Fusion Analysis: Merging Fundamental, Technical, Behavioral, and Quantitative Analysis for Risk-Adjusted Excess Returns (McGraw-Hill, 2012) is based on a course V. John Palicka offered at the New York Institute for Finance. As the subtitle indicates, the book advocates a multi-pronged or, perhaps better put, a blended approach to investing.
Palicka’s general approach is not new. Many stock screeners and investment services score stocks using a variety of inputs. For the investor who wants to do it himself, however, it’s not always obvious what kinds of inputs make the most sense—and potentially the most dollars. It’s also not intuitive what the theoretical underpinnings of the various approaches are. For instance, in a somewhat philosophically muddled chapter that touches on faith, physics, and time travel the author argues that determinism (here most frequently contrasted with free will) is the basis for technical analysis.
Palicka throws a lot at the reader in this book. He blends Elliott Wave analysis with the fundamentals of real estate within the context of determinism. He studies gold trading and business cycles, describes buy and sell decisions using Gann analysis, outlines measures for risk-adjusted excess returns (Sharpe, Treynor, Jensen Alpha, and the Information Ratio), and explains how to use swaps for market timing.
Fortunately Palicka provides a case study (Steve Madden—SHOO) to illustrate how the fusion process works. He uses a scoring system to determine a rating but cautions that “my actual and proprietary system may differ from that illustrated for confidentiality reasons.” (p. 374) In his illustration he incorporates five categories—short-term return, price/book, expected P/E divided by the expected P/E on the S&P 500, price/cash flow, and technical (a weighted average of MA, support/resistance, MACD, OBV, market relative strength, and Fibonacci). The categories are weighted—in this case between 10% and 35%--and each category gets a rating. So, to determine the total rating you simply take each individual rating times its weight and sum the products. Then you compare the summed rating to a table which correlates ratings (buy, buy/hold, hold, sell/hold, sell) with points. If, for instance, in this example the total rating is under 1.5, the stock gets a buy rating.
Of course, in the investing world nothing is cut and dried, and Palicka’s book makes that patently obvious. Backtesting helps, numbers are critical, but ultimately fusion analysis relies on market experience, the integration of disparate fields of study, and the touch of an artist. Even though I doubt that the author would find this an apt description of his method, I intend it as a compliment.