examines the performance attribution and robustness of fundamental indices against cap-weighted portfolios. The results indicate that most fundamental indices constructed from the top 50 stocks are less mean-variance efficient than the TAIEX but more mean-variance efficient than the cap-weighted reference portfolio. All fundamental indices of the mid-100 stocks are more mean-variance efficient than the TAIEX and the reference portfolio. The return drag observed in the cap-weighted TAIEX and reference portfolio evidences the presence of mean reversion of stocks. Moreover, the returns of fundamental indices of the top 50 stocks are partly influenced by size risk premium but the fundamental indices comprised of the mid-100 stocks display return variations with statistically significant factor loading on the small cap (size) risk premium and value risk premium. Fundamental indices, on average show a higher resilience against the cap-weighted portfolios in both bull and bear markets. The sales index and fundamental composite index are the most mean-variance efficient fundamental indices and generate statistically significant alphas post accounting for both size and value risk premia.

Modern geographical databases store a rich set of aspatial attributes in addition to geographic data. Retrieving spatial records constrained on spatial and aspatial attributes provides users the ability to perform more interesting spatial analyses via composite spatial searches; e.g., in a real estate database, "Find the nearest homes for sale to my current location that have backyard and whose prices are between $50,000 and $80,000". Efficient processing of such composite searches requires combined indexing strategies of multiple types of data. Existing spatial query engines commonly apply a two-filter approach (spatial filter followed by non-spatial filter, or viceversa), which can incur large performance overheads. On the other hand, the amount of geolocation data in databases is rapidly increasing due in part to advances in geolocation technologies (e.g., GPS- enabled mobile devices) that allow to associate location data to nearly every object or event. Hence, practical spatial databases may face data ingestion challenges of large data volumes. In this dissertation, we first show how indexing spatial data with R-trees (a typical data pre- processing task) can be scaled in MapReduce – a well-adopted parallel programming model, developed by Google, for data intensive problems. Close to linear scalability was observed in index construction tasks over large spatial datasets. Subsequently, we develop novel techniques for simultaneously indexing spatial with textual and numeric data to process k-nearest neighbor searches with aspatial Boolean selection constraints. In particular, numeric ranges are compactly encoded and explicitly indexed. Experimental evaluations with real spatial databases showed query response times within acceptable ranges for interactive search systems.

No / Modern manufacturing involves multiple stages of complex process chains where Finite Element Analysis is frequently used as a simulation method on a discretized mesh to provide an accurate estimation of factors such as stresses, strains, and displacements. The choice of the most suitable element type and density is dependent on the individual manufacturing process or treatment applied at each stage of the process chain. To map between unalike Finite Element meshes, differing in density and/or element type, an Octree spatial index was evaluated as a solution for highly performant mapping. Compared to existing solutions, the Octree spatial index introduces parallelism within index creation and provides a strategy to perform the most complex interpolation technique, Element Shape Function, in a more computationally efficient manner.

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