Below is a weekly summary of our research findings for 6/20/16 through 6/24/16. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.
June 24, 2016
June 24, 2016
Does availability of liquid tracking products change short-term trending/reversal tendencies of equity indexes? In their May 2016 paper entitled “Indexing and Stock Market Serial Dependence Around the World”, Guido Baltussen, Sjoerd van Bekkum and Zhi Da investigate how introduction of index futures, exchange-traded funds (ETF) and mutual funds affects measures of index serial dependence. They hypothesize that technical interplay in index products among investors, market makers and arbitrageurs stimulates short-term reversal. They measure serial dependence with daily lags and one-week lag in two ways: (1) simple autocorrelations; and, (2) returns to a “MAC(5)” trading strategy based on a weighted average of autocorrelations for lags 1 to 4, with positive (negative) returns indicating trending (reversal). Using daily data for 21 major global equity indexes and associated index futures and ETFs and for mutual funds tracking the S&P 500 Index as available through mid-May 2013, they find that: Keep Reading
June 23, 2016
The middle of the year might be a time for funds to dress their windows and investors to review and revise portfolios. The 4th of July celebration might engender optimism among U.S. investors. Are there any reliable patterns to daily U.S. stock market returns around mid-year and the 4th of July? To check, we analyze the historical behavior of the S&P 500 Index from five trading days before through trading days after both the end of June and the 4th of July. Using daily closing levels of the index for 1950-2015 (66 years), we find that: Keep Reading
June 22, 2016
Do data-intensive, high-frequency investor sentiment measurements usefully predict stock index performance? In his May 2016 paper entitled “Can Sentiment Indicators Signal Market Reversals?”, Arnaud Lagarde applies a random forest machine learning algorithm to test the power of Amareos sentiment indications to predict stock index reversals. Algorithm training data relates sentiment to known stock index return for the next 182 days (six months). If this return is -20% or lower (+10% or higher), he designates the condition at the time of forecast as a market top (bottom). Otherwise, he designates the condition as neutral. He starts with 20 global equity indexes. He holds out four indexes (CAC40, CSI300, Nikkei and S&P500) for out-of-sample testing. He then randomly selects 80% of daily observations on the other 16 indexes for algorithm training, with the remaining 20% reserved for additional out-of-sample testing. Out-of-sample testing includes tabulation of raw top/bottom identification accuracy and a simple trading strategy that is long (in cash) after a bottom (top) indication and does not react to a neutral indications. He focuses trading strategy testing on: (1) the four hold-out indexes over the entire sample period; and, (2) the last six weeks of data for all indexes, which cannot be used for training. Using daily Amareos market sentiment readings and returns for the 20 equity indexes during January 2005 through mid-April 2016, he finds that: Keep Reading
June 21, 2016
When does a cointegration test, which looks for a connection between two apparently wandering price paths, work for pairs trading? In their May 2016 paper entitled “Cointegration and Relative Value Arbitrage”, Binh Do and Robert Faff investigate the conditions under which cointegration successfully identifies stocks for pairs trading. Their basic pairs trading strategy is to each month:
- Identify cointegrated pairs based on daily total returns over the last 12 months.
- Over the next six months, buy (sell) the relatively undervalued (overvalued) stock when cointegrated pair spread exceeds its selection interval mean by two standard deviations.
- Close positions when the spread reverts to its historical mean or the trading period ends, whichever occurs first.
- Closed trades may be reopened as signaled, if there is more than a month left in the trading interval.
They then refine the strategy by constraining selected pairs to those that are close economic substitutes, corresponding to a low cointegration coefficient. Pairs passing (failing) this constraint move together in the long run without any price scaling (only with scaling of prices for one member of the pair). While they focus on pairs of individual stocks, they also consider trading of pairs of small groups (baskets) of stocks. Their benchmark is a conventional pairs trading strategy that identifies pairs with the smallest sums of squared differences in normalized daily prices over the past 12 months, and then trades as specified above over the next six months. Using daily data for a broad sample of U.S. common stocks during July 1962 through December 2013, they find that: Keep Reading
June 20, 2016
Do asset returns exhibit cyclic relative strength? In the December 2015 revision of their paper entitled “Return Seasonalities”, Matti Keloharju, Juhani Linnainmaa and Peter Nyberg examine 12-month relative strength cycles via a strategy that is each month long (short) assets with the highest (lowest) returns during the same calendar month over the past 20 years. They apply this strategy to individual U.S. stocks, factor and anomaly portfolios of U.S. stocks, industry portfolios of U.S. stocks, developed country stock indexes and commodity futures contract series. They also test a 5-day relative strength cycle across individual U.S. stocks. They perform ancillary tests to investigate sources and interactions of relative strength cycles. Using monthly and daily data for a broad sample of U.S. common stocks, industry portfolios and factor/anomaly portfolios mostly since July 1963 and monthly data for 24 commodity futures series and 15 country stock indexes since January 1970, all through December 2011, they find that: Keep Reading
June 17, 2016
Below is a weekly summary of our research findings for 6/13/16 through 6/17/16. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.
How fast should individuals plan to grow net worth as they age? To investigate, we examine median levels of household (1) total net worth and (2) net worth excluding home equity from several vintages of U.S. Census Bureau data. We make the following head-of-household age cohort assumptions:
- “Less than 35 years” means about age 30.
- “35 to 44 years” means about age 39.
- “45 to 54 years” means about age 49.
- “55 to 64 years” means about age 59.
- “65 to 69 years” means about age 67.
- “70 to 74 years” means about age 72.
- “75 and over” means about age 78.
We also assume that wealth growth between these ages is constant via compound annual growth rate (CAGR) calculations. Using median levels of total net worth and net worth excluding home equity from 2000. 2005, 2010 and 2011 Census Bureau summary tables, we find that: Keep Reading
June 16, 2016
The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for May 2016. The actual total (core) inflation rate for May is slightly higher than (slightly higher than) forecasted.
June 16, 2016
Do changes in the Effective Federal Funds Rate (EFFR), the actual cost of short-term liquidity derived from a combination of market demand and Federal Reserve open market operations designed to maintain the Federal Funds Rate (FFR) target, predictably influence the U.S. stock market over the intermediate term? To investigate, we relate smoothed (volume-weighted median) monthly levels of EFFR to monthly U.S. stock market returns (S&P 500 Index or Russell 2000 Index) over available sample periods. Using monthly data as specified since July 1954 for EFFR and the S&P 500 Index (limited by EFFR) and since September 1987 for the Russell 2000 Index, all through April 2016, we find that: Keep Reading