Quantpedia Premium Update – 31st July

New strategies:

#769 – Disposition Effect in China

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1999-2016
Indicative performance: 20.41%
Estimated volatility: 36.7%

Source paper:

Liu, Jinpeng: Behavioural Factors in China Stock Market
https://ssrn.com/abstract=4134890
Abstract:
Based on three behavioural biases: overconfidence; disposition effect and herding, we construct three behavioural factors for China stock market. Compared with traditional factors, behavioural factors are able to provide incremental explanation ability and predictability of portfolio returns. To accommodate significant anomalies, our behavioural models outperform the traditional factor models and two popular new factor models. This paper sheds light on the behavioural approach to study China stock market both for academia and industry.

#770 – Coreversal in Chinese Equities

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex Strategy
Backtest period: 2000-2020
Indicative performance: 12.42%
Estimated volatility: 13.75%

Source paper:

Liu, Xin, and Qiu, Zhigang and Shen, Luyao and Zheng, Weinan, Coreversal: The Booms and Busts of Arbitrage Activities in China
https://ssrn.com/abstract=4122691
Abstract:
This paper investigates arbitrage activities in China’s stock market to examine whether arbitrageurs destabilize stock prices. We focus on reversal anomaly and construct a measure of arbitrage intensity, coreversal, which captures the abnormal return correlation among stocks on which a reversal strategy would speculate. In times of low reversal arbitrage, the reversal strategy exhibits delayed correction, taking up to three years for abnormal returns to be realized. However, when reversal arbitrage is high, prices overshoot and then revert in the long run, reflecting prior overreaction from crowded reversal trading which pushes prices away from fundamentals.

#771 – Enduring Momentum in Stocks

Period of rebalancing: 6 Months
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1980-2018
Indicative performance: 26.31%
Estimated volatility: 38.21%

Source paper:

Zeng, Hui and Marshall, Ben R. and Nguyen, Nhut H. and Visaltanachoti, Nuttawat: Enduring Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4082543
Abstract:
We use firm characteristics to estimate the enduring momentum probabilities for past winners (losers) to continue to be future winners (losers). The enduring momentum probability is significantly related to stock return persistence and explains cross-sectional expected returns. In addition, it contains different information from momentum signals. Combining the two pieces of information generates an enduring momentum strategy that produces a 2.19% return per month, almost doubling the momentum return. Factors that drive the price momentum strategy, such as seasonality, limit to arbitrage, and transaction costs, do not fully capture the performance of the enduring momentum strategy.

#772 – Geopolitical Risk and the Cross-Section of Cryptocurrency Returns

Period of rebalancing: Weekly
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Moderately complex strategy
Backtest period: 1980-2019
Indicative performance: 187.05%
Estimated volatility: 39.8%

Source paper:

Long, Huaigang and Demir, Ender and Bedowska-Sojka, Barbara and Zaremba, Adam and Hussain Shahzad, Syed Jawad: Is Geopolitical Risk Priced in the Cross-Section of Cryptocurrency Returns?
https://ssrn.com/abstract=4109293
Abstract:
We examine the role of geopolitical risk in the cross-sectional pricing of cryptocurrencies. We calculate cryptocurrency exposure to changes in the geopolitical risk index and document that coins with the lowest geopolitical beta outperform those with high geopolitical beta. Our findings suggest that risk-averse investors require additional compensation as motivation to hold cryptocurrencies with low and negative geopolitical betas, and they are willing to pay a premium for assets with high and positiv

New research papers related to existing strategies:

#685 – Boosted Trees and Cryptocurrency Return Prediction

Toledo, Jefferson de Morais and Souza, Damires Yluska: Signal Prediction in Cryptocurrency Tradeoperations: A Machine Learning-Based Approach
https://ssrn.com/abstract=4062476
Abstract:
Deciding the right time to purchase a cryptocurrency is a crucial factor in enhancing a return on a given investment. In this work, we propose the use of gradient boosting algorithms (XGBoost and LightGBM) to perform the prediction of a binary market entry signal. For that, we use as features, in addition to prices and trading volumes, some technical market indicators. We use PCA (Principal Component Analysis) to reduce the dimensionality of the training/test datasets and Bayesian optimization to tune hyperparameters of the classification models. We have verified that the resulting strategy presents better results than a simple buy-and-hold of cryptocurrencies in the portfolio.

#314 – Sector Rotation Strategy Based on Multivariate Regression Analysis

Hu, Qiaozhi (George): A Markov Regime Switching Model for Asset Allocation
https://ssrn.com/abstract=4094459
Abstract:
This paper develops an innovative regime switching multi-factor model accounting for the different regime switching behaviors in the systematic and idiosyncratic components of asset returns. A Gibbs sampling approach for estimation is proposed to deal with the computational challenges that arise from a large number of assets and multiple Markov chains. Finally, a dynamic asset allocation problem is studied under this model. In the empirical analysis, the model is applied to study the sector ETFs. The idiosyncratic volatilities of different sectors’ returns exhibit a strong degree of covariation and state-dependent patterns, which are different from the dynamics of their systematic components. The out-of-sample dynamic asset allocation experiments show that the new regime switching model statistically significantly outperformed the linear multi-factor model and conventional regime switching models driven by a common Markov chain. These results suggest that it is not only important to account for regimes in portfolio decisions, but correct specification about the regimes’ structure and number is of equal importance.

#699 – Stock and Bond Returns Predict Currency Returns

Phylaktis, Kate and Yamani, Ehab Abdel-Tawab: The Internet Appendix to Accompany Foreign Currency Forecasting: What Can Stock and Bond Markets Tell Us?
https://ssrn.com/abstract=4032002
Abstract:
In this Appendix, we present the results of four supplementary robustness checks including controlling for various macro risks (Appendix A), different time periods (Appendix B), a different method of forecast construction (Appendix C), and different trading strategy (Appendix D).

#5 – FX Carry Trade

Söderlind, Paul and Somogyi, Fabricius: FX Liquidity Risk and Carry Trade Premia
https://ssrn.com/abstract=4067387
Abstract:
The foreign exchange (FX) market is considered to be the largest and presumably most liquid financial market in the world. We show that even in this market exposure to liquidity risk commands a non-trivial risk premium of up to 3.6% per annum. In particular, systematic and currency-specific liquidity risk are not subsumed by existing risk factors and successfully price the cross-section of currency returns. However, we also find that liquidity and carry trade premia are significantly correlated. This lends support to a liquidity-based explanation of the carry trade risk premium. To illustrate this point, we decompose carry trade returns and show that the commonality with liquidity risk stems from periods of high market stress and is confined to the static but not the dynamic carry trade.

#753 – Overnight Seasonality in Bitcoin

Jahanshahloo, Hossein and Corbet, Shaen and Oxley, Les, Seeking Sigma: Time-of-the-Day Effects on the Bitcoin Network
https://ssrn.com/abstract=4055551
Abstract:
This research investigates and tests for the presence of time-of-the-day effects on the Bitcoin network. Results indicate that NYSE trading sessions lead Bitcoin trading activity, both on the blockchain and centralised exchanges. Effects are found to have strengthened over time, however, simultaneously diminished at the weekend indicating significant exchange interactions, and that Bitcoin has developed somewhat outside its intended design parameters, and is influenced by other forces such as those originating from NYSE trading. While proponents consider Bitcoin trading to be `24/7′, our findings suggest that both transaction and on-chain network activity are best described to be, at best, `12/5′, presenting significant implications for traders, with regards to centralised exchange liquidity and the speed of their transaction inclusion on the blockchain. Finally, the role and influence of both algorithm and volatility traders cannot be eliminated.

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

The Importance of Factor Construction Choices

Choosing the correct portfolio-construction techniques is very important. The new paper that is written by Amar Soebhag, Bart van Vliet, and Patrick Verwijmeren explores the various ways in which different design choices in portfolio construction can, either intentionally or unintentionally, influence and distort the statistical results of a market factor’s research. Their takeaway is that seemingly small differences in design can significantly impact the resultant portfolio’s performance.

The Buffett Indicator

Despite Warren Buffett’s claim that the MVE/GDP ratio is “probably the best single measure of where valuations stand at any given moment,” its predictive ability has been the subject of relatively little academic scrutiny. A novel paper by Swinkels and Umlauft (2022) fills this gap and examines whether the MVE/GDP ratio can forecast international equity returns, which complements the existing research limited to the United States. A simple trading strategy that invests in countries with the highest model-predicted returns yields statistically significant and economically meaningful alpha over a corresponding buy-and-hold benchmark while experiencing lower volatility and maximum drawdown.