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Avoiding Investment Strategy Flame-outs

 “…markets always eventually outwit us. Even if markets are not strictly random, their vagaries are too rich to capture in a few sentences or equations. So die the dreams of financial theories. Only imperfect models remain. …Given that finance’s best tools are shaky models, the best strategy is to use models as little as possible, and to replicate making as little assumptions as you can. …Every financial axiom is pretty much wrong; the practical question is: how wrong, and can you still make use of it?”

 “The world of markets doesn’t exactly match the ideal circumstances a model assumes, but a robust model allows a savvy user to qualitatively adjust for those mismatches. A user should know what has been assumed when he uses the model, and he should know exactly what has been swept out of view.”

 “Financial modelers must therefore compromise, must firmly decide what small part of the financial world is of greatest current interest, decide on its key features, and make a mock-up of only those. …A successful financial model must have limited scope; you must work with simple analogies…”

 –          Emanuel Derman in “Metaphors, Models & Theories”

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Introduction

Why do investment/trading strategies that test well on historical data flame out when put to actual use? Are there steps investors can take to improve the odds that strategies they develop will perform as tested? This book draws upon reviews of hundreds of academic and practitioner studies that seek to predict asset prices and exploit the predictions. It focuses on widespread weaknesses and limitations in these studies to help investors: (1) avoid or mitigate the weaknesses in developing their own strategies; and, (2) perform due diligence on strategies offered by others.

Suggestions in the book address:

Chapter 1: Some Statistical Practices that Make Sense

Chapter 2: Making the Strategy Logical

Chapter 3: Avoiding or Mitigating Snooping Bias

Chapter 4: Accounting for Implementation Frictions

Chapter 5: Checking for Market Adaptation

Chapter 6: Modeling at the Portfolio Level

Chapter 7: Thinking about Taxes

Chapter 8: Two Analysis Regimes

Chapter 9: Getting Expert Advice (Delegating Strategy Development)

Discussions are concise and (mostly) non-mathematical, with figures to illustrate many points. The book is by no means a comprehensive treatment of investing analysis and statistical methods. The discussions likely apply, with some translation, to other areas involving complex, interacting, learning systems such as economics, politics and sports.

In lieu of specific citations scattered throughout the book to support assertions, we generally refer here to CXOadvisory.com for a sense of studies reviewed. Many of the academic studies are drafts and preliminaries. Some of the practitioner studies are formal papers, and some (offered in the context of the marketing of advisory services) are informal.

Data used to generate illustrative figures come are freely available from sources such as Yahoo!Finance and the Federal Reserve Economic Data (FRED) maintained by the Federal Reserve Bank of St. Louis.

Comments and questions from CXOadvisory.com readers and subscribers have been useful. Any errors in the book are ours. We will fix them as we find them.

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