A Metadata-Driven Approach to Computing Financial Analytics in a Relational Database

Jan 3, 20071 like834 views

inscit2006

This document discusses building a financial analytics system within a relational database using a metadata-driven approach. It describes representing analytic formulas as metadata in an annotated directed acyclic graph stored as a table. An interpreter is implemented as a special graph traversal algorithm in SQL to efficiently compute multiple analytic formulas for all data at once within the database layer. The paper describes an actual system built at Reuters that computes thousands of formulas per second on a $10,000 computer using this novel breadth-first graph traversal approach.

A Metadata-Driven Approach to Computing Financial Analytics in a Relational Database by David Rozenshtein, PhD Sandip K. Mehta Reuters October 2006

What are Financial Analytics? Algebraic formulas: Calculate financial results for companies, instruments, indices, industries, etc. Inputs: Financial metrics: items from financial statements, estimates, prices, interest rates, etc. Values computed for other analytics. Constants. Operations: Mathematical and logical operators and functions.

Approaches to Financial Analysis Systems Typical approach: “Hardcode” analytic formulas into the system’s source code. Important disadvantage – need to change this source code as analytics are added, deleted or modified. Better approach: Represent analytic formulas as “metadata.” Build the analytic system as a formula interpreter . Analytics can now be added, deleted or modified without having to make any changes to the analytic system’s source code.

Where and How to Build the Interpreter? Possibility 1: Build the interpreter in standard 3GL – C++, C#, Java, etc. Advantage: Well known algorithms exist. Problem: Financial data is in the database. Analytic results should be placed in the database as well. Interpreter itself would be outside the database layer. Too much data movement between the database and the interpreter. Too slow!

Where and How to Build the Interpreter? Possibility 2: Build the interpreter in SQL. Advantage: Both data and computation are now completely within the database layer. Issues: How to represent formulas as data? How to actually code the interpreter? How to make all of this very efficient?

Building the Formula Interpreter in SQL Representing formulas: The set of analytic formulas is represented as an annotated directed acyclic graph (ADAG). The nodes of the ADAG represent inputs and outputs of the formulas. The edges represent operations and functions . The ADAG itself is stored as a table in the database. Implementing the interpreter: The interpreter is implemented as a special kind of graph traversal algorithm.

Standard SQL Graph Traversal Algorithms Standard graph traversal algorithms are depth-first . Not suitable for SQL: Lead to “one/few rows at a time” computation. Too slow!

A Novel SQL Graph Traversal Algorithm We have developed a novel breadth-first graph traversal algorithm. Very good for SQL: Our system computes multiple analytic formulas for all business entities/fiscal period combinations at a time. The number of SQL statement executions is completely independent of the number of business entities or fiscal periods involved, and is proportional only to the depth of the analytic graph, which is usually the logarithm of the number of distinct financial metrics and analytic formulas. Very efficient!

The System and the Paper This paper describes an actual system built at Reuters. Computes at the rate of approx. 12 thousand analytic formulas per second. Runs on an approx. $10,000 computer. The system supports arithmetic and logical formulas over numerics. For arithmetic formulas, it supports arithmetic operators: +, -, * and /, and a limited number of function symbols. For logical formulas, it support binary comparators: =, !=, <, <=, > and >=, and the logical operators: NOT, AND and OR. Currently, the system is limited to formulas over numeric values only; however, it is trivial to extend it to other data types. Due to space limitations, the paper presents only the arithmetic component of the interpreter.

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Visualizing Search Term Relevance, Boolean Operators, and Phrases using the C...inscit2006

The document proposes a visualization called the CirSA (Circle-Sector-Arc) Chart to represent the relevance of terms in a search query using a circle divided into sectors with arcs showing relevance. It describes how the CirSA Chart works and previous tests showing users could correctly interpret queries represented in this format. The document then describes new tests examining if users can decode Boolean operators and specify them on a CirSA Chart, as well as represent phrases.

Visualization of Multidimensional Information from Scientific Computationsinscit2006

High dimensional Data Visualization using Star Coordinates on Three Dimensionsinscit2006

This paper enhances the existing 2D star coordinates technique for visualizing high dimensional data and extends it to 3 dimensions. The authors introduce new parameters to improve the visualization and avoid overlapping points. Data points are represented as vectors on 3D axes based on their attribute values. Overlapping points can be distinguished using wireframes or varying opacity. The technique is demonstrated on the iris dataset.

Knowledge Discovery in Environmental Impact Report’s summary texts: an explor...inscit2006

Improvement in Quality of Speech associated with Braille codes - A Reviewinscit2006

Identificación de Nombres de Genes en la Literatura Biomédicainscit2006

An Intuitive Natural Language Understanding Systeminscit2006

The document describes the development of a natural language understanding system with 6 modules for morphological analysis, synonym matching, syntax analysis, semantic analysis, and knowledge base interaction to understand commands in English sentences and execute the corresponding shell command. It discusses the methodology used in building the modules and evaluates the system's performance on 50 test sentences, achieving a 94% precision in generating the correct responses.

Requirement analysis for mobile information exchange in the police using a ti...inscit2006

Parametric Study to Enhance Genetic Algorithm's Performance using Ranked base...inscit2006

Information updated and conveyed by the neural network systemsinscit2006

The document discusses using neural networks and information theory to model how information is conveyed in the brain. It presents a neural network model that was trained on Walsh patterns as stimuli to test how much information could be encoded and decoded. Computational experiments showed the model was able to code and decode information by implementing an artificial neural network, though more research is still needed to apply this to practical communication systems.

Mensajería instantánea: una puerta para una nueva percepción del mundo para n...inscit2006