Quantpedia Premium Update – 31st October 2020

New strategies:

#549 – Basis Momentum Commodity Premia in China

Period of rebalancing: Monthly
Markets traded: commodities
Instruments used for trading: futures
Complexity: Moderately complex strategy
Backtest period: 2004-2018
Indicative performance: 7.93%
Estimated volatility: 10.06%

Source paper:

Robert J. Bianchi, John Hua Fan, Tingxi Zhang: Investable Commodity Premia in China
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3525612
Abstract:
We investigate the investability of commodity risk premia in China. Previously documented standard momentum, carry and basis-momentum factors are not investable due to the unique liquidity patterns along the futures curves in China. However, dynamic rolling and strategic portfolio weights significantly boost the investment capacity of such premia without compromising its statistical and economic significance. Meanwhile, style integration delivers enhanced performance and improved opportunity sets. Furthermore, the observed investable premia are robust to execution lags, stop-loss, illiquidity, sub-period specifications, seasonality, transaction costs and offer portfolio diversification for investors. Finally, investable commodity premia in China reveal strong predictive ability with global real economic growth.

#550 – Carry Commodity Premia in China

Period of rebalancing: Monthly
Markets traded: commodities
Instruments used for trading: futures
Complexity: Simple strategy
Backtest period: 2004-2018
Indicative performance: 8.07%
Estimated volatility: 9.94%

Source paper:

#551 – Momentum Commodity Premia in China

Source paper:

#552 – Betting against beta in China

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 1996-2016
Indicative performance: 12.54%
Estimated volatility: 11.98%

Source paper:

Xing Hana, Kai Li, Youwei Lid: Investor Overconfidence and the Security Market Line: New Evidence from China
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3284886
Abstract:
This paper documents a highly downward-sloping security market line (SML) in China, which is more puzzling than the typical “flattened” SML in the US, and does not reconcile with existing theories of the low-beta anomaly. We show that investor overconfidence offers some promises in resolving the puzzle in China: In the time-series dimension, the slope of the SML becomes more “inverted” when investors get more overconfident. This dynamic overconfidence effect is intensified with biased self-attribution. As a general symptom of overconfidence in the cross section, high-beta stocks are also the mostly heavily traded. After accounting for trading volume, there is no longer the low-beta anomaly at both the firm and portfolio levels. Mutual fund evidence reinforces the view that institutional investors actively exploit the portfolio implications of a downward-sloping SML by shying away from high-beta stocks and betting on low-beta stocks for superior performance.

#553 – Cross-sectional Effects of Bases in Equity Indexes Futures

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: futures
Complexity: Complex strategy
Backtest period: 2000-2017
Indicative performance: 5.84%
Estimated volatility: 6.97%

Source paper:

Hazelkorn, Todd and Moskowitz, Tobias J. and Vasudevan, Kaushik: Beyond Basis Basics: Liquidity Demand and Deviations from the Law of One Price
https://ssrn.com/abstract=3658560
Abstract:
We argue that deviations from the law of one price between futures and spot prices, known as bases, capture important information about liquidity demand for equity market exposure in global equity index futures markets. We show that bases (1) co-move with dealer and investor futures positions, (2) are contemporaneously positively correlated with spot and futures markets with the same sign, and (3) negatively predict futures and spot market returns with the same sign. These findings are uniquely consistent with our liquidity demand model and distinct from other explanations for bases, such as arbitrage opportunities or intermediary balance sheet costs. We show persistent supply-demand imbalances for equity index exposure reflected in bases, where compensation for meeting liquidity demand for that exposure is large (5-6% annual premium).

#554 – Size Factor in Chin a

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2000-2016
Indicative performance: 13.08%
Estimated volatility: 15.66%

Source paper:

Jianan Liua, Robert F. Stambaugh, Yu Yuan: Size and Value in China
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3108175
Abstract:
We construct size and value factors in China. The size factor excludes the smallest 30% of firms, which are companies valued significantly as potential shells in reverse mergers that circumvent tight IPO constraints. The value factor is based on the earnings-price ratio, which subsumes the book-to-market ratio in capturing all Chinese value effects. Our three-factor model strongly dominates a model formed by just replicating the Fama and French (1993) procedure in China. Unlike that model, which leaves a 17% annual alpha on the earnings-price factor, our model explains most reported Chinese anomalies, including profitability and volatility anomalies.

#555 – Value Factor in China

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2000-2016
Indicative performance: 14.57%
Estimated volatility: 12.99%

Source paper:

#556 – Long-Term Institutional Trades and the Cross-Section of Returns

Period of rebalancing: Quarterly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 1980-2010
Indicative performance: 8.24%
Estimated volatility: 11.04%

James Bulsiewicz: Long-Term Institutional Trades and the Cross-Section of Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3669224
Abstract:
I investigate the relation between long-term institutional trades and future returns, and find that the cumulative number of shares purchased in net by financial institutions over the prior ten quarters is negatively related to future returns. A long-short portfolio constructed on this measure earns an annualized average Carhart alpha of 9.9%. Overall, I find that long-term institutional trades contain information about future returns that is not already captured by existing short-term institutional trades measures.

New research papers related to existing strategies:

#536 – Machine Learning Stock Picking

Noguer i Alonso, Miquel and Srivastava, Sonam, Deep Reinforcement Learning for Asset Allocation in US Equities
https://ssrn.com/abstract=3711487
Abstract:
Reinforcement learning is a machine learning approach concerned with solving dynamic optimization problems in an almost model-free way by maximizing a reward function in state and action spaces. This property makes it an exciting area of research for financial problems. Asset allocation, where the goal is to obtain the weights of the assets that maximize the rewards in a given state of the market considering risk and transaction costs, is a problem easily framed using a reinforcement learning framework. So it is first a prediction problem for the vector of expected returns and covariance matrix and then an optimization problem for returns, risk, and market impact, usually a quadratic programming one. Investors and financial researchers have been working with approaches like mean-variance optimization, minimum variance, risk parity, and equally weighted and several methods to make expected returns and covariance matrices’ predictions more robust and after use mean-variance like the Black Litterman model. This paper demonstrates the application of reinforcement learning to create a financial model-free solution to the asset allocation problem, learning to solve the problem using time series and deep neural networks. We demonstrate this on daily data for the top 24 stocks in the US equities universe with daily rebalancing. We use a deep reinforcement model on US stocks using different deep learning architectures. We use Long Short Term Memory networks, Convolutional Neural Networks, and Recurrent Neural Networks and compare them with more traditional portfolio management approaches like mean-variance, minimum variance, risk parity, and equally weighted. The Deep Reinforcement Learning approach shows better results than traditional approaches using a simple reward function and only being given the time series of stocks. In Finance, no training to test error generalization results come guaranteed. We can say that the modeling framework can deal with time series prediction and asset allocation, including transaction costs.

#102 – Option-Expiration Week Effect
#7 – Low Volatility Factor Effect in Stocks – Long-Only Version
#456 – Timing High and Low Volatility Equity Factor Strategy

Cao, Jie and Chordia, Tarun and Zhan, Xintong, The Calendar Effects of the Idiosyncratic-Volatility Puzzle: A Tale of Two Days?
https://ssrn.com/abstract=3659479
Abstract:
The idiosyncratic volatility (IVOL) anomaly exhibits strong calendar effects. The negative relation between IVOL and the next month return obtains mainly in the third week of the month. The IVOL-return relation is generally negative on Mondays and positive on Fridays. However, the positive impact is absent on the third Friday due to selling pressure from stocks delivered at option expiration. This imbalance between the negative and positive returns during the third week of the month has a large impact on the IVOL-return relation. Removing the third Friday and subsequent Monday return reduces the monthly IVOL effect by at least 40%.

#99 – FX Carry Trade Combined with PPP (Value) Strategy
#5 – FX Carry Trade

Dai, Wei and Schneller, Warwick, To Hedge or Not to Hedge: A Framework for Currency Hedging Decisions in Global Equity & Fixed Income Portfolios
https://ssrn.com/abstract=3703333
Abstract:
This paper develops a framework for evaluating the impact of currency hedging on expected returns and volatility and tests the implications for global equity and fixed income portfolios using data on 12 developed markets from 1985 to 2019. We show that the impact of currency hedging on overall portfolio volatility depends on the magnitude of asset volatility relative to that of currency volatility. We also find that currency returns cannot be reliably predicted using the prior month currency returns or interest rate differentials. The forward currency premium, however, contains reliable information about differences in expected returns between unhedged and hedged portfolios, and can be used to pursue higher expected returns through a selectively hedged strategy.

#216 – Active Collar Strategy

Guo, Ivan and Loeper, Gregoire, Designing All-Weather Overlays — A Study on Option-Based Systematic Strategies
https://ssrn.com/abstract=3688843
Abstract:
We perform an empirical analysis of systematic trading strategies on options. Namely, we focus on strategies which sell out of the money (OTM) call options to harvest the premium, and buy downside protection through OTM puts. We compare the risk adjusted performance across different choices of strike, maturity and option notional. In this paper we mostly focus on the S&P 500 index over the period 2007–2018. There is also a brief look of the performances of the best strategies during the COVID-19 pandemic in early 2020.

#460 – ESG Level Factor Investing Strategy

Gougler, Arnaud and Utz, Sebastian, Factor Exposures and Diversification: Are Sustainably-Screened Portfolios Any Different?
https://ssrn.com/abstract=3660986
Abstract:
We analyze the performance, risk, and diversification characteristics of global screened and best-in-class equity portfolios constructed according to Inrate’s sustainability ratings. The financial performance of sustainably high-rated portfolios is similar to the risk-adjusted market performance in terms of abnormal returns of a five-factor market model. In contrast, low-rated portfolios exhibit negative abnormal returns. Firms with high sustainability ratings show lower idiosyncratic risk, and higher exposure towards the high-minus-low and the conservative-minus-aggressive factor.

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

Not all Gold Shines in Crisis Times – COVID-19 Evidence

Gold is a hot topic nowadays, but that is not a surprise given the worldwide situation. Gold is by the majority considered as a hedge, safe haven and often recognized for its ability to preserve the value in the long term. However, gold itself is not the only gold-related investable asset. There are numerous gold-related stocks – producers, explorers and developers. Common sense might suggest that the price of such stocks should reflect the gold prices, but the novel research by Baur and Trench (2020) shows that this logic is not always correct. Results suggest that gold equities cannot be considered as safe havens and investors differentiate between producers, explorers and developers during regular times. On the other hand, during the recent (and lasting) stressful COVID period, all types of gold stocks moved similarly to gold.

Implied Volatility Indexes for European Government Bond Markets

Volatility indexes are essential parts of the financial markets. They offer investable opportunities and exposure to the volatility, but most importantly, those indexes offer forward-looking measures of option-implied uncertainty. Therefore, such indexes are often used as indicators of risk or sentiment in the markets. For example, the well-known VIX index is often called the fear-index. The volatility indexes are not exclusive to the equity market. There are fixed-income option-implied volatility indexes for US Treasury futures, but the European fixed income market lacks such index. This novel research paper by Jaroslav Baran and Jan Voříšek fills this gap and proposes volatility indexes, connected to the euro bond futures using the Cboe TYVIX (US Treasury implied volatility index) (2018) methodology. As a result, the TYVIX and euro bond futures volatility indexes are directly comparable.

Plus, the following six trading strategies have been backtested in QuantConnect in the previous two weeks:

#379 – Carry Factor in Cryptocurrencies
#380 – Value Factor in Cryptocurrencies
#394 – Curvature Factor in Currencies
#407 – Timing Betting Against Beta with Small Stocks
#408 – Cointegrated Cryptocurrency Portfolios
#420 – Geographical Country Momentum

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