Table Of ContentFUSION Analysis
MERGING FUNDAMENTAL, TECHNICAL,
BEHAVIORAL, AND QUANTITATIVE ANALYSIS
FOR RISK-ADJUSTED EXCESS RETURNS
V. J P CFA CMT
OHN ALICKA
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CONTENTS
Acknowledgments
Introduction
Part 1: The Big Picture
1. Forecasting the Economy
2. Big Question: Where Is the Market Going?
3. Technical and Fundamental Decision
Part 2: The Fluctuating Moves
4. Valuation and Broad Approaches in Fundamental Analysis
5. Valuation Is Just the Beginning
6. Investing, Trading, and Gold
7. Gold and Paper Money
Part 3: The Ticking Clock: Growth of Concentric Circles
8. Determinism, Investing, and Trading
9. Time Travel Revisited
10. Other Economic Cycles
11. Sentiment and Technical Tools
12. Elliott Wave Principle and Real Estate: Mixing a Technical Tool
and a Fundamental
Part 4: Fusion Process
13. Quant Systems
14. Financial and Emotional Blend and Trading Strategies
15. Derivatives Inputs and Trading Strategies
16. Developing Our Model
17. Fusion Process and Steve Madden Case Study
18. Fusion Demonstration for New Ideas and Necessary
Investment Knowledge
Conclusion: The Future and the Golden Butterfly
Appendix
Further Reading
Endnotes
Index
ACKNOWLEDGMENTS
As a special thanks, I would like to mention some of my early
mentors at Prudential. In the first few years, my first boss was Ray
Kurtz, who was an excellent analyst and technician. Bill Rankin, our
lead senior analyst, offered good analytical leadership, and we had
great academic guidance from Ed Zinbarg, who helped write leading
financial textbooks. Once Bill left and Ray passed away, I took over
the funds and enhanced a fusion process that we were using. I
thank all the support staff and analysts who worked on the funds at
Prudential. I am also grateful for the support I got from my family,
especially my wife Cindy, and the many students who took my
Fusion courses around the world. I also wish to thank Pat Sparacio
who as head of NYIF’s courses gave me the opportunity to teach my
Fusion course. I also wish to thank my friend Esam Hassanyeh who
helped me take the course overseas and who also plays a great
game of tennis. Speaking of tennis, I wish to thank my daily partners
such as Rich, Ernie and Zoran whose questions helped me shape
some of the paths to the end conclusions. Naturally, any errors are
mine.
INTRODUCTION
What is the best way to make money when investing?
This question is one any investor asks, no matter if he or she is
an “ordinary” investor, a professional money manager, an academic,
or a fund consultant. Some of these people are naive and know little
about investing, while others are very knowledgeable. Their
objectives and investment processes range from the most
conservative and sound to the most speculative.
For the most part, money is actively managed. While index
funds and their similar exchange-traded funds (ETFs) cousins are
rapidly growing, investors still seek active management in the hopes
of getting an edge on the market returns, called seeking risk-
adjusted excess returns, leading to the selection of an investment
discipline. Investment disciplines should then lead to better returns
than that of the market. Putting aside hot tips, indexing, hunches,
and so on, professional money managers will seek such an
investment discipline. Managers check their investment disciplines
against benchmarks, such as an index, to see if the results are any
good. The starting point in this process involves fundamentals or the
use of accounting and other financial information to create valuation.
These may include earnings or even qualitative factors like brand
images of consumer companies.
However, results are not too good for those managers who only
rely on the fundamentals, as the vast majority cannot beat their
benchmarks, especially when one adjusts returns for the risk they
take. As we shall see, the market gets rather efficient as many
professionals seek the best returns. There are also statistical issues
that surprise us. For example, we got faked out by the devastation
of Hurricane Katrina. The event and eventual damage were much
greater than anyone expected, and therefore the insurance
companies were under-reserved against the potential.
Not Just Fundamentals
An increasing number of portfolio managers have now realized that
fundamental analysis alone often does not make the best
investment approach and have therefore begun to utilize technical,
behavioral, and quant analyses as well. Proper use of fundamental
techniques is required for selecting investments that are designed to
generate risk-adjusted excess returns. However, users of technical
analysis, from institutional investors to short-term traders, must use
non-fundamental information to get another perspective in activities
such as market timing and the minimization of transaction costs.
Fundamental analysts have increased their caution when taking
reported financial numbers—consider the large audited firm Enron
that went up in smoke with fraud in 2001. So which numbers can,
and do, you trust? For example, is China’s success for real or is the
government fudging numbers?
Technical analysis includes the study of charts, volume, and
other market statistics, and uses little if any fundamentals.
Behavioral analysis is the study of moods and emotions. In more
recent years, these analyses, and not fundamentals, have become
the drivers of investment decisions. Technicals also utilize investor
moods (technicians call this sentiment). At times, technical and
behavioral analysis can be close cousins. Quants use numbers in
some mathematical formula to make a decision.
There is also a feeling that the market is not rational and that
there are emotions which drive investment decisions, hence the
interest in behavioral and technical. For this reason quant is also
popular, as it is felt that quant can eliminate these emotions by just
investing on the numbers. Of course, behaviorally, managers may
start to make exceptions at times, and this could lead them into
trouble.
Another reason why managers have sought areas outside
fundamentals is that the markets can be volatile. Fundamentals
can’t seem to capture these swings, as we saw in 2008 and 2009.
The news looks good, but the market falls, and then the news looks
bad, but the market sharply rallies. For some, technicals can take
advantage of this type of situation, more so than fundamentals. With
