New strategies:
#563 – Currency Factor Momentum
Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: CFDs, futures
Complexity: Moderately complex strategy
Backtest period: 1983-2020
Indicative performance: 4.9%
Estimated volatility: 6.42%
Source paper:
Zhang, Shaojun: Dissecting Currency Momentum
https://ssrn.com/abstract=3643855
Abstract:
This paper shows that currency momentum, which cannot be explained by carry and dollar factors, summarizes the autocorrelation of these factors. Carry and dollar factors are strongly autocorrelated and only earn significantly positive excess returns following positive factor returns. Currency momentum longs the factors following positive factor returns and shorts them following losses. Factor momentum not only outperforms currency momentum but also explains it, whereas idiosyncratic returns do not contain or explain momentum. Further evidence shows that factor momentum is inconsistent with the time-varying risk premium but supports mispricing. In particular, the mispricing prevails across markets.
#564 – Intraday Momentum in Fixed Income
Period of rebalancing: Intraday
Markets traded: bonds
Instruments used for trading: futures, CFDs
Complexity: Simple strategy
Backtest period: 1974-2020
Indicative performance: 2.16%
Estimated volatility: 1.33%
Source paper:
G. Baltussen, Z. Da, S. Lammers, M. Martens: Low-Risk Effect: Hedging demand and market intraday momentum
https://www3.nd.edu/~zda/intramom.pdf
Abstract
Hedging short gamma exposure requires trading in Hedging short gamma exposure requires trading in the direction of price movements, thereby creating price momentum. Using intraday returns on over 60 futures on equities, bonds, commodities, and currencies between 1974 and 2020, we document strong “market intraday momentum” everywhere. The return during the last 30 minutes before the market close is positively predicted by the return during the rest of the day (from previous market close to the last 30 minutes). The predictive power is economically and statistically highly significant, and reverts over the next days. We provide novel evidence that links market intraday momentum to the gamma hedging demand from market participants such as market makers of options and leveraged ETFs.
#565 – The Ex-dividend Date in the European Market
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 2018-2020
Indicative performance: 1.8% (CAAR over a [-5; 0] window)
Estimated volatility: not stated
Source paper:
Chasing dividends during the COVID-19 pandemic : Nicolas Eugstera, Romain Ducretb, Dušan Isakovc & Jean-Philippe Weisskopfd https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3716174
Abstract:
This paper investigates the impact of the COVID-19 pandemic on the trading behavior of investors around ex-dividend dates in Europe. The sudden decrease in the number of companies paying dividends reduced the opportunities to capture dividends. The firms that have maintained dividend payments during the pandemic thus attracted more interest than before. This led to a doubling in the magnitude of stock return patterns usually observed around exdividend days. Our evidence indicates that dividend-seeking investors are likely to be the main driver of the price patterns observed around ex-dividend dates.
#566 – Multi Asset Pairs Momentum
Period of rebalancing: Monthly
Markets traded: bonds, commodities, equities, REITs
Instruments used for trading: bonds, ETFs, futures, stocks
Complexity: Very complex strategy
Backtest period: 2000-2020
Indicative performance: 5.3%
Estimated volatility: 8.1%
Source paper:
Goulding, Christian L. and Harvey, Campbell R. and Pickard, Alex: Decoding Systematic Relative Investing: A Pairs Approach
https://ssrn.com/abstract=3680314
Abstract:
We propose a novel theory that brings to light three fundamental performance drivers of zero-cost systematic investment strategies:
(1) high (positive) own-asset signal-return predictability;
(2) low (or negative) cross-asset signal correlation; and
(3) low (or negative) cross-asset signal-return predictability.
We develop these insights in the context of long-short pair strategies used as portfolio building blocks. We test our approach empirically using momentum signals for major asset classes, though our method can generalize to any signal. Our investable pairs-based portfolio harvests over double the average returns of a conventional rank-based portfolio over the last 20 years.
#567 – Low-risk Anomaly Index
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1963-2018
Indicative performance: 9.25%
Estimated volatility: 29.4%
Source paper:
Turan G. Bali, A. Doruk Gunaydin, Thomas Jansson, and Yigitcan Karabulut : Do the Rich Gamble in the Stock Market? Low Risk Anomalies and Wealthy Households
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3664501
Abstract:
We propose a low risk anomaly (LRA) index with high values indicating high-risk stocks with high-beta, high-volatility, and high-lottery-payoffs. We document a significantly negative crosssectional relation between the LRA index and future returns on individual stocks trading in the U.S. and international countries. We show that the high-LRA stocks are subject to significant overpricing and primarily held by retail investors, whereas the degree of underpricing of low-LRA stocks is so small that the low risk anomaly is driven by retail investors’ demand for high-LRA stocks, leading to temporary overpricing and negative future abnormal returns for
these high-beta, high-volatility stocks with large lottery payoffs. To understand how and why individual investors contribute to the low risk anomalies, we use a large-scale individual-level panel dataset from Sweden that allows us to directly observe the stock investments of the entire population at the individual security level. We find that the anomalous negative relation between risk and future abnormal returns is only confined to those stocks held by rich households, whereas there is no evidence of low risk anomaly for stocks held by non-rich households and institutional investors. Further tests also reveal that the skewness preferences of rich households have the potential to explain the demand of wealthy investors for high-risk stocks. In contrast, other potential explanations such as the overconfidence-based preferences, constraints on financial leverage, downside risk, and hedging demand receive little support from the data.
#568 – Momentum effect in Chinese B-shares
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2002-2018
Indicative performance: 14.72%
Estimated volatility: 16.59%
Source paper:
Andy C.W. Chui, A. Subrahmanyam, Sheridan Titman: Momentum, Reversals, and Investor Clientele
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3674871
Abstract:
The identical cash flow rights of Chinese A and B shares provide a natural experiment that allows us to explore how investor clienteles affect stock return patterns. Chinese domestic retail investors are responsible for the majority of trades in A shares, while foreign institutional investors have a significant presence in B shares. We find that B shares exhibit strong momentum while their corresponding A shares do not. In contrast, A shares exhibit significant short-term reversals while their B share counterparts do not. Furthermore, we document that institutional ownership strengthens momentum in B shares. These return patterns are consistent with a simple model where the trades of overconfident informed investors generate momentum and the trades of uninformed noise traders generate reversals.
#569 – Intraday Time-series Momentum in Chinese Futures
Period of rebalancing: Monthly
Markets traded: commodities
Instruments used for trading: futures
Complexity: Simple strategy
Backtest period: 2002-2013
Indicative performance: 11.92%
Estimated volatility: not stated
Source paper:
Muzhao Jin et al.: Intraday Time-series Momentum: Evidence from China
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3493927
Abstract:
This study conducts an investigation of intraday time-series momentum across four Chinese commodity futures contracts: copper, steel, soybean, and soybean meal. Our results indicate that the first half-hour return positively predicts the last half-hour return across all four futures. Furthermore, in metals markets, we find that first trading sessions with high volume or volatility are associated with the strongest intraday time-series momentum dynamics. Based on this, we propose an intraday momentum informed trading strategy that earns a return in excess of standard always long and buy-and-hold benchmarks.
New research papers related to existing strategies:
#526 – Principal Portfolios
Ding, Yi and Li, Yingying and Zheng, Xinghua, High Dimensional Minimum Variance Portfolio Estimation under Statistical Factor Models
https://ssrn.com/abstract=3628496
Abstract:
We propose a high dimensional minimum variance portfolio estimator under statistical factor models, and show that our estimated portfolio enjoys sharp risk consistency. Our approach relies on properly integrating l1 constraint on portfolio weights with an appropriate covariance matrix estimator. In terms of covariance matrix estimation, we extend the theoretical results of POET(Fan et al. (2013)) to a setting that is coherent with principal component analysis. Simulation and extensive empirical studies on S&P 100 Index constituent stocks demonstrate favorable performance of our MVP estimator compared with benchmark portfolios.
#14– Momentum Factor Effect in Stocks
#534– Time Series Factor Momentum
#510– Factor Momentum
Falck, Antoine and Rej, Adam and Thesmar, David, Is Factor Momentum More than Stock Momentum?
https://ssrn.com/abstract=3688983
Abstract:
Yes, but only at short lags. In this paper we investigate the relationship between factor momentum and stock momentum. Using a sample of 72 factors documented in the literature, we first replicate earlier findings that factor momentum exists and works both directionally and cross-sectionally. We then ask if factor momentum is spanned by stock momentum. A simple spanning test reveals that after controlling for stock momentum and factor exposure, statistically significant Sharpe ratios only belong to implementations which include the last month of returns. We conclude this study with a simple theoretical model that captures these forces: (1) there is stock-level mean reversion at short lags and momentum at longer lags, (2) there is stock and factor momentum at all lags and (3) there is natural comovement between the PNLs of stock and factor momentums at all horizons.
#524 – Deviations of Fundamentals and Machine Learning
#480 – Machine Learning-Based Financial Statement Analysis
Cao, Kai and You, Haifeng, Fundamental Analysis Via Machine Learning
https://ssrn.com/abstract=3706532
Abstract:
We examine the efficacy of machine learning in one of the most important tasks in fundamental analysis, forecasting corporate earnings. Our analyses show that machine learning models, especially those that accommodate nonlinearities, generate significantly more accurate and informative forecasts than a host of state-of-the-art earnings prediction models in the extant literature. Further analysis suggests that machine learning models uncover economically sensible relationships between historical financial information and future earnings. We also find that the new information uncovered by machine learning models is of considerable economic significance to investors. The new information component of the machine learning-based forecasts is significantly associated with future stock returns. Stocks in the quintiles with the most favorable new information outperform those in the least favorable quintiles by approximately 70 bps per month, suggesting that the new information is not well understood by investors. Finally, insights from machine learning models are useful for improving the extant models.
#162 – Momentum Effect in Stocks in Small Portfolios
Piras, Antonio, Concentrated Portfolios of Momentum Stocks
https://ssrn.com/abstract=3695892
Abstract:
There exists abundant academic literature showing that momentum, i.e. a positive correlation between initial ranking of stocks by their past returns and subsequent returns, is pervasive across different markets and time periods. Although recent criticism speculates on the disappearing of momentum returns, as-set managers have launched strategies to harvest the risk premia in form of well diversified equities funds based. In this research paper I delve deeper into the topic of the construction of concentrat-ed portfolios with less than 50 stocks. Based on monthly data for the US market universe I first investigate the consistency of momentum returns over the latest 20 years (1999-2019) with deciles analysis and then study the characteristics both of unconstrained and sector neutral concentrated portfolios. The empirical results show that momentum in the last decade (2010-2019), measured as the performance of a zero investment portfolio (long winners and short losers), is present but with minor intensity compared to the previous decade and that on the same period the top decile “long only” portfolio, built with previous winners’ stocks, still keeps beating the markets index with better Sharpe and Sortino ratios. The results on concentrated portfolios, in particular portfolios with less than 10 stocks, show clear dependency on the given universe constituents, to make the analysis less dependent on particular universe constituents I propose to run the momentum strategy on 1000 random subsampled stocks universes and show empirically that the relation between the number of stocks in the portfolios and the corresponding performances is statistically significant monotonic (the less stocks the more performance). Finally, I report that a sector neutral portfolio, i.e. a portfolio with the same number of stocks for each industrial sector, shows superior risk return characteristics than unconstrained ones. In the last 20 years a long only portfolio based on overlapping sector neutral sub-portfolios with 10 stocks each, gained an annualized return of 11.3% with a Sharpe ratio of 0.59, compared to a 6.00% and 0.24 for the MSCI USA and a 8.5% and 0.38 for the equal weighted benchmark.
#7 – Low Volatility Factor Effect in Stocks – Long-Only Version
Bellone, Benoit and Carvalho, Raul Leote de, The Low Volatility Anomaly in Equity Sectors – 10 Years Later!
https://ssrn.com/abstract=3697914
Abstract:
Ten years after showing that the low volatility anomaly in the performance of stocks is a phenomenon that should be considered in each sector as opposed to on an absolute basis ignoring sectors, we present evidence that this observation has held up well, and that if anything, has become even more valid.
And two interesting free blog posts have been published during last 2 weeks:
Stock Price Overreaction to ESG Controversies
Nobody can doubt that in the recent period, ESG investing has significantly grown and is a staple part of the financial markets. The academic literature has also grown with the popularity of ESG investing. The negative, mixed and positive results for ESG scores in portfolios have evolved, and generally, there is a consent that ESG scoring can be a vital part of the portfolio management process. It can be observed that in the past, the ESG scores were not priced in the equity market and still, the ESG is not priced in the corporate bond market (apart from Europe). Nowadays, the investors react to the ESG scores, but the research paper of Cui and Docherty (2020) has novel insights that investors may react too much to the ESG. Their research shows that investors overreact to the negative ESG events and stocks connected with negative ESG events sharply fall, but the prices have mean-reverting properties. As a result, there is a reversal after bad ESG events. Stocks firstly sharply fall, but then their prices are reverted to the previous values. Therefore, this paper is interesting from the market pricing or efficiency point, but it also can be utilized by a reversal investor.
Novel Market Structure Insights From Intraday Data
In recent years, financial markets have experienced a boom in passive and index-based strategies, which could have caused a change in the trading volume, volatility, beta or correlations. The reason is straightforward: the index investing causes a lot of stocks to move in the same direction. A novel research Shen and Shi (2020), using high-frequency data, suggests that over the last two decades, the patterns mentioned above have changed and the index investing is the cause. Both the trading volume and stock correlations are increased at the end of trading sessions. Betas are firstly dispersed, but in general, converge to one during the rest of the day. Trading volume has high dispersion at the market open, but low dispersion at the market close. Overall, the paper has many important implications for portfolio managers, risk managers and traders as well since it is closely related to the transaction costs, intraday price fluctuations, correlations or liquidity. Moreover, it is full of exciting charts that are worth seeing.
Plus, the following six trading strategies have been backtested in QuantConnect in the previous two weeks:
#163 – Net Emotional Volatility Index Effect
#174 – Institutional Ownership Effect
#202 – Dividend Announcement Effect
#240 – Sector Rotation via Credit Relative Value
#245 – Post-Split Drift Combined with PEAD Anomaly
#315 – Stock Splits Strategy Based on Earnings Management
Are you looking for more strategies to read about? Sign up for our newsletter or visit our Blog or Screener.
Do you want to learn more about Quantpedia Premium service? Check how Quantpedia works, our mission and Premium pricing offer.
Do you want to learn more about Quantpedia Pro service? Check its description, watch videos, review reporting capabilities and visit our pricing offer.
Are you looking for historical data or backtesting platforms? Check our list of Algo Trading Discounts.
Would you like free access to our services? Then, open an account with Lightspeed and enjoy one year of Quantpedia Premium at no cost.
Or follow us on:
Facebook Group, Facebook Page, Twitter, Linkedin, Medium or Youtube
