Diversified Portfolio

 TST Equities Systems – Diversifed Portfolio

This application was developed by an analyst for a large mutual fund. The analyst used TST’s methodology and algorithms to build a model to analyze, compare and rank 65 stocks in 16 different groups. The analyst had a high level of quantitative skills and used TST’s non-numerical algorithms to quantify 43 macro scenarios, technical analysis, and 3 qualitative factors and integrate them with his pre-existing discounted cash flow models to quantify and visualize his whole thought process. The models include a 1PAGER that synthesized the most important elements of his thought process on one dynamic page plus multiple sub-models that provided a high level of detail to his model.

Model Framework

The Analys’s goal was to forecast the target prices for his 64 stocks and rank them by relative intrinsic value over a 12 month forecast period.   The methodology was a Discounted Cash Flow Analsyis that combined his existing projected cash flow models for each stock with a risk-adjusted discounted rate based upon a new prospective approach to risk.

Risk-Adjusted Intrinsic Value

PrestonModelFramework3

Projected Cash Flows

The Analysis used his existing models to forecast the cash flows for each company for 5 years and enhanced them with the TST-based Macro and Risk Models.

Macro + Risk Models

PrestonModelFramework4

 Analyst Overview

The TST model presents a new framework for equity investment analysis, and approaches the concept and measurement of risk in novel ways.  In both design and in use, the TST model differs from conventional models or analysis.  Conventional equity analysis typically employs spreadsheets for valuation analysis, supplemented with written analysis.  Spreadsheets, however, are limited in the respect that judgments, in the qualitative form, are difficult to incorporate.

For example, even though the quality of a company’s management is often considered to be a key factor in the investment decision process, attempting to quantify its impact is not a standard practice.  This is likely not because analysts do not consider the quality of management to be an important factor because analysts usually address this issue in their written analysis. Rather, it is more likely that the analyst does not know how to model it or does not have the tools to do so.

Investing in equity securities, however, requires judgments to be made constantly. Therefore, since it is necessary to make judgments during the investment process, but they cannot be easily modeled, most analysis conducted currently is limited by definition. TST-based models, however, have the ability to integrate qualitative judgments with numerical analysis, allowing for a more comprehensive analysis. The model discussed below (heretofore known as the  “Model’) is intended to demonstrate the flexibility of the TST software.

Two Sides of Investment Ledger

The approach taken by the analyst is that there are two sides to the investment decision ledger.  One side comprises quantitative analysis based on common valuation frameworks such as discounted cash flow analysis. The other side constitutes the qualitative variables that influence the value of equity securities. The Model demonstrates how the two sides can be meshed together, which fundamentally changes the methodology of equity valuation, and which may result in estimated intrinsic equity values different than those suggested by conventional equity analysis, but which may more accurately reflect an equity’s true worth.

What’s New

The main innovations of the Model are the analyst’s development and use of:

  • TST Methodology
  • Numerical & Non-Numerical Models
  • Historical & Forward-Looking Analysis
  • New Prospective Approach To Risk
  • Backtested Macro Scenarios
  • Peer Group Comparisons
  • Synthesis Of Thought Process.

TST Methodology

The analyst used ThinkSHEET’s methodology to quantify and synthesize his whole thought process. The methodology is to break down his thought process into its component parts (metrics), communicate and measure each assumption for each metric in the most natural way, combine sets of metrics into submodels and synthesize the most important on

Back-Tested Macroeconomic Models

The Model essentially “backtests” how different stock sectors performed under different scenarios in the past.  A potential flaw in the methodology is that, since The Model attempts to model the impact of future events, backtested results are a poor proxy for future results.

The macroeconomic model attempts to measure the impact that individual macroeconomic factors have on stock performance. It is acknowledged that it is impossible to isolate the impact of specific macroeconomic factors on stock performance, as multiple factors affect the movement of stock prices. However, by measuring changes in stock prices in different macroeconomic environments over a long period of time, it is the analyst’s hope that trends in performance can be detected, and conclusions can therefore be assumed as to how stock sectors perform under different macroeconomic scenarios.

To make the analysis less cumbersome, sector performance data classified by S&P and an outside vendor was used in place of individual security performance. For example, the automobiles and components sector performance is a proxy for Ford’s stock price performance. The sectors used as proxies for the universe of stocks in The Model are: automobiles and components, [basic] materials, telecommunication services, and transportation. Matching the sectors in The Model with S&P/outside vendor sector classifications was done as follows:

Modeling Risk: A Prospective Approach

Modern portfolio theory argues that risk may be defined and quantified by “Beta”.[1] Simply put, the theory posits that the expected risk of individual securities is a function of beta, and measured by the capital asset pricing model (CAPM).

(Long dissertation of Beata_asure the covariance and variance terms in the equation above).

As such, beta is an ex-post measure of risk rather than ex-ante.  In the context of valuation, as future cash flows should be discounted by a similarly prospective measure of risk, then, there is scope for a more appropriate measure of risk.  Risk can come in many forms, such as judgments as to the ability of management in making future decisions for the firm or the expectation that industry dynamics may change in the future.  The Model specifies factors which are expected to constitute a firm’s risk. However, the factors specifically identified in the Model are not meant to convey that these are all the known risk factors. Unexpected events will occur, but which are not and cannot be taken into account.  Rather, The Model provides a framework for constructing the definition of risk on a prospective basis and in terms accommodative to such definitions (i.e. words, phrases, numbers).

Additionally, the prospective risk of a firm is based on the expectations and subjective notions of the analyst per se; it is not based on a “collective” view. That is, risk is in the eyes of the beholder, and may differ from analyst to analyst based on the degree of knowledge and/or psychological state, including individual risk tolerance. Only by observing a firm’s stock price and “backing out” risk through the equity risk premium can one say what “collective” risk is, which may or may not turn out to be correct.

Stocks & Industries

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