In the June 2018 draft of their paper entitled “An Information Factor: Can Informed Traders Make Abnormal Profits?”, Matthew Ma, Xiumin Martin, Matthew Ringgenberg and Guofu Zhou construct and test a long-short information factor (INFO) based on observed trading of firm insiders, short sellers and option traders. Specifically, the INFO portfolio:

• Is each month long the 10% (decile) of stocks with the highest levels of net buying (purchases minus sales) by top managers scaled by the average number of shares held by all top managers over the calendar year.
• Is each month short stocks based on both short interest (number of shares short divided by shares outstanding) and associated option trading activity (volume of liquid put and call options divided by volume of associated stock). They sort stocks independently on short interest and option trading activity, add the two ranks for each stock and short the decile of stocks with the highest combined ranks.

They further examine whether INFO  is a key driver of hedge fund returns. Using monthly data for specified variables, monthly returns for a broad sample of U.S. stocks priced over $5 and monthly returns for 13 hedge fund indexes and 5,565 individual U.S. equity hedge funds during February 1996 (limited by options data) through December 2015, they find that: Keep Reading

Does quantified uncertainty in government economic policy reliably predict stock market returns? To investigate, we consider the U.S. Economic Policy Uncertainty (EPU) Index, created by Scott Baker, Nicholas Bloom and Steven Davis and constructed from three components: (1) coverage of policy-related economic uncertainty by prominent newspapers: (2) the number of temporary federal tax code provisions set to expire in future years; and, (3) the level of disagreement in one-year forecasts among participants in the Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters for both (a) the consumer price index (CPI) and (b) purchasing of goods and services by federal, state and local governments. They first normalize each component by its own standard deviation prior to January 2012. They then compute a weighted average of components, assigning a weight of one half to news coverage and one sixth each to tax code uncertainty, CPI forecast disagreement and government purchasing forecast disagreement. They update the EPU index monthly with a delay of about one month, including revisions to recent months. Using monthly levels of the EPU Index and the S&P 500 Index during January 1985 through June 2018, we find that: Keep Reading

Patrick Donohoe introduces his 2018 book, Heads I Win, Tails You Lose: A Financial Strategy to Reignite the American Dream, by stating that the book: “…will teach you many of the principles and strategies to help discover your own path to financial freedom. Most importantly, it will show you the mindset required to carry out a successful plan. …almost everything you will gain from this book conflicts with what the typical financial planner, financial celebrity, and most financial publications tell you to do. …You will…discover how to pivot the foundation of your wealth to…the private mutual insurance company.” Based on his experience, market research and many examples, he concludes that: Keep Reading

Below is a weekly summary of our research findings for 8/6/18 through 8/10/18. 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

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for July 2018. The actual total (core) inflation rate for July is a little higher than (a little higher than) forecasted.

To determine whether the stock market is expensive or cheap, some experts use aggregate valuation ratios, either trailing or forward-looking, such as earnings-price ratio (E/P) and dividend yield. Operating under a belief that such ratios are mean-reverting, most imminently due to movement of stock prices, these experts expect high (low) future stock market returns when these ratios are high (low). Where are the ratios now? Using the most recent actual and forecasted earnings and dividend data from Standard & Poor’s, we find that: Keep Reading

A subscriber requested testing of the relationship between U.S. M2 Money Stock and gold, offered in one form via “Why Gold May Be Looking Cheap”: “[O]ne measure I’ve found useful is the ratio of the price of gold to the U.S. money supply, measured by M2, which includes cash as well as things like money market funds, savings deposits and the like. The logic is that over the long term the price of gold should move with the change in the supply of money… That equilibrium level is also relevant for future price action. When the ratio is low, defined as 25% below equilibrium, the medium 12-month return has been over 12%. Conversely, when the ratio is high, defined as 25% above equilibrium, the 12-month median return has been -6%. …This measure can be refined further. [G]old tends to trade at a higher ratio to M2 when inflation is elevated.” Because it defines specific valuation thresholds, this approach is susceptible to data snooping bias in threshold selection. We consider an alternative setup that relates monthly change in M2 to monthly gold return. We also consider the effect of inflation on this relationship. Using monthly seasonally adjusted M2 and end-of-month London gold price fix during January 1976 (to ensure a free U.S. gold market) through June 2018 (510 months), we find that: Keep Reading

Should investors consider Bitcoin as a safe haven from turbulent financial markets? In their June 2018 paper entitled “Bitcoin as a Safe Haven: Is It Even Worth Considering?”, Lee Smales and Dirk Baur assess the potential for Bitcoin as a safe haven, focusing on considerations beyond its low return correlations with other assets during times of market stress. Their comparison set of assets consists of gold (GLD) and bonds (10-year U.S. Treasury futures) as traditional safe havens,  a proxy for the U.S. stock index (SPY) and mature (Apple) and immature (Twitter) individual stocks. They match samples by removing Bitcoin data for weekends and holidays. Using daily returns for Bitcoin and the comparison set of assets during August 2011 through May 2018, they find that: Keep Reading

Will equity funds “managed” by artificial intelligence (AI) outperform human investors? To investigate, we consider the performance of AI Powered Equity ETF (AIEQ), which “seeks to provide investment results that exceed broad U.S. Equity benchmark indices at equivalent levels of volatility.” More specifically, offeror EquBot: “…leverages IBM’s Watson AI to conduct an objective, fundamental analysis of U.S.-listed common stocks and real estate investment trusts…based on up to ten years of historical data and apply that analysis to recent economic and news data. Each day, the EquBot Model ranks each company based on the probability of the company benefiting from current economic conditions, trends, and world events and identifies approximately 30 to 70 companies with the greatest potential over the next twelve months for appreciation and their corresponding weights, while maintaining volatility…comparable to the broader U.S. equity market. The Fund may invest in the securities of companies of any market capitalization. The EquBot model recommends a weight for each company based on its potential for appreciation and correlation to the other companies in the Fund’s portfolio. The EquBot model limits the weight of any individual company to 10%.” We use SPDR S&P 500 (SPY) as a simple benchmark for AIEQ performance. Using daily dividend-adjusted closes of AIEQ and SPY from AIEQ inception (October 18, 2017) through July 27, 2018, we find that: Keep Reading

Is there an exploitable way to predict when short-term stock market return will be negative? In his June 2018 paper entitled “Predictable Downturns”, Carter Davis tests a random forest regression-based forecasting model to predict next-day U.S. stock market downturns. He uses the value-weighted return of a portfolio of the 10 U.S. stocks with the largest market capitalizations at the end of the prior year minus the U.S. Treasury bill (T-bill) yield as a proxy for excess market return. He employs a two-step test process:

1. Use a rolling 10-year historical window of 143 input variables (economic, equity factor, market volatility, stock trading, calendar) to find when the probability of negative portfolio daily excess return is at least 55%.
2. Calculate whether the average portfolio gross excess return of all such days is in fact significantly less than zero.

He corrects for data snooping bias associated with the modeling approach. He further investigates which input variables are most important and tests a market timing strategy that holds the 10-stock portfolio (T-bills) when predicted portfolio return is negative (non-negative) as specified above. Using data for the input variables and returns for test portfolio stocks during July 1926 through July 2017, he finds that: Keep Reading