The Lazy Fundamental Analyst: Applying quantitative techniques to fundamental stock analysis

A must read for investors who use screeners and backtesters. Dr Piard shows a straightforward yet sophisticated approach to using screeners and backtesters (He uses Portfolio123) to build portfolios for various sectors. He notes that different factors are relevant to different sectors, then proceeds to build and demonstrate independent models for each sector. He does this once with S&P500 stocks, then once with Russell 2000 stocks, while adjusting the slippage appropriately as he switches from one universe of stocks to the other. He also describes simple timing and hedging methods for reducing maximum drawdown (Portfolio123 allows backtesting hedging for the "benchmarks," which can be SPY.) Dr Piard also demonstrates an interesting approach to evaluating his models, in which is looks at the returns of each model over three separate 5-year period, a good technique for building robust models. The models Dr Piard present have more drawdown than I prefer, but they are designed as starting points for model builders, not complete models, If you want to reduce drawdowns I suggest you review the market timing suggestions in his other book, "Quantitative Analysis." If you are interested in quantitative stock analysis, these two books should be on the required reading list for any "Quant 101" course!

This is a book with a number of serious problems and one virtue.Fundamental value factors are company balance sheet statistics like price earnings or return on capital. The book lists a number of these factors.Fundamental investing builds a factor model based on these balance sheet statistics. The factor model is used to select stocks that, on the basis of the factors, are expected to outperform a benchmark in terms of either absolute return or lower risk.Most of the book is divided into chapters that cover an S&P 500 sector (e.g., health care, consumer discretionary, energy, etc...) For each sector, a set of factors are given, along with ten stocks that have the highest factor values. An equally weighted portfolio is constructed from these ten stocks.Each chapter includes a plot of the factor portfolio, along with a plot of the S&P 500. Another plot shows a hedged version of the portfolio and the S&P 500.The plots in the book are in black and white, with very small print for the plot legend. The plots are difficult to read.An even greater problem is the selection of factors. The author uses online stock screening tools to define portfolios and select factors. Showing that a set of factors delivers performance that is higher than the overall sector is problematic. The author provides only a sketchy outline of exactly how the factors are selected. Factors are likely to change over time, so applying the factors listed in the book may not deliver the returns expected outside of the author's backtest time period.Each sector portfolio of ten stocks is "hedged" using a market timing signal that is constructed from the S&P 500 yearly earnings per share, by month, and the monthly unemployment rate. Compared to technical signals like the 200-day moving average (see The Stock Market Cash Trigger by David Alan Carter), the author's market timing signal has the advantage of being based on fundamental macroeconomic features. I have tested this market timing signal as a portfolio hedge using Python code. No market timing signal is always right, but this does seem to be a strong market timing signal. This signal was worth the price of the book. The attached plot shows a portfolio consisting of a technology ETF hedged using the market timing signal.A problem with this signal is the data needed to create it. The author states that unemployment data is widely announced in the news. This is true, but this is not sufficient to build backtests. I downloaded the unemployment data from the US Bureau of Labor website (they provide a Python interface). I was able to find the monthly S&P 500 composite stock earnings on the NASDAQ website (also downloadable via Python).I have found that online stock screeners and portfolio backtest tools are limited in the strategies that they can support. I have also seen significant differences between the results given by on-line stock screeners and my custom Python code. These differences seem to involve the calculation of backtest portfolio values.The author states that the reader can spend a few minutes a month. This may be underestimating the time required. The results may also not be reliable when it comes to constructing portfolios that outperform the overall sector.An investor who cannot independently backtest the results in an investment strategy described in a book like The Lazy Fundamental Analyst is left gambling that the author's strategy will work as described. A prudent investor might be better off investing in a diversified set of ETFs.

As someone who loves to read as much about quantitative analysis as I can, it is decent book for someone who wishes to apply most important factors to specific groups of stocks. Initially gave 3 stars because I liked his other book so much more. Individual stocks show price behavior inherent to their specific groups, but for my needs preferred his other book. I subscribed to one of his services (which, thankfully, he doesn't beat you over the head with in either book- only mentioned it once...). Between both books he shows how to use the processes- but I am incredibly busy and find service well worth it for parts I use. Both books were handy to me to understand the parts I use from both books, but really enjoyed his other book more and find it much more useful- though I use one of his methodologies in this book.

Might be that I'm too skeptical when it comes question of related topic but of course it's possible to back test and to find 2-3 the most important ratios for each sector but nobody could not be sure whether those would work in following years. Instead I prefer a wider range of different ratios, which all play important role in finding the best stock in market. Therefore I only gave one star and propose to skip this book, as I see reading it is just waste of time.

Succinct and well-researched. I thoroughly enjoyed the no-nonsense, rigorous writing style that cuts straight to the chaseIn particular, I enjoyed the following:(i) Very succinctly written without needing to regurgitate the history of modern finance, as befitting a PhD computer science engineer. A breath of fresh air from other practitioner tomes(ii) Does not give you the strongest backtest result upfront. Unlike other books where they give you the most robust strategy right away and then justify it, you have to comb through the book to find the one that performs best. Which means you build up understanding of why certain backtests would work and others not in a more deeply entrenched way.(iii) Presents a holistic way to complete the 'down' part of 'top-down' investment process first implemented on the 'top' side through the tactical asset allocation techniques mentioned in his first book

Not helpful, simplified screens. Limits to 1-2 metrics to rank. Limited data outside of outputs - would like to see how this worked out of sample set