Bob Bronson

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About Bob Bronson

For the past 46 years, Robert E. Bronson, III has applied a disciplined, analytical approach to understanding and forecasting capital markets and advising institutional and other professional investors. Through his rigorous analysis of capital markets and economic data and his background in mathematics and financial economics, he has developed a number of unique investment concepts and refined portfolio-management techniques that improve returns and lower downside-volatility risk.

One of the most important investment concepts Bob has developed is a forecast-driven, tactical and strategic asset-allocation forecasting and portfolio-management style that he calls Cycle-Trend Anticipation. This forward-looking and primarily contrarian approach combines a full range of fundamental and technical indicators into weight-of-the-evidence, bottom-up explanatory models, which are further integrated into top-down, composite forecasting scenarios. Bob uses his dynamic models to forecast significant buy-sell turning points and the relative magnitude and duration of those cycle-trends. His quantitative models cover domestic and foreign capital markets, financial and tangible asset classes, and various investment styles.

He combines an unusually comprehensive range of indicators in his models, blending conventional fundamental, technical, and quantitative factors with dozens of customized indicators that track monetary/economic, valuation/sentiment, social/political, and inter/intra-market technical data over multiple time frames, ranging from hours and days to decades and multiple generations. He continually reweights, reformulates, and creates indicators in his models in response to the ever-changing capital markets.

Bob’s Cycle-Trend Anticipation style is reflected in his portfolio management techniques of absolute performance targets, downside-volatility risk control, partial positioning, selective diversification, and forecasts over multiple, nested time horizons.

To more accurately model and forecast the dynamically interrelated capital markets, he has developed proprietary, advanced computational methodologies by using a multi-disciplinary approach, applying selected concepts from cutting-edge developments in economics, mathematics, physics and other fields.¹

Bob has enhanced conventional computer-modeling techniques by developing new forecasting algorithms and advanced methods for recognizing and applying aperiodically cyclic, fundamentally-driven patterns in the capital markets that he calls Growth Cycle analysis. For example, the technical aspect of it corrects for the pattern failures and non-falsifiability deficiency of Elliot Wave Theory. It includes his Growth Cycle Trend Projector Algorithm (GCTPA) which reconciles all possible price (swing) patterns, including more accurately projecting the classical chart patterns. This has never been done before.

Bob is a long-time critic of the popular investment strategy of buy-and-hold, which ignores and fails during secularly underperforming BAAC Supercycle bear market periods. He is also a critic of stationary (non-time-varying) mean-variance analysis applications from Modern Portfolio Theory (MPT), such as the widely promoted asset-allocation optimization method known as the Efficient Frontier, which is based on extrapolations from stationary assumptions that ignores both the fundamental causes of capital-market movements and the intrinsically cyclical nature of performance and (especially) downside-volatility risk. Such rearview-mirror statistical optimizations encourage investors to pile-in at tops and panic-sell at bottoms of significant market moves by overweighting the recent best-performing assets and investment styles, which are vulnerable to greater losses during inevitable bear markets - especially those of severe magnitude and/or long duration, as has been demonstrated for more than a decade.

Bob is also a critic of Monte Carlo simulations, which wrongly randomize market performance that actually occur in runs, or extended trends, consistent with mean reversion of all capital market returns. This statistical short-cut, which doesn’t adequately account for fatter left-tailed returns, attempts to bypass the need to understand capital-markets history and to apply fundamental analysis, thus needlessly exposing investors to downside-volatility risk.

Rigorous methodology for identifying the secular periods of under- and over-performance in various asset classes, investment styles and business cycles is among the significant contributions Bob has made to the Post-MPT era. His SMECT model completes the unfinished work of Joseph Schumpeter integrating various business cycles.

Other key concepts he has developed include: Downside-Volatility-Risk, perhaps the most effective measure of investment risk as perceived by investors; the Growth Cycle, a pattern recognition tool that reconciles the consequences of all technical chart patterns (summarized above); multi-cycle correlation analysis, which corrects the problem of misuse of the Gaussian copula function that led to the collapse of exotic derivatives in the financial crisis (which he forecasted well in advance); Mass-Correlation, Hyper-Volatility Illiquidity Events, the fundamental explanation and analytical formulation of meltdown contagions and severe bear market selling climaxes; and Extremely Small Dataset Inference analysis (an extension of large number statistics that algorithmically drives his GCTPA.

Bob has authored a number of ground-breaking investment research papers, including: “The Case for the Third Supercycle Bear Market Period of This Century,” which called the top and bust of the dot.com bubble; “SMECT: A Long- Term Stock Market and Economic Cycle Forecasting Model”; “Market P/Es as a Forecasting Tool” (P/E Predictor Study I); and “Searching for the Dominance of Asset Classes and Investment Styles.” His report “Are You Prepared for the First of Three Perfect Storms of Business Cycles?” called the top of the housing-led stock market bubble in 2007 and the follow-on shadow banking system bust and consequential Great Recession. He currently is completing “A Multifactor Valuation Model” (P/E Predictor Study II) and co-authoring “Profiting from Supercycles.” His research and capital market forecasts have been quoted and referenced in The Wall Street Journal, Barron’s, The Wall Street Transcript, Institutional Investor, Derivatives Week, Financial Planner, Investment Advisor, Stocks and Commodities, Futures, FinancialSense.com, dShort.com and The Big Picture.

Bob’s 46-year career in the financial services industry has spanned investment research, portfolio management, financial planning, due diligence, syndication, and consulting. At age 23, he and his partner founded an investment research firm for institutional clients and were among the first to use mainframe computers for investment research. They developed an equity investment strategy that grew over 20-fold in less than the eight years from 1966 through 1974, a secular bear market period. That 44% annualized return has never been beaten since, and latest his Proprietary Alpha strategy is expected to perform even better in the years ahead. Since 1967, he has served as an investment strategist and consultant to various investment advisory firms and is the principal of Bronson Capital Markets Research

¹ Selected concepts from various fields that he integrates in his research include: history (capital markets, economic, political and military); behavioral finance (systematic irrationalities [popular myths and fallacies] and other decision traps, applied game theory); neuroeconomics; social psychology (deviant behaviorism and cultural/religious mythology); evolutionary economics; bioeconomics; econophysics; computational economics (agent-based modeling in complex adaptive systems, emergent cellular automata and other self-organizational patterning); phase state chaotic attractors-repellers and symmetry-breaking; catastrophe theory (sand-pile physics); pulse-coupled mechanical and biological oscillators; fractal geometry; extremely small dataset inference; number theory; multiple band-pass signal filters and other time-series oscillators; computer inference technologies (expert systems, polynomial networks [best fit optimizers], fuzzy logic [approximate reasoning], genetic algorithms [empirical discovery], neural networks [trainable classifiers]); multivariate econometric modeling; and non-parametric statistical analysis techniques.