Quantpedia Premium Update – 5th of June

New strategies:

#875 – Trend-Based Machine Learning Crypto Strategy

Period of rebalancing: Weekly
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Very complex strategy
Backtest period: 2018-2022
Indicative performance: 12.6%
Estimated volatility: 18.26%

Source paper:

Tan, Xilong and Tao, Yubo: Trend-based Forecast of Cryptocurrency Returns
https://ssrn.com/abstract=4222864
Abstract
We systematically re-examine the efficacy of trend-based technical indicators in predicting cryptocurrency market returns at daily, weekly, and monthly horizons. It shows that the price-based signals are more effective than the volume-based signals in the short horizon (daily and weekly), while the volume-based signals are more powerful in the long horizon (monthly). We also document that machine learning techniques can significantly improve the performance of technical indicators both in and out of sample at all horizons. Further analysis reveals that leading cryptos are more predictable by technical analysis, and technical indicators based on different information respond differently to the COVID-19 outbreak.

#876 – Momentum (and Value) Enhanced by Institutional Prediction

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1981-2020
Indicative performance: 5.58%
Estimated volatility: 12.56%

Source paper:

Chen, AJ and Hoberg, Gerard and Zhang, Miao Ben: Wisdom of the Institutional Crowd: Implications for Anomaly Returns
https://ssrn.com/abstract=4207132
Abstract
We hypothesize that when price correction requires more capital than any one investor can provide, institutions coordinate trading via crowd-sourcing in the media. When the crowd reaches a consensus, synchronized trading occurs, prices are corrected, and anomaly returns result. We use over one million Wall Street Journal articles from 1980 to 2020 to develop a novel textual measure of institutional investors making predictions in the media (InstPred). We show that (i) both value and momentum anomaly returns are 34% to 63% larger when InstPred is higher; (ii) these effects are driven by stocks whose institutional investors are highly cited in WSJ articles; and (iii) institutional investors collectively trade the anomalies more aggressively when InstPred is higher. Our results cannot be explained by existing measures such as document tone.

#877 – Weighted Frequency of Losses

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 1963-2018
Indicative performance: 11.9%
Estimated volatility: 12.34%

Source paper:

Koval, Borys and Steshkova, Alina : Do investors care about negative returns?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4183853
Abstract
In this paper, we analyse the impact of frequent negative returns and the recency bias on future stock market returns. Specifically, we propose a strategy that is based on counting of daily negative returns during the previous month, where investors earn 11.9% p.a. Our results show that weighting returns exponentially outperforms equal-weighting and is robust to a range of existing risk factors and the firms’ characteristics, suggesting that the most recent observations during the month receive more investors’ attention and are the most relevant for future performance. The exponentially weighted strategy remains significant for stocks in the S&P 500 after transaction costs. Although the return of the exponentially weighted strategy is positive for stocks held by institutional and retail investors, it is the highest for stocks that are largely held by retail traders.

#878 – Factor Ownership Predicts Factor Returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1982-2021
Indicative performance: 16.22%
Estimated volatility: 17.08%

Source paper:

Song, Jihong: Smart-Beta Institutional Ownership and Stock Return Anomalies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4321764
Abstract
I document a novel asset-pricing fact that the expected returns of 7 anomaly factors are lower among stocks with higher ownership share by smart-beta institutional investors who trade according to these anomalies. The factor-oriented demand of smart-beta investors contributes to lower price-of-risk or mispricing for the anomaly factors in equilibrium; and stocks have different levels of smart-beta demand from their owners due to investor heterogeneity and market segmentation. As a result, the return predictability of anomaly factors is decreasing in smart-beta institutional ownership in the cross-section of stocks. I provide persistent and robust empirical evidence for this relationship based on new measures of smart-beta factor demand at investor level. As the results show significant market segmentation across stocks, I further document that the market segmentation is driven by idiosyncratic volatility, benchmarking, and strategic considerations in smart-beta investing.

#879 – Implied Put-Call Volatility Spread in US Equities

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1996-2017
Indicative performance: 6.29%
Estimated volatility: 5.09%

Source paper:

Campbell, T. Colin and Gallmeyer, Michael and Petkevich, Alex: The Only Constant Is Change: Non-constant Volatility and Implied Volatility Spreads
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4251821
Abstract
We examine the predictability of stock returns using implied volatility spreads (VS) from individual (non-index) options. Volatility spreads can occur under simple no-arbitrage conditions for American options when volatility is time-varying, suggesting that the VS-return predictability could be an artifact of firms’ sensitivities to aggregate volatility. Examining this empirically, we find that the predictability changes systematically with aggregate volatility and is positively related to the firms’ sensitivities to volatility risk. The alpha generated by VS hedge portfolios can be explained by aggregate volatility risk factors. Our results cannot be explained by firm-specific informed trading, transaction costs, or liquidity.

New research papers related to existing strategies:

#697 – Multifactor Corporate Bond Strategy

Martina Andreani, Diogo Palhares, Scott A. Richardson: Computing Corporate Bond Returns: A Word (or Two) of Caution
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4411611
Abstrac:
We offer several suggestions for researchers using corporate bond return data. First, despite clear instructions from older papers (e.g., Bessembinder et al. 2009) about ways to compute credit excess returns, a lot of recent research simply subtracts a Treasury-bill return. We show that this imprecision is likely to contaminate inferences as the rate component of returns is negatively correlated to the spread component. This is a problem for all research looking at corporate bonds returns, especially time series analysis and safer corporate bonds (e.g., Investment Grade). We provide a simple approach using WRDS data to remove the interest rate component of corporate bond returns. Second, we note significant differences in coverage of corporate bonds across the Trade Reporting and Compliance Engine (TRACE) platform and typical corporate bond indices. We provide some simple rules for researchers using TRACE to select a subset of bonds closest to those contained inside corporate bond indices used by institutional investors. Third, we note differential quality in the prices and hence returns between TRACE and typical corporate bond indices. Corporate bond returns provided by corporate bond indices (i) correctly estimate credit excess returns, (ii) are synchronous for the entire set of bonds allowing for consistent cross-sectional comparability, and (iii) suffer less from stale pricing issues. Where possible researchers should try to source return data from multiple sources to ensure the robustness of their results due to these coverage and data quality issues.

#818 – ETF Flows Predict Subsequent ETF Performance

Baixiao Liu, Linlin Ma: Media Negativity on Foreign Countries and International Asset Allocation
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4440107
Abstract:
We investigate the relationship between media negativity on foreign countries and flows to country-specific international mutual funds in the US. We find that media negativity, in combination with media attention given to a foreign country, is negatively correlated with the flows to international mutual funds that target the country. The correlation is more pronounced in non-economics-related media coverage, in the abnormal negative media coverage of the Wall Street Journal following the Murdoch acquisition, in retail fund flows as opposed to institutional fund flows, and when negative media coverage can reach a broader audience through the launch of media outlets’ mobile apps. We further find that fund flows induced by negative media coverage are negatively correlated with subsequent fund performance. Moreover, the media negativity of a country also influences international mutual funds targeting the region that includes the country. We interpret our findings as suggesting that slanted media negativity towards foreign countries adversely affects domestic investors’ international asset allocation.

#167 – Idiosyncratic Momentum in Stocks

Cheoljun Eom: Short-Term Idiosyncratic Momentum in Cross-Sectional Stock Returns: Empirical Evidence
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4370153
Abstract:
This study finds short-term idiosyncratic momentum (iMOM) in cross-sectional stock returns. The short-term iMOM utilizes the daily residual returns estimated by pricing models in the previous month. This is different from idiosyncratic volatility (IVOL), which uses the volatility of the same residual returns and short-term reversals (SREV) using returns in the previous month. The short-term iMOM portfolios persistently show significant positive performance from one to eight months in future periods. This persistent pattern is due to the under-reacted trading behavior of institutional investors’ over-selling of winner stocks in short-term iMOM. Moreover, the short-term iMOM has unique information that cannot be explained by known factor premiums and shows significant evidence even after controlling for firm-specific variables related to arbitrage constraints in the previous month. An interesting finding is that short-term iMOM and IVOL are in a reciprocal barometer relationship on whether or not to have predictive power on the expected returns of stocks; that is, the existence of (non-) significant short-term iMOM implies the existence of (non-) significant IVOL, and vice versa. Therefore, this finding is expected to lead to further studies that verify short-term iMOM and provide additional insight into the IVOL puzzle.

#663 – R&D Expenditures and Stock Returns

Amit Goyal, Sunil Wahal: R&D, Expected Profitability, and Expected Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4339765
Abstract:
Current R&D expenditures forecast cash-based operating profitability up to three years in the future and sometimes as much as ten years, but do not forecast asset growth. High R&D firms have positive loadings on a cash-based operating profitability factor, and zero alphas. Capitalizing R&D to augment book values with intangible assets is unnecessary for asset pricing, so long as expected profitability is explicitly recognized as a determinant of expected returns.

And several interesting free blog posts have been published during last 2 weeks:

Exploration of the Arbitrage Co-movement Effect in ETFs

We continue our short series of articles dedicated to the exploration of trading strategies that derive their functionality from the deep understanding of how Exchange Trading Funds (ETFs) work. In our first post, we discussed how we could use the ETF flows to predict subsequent daily ETF performance. In today’s article, we will analyze how we can use the information about the sensitivity of individual stocks to the ETF arbitrage activity to build a profitable equity factor trading strategy.

An Evaluation of the Skewness Model on 22 Commodities Futures

Skewness is one of the less-known but practical measures from statistics that can be used in trading. It is defined as a measure of the asymmetry of the probability distribution of a random variable around its mean. The goal of this analysis is to explore the commodity skewness trading strategy and perform the battery of robustness tests to see how sensitivity analysis changes overall results regarding performance, volatility, and Sharpe ratios.

Factor Trends and Cycles

Bearish trends or deep corrections in international equity markets starting in 2022 and rising interest rates worldwide brought investors’ attention back to not only once-proclaimed dead factor investing. From long-run and short run, during different market cycles, different factors behave differently. What’s fortunate is that it is pretty predictable to some extent. Andrew Ang, Head of Factor Investing Strategies at BlackRock, in his Trends and Cycles of Style Factors in the 20th and 21st Centuries (2022), used Hodrick-Prescott (HP) filter and spectral analysis to investigate different models to draw some general conclusions on most-widely used factors. We will take a look at a few of quite the most interesting ones of them.

In-Sample vs. Out-Of-Sample Analysis of Trading Strategies

Science has been in a “replication crisis” for more than a decade. But what does it mean to us, investors and traders? Is there any “edge” in purely academic-developed trading strategies and investment approaches after publishing, or will they perish shortly after becoming public? After some time, we will revisit our older blog on this theme and test the out-of-sample decay of trading strategies. But this time, we have hard data – our regularly updated database of replicated quant strategies.