In this book, we demonstrate the performance of various strategies, which can require only a single input: historical prices. In this section, we will begin our journey to the world of price-based investing with a short description of how we both calculated and tested these strategies on real historical data. All the strategies have been implemented in a consistent and identical way so as to assure their comparability. Below, we describe three major aspects of our examinations: (1) the data we use, (2) the method we form the portfolios, and (3) the method we evaluate their performance.
What Data We Use?
Todayās financial markets know almost no borders. Sitting in his living room in Berlin an investor can access equity markets in London, New York, or even Tokyo with a single mouse-click. The world of investing has become more interconnected and accessible than ever before. As a result, we do not test our strategies in a single market, even if itās as large as the American market, but instead, we test them in a robust sample of 24 developed countries with extensive and well-established stock marketsāthat is, Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Hong Kong, Ireland, Israel, Italy, Japan, the Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, the UK, and the USA. These markets span across many continents and cultures and account for the majority of capitalization in global equity markets. We have based our computations on the price data sourced from FactSet. Naturally, our tests could be further extended to include the emerging or frontier markets, but our focus on the developed economies guarantees the strategies to be accessible to most of the developed-market investors.
As we have focused on the period from January 1995 to June 2017, our sample is fresh and timely, reflecting the recent changes and developments in financial markets. We also used older data, for instance, when forming a strategy for January 1995 requires data from the earlier periods as, for example, a momentum strategy which relies on past performance. At times, the return data for some or all of the countries is available for the shorter periods, in which case we use them. We calculate all of the strategies separately for individual countries.
We collected the initial data in local currencies as comparisons based on various currencies could be misleading (Liew and Vassalou 2000; Bali et al. 2013). This is especially reasonable for countries where inflation and risk-free rates are very high and differ significantly across the markets. As most studies adopt the dollar-denominated approach (Waszczuk 2014a), we also denominated all the data in US dollars to obtain comparable results on an international scale.1 For consistency, whenever we needed to use the risk-free rate (e.g., to calculate excess returns), we used the benchmark returns on the US three-month Treasury bills. Throughout the book, we have used gross returns, that is, returns unadjusted for tax (whether income taxes or taxes on dividends), and rely on monthly returns, which is probably most prevalent among such studies, although most of the accounting data would change only quarterly.2
Finally, being aware that not all stocks in equity market are tradable, for example, stocks of companies with extremely low liquidity and market capitalization would be very difficult to trade freely, we applied a series of various static and dynamic filters to the common stocks within our calculations at the beginning of each month when forming the investment portfolio. We took account of only companies with the total stock market capitalization exceeding $100 million and the average daily trailing six-month turnover beyond $100,000. As a very low price may also lead to practical difficulties with trading, due to a wide bid-ask spread, we discarded stocks with the trading price below $1.00 at the beginning of a given month.3
Portfolios Structure
As in our study we have reviewed a lot of different strategies, to make them easily comparable, we investigated the strategies using portfolios designed in an identical fashion. To test various investment approaches, we applied the so-called one-way sorted portfolios by ranking all the stocks in our universe on a characteristic which in academia is called the āreturn-predictive variableā for it helps forecast future price changes. Naturally, for our purposes, we used price-based return-predictive variables . Having thus sorted the securities, we formed a long portfolio of stocks ranked with the highest predicted return and a short portfolio of securities with the lowest predicted returns.
In order to calculate returns in a given month, typically called month t, we sorted the stocks within the sample at the end of the previous month (month tā1) according to the investigated characteristic, for example, short-run return and long-run return. Having ranked the markets by the investigated characteristics, we then determined the 20th and 80th percentile breakpoints for each measure. In other words, by focusing only on the 20% of the securities with the highest expected returns and the 20% of the stocks with the lowest predicted future returns, we consequently arrived at two quintile subgroups.4
Subsequently, we weighted the respective equities from portfolios. For simplicity, we used a straightforward weighting methodāequal weighting, under which each of the best (or worst) stocks from the top (or bottom) quintiles of the ranking was assigned the same weight, that is, a fraction of the portfolio. In other words, we divided the portfolio in...