In this updated and enlarged second edition of How Markets Really Work: A Quantitative Guide to Stock Market Behavior (Bloomberg/Wiley, 2012) Laurence A. Connors (with Cesar Alvarez and Connors Research) offers a plethora of trading hypotheses based on market data from 1989 through the third quarter of 2011. There are two main themes: (1) despite the radical shifts in longer-term market conditions short-term market behavior rarely changes and (2) “on a short-term basis, oversold markets tend to move higher over the next few days and overbought markets tend to move lower on a short-term basis over the next few days.” (p. 5)
For those who read the first edition, what’s new in addition to the updated data? Three chapters. The first explores the two-period RSI, which “may be the best oscillator to identify overbought and oversold market conditions.” The second looks at the one-year performance of low volatility as opposed to high volatility stocks. The third describes a short-term strategy that trades only in S&P 500 stocks and that has outperformed the index by over 10% a year in simulated trading and did so with 70% lower volatility. Is your appetite whetted?
I suppose I should ask another question: do you like text far more than tables and graphs? If you do, you’ll be sorely disappointed. This is decidedly not a wordy book. In each chapter the authors look at a few market scenarios such as the SPX having risen three days in a row or having made a new five-day intraday high and then ask how the market performed one week later. They provide charts and graphs showing the average one-week returns of both the S&P 500 and the Nasdaq 100. They also have tables showing the performance over the entire timeframe of one-day, two-day, and one-week returns in a variety of scenarios.
Among the themes analyzed are market breadth, volume, large moves, new 52-week highs and lows, the put/call ratio, and the VIX.
The authors caution the reader not to use any of their indicators blindly. “No matter how big the edge has been during some of these times, there have also been large drawdowns in many along the way. Prudent money management and portfolio management (risk control and position size) is a must.” (p. 164)
Swing traders, especially those who are trying to develop algorithmic systems, can definitely benefit from this book. It offers case studies in hypothesis testing and explains (admittedly very briefly) how the described systems can be improved upon. And it takes no time at all to read.