New strategies:
#467 – Bitcoin Intraday Momentum
Period of rebalancing: Intraday
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Simple strategy
Backtest period: 2015 – 2019
Indicative performance: 28.62%
Estimated volatility: 30.26%
Source paper:
Caporale, Plastun: Momentum Effects in the Cryptocurrency Market after One-Day Abnormal Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3480923
Abstract:
This paper examines whether there exists a momentum effect after one-day abnormal returns in the cryptocurrency market. For this purpose a number of hypotheses of interest are tested for the BitCoin, Ethereum and LiteCoin exchange rates vis-à-vis the US dollar over the period 01.01.2017-01.09.2019, specifically whether or not: H1) the intraday behaviour of hourly returns is different on overreaction days compared to normal days; H2) there is a momentum effect on overreaction days, and H3) after one-day abnormal returns. The methods used for the analysis include a number of statistical methods as well as a trading simulation approach. The results suggest that hourly returns during the day of positive/negative overreactions are significantly higher/lower than those during the average positive/negative day. Overreactions can usually be detected before the day ends by estimating specific timing parameters. Prices tend to move in the direction of the overreaction till the end of the day when it occurs, which implies the existence of a momentum effect on that day giving rise to exploitable profit opportunities. This effect (together with profit opportunities) is also observed on the following day. In two cases (BTCUSD positive overreactions and ETHUSD negative overreactions) a contrarian effect is detected instead.
#468 – Dynamic Momentum Strategy
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2004 – 2018
Indicative performance: 6.11%
Estimated volatility: 10.00%
Source paper:
Garg, Ashish and Goulding, Christian L. and Harvey, Campbell R. and Mazzoleni, Michele: Momentum Turning Points
https://ssrn.com/abstract=3489539
Abstract:
Turning points are the Achilles’ heel of time-series momentum portfolios. Slow signals fail to react quickly to changes in trend while fast signals are often false alarms. We examine theoretically and empirically how momentum portfolios of various intermediate speeds, formed by blending slow and fast strategies, cope with turning points. Our model predicts an optimal dynamic speed selection strategy. We apply this strategy across domestic and international equity markets and document efficient out-of-sample performance. We also propose a novel decomposition of momentum strategy alpha, highlighting the role of volatility timing.
New research papers related to existing strategies:
#26 – Value (Book-to-Market) Factor
Arnott, Harvey, Kalesnik, Linnainmaa: Reports of Value’s Death May Be Greatly Exaggerated
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3488748
Abstract:
Value investing has underperformed growth investing for over 12 years with a -39.1% drawdown from peak to trough using the classic Fama-French definition of the value factor. The second-longest duration of underperformance occurred during the tech bubble, and while deeper than the recent drawdown, lasted less than 4 years. As a result, many now argue, relying on a variety of narratives, that the value investing style is no longer viable. We examine some of these narratives and find them wanting. We use a bootstrap analysis to show that the likelihood (given the historical data) of observing such a large drawdown is about 1 in 20—unusual but not enough to support structural impairment. We then decompose the value–growth performance into three components: the migration of securities, a profit differential, and the change in a valuation spread. Our analysis of pre- and post-2007 data reveal no significant difference between the migration of stocks (from value to neutral or growth or from growth to neutral or value) in the two periods nor do we observe a difference in profitability. The drawdown is explained by the third component: value has become unusually cheap relative to growth with the valuation now in the 97th percentile of the historical distribution. We show that, even accounting for intangibles, which have eroded the relevance of book value, the drawdown is explained by value becoming exceptionally relatively cheap. Even given the noisy nature of returns, expected returns are always elevated when in the extreme lower tail of a distribution.
Tokat-Acikel, Aiolfi, Jin: Multi-Asset Value Payoff: Is Recent Underperformance Cyclical?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3493335
Abstract:
Value is one of the most studied risk premia strategies across asset classes. Value factors, however, have struggled lately. To uncover the drivers of recent value factor underperformance, it is important to understand how value returns are affected by macroeconomic conditions. We build on the existing literature by directly measuring the macroeconomic characteristics of value factor portfolios, namely real economic growth and inflation exposures. By pairing methodologies commonly used to derive fundamental characteristics of equity portfolios, we are able to identify macro linkages that have not been previously made evident. Our holdings-based and factor-mimicking portfolio analyses provide insights into the behavior of value strategies across various asset classes, looking at both cyclical and idiosyncratic drivers.
#199 – ROA Effect within Stocks
#224 – Profitability Factor Combined with Value Factor
#229 – Earnings Quality Factor
Hsu, Kalesnik, Kose: What Is Quality?
https://www.tandfonline.com/doi/full/10.1080/0015198X.2019.1567194
Abstract:
Unlike standard factors, such as value, momentum, and size, “quality” lacks a commonly accepted definition. Practitioners, however, are increasingly gravitating to this style factor. They define quality to be various signals or combinations of signals—some that have been thoroughly explored in the academic literature and others that have received limited attention. Among a comprehensive group of the quality categories used by practitioners, we find that profitability, accounting quality, payout/dilution, and investment tend to be associated with a return premium whereas capital structure, earnings stability, and growth in profitability show little evidence of a premium. Profitability and investment-related characteristics tend to capture most of the quality return premium.
And two interesting free blog post has been published during last 2 weeks:
The CAPE Ratio and Machine Learning
Professor Robert Shiller’s work and his famous CAPE (cyclically-adjusted price-to-earnings) ratio is well known among the investment community. His methodology for assessing a valuation of the U.S. equity market is not the first one but is surely the most cited and the most discussed. There are numerous papers that tweak or adjust Shiller’s methodology to assess better if U.S. equities are under- or over-valued. We recommend the work of Wang, Ahluwalia, Aliaga-Diaz, and Davis (all from The Vanguard Group ) in which they use a combination of machine learning and a regression-based approach to obtain forecasted CAPE ratio, and subsequently, U.S. stock market returns, more accurately.
Authors: Wang, Ahluwalia, Aliaga-Diaz, Davis
Title: The Best of Both Worlds: Forecasting US Equity Market Returns using a Hybrid Machine Learning – Time Series Approach
Alternative Fair-Value Models for Currency Value Strategy
The idea of buying an investment asset for a lower price than a fair-value is the cornerstone of value factor strategies. Various value strategies were popularized by famous investor Benjamin Graham (and his successors like Warren Buffett) and were firstly employed in the stock market. This idea of looking for investment opportunities that can be bought cheaply can also be applied in currency markets – Currency Value Factor strategy. There is, however, one catch – an investor must know the fair-value exchange rate for currencies. The most popular equilibrium exchange rate model used for this purpose is based on PPP (purchasing power parity). A new research paper written by Ca’ Zorzi, Cap, Mijakovic, and Rubaszek analyzes two additional models – Behavioral Equilibrium Exchange Rate (BEER) and the Macroeconomic Balance (MB) approach to assess which model has the best forecasting power.
Authors: Ca’ Zorzi, Cap, Mijakovic, Rubaszek
Title: The Predictive Power of Equilibrium Exchange Rate Models
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