Happy New Year

May you all find peace, prosperity, health, and happiness in 2018.

Happy holidays

If you have a book you didn't like and time on your hands, you too can produce a holiday decoration. Just fold away.

Chambers, Trading Cryptocurrencies

If you’re itching to get in on the cryptocurrency mania and don’t have enough capital to trade in either of the two new futures markets, Clem Chambers’ book is a useful how-to manual. Trading Cryptocurrencies: A Beginner’s Guide (ADVFN Books, 2017) is brief, at only 70 pages of text, but it covers the basics of both trading and mining and explains at least part of the rationale behind these digital currencies. It also issues appropriate caveats all along the way.

If you want to mine, don’t expect big bucks using only your PC—and you can’t even play if you have a Mac. At the moment “you can make pennies [a day] mining with your PC, dollars with a gaming computer, $30 upwards with a mining rig and $50+ with the right ASIC miner. The costs are from nothing to a lot, say $10,000 for a single ASIC device.” And, of course, the cost of electricity to run these hot, noisy devices. The fancier the graphics card (think top-of-the-line NVDA and AMD) the more money you can bring in. But the potential for making money as a miner is ever in flux, as with practically everything cryptocurrency-related.

If, instead, you want to trade, Chambers points to the most reliable sites, warning that the cryptocurrency world is “awash with scammers.”

I personally don’t like trading what appears to be a bubble, so this book isn’t for me. But, if I’m wrong and cryptocurrencies are just in their infancy, traders who want to get on the bandwagon should do their homework. Chambers has made that task a lot easier.

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.

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.

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.

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

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.)