Trading Ideas

In Idea

... based upon my prior work with sports outcome predictions and sensor net architecture.

Conceptual Framework for Financial Technical Analysis and Future Position Prediction

Abstract

This paper proposes a novel approach to financial technical analysis and the prediction of future market positions by drawing upon a methodology previously applied in the assessment of professional football games. The core idea involves the development and application of a numerical rating system, similar to one created in the 1980s for evaluating the performance of football teams by analyzing the strengths and weaknesses of their offensive, defensive, and special teams' statistics. The aim of this approach is to extend the principles of this rating system to the evaluation of companies by examining their financial fundamentals and technical analysis metrics.

Introduction

In the realm of sports analytics, the creation of a power rating system transformed the evaluation of professional football games by quantifying the performance of teams based on a comprehensive analysis of their statistics. This methodology involved a comparative analysis between the offensive capabilities of one team against the defensive competencies of their opponents, in addition to assessing the performance of special teams. The derived power rating was instrumental in predicting the likelihood of a team to "cover the points" in a game, essentially forecasting game outcomes with a higher degree of accuracy.

The proposed study seeks to adapt this successful analytical framework to the financial domain, specifically to the assessment of company performance and market position prediction. The adaptation involves considering company financial fundamentals and technical analysis indicators as analogous to the offensive, defensive, and special teams' statistics in football.

Methodology

The methodology encompasses the conceptualization of financial fundamental and technical analysis indicators as individual "sensors," each attributed with a distinct reliability weight. This is inspired by prior work on sensor networks, where each sensor's input was weighted according to its reliability. Through historical analysis within 15-day windows, weights will be assigned to each "sensor" (i.e., financial indicator), determining its influence on buy, sell, or hold decisions.

The study will explore multiple combinations of these weighted sensors to establish a comprehensive set of indicators. These indicators will cover various trading horizons, specifically 15, 30, 45, 60, 90, 120, 180, 240, and 360 trading days. The intent is to produce a versatile suite of both short-term and long-term indicators that can guide investment decisions.