Preface

There is an endless supply of books about investing out there, and I have at least forty of them on my shelf. Some of these books provide helpful knowledge about budgeting and personal finance. Others offer valuable information about the concepts and mechanics of investing. And a few include great investment strategies, or even the code for trading algorithms. However, none of them seems to scratch a specific itch: how to optimize investment decisions, based on an investor’s personal financial situation, risk appetite, and goals for the future.

Over the years, my company has been helping many people with financial planning and wealth management. The first question we get asked by half of our clients is “do you beat the market?” In further discussions, some clients express the urge to invest aggressively to make up for lost time, while others feel paralyzed by their own fear of losing capital. The common thread in these sentiments is the difficulty in finding the optimal (and personalized) balance between risk and returns.

Obviously, there is a relationship between investment risk, returns, and time. Common literature often mentions that investment horizons need to be long enough to even out volatility, and that investors should reduce their risk levels when nearing retirement. However, this suggestion remains vague, and lacking any systematic and quantifiable approach. Unfortunately, in the absence of such a systematic approach, the path from financial planning to an executable investment strategy remains unclear.

As part of the TuringTrader open-source project, I have developed a new type of Monte Carlo simulation, solving this issue by pessimistically estimating investment returns as a function of time, accompanied by a unique visual presentation. This feature has been a game changer for me. It has highlighted what matters and what doesn’t – and has helped me explain the peculiar risk-versus-reward conundrum to my clients.

As a result, my discussions with clients have shifted away from a return-centric view (“do you beat the market?”) and toward a goal-driven approach. Here, we synthesize the overall investment strategy as the sum of several modules, each addressing a specific financial goal. For critical goals that we must not miss (or for income), we invest conservatively, optimizing the likelihood of success, even if we find times of economic stress ahead. For lofty blue-sky goals (or for generational wealth), we invest more aggressively, optimizing for the best expected outcome. With this model, setting the optimal portfolio risk level becomes straight forward, and the investor’s personal risk tolerance becomes a simple selection criterion.

The outcome success of this approach is so powerful, that I wanted to share these findings with a broader audience, along with many articles I had scattered across numerous sites and blogs over time. I considered starting a new blog, but quickly abandoned the idea because the nature of blogs is to sort topics chronologically, which is pretty much randomly. I wanted more structure than that, and so I embarked on my journey to write this book.

As a software engineer, I felt that the waterfall approach that inevitably comes with a printed book didn’t fit my way of getting things done. Instead, I wanted to stay agile, with writing and publishing closely intertwined. This called for an online-first publication. The notion of a book stays alive with a defined structure and table of contents, but the implementation is different: I am writing and publishing incrementally, one thought at a time, almost immediately creating tangible value. The result lives on this site.

I have not abandoned the idea of a self-published printed copy. On the contrary, my hope is that the final work on this site results in a printed edition that you will one day get to place on your bookshelf.