What is the body of academic research on crypto-assets? In their March 2018 paper entitled “Cryptocurrencies as a Financial Asset: A Systematic Analysis”, Shaen Corbet, Brian Lucey, Andrew Urquhart and Larisa Yarovaya review available research on cryptocurrencies as financial assets. They define crypto-assets as peer-to-peer electronic transaction systems which allow payment by one party directly to another without an intermediary. Such assets are therefore are infinitely divisible and have no physical representation or association with higher authority. Theses assets derive value from the security of an algorithm that records all transactions. The authors segment research into five areas: (1) bubble dynamics, (2) regulation, (3) cybercriminality, (4) diversification, and (5) market efficiency. Based on 87 papers published during 2013 through early 2018 (accelerating in frequency), they conclude that: Keep Reading

A subscriber asked about the effectiveness of applying a two-period Relative Strength Index, RSI(2), to leveraged exchange-traded funds (ETF), suggesting two pairs of trade entry (oversold) and exit (overbought) settings:

1. Buy when RSI(2) falls below 10 and sell when it subsequently rises over 90 (10-90).
2. More conservatively, buy when RSI(2) falls below 5 and exit when it subsequently rises over 70 (5-70).

To investigate, we run simple tests on ProShares Ultra S&P 500 (SSO) with RSI(2) calculations based on the RSI template from StockCharts. Using daily adjusted SSO opens and closes during July 2006 (the first full month SSO is available) through March 2018, we find that: Keep Reading

A subscriber asked which asset (short stocks, cash, bonds by subclass) is best to hold during equity bear markets, defined simply as intervals when SPDR S&P 500 (SPY) is below its 10-month simple moving average (SMA10). To investigate, we test the following nine alternatives, five of which are bond-like mutual funds and two of which are gold-related:

Short SPY
Cash, with return estimated as the yield on 13-week U.S. Treasury bills (T-bill)
Vanguard GNMA Securities (VFIIX)
T. Rowe Price International Bonds (RPIBX)
Vanguard Long-Term Treasury Bonds (VUSTX)
Fidelity Convertible Securities (FCVSX)
T. Rowe Price High-Yield Bonds (PRHYX)
Fidelity Select Gold Portfolio (FSAGX)
Spot Gold

Specifically, we compare monthly return statistics, cumulative performances and maximum (peak-to-trough) drawdowns of these nine alternatives for months during which SPY is below its SMA10. Using monthly T-bill yield and monthly dividend-adjusted closing prices for the above assets during January 1993 (as limited by SPY) through Mar 2018, we find that: Keep Reading

Below is a weekly summary of our research findings for 4/16/18 through 4/20/18. 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.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

A subscriber asked whether the Simple Asset Class ETF Momentum Strategy (SACEMS) exhibits monthly calendar effects. In investigating, we consider also the Simple Asset Class ETF Value Strategy (SACEVS)? We focus on: (1) the “Best Value” version of SACEVS, which each month picks one of three exchange-traded funds (ETF) corresponding to the most undervalued of U.S. term, credit and equity risk premiums (or cash if none of the three premiums are undervalued); and, (2) the “EW Top 3” version of SACEMS, which each month equally weights the top three of nine ETFs/cash with the highest total returns over a specified lookback interval. Using monthly total returns for SACEVS Best Value asset selections since August 2002 and for SACEMS EW Top 3 asset selections since August 2006, all through March 2018, we find that:

Keep Reading

How do the corporate experts most responsible for assessing the cost of equity currently feel about future U.S stock market returns? In their March 2018 paper entitled “The Equity Risk Premium in 2018”, John Graham and Campbell Harvey update their continuing study of the views of U.S. Chief Financial Officers (CFOs) and equivalent corporate officers on the prospective U.S. equity risk premium (ERP) relative to the 10-year U.S. Treasury note (T-note) yield, assuming a 10-year investment horizon. Based on 71 quarterly surveys over the period June 2000 through December 2017 (an average 351 responses per survey), they find that: Keep Reading

What is the best way to put strategic edges and tactical views into investment portfolios? In their March 2018 paper entitled “Model Portfolios”, Debarshi Basu, Michael Gates, Vishal Karir and Andrew Ang describe and illustrate a three-step optimized asset allocation process incorporating investor preferences and beliefs that is rigorous, repeatable, transparent and scalable. The three steps are: 

1. Select a benchmark portfolio matched to investor risk tolerance via simple combination of stocks and bonds. They represent stocks with a mix of 70% MSCI All World Country Index and 30% MSCI USA Index. They represent bonds with Barclays US Universal Bond Index. In their first illustration, they focus on 20-80, 60-40 and 80-20 stocks-bonds benchmarks, rebalanced quarterly.
2. Construct a strategic portfolio with the same expected volatility as the selected benchmark but generates a higher long-term Sharpe ratio by including optimized exposure to styles/factors expected to outperform the market over the long run. Key inputs are long-run asset returns and covariances plus a risk aversion parameter. In their first illustration, they constrain the strategic model portfolio to have the same overall equity exposure and regional equity exposures as the selected benchmark.
3. Add tactical modifications to the strategic portfolio by varying strategic positions based on short-term expected returns and risks. In their second illustration, they employ a 100-0 stocks-bonds benchmark consisting of 80% MSCI USA Net Total Return Index and 20% MSCI USA Minimum Volatility Net Total Return Index. The corresponding strategic portfolio reflecting long-term expectations is an equally weighted combination of value, momentum, quality, size and minimum volatility equity factor indexes. They specify short-term return and risk expectations based on four indicators involving: economic cycle variables; aggregate stock valuation metrics; factor momentum; and, dispersion of factor measures (such as difference in valuations between value stocks and growth stocks). They apply these indicators to underweight or overweight strategic positions using an optimizer. They rebalance these portfolios monthly. 

For their asset universe, they focus on indexes accessible via Exchanged Traded Funds (ETFs). Using monthly data for five broad capitalization-weighted equity indexes, six broad bond/credit indexes of varying durations and six style/factor (smart beta) equity indexes as available during January 2000 through June 2017, they find that: Keep Reading

A subscriber wondered whether U.S. stock market movements predict Federal Funds Rate (FFR) actions taken by the Federal Reserve open market operations committee. To investigate and evaluate usefulness of findings, we relate three series:

1. FFR actions per the above source, along with recent and historical committee meeting dates.
2. S&P 500 Index returns.
3. Changes in yield for the 10-Year U.S. Constant Maturity Treasury note (T-note).

In constructing the first series, for Federal Reserve open market operations committee meeting dates which do not produce FFR changes, we quantify committee actions as 0%. We ignore committee conference calls that result in no changes in FFR. We calculate the second and third series between committee meeting dates because that irregular interval represents new information to the committee and potential exploitation points for investors. Using data for the three series during January 1990 through March 2018, we find that:

Keep Reading

Does investing “leader” overconfidence (self-attribution bias) transfer bad trading practices to other non-professional investors who participate in a social trading platform? In their March 2018 paper entitled “Self-Attribution Bias and Overconfidence Among Nonprofessional Traders”, Daniel Czaja and Florian Röder employ data from a large European social trading platform to examine: (1) how self-enhancement (attributing successes to self) and self-protection (attributing failures to external factors) components of self-attribution bias affect non-professional trading performance; and, (2) how social trading platforms transfer any such effects to other non-professional traders. The selected platform lets traders (leaders) manage and comment on virtual portfolios publicly. When enough other traders (followers) express interest in such a portfolio, a business partner of the platform offers a product that replicates its performance. After excluding portfolios managed by professional asset management firms, the authors perform content analysis on leader trading comments to measure the difference between first-person pronouns and third-person pronouns as indicators of self-enhancement and self-protection biases. They then relate leader bias to leader future performance and to inflows of associated investable portfolios from followers. Using daily transaction and performance data for 3,519 social trading portfolios managed by 2,010 European non-professional traders and available for investment for at least 360 days, including 45,623 leader comments, during June 2012 through November 2016, they find that:

Keep Reading

Below is a weekly summary of our research findings for 4/9/18 through 4/13/18. 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.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading