Sunday, December 17, 2017

Sonnenfeldt, Think Bigger

Michael W. Sonnenfeldt is the founder and chairman of Tiger 21, a peer-to-peer learning network for highly successful entrepreneurs. The 40 groups in 35 cities in the U.S. and Canada and a new group in London get together once a month to talk about their businesses, share investment advice, and offer one another support. Drawing on the knowledge he gained over the 19 years of Tiger 21’s existence, Sonnenfeldt, with extensive help from reporter and writer Ed Tivnan, has produced Think Bigger and 39 Other Winning Strategies from Successful Entrepreneurs (Bloomberg/Wiley, 2017).

Most of the so-called strategies are not original. We are urged, for instance, to “know thyself.” We read that “grit beats IQ—most of the time,” that you should “surround yourself with people who are smarter than you,” and that you should “live below your means.”

Where this book shines is in its accounts of entrepreneurial struggles and successes. It often gets beneath business truisms and feel-good stories to go into the trenches, where entrepreneurs file lawsuits, struggle to remove a demented father from executive control, and are burned by sketchy partners. The book also includes more examples of women entrepreneurs than most business books.

Think Bigger is a quick, easy read. It’s a good complement to the “everyone can get rich” books (as Sonnenfeldt clearly demonstrates, they can’t) and to one-size-fits-all manuals on how to start and grow a business.

Wednesday, December 13, 2017

Harriman’s Stock Market Almanac 2018

Harriman’s Stock Market Almanac 2018, compiled by Stephen Eckett, is now in its eleventh edition. And it comes complete with a new and improved format. Gone is the weekly diary that formerly took up a large part of the book and made it look like something of a clone of Hirsch’s Stock Trader’s Almanac. (A minimalist version of the diary is now available online.) In its place is an expanded explication of seasonal market strategies and analysis.

The almanac, which focuses on the UK market, is divided into four parts—calendar, strategies, analysis, and reference.

The calendar section gives a two-page summary of the main features of each month’s historical performance and anomalies associated with the month.

The strategies section “describes the major anomalies and seasonality effects in the UK market and how they can be exploited.” These include the bounceback portfolio, construction sector 4M strategy, sell in May and sell in May sector strategies, summer share portfolio, sell Rosh Hashanah buy Yom Kippur, Santa rally, day of the week strategy, Tuesday reverses Monday, turn of the month strategy, FTSE 100/250 monthly switching strategy, FTSE 100/S&P 500 monthly switching strategy (investing in the FTSE 100 in April, July, and December, and in the S&P 500 for the rest of the year is a winner), monthly seasonality of oil, monthly share momentum, quarterly sector strategy, quarterly sector momentum strategy, the low-high price portfolio, and the world’s simplest trading system.

The analysis section looks at market indices, sectors, companies, long term patterns, and interest rate considerations. For instance, under the heading of market indices Eckett discusses such topics as intra-day volatility, first and last trading days of the month, and the Chinese calendar and the S&P 500. As for the last item, the best performing market animals since 1950 have been the goat and the dog. The Chinese year starting February 16, 2018 is the year of the dog, in which the S&P 500 has had an average return of 16.8%. Under the long-term heading he analyzes the correlation between UK and US markets, the ultimate death cross (when the 50-month moving average moves down through the 200-month moving average), and gold.

All in all, this almanac is a treasure trove of seasonal strategies that can be tested out on, and probably tweaked for, any market.

Sunday, December 10, 2017

Harriman’s New Book of Investing Rules

Harriman’s New Book of Investing Rules: The Do’s & Don’ts of the World’s Best Investors, edited by Christopher Parker, contains over 500 pages of wisdom from 64 noted American and British investors. It’s a smorgasbord of ideas from which the reader can pick and choose. Don’t like Brussel sprouts? Here, have some cheesecake. But, said in a cautionary whisper, you’d be better off with the Brussel sprouts.

I hate to think how many years of successful investing experience are encapsulated in this volume. Probably somewhere in the neighborhood of 2,000. There aren’t too many resources that can claim this much collective experience.

Herewith a tiny sampling of some of the rules, minus the often much more insightful explanation that follows each of them.

Diversify, but not to mediocrity.

Concentrate, but not too much.

Hedge when the market’s expensive and falling.

Unless you are a genius use a system.

Don’t rely too heavily on models.

Beware of geeks bearing formulas.

Demographics are destiny.

Price is the paramount trading signal.

Be happy doing nothing.

Question the persistency of anomalies.

Understand your edge and why it is sustainable.

Review past stupidities, but don’t let them make you timid.

Only bet on one variable at a time.

It’s important that your process does not work in every market environment.

Don’t chop and change too much.

Be patient—fortune sometimes take a while to favor the bold.

Time, not timing, is the key to investment success. The best time to invest, therefore, is now.

Always remember that investing is hard.

Wednesday, December 6, 2017

Best books of 2017

Bowing to reader demand, I'm sharing my personal, idiosyncratic choices for the best books of 2017, with links to my reviews.

Eric Barker, Barking Up the Wrong Tree

Richard Bookstaber, The End of Theory

Robert Carver, Smart Portfolios



Michael W. Covel, Trend Following, 5th ed.

Aswath Damodaran, Narrative and Numbers

Diana B. Henriques, A First-Class Catastrophe



Hari P. Krishnan, The Second Leg Down

Bill Martin, The Smart Financial Advisor

Edward O. Thorp, A Man for All Markets


Sunday, December 3, 2017

Brandimarte, An Introduction to Financial Markets

Paolo Brandimarte’s An Introduction to Financial Markets: A Quantitative Approach (Wiley, 2018) is an imposing 750-page book. It is meant as a textbook for students who want a thorough grounding in the mathematics and statistics of finance. It arose out of courses the author offered at Politecnico di Torino, where he is a professor in the department of mathematical sciences, to graduate students in mathematical engineering. Like most textbooks, at the each of each chapter it has a set of problems (the answers to which can be found on the book’s website) and a bibliography.

After an overview of markets and an outline of basic problems in quantitative finance, the author analyzes fixed-income assets, equity portfolios, derivatives, and advanced optimization models. Devoting nearly 150 pages to optimization models may seem a bit eccentric, but Brandimarte is particularly interested in this topic and has done extensive research on it. For instance, in 1995 he co-authored a book titled Optimization Models and Concepts in Production Management. And in 2013 he co-authored a book on distribution logistics, which is essentially an optimization problem.

Zeroing in on the third part of the book, on equity portfolios, we find four chapters that deal with (1) decision-making under uncertainty: the static case, (2) mean-variance efficient portfolios, (3) factor models, and (4) equilibrium models: CAPM and APT. Here I’ll look very briefly, and somewhat telegraphically, at the first problem—decision-making under uncertainty—to give some sense of the book’s approach.

Brandimarte distinguishes between a static decision model and a multistage decision model. In a static model, we assume that we are not “adjusting our decisions along the way, when we observe the actual unfolding of uncertain risk factors.” In a multistage model, we can update our decisions, “depending on the incoming information flow over time.” He is simplifying his discussion by considering only the static case.

If we are trying to choose among a set of lotteries, let’s say, we might use a utility function. Brandimarte spends ten pages on the math involved in explicating and applying these functions. But utility functions have been severely criticized over the years, most notably for mixing objective risk measurement and subjective risk aversion in decision-making. Therefore, academics and practitioners have proposed mean-risk models, using risk measures such as standard deviation and variance and quantile-based risk measures such as V@R and CV@R. (More math.) These “may provide us with a partial ordering of alternatives, as well as a set of efficient portfolios.”

A third alternative framework is the stochastic dominance framework, “resulting in a partial ordering that may be related to broad families of utility functions.” (Math.) Brandimarte finds stochastic dominance “an interesting concept, allowing us to establish a partial ordering between portfolios, which applies to a large range of sensible utility functions. Unfortunately, it is not quite trivial to translate the concept into computational terms, in order to make it suitable to portfolio optimization. Nevertheless, it is possible to build optimization models using stochastic dominance constraints with respect to a benchmark portfolio.” (Two theorem proofs, problems, bibliography, end of chapter.)

Wednesday, November 29, 2017

Mulgan, Big Mind

Geoff Mulgan’s Big Mind: How Collective Intelligence Can Change Our World (Princeton University Press, 2018) is a thoughtful, quasi-philosophical book on a topic where “the stakes could not be higher. Progressing collective intelligence is in many ways humanity’s grandest challenge since there’s little prospect of solving the other grand challenges of climate, health, prosperity, or war without progress in how we think and act together.”

Collective intelligence requires a diversity of elements and capabilities: a live model of the world, observation, focus, memory, empathy, motor coordination, creativity, judgment, and wisdom. It is supported by infrastructures, such as networks. A general theory of collective intelligence also needs to address the dimensionality of choices—not only the number of variables involved but “cognitive dimensionality (how many different ways of thinking, disciplines, or models are necessary to understand the choice), its social dimensionality (how many people or organizations have some power or influence over the decision, and how much are they in conflict with each other), and its temporal dimensionality (how long are the feedback loops).” And this is just the beginning. The lists and requirements keep multiplying. Collective intelligence is complicated.

Mulgan analyzes how collective intelligence (though, in practice, all too often collective stupidity) functions in everyday life—in meetings, in cities and governments, in economies and firms, in universities.

Big Mind is a call to action, even though the author admits that there’s no single path to success. Because of this, we need to nurture people with skills in “intelligence design.”

Sunday, November 26, 2017

Martin, The Smart Financial Advisor

The subtitle of Bill Martin’s The Smart Financial Advisor (Harriman House, 2017) says it all: How Financial Advisors Can Thrive by Embracing Fintech and Goals-Based Investing. Martin, the chief investment officer at INTRUST Bank, beats the drum for goal-based investing since it has so many benefits, for both advisors and clients. For instance, it enables advisors to manage more of their clients’ wealth, it better matches assets and liabilities, it determines the optimal asset allocation, it minimizes taxes, and it increases an advisor’s value. He also advocates using fintech within a goals-based investing framework to efficiently manage, monitor, and report goal progress.

Most investors fall victim to a bevy of investing hazards. They react to external factors, fail to plan, quarantine their portfolios, mismanage risks, rely on alluring stories, ignore taxes, and focus on past performance. Advisors can address these hazards by adopting goals-based investing, identifying and prioritizing client goals, managing client wealth holistically, assessing risk comprehensively, determining the optimal mix of assets and constructing portfolios intelligently, utilizing tax-smart investment strategies, and tracking goal progress.

Goals-based investing is gaining traction among advisors and “finally appears positioned to become the new industry norm in managing wealth.” Historically, from the 1900s to the 1960s investors held concentrated stock portfolios and evaluated their stocks independently. From the 1960s to the 1980s Markowitz’s theory of portfolio selection became the guiding model, then from the 1980s through the 1990s strategic asset allocation took over. Employing Markowitz's model added value to investors' portfolios; strategic asset allocation added more value. Today, in part as a result of the findings of behavioral finance, goals-based investing is beginning to take center stage, and it is adding yet more value.

Martin explains the ins and outs of goals-based investing, step by step, element by element. Although he is writing for financial advisors, I consider his book a must-read for the DIY investor as well. Admittedly, it’s a lot harder for the individual investor to stay on track without the assistance of an advisor, but it’s virtually impossible without a viable, forward-looking plan. If you’re acting as your own advisor, you couldn’t ask for a clearer, more useful book than The Smart Financial Advisor.