In short, if you manage less than $10 million, then you may skip this book. Instead, google what an all-weather portfolio is. Buy and hold it, rebalancing as needed.
To read this book and put its concepts into practice, you will need to invest a considerable amount of time and effort. In your case, it may not pay off. You may still want to read Part Four, devoted to portfolio rebalancing.
However, if you manage a substantial amount of money, this book is a piece of gold. It offers exceptionally reasonable and practical ideas for constructing an investment portfolio. Furthermore, I have not seen such great portfolio-building insights in any of the dozens of financial books I have read.
Unfortunately, the book has significant shortcomings. While the author is an outstanding financial thinker, he is not a great teacher or writer.
First and foremost, before reading this book, you should have a thorough understanding of statistics, including not only normal distribution but also bootstrap. You need to be familiar with the normal distribution's limitations in finance. It is crucial to understand why using bootstrap simulations is preferable to relying on Student's t-distribution tables for obtaining confidence intervals for the mean value and other statistics.
Without a solid grasp of the statistical concepts mentioned above, comprehending Part One will be challenging. Furthermore, you may not fully appreciate the wisdom of the insights presented by the author in Parts 2 and 3 of the book.
Unfortunately, Robert Carver did not take the time to explain the fundamentals of statistics to his readers. While the book includes Appendix C, which addresses technical issues, it proves inadequate. Before diving into this book, you could work through textbooks for a solid understanding of statistics, including the bootstrap approach.
I highly recommend getting your hands dirty applying bootstrap methods to financial data. While you can do it in Excel, utilizing Python or R is more convenient. It is highly beneficial for aspiring portfolio managers to verify firsthand that investment return estimates are unstable and unreliable. It underscores the rationale behind assuming equal risk-adjusted returns for all assets when constructing an investment portfolio.
Once you've looked at the estimates of investment returns, turn your attention to the estimates of the correlation between stocks and bonds. By examining the data, you'll notice that these estimates are the same unstable as investment return estimates. This realization should prompt you to completely discard the classic portfolio optimization theory once and for all.
In Part 4 of the book, the author suggests, among other things, rebalancing your portfolio if there are significant changes in your estimates of the correlation between assets. However, this approach doesn't make sense when considering the correlation between stocks and bonds. This correlation tends to fluctuate wildly. During crises, it often approaches 1, making rebalancing strategies impractical.
In addition to demanding a solid grasp of statistics from its readers, this book is quite bloated. Be prepared to feel drowsy while reading it. Nonetheless, I'm pleased that I persevered and completed it. Parts 2, 3, and 4 present numerous unique and intelligent ideas for constructing an investment portfolio. The author's methodology will benefit individuals who manage substantial sums of money.