SpireStocks.com provides an objective, tangible, measurable and reliable means to enable the already engaged professional, to personally direct and control their equity portfolio.  After providing only the initial dollar amount of their portfolio, SpireStocks.com calculates which stocks have the highest potential to outperform the major market indices.  SpireStocks.com then provides the dollar amount that should be invested into each stock.  This is all accomplished through a process called the Alchemy Intangible Transformation Process™ (AITP).   AITP can be used for a wide range of applications including job performance assessment, choosing a car or house, hiring personnel, and evaluating products.

AITP was developed as a strategic and systematic method to compile a large range of statistical information into an output, that can be used for comparisons/rank-ordering.  This is accomplished, by establishing evaluation factors and scenarios that combine qualitative data and quantitative algorithms. Through rigorous and thorough definition, qualification, and assessment phases, the observer gains an in-depth understanding on how each input effects the desired outcome and adjusts the evaluation factors accordingly.  By implementing AITP, a model is typically developed with the outputs being single order of merits for each subject being evaluated.  These outputs not only capture the relationships to the desired outcome, but provides a decision tool to rank order and induce normalization into what would ordinarily be construed as being unfeasible to evaluate. Through this process, evaluations and assessments can be performed with confidence resulting in a tremendous capability for insightful decisions.  By using AITP to develop a systematic, automated, and integrated approach to direct stock investing, the SpireStocks.com members can invest in companies that have the potential and propensity to outperform Wall Street.

1. Inputs are subjected to rigorous and robust definition, qualification, and assessment evaluation.  Further understanding of the intent of the desired outcome and establishing the evaluation factors, that directly correlate to the desired outcome, are accomplished. [Note: there may be incidents where the necessary data/process does not exist in order to have insight into what factors are affecting the desired outcome; thus, processes may have to be developed to create the necessary data].
2. Data bounding is accomplished and measurement instruments are developed.  Algorithms and weights are then derived to produce a model that has the ability to transform the subjective inferences into a tangible and objective observable.
3. Case studies are produced and simulations run to tune the algorithms and weights of each evaluation factor/variable to ensure direct correlation to the desired outcome. Model output is compared to the subjective inference lists.  After this iterative and systematic process, the model can now be used as a tangible and objective means to declare expectations, provide insight as to how merit decisions are going to be accomplished, and utilized as a feedback mechanism based on the single order of merit/output of the model, that provide the necessary insight required to make formulated and correct decisions, after knowing how the evaluation factors effect the outcome.
4. Training or testing ensures that data input and model effects are understood allowing the implementation of the model to improve over time and adjust to changes.