{"id":38139,"date":"2024-12-27T22:59:13","date_gmt":"2024-12-27T21:59:13","guid":{"rendered":"https:\/\/quantpedia.com\/?p=38139"},"modified":"2025-02-04T13:14:04","modified_gmt":"2025-02-04T12:14:04","slug":"quantpedia-premium-update-december-26th","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-december-26th\/","title":{"rendered":"Quantpedia Premium Update – December 26th"},"content":{"rendered":"

New strategies:<\/h4>\n\n\n\n

#1080 – Stock-Bond Correlations and the Expected Country Stock Returns<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> ETFs, futures
Complexity:<\/strong> Moderately complex strategy
Backtest period:<\/strong> 1999-2022
Indicative performance:<\/strong> 7.06%
Estimated volatility:<\/strong> 13.61%<\/p>\n\n\n\n

Source paper:<\/strong>

Pyun, Sungjune: Stock-Bond Return Dynamics and the Expected Country Stock Returns<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4972568<\/a>
Abstract:
Stock and bond prices of a country move together with increasing country-specific risk. Bonds effectively hedge growth expectation risk when country-specific risk is low, resulting in a negative stock-bond correlation. However, as country-specific risk increases, hedging is less effective because 1) rising domestic prices tend to reduce a country\u2019s growth potential and 2) global growth expectation shocks are more persistent than country-specific ones. Consequently, countries with greater country-specific risk exhibit a relatively positive stock-bond correlation. Equity investments in these countries outperform those with negative relationships by 7\u201311% annually. The superior performance is not driven by investing in a fixed set of countries.<\/p>\n\n\n\n

#1081 – Price-to-Low Effect in Cryptocurrencies<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Weekly
Markets traded:<\/strong> cryptocurrencies
Instruments used for trading:<\/strong> cryptocurrencies
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2017-2024
Indicative performance:<\/strong> 161.2%
Estimated volatility:<\/strong> 150.21%<\/p>\n\n\n\n

Source paper:<\/strong>

Nakagawa, Kei and Sakemoto, Ryuta: Cross-sectional reversal portfolios in the cryptocurrency market and market uncertainty<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5001299<\/a>
Abstract:
This study explores whether a behavioral approach can enhance the profitability of cross-sectional reversal strategy in the cryptocurrency market. Building on findings from stock and commodity futures markets, we propose a new decomposition method for reversal portfolios where the highest and lowest prices during the formation periods serve as anchoring points. We find that these reversal portfolios generate higher returns than conventional cross-sectional momentum and reversal portfolios. These results hold across different portfolio formation periods, suggesting the presence of heterogeneous investment horizons. Moreover, our cryptocurrency reversal portfolios act as a hedge against increases in stock and gold market uncertainty.<\/p>\n\n\n\n

#1082 – Global Hairy Premium Strategy<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded:<\/strong> bonds
Instruments used for trading:<\/strong> swaps
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1920-2024
Indicative performance:<\/strong> 2.74%
Estimated volatility:<\/strong> 0.97%<\/p>\n\n\n\n

Source paper:<\/strong>

Della Corte, Pasquale and Georgievska, Ljubica and Saunders, Anthony and Tancheva, Zhaneta K.: The Hairy Premium<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4475821<\/a>
Abstract:
This paper studies the tendency of forward rates to systematically overestimate future spot rates in the bond market through the lens of a strategy that pays the floating rate in exchange for the fixed rate over a long-term holding period. We name this strategy the \u201chairy strategy\u201d because the overshooting behaviour of forward rates looks like hairs spread out over time. Since the late 80s, the hairy strategy based on 10-year US interest rates swaps has generated a hairy premium of 2.7% per annum, with a minimum of 0.6% and a maximum of 4.7%. The hairy premium remains sizeable to using more than a century\u2019s worth of US data and also holds for other major countries. We document that 45% of the Hairy premium variation can be explained by a single global factor, while 14% can be attributed to the conventional empirical term premium. Unlike the conventional empirical term premium, moreover, the Hairy premium exhibits a countercyclical dynamic that is positively related to recessions and negatively to inflation expectations, thus providing a hedge during bad times.<\/p>\n\n\n\n

New research papers related to existing strategies:
<\/h5>\n\n\n\n

#822 – Negative ESG Premium in Chinese Stock Market<\/a><\/strong><\/p>\n\n\n\n

Jiao, Diyang and Peng, Yumeng and Sun, Yihan and Yang, Yiqu and Hu, Sang and Zhou, Zihan: ESG Impact on Stock and Mean-Variance Portfolio: Evidence from China’s A-share Market<\/strong>
https:\/\/ssrn.com\/abstract=4993962<\/a>
Abstract:
This paper investigates the impact of ESG on individual stocks and mean-variance portfolios in China’s A-share market. Using stock data from 2017 to 2022, the regression results show that a higher ESG score leads to lower volatility, while the stock return and Sharpe ratio exhibit an inverse U-shape in the ESG score. By directly incorporating the ESG preference into the classical mean-variance framework, we examine the performance of portfolios given different ESG preferences using market data in 2023. We find that the portfolio with moderate ESG preference reaches the highest Sharpe ratio, striking a balance between profitability and sustainability. Furthermore, we compare the performances of the classical mean-variance portfolio and the ESG-motivated mean-variance portfolio, and it is shown that the portfolio with moderate ESG preference outperforms that without any ESG preference, indicating that investors can significantly improve their portfolio performance by considering ESG.<\/p>\n\n\n\n

#485 – Toxical Releases and Stock\u2019s Performance<\/a>
#614 – Climate sentiment, carbon prices and Emission minus Clean Portfolio<\/a><\/strong><\/p>\n\n\n\n

Atilgan, Yigit and Demirtas, K. Ozgur and Gunaydin, A. Doruk: Pollution Premium: Further Evidence<\/strong>
https:\/\/ssrn.com\/abstract=4974375<\/a>
Abstract:
This paper presents novel empirical evidence related to the pollution premium. First, investors are concerned about whether a firm has higher scaled emissions relative to its industry peers rather than companies in other sectors. Second, it is the emission intensity but not the level of or growth rate in total emissions that is priced in the cross-section. Third, the positive relation between emission intensity and future returns in a multivariate setting is dependent on the inclusion pf industry-fixed effects. Fourth, variables that proxy for limits-to-arbitrage and informational frictions do not account for the pollution premium. Fifth, there is no indication that firms with higher scaled emissions produce higher earnings surprises which does not support a mispricingbased explanation. Finally, firms with higher emission intensities have lower institutional ownership by investment advisers.<\/p>\n\n\n\n

#371 – Supply Chain Based Equity Strategy<\/a><\/strong><\/p>\n\n\n\n

Capponi, Agostino and Sidaoui, J. Antonio and Zou, Jiacheng: Graph Machine Learning for Asset Pricing: Traversing the Supply Chain and Factor Zoo<\/strong>
https:\/\/ssrn.com\/abstract=5031617<\/a>
Abstract:
We propose a nonparametric method to aggregate rich firm characteristics over a large supply chain network to explain the cross-section of expected returns. Each target firm receives a nonlinearly constructed pricing signal passed from neighboring firms that are within d-hops on the supply chain network. Analyzing all US-listed stocks with supply chain data, our model achieves over 50% higher out-of-sample Sharpe ratios compared to models using only direct suppliers and consumers, outperforming Fama-French five-factor and principal component models. Through a graph-Monte Carlo experiment, we demonstrate the interplay between d and degree centrality, showing that the most central firms are twice as sensitive as peripheral firms. Our recommended d = 6 balances bias-variance and ensures robustness.<\/p>\n\n\n\n

And several interesting free blog posts that have been published during the last 2 weeks:<\/h4>\n\n\n\n
Can We Use Active Share Measure as a Predictor?<\/a><\/h5>\n\n\n\n

Active Share is a popular metric used to gauge how actively managed a portfolio is compared to its benchmark, but its predictive power for fund performance is questionable. Our research suggests that high Active Share often reflects exposure to systematic equity factors rather than genuine stock-picking skill. Additionally, inaccuracies in benchmark selection can distort the metric\u2019s insights, making it unreliable as a standalone measure. A more effective approach is to conduct a factor analysis of alpha<\/a> to better understand a manager\u2019s performance and true sources of over\/underperformance.
<\/a><\/p>\n\n\n\n

Design Choices in ML and the Cross-Section of Stock Returns<\/a><\/h5>\n\n\n\n

For those who have not yet had the chance to read it, we recommend the latest empirical study by Minghui Chen, Matthias X. Hanauer, and Tobias Kalsbach, which shows that design choices in machine learning models, such as feature selection and hyperparameter tuning, are crucial to improving portfolio performance. Non-standard errors in machine learning predictions can lead to substantial portfolio return variations, and authors are highlighting the importance of robust model evaluation techniques.<\/p>\n\n\n\n

Front-Running Seasonality in US Stock Sectors<\/a><\/h5>\n\n\n\n

Seasonality plays a significant role in financial markets and has become an essential concept for both practitioners and researchers. This phenomenon is particularly prominent in commodities, where natural cycles like weather or harvest periods directly affect supply and demand, leading to predictable price movements. However, seasonality also plays a role in equity markets, influencing stock prices based on recurring calendar patterns, such as month-end effects or holiday periods. Recognizing these patterns can provide investors with an edge by identifying windows of opportunity or risk in their investment strategies.<\/p>\n\n\n\n

Plus, the following trading strategies have been backtested in QuantConnect<\/a> in the previous two weeks:<\/h4>\n\n\n\n

487 – Mean-Variance Market Timing in the FX Market<\/a>
1076 – Impact of EIA Inventory Announcements on Crude Oil Prices<\/a>
1079 – Commodities Seasonal Front-Run Strategy<\/a><\/p>\n\n\n\n

<\/p>","protected":false},"excerpt":{"rendered":"

Three new strategies have been added. Three new related research papers have been included into existing strategy reviews and three short free blog posts<\/strong><\/a> have been published during last few weeks. Plus, three trading strategies have been backtested in QuantConnect<\/strong><\/a> in the previous two weeks.<\/p>","protected":false},"author":25721,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-38139","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/38139","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/users\/25721"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=38139"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/38139\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=38139"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=38139"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=38139"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}