{"id":37020,"date":"2024-11-10T17:38:30","date_gmt":"2024-11-10T16:38:30","guid":{"rendered":"https:\/\/quantpedia.com\/?p=37020"},"modified":"2024-12-14T11:08:06","modified_gmt":"2024-12-14T10:08:06","slug":"quantpedia-premium-update-november-7th","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-november-7th\/","title":{"rendered":"Quantpedia Premium Update – November 7th"},"content":{"rendered":"

New Strategies<\/h4>\n\n\n\n

#1065 – Enhanced ETF Sector Momentum Strategy<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Moderately complex strategy
Backtest period:<\/strong> 1998-2024
Indicative performance:<\/strong> 9.16%
Estimated volatility:<\/strong> 12.83%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Belusk\u00e1, So\u0148a and Vojtko, Radovan: How to Improve ETF Sector Momentum<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4988543<\/a>
Abstract:
In this article, we explore the historical performance of sector momentum strategies and examine how their alpha has diminished over time. By analyzing the underlying causes behind this decline, we identify key factors contributing to the underperformance. Most importantly, we introduce an enhanced approach to sector momentum, demonstrating how this solution significantly improves the performance of an ETF sector momentum strategy, making it once again an effective tool for systematic investors.<\/p>\n\n\n\n

#1066 – Bitcoin as a Leading Indicator for Stock Market<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Intraday
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> CFDs, ETFs, futures
Complexity:<\/strong> Moderately complex strategy
Backtest period:<\/strong> 2020-2024
Indicative performance:<\/strong> 15.96%
Estimated volatility:<\/strong> 15.5%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Melchin, Derek: Bitcoin as a Leading Indicator<\/strong>
https:\/\/www.quantconnect.com\/research\/17902\/bitcoin-as-a-leading-indicator\/p1<\/a>
Abstract:
Leading and lagging indicators serve as crucial tools for traders seeking to manage capital in the financial markets. In this research post, we\u2019ll review what leading indicators are, why Bitcoin\u2019s price action can be used as a leading indicator for upcoming turbulence in the US Equity markets, and how to implement a trading strategy with the LEAN trading engine that uses this information. The results show that rotating capital between US Equities and cash based on Bitcoin\u2019s price action increases the risk-adjusted returns of long-term Equity investors.<\/p>\n\n\n\n

#1067 – Factor Pockets<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 1975-2022
Indicative performance:<\/strong> 39.73%
Estimated volatility:<\/strong> 21.98%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Li, Sophia Zhengzi and Yuan, Peixuan and Zhou, Guofu: Pockets of Factor Pricing<\/strong>
https:\/\/ssrn.com\/abstract=4661444<\/a>
Abstract:
Current factor models assume certain pre-specified factors can price or explain asset returns with the same level of ability across time. In contrast with this conventional wisdom, we find that the factor\u2019s pricing ability exhibits notable temporal variations, and it tends to cluster in certain periods referred to as \u201cpockets.\u201d We propose a real-time approach to effectively identify the pockets, and apply it to a comprehensive set of firm characteristics. We find episodic and distinct dynamics of return predictability for different types of characteristics, challenging the notion of continuous presence of the same factors with consistent pricing ability. By leveraging the time-varying predictive power of factors, we construct a composite predictor that achieves a value-weighted hedge return of 3.94% per month with a high t-statistic of 13.87. Furthermore, the composite factor pricing model, which incorporates a selection of factors with factor timing, demonstrates superior effectiveness in both explaining and predicting market anomalies. The factor also provides a comprehensive explanation for factor momentum, which is shown as a consequence of the past performance of factor returns.<\/p>\n\n\n\n

#1068 – Exponential FX Mean Reversion Strategy<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded:<\/strong> currencies
Instruments used for trading:<\/strong> CFDs, futures
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2007-2024
Indicative performance:<\/strong> 2.98%
Estimated volatility:<\/strong> 8.62%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Belusk\u00e1, So\u0148a and Vojtko, Radovan: How to Build Mean Reversion Strategies in Currencies<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5002058<\/a>
Abstract:
Our article explores a simple mean reversion trading strategy applied to FX futures, focusing on identifying undervalued and overvalued currencies to generate returns. Using FX futures rather than spot rates allows for the inclusion of interest rate differentials, simplifying the analysis. The strategy employs two position-sizing methods\u2014linear and exponential\u2014both rebalanced monthly based on currency deviations from their mean. While the linear method offers stability, its returns are limited. In contrast, the exponential method, despite higher risk and deeper drawdowns, ultimately delivers stronger growth and better overall performance by leveraging the mean reversion tendencies of FX pairs.<\/p>\n\n\n\n

#1069 – Holiday Momentum for Amazon<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> options
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2012-2024
Indicative performance:<\/strong> 1.39%
Estimated volatility:<\/strong> 1.4%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Melchin, Derek: Upcoming Holiday Momentum for Amazon<\/strong>
https:\/\/www.quantconnect.com\/research\/18186\/upcoming-holiday-momentum-for-amazon\/p1<\/a>
Abstract:
Retailers typically see an increase in sales volume leading into a holiday. The increase in sales can be from shoppers taking advantage of holiday sales or spending extra cash to buy gifts for friends and family. In this research post, we analyze trading opportunities for Amazon around Black Friday and Prime Day.<\/p>\n\n\n\n

#1070 – Threshold Overnight Comovement Strategy for FXI ETF<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2018-2023
Indicative performance:<\/strong> 14.34%
Estimated volatility:<\/strong> <\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Jung, Jiwon and Lee, Kiseop and Leung, Tim: Threshold Overnight Comovement Analysis of Intraday and Overnight Returns<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4946188<\/a>
Abstract:
This paper presents a novel practical framework for analyzing the interdependency between two stock markets with non-overlapping trading hours. We propose a statistical method to quantify and interpret the lead-lag effects in these asynchronous markets. To account for the lead-lag effects between intraday and overnight returns, we introduce a class of counting processes called the Threshold Overnight Comovement (TOC) processes, that measures the comovement frequency given a strength threshold. We examine the lagged correlations in returns between the S&P500 ETF (SPY) and China large-cap ETF (FXI), including the cross-correlations between intraday and overnight markets. Furthermore, we discuss a class of trading strategies based on lead-lag dynamics.<\/p>\n\n\n\n

New research papers related to existing strategies:<\/h4>\n\n\n\n

#207 – Value Factor \u2013 CAPE Effect within Countries<\/a><\/strong><\/p>\n\n\n\n

Jacob, Joshy and Raju, Rajan: Forecast or Fallacy? Shiller’s CAPE: Market and Style Factor Forward Returns in Indian Equities
<\/strong>https:\/\/ssrn.com\/abstract=4911989<\/a>
Abstract:
This paper explores the relationship between CAPE and future returns across a range of market benchmarks and long-only styles in the Indian equity market. We create and use a new Shiller CAPE series for India using indices that are publicly available. We find a statistically significant inverse relationship between CAPE values and forward returns across styles and periods, although CAPE may not strictly qualify as a market timing tool. In addition, there is a direct relationship between CAPE and drawdowns. These findings suggest that investors should adapt their expectations of investment returns based on CAPE value. We show that different size-and style-based indices have different reaction functions to the same starting value of CAPE with value and low volatility showing higher resilience. The study contributes to the evidence from the Indian market on the predictive power of valuations for future returns, improving our understanding of market dynamics.<\/p>\n\n\n\n

#21 – Momentum Effect in Commodities<\/a><\/strong>
#22 – Term Structure Effect in Commodities<\/a><\/strong>
#111 – Hedgers\u2019 Effect in Commodities<\/a><\/strong>
#285 – Spread (Basis) Momentum within Commodities<\/a><\/strong>
#424 – Long-Run Reversal in Commodity Returns<\/a><\/strong><\/p>\n\n\n\n

Nakagawa, Kei and Sakemoto, Ryuta: Prices of Risk Estimation for Commodity Factors<\/strong>
https:\/\/ssrn.com\/abstract=4952943<\/a>
Abstract:
This study investigates the prices of risk in cross-sectional commodity futures portfolios using a three-pass regression approach that is robust to model specification. We find that the prices of risk for commodity basis and value factors are important, with values of 1.2% and 2.0% per month, respectively. Moreover, we observe that the commodity factors do not price cross-sectional equity portfolios, resulting in a combination of the equity market and commodity factor portfolios achieving a high Sharpe ratio. Additionally, we demonstrate that the equity market factor has recently become more strongly associated with the cross-sectional commodity futures portfolios, suggesting the effects of financialization.<\/p>\n\n\n\n

#210 – Adaptive Asset Allocation<\/a><\/strong>
#851 – Adaptive Asset Allocation v.2<\/a><\/strong><\/p>\n\n\n\n

Valeyre, S\u00e9bastien: Optimal Trend Following Portfolios<\/strong>
https:\/\/ssrn.com\/abstract=4908749<\/a>
Abstract:
This paper derives an optimal portfolio that is based on trend-following signal. Building on an earlier related article, it provides a unifying theoretical setting to introduce an autocorrelation model with the covariance matrix of trends and risk premia. We specify practically relevant models for the covariance matrix of trends. The optimal portfolio is decomposed into four basic components that yield four basic portfolios: Markowitz, risk parity, agnostic risk parity, and trend following on risk parity. The overperformance of the proposed optimal portfolio, applied to cross-asset trading universe, is confirmed by empirical backtests. We provide thus a unifying framework to describe and rationalize earlier developed portfolios.<\/p>\n\n\n\n

#605 – Momentum on Straddles<\/a><\/strong><\/p>\n\n\n\n

Li, Shuaiqi and Yang, Lihai: Peer Option Momentum<\/strong>
https:\/\/ssrn.com\/abstract=4964637<\/a>
Abstract:
We document option momentum spillovers across peer firms with shared analyst coverage. Firms whose peers have higher past-12-month delta-hedged straddle returns tend to have higher future option returns. Peer option momentum has a magnitude similar to the option momentum documented by Heston et al. (2023), but it is distinct from option momentum. Past option returns of a firm’s peers can predict the firm’s realized variance even after we control the firm’s option-implied variance and its own past option returns. In addition, peer momentum is stronger for indirect linkages. The findings are consistent with limited investor attention on volatility information from peer firms. Compared with peer stock momentum, peer option momentum persists longer and exhibits spillovers via more economic linkages that cannot be subsumed by analyst-based linkages.<\/p>\n\n\n\n

#239 – Large Price Changes combined with Analyst Revisions<\/a><\/strong><\/p>\n\n\n\n

Wang, Lei: Analysts Disagreement and the Cross-Section of Stock Returns<\/strong>
https:\/\/ssrn.com\/abstract=4923972<\/a>
Abstract:
This dissertation investigates the relationship between market uncertainty and analyst forecast errors, focusing on how these forecast errors and analyst disagreement can be utilized as signals for constructing investment portfolios. The empirical analysis employs portfolio sorting methods to examine the performance of portfolios based on three anomalies: Adjusted Forecast Error (AFE), Analyst Disagreement (SDS), and Volatility in Disagreement (VDS). Quintile and decile sorts are used to compare equally-weighted and value-weighted portfolios. The results indicate that portfolios formed based on these signals, particularly when sorted into deciles, exhibit significant alpha, suggesting that larger forecast errors and greater analyst disagreement, which indicate higher market uncertainty, are associated with higher expected returns. Furthermore, the study explores the relationship between these signals and traditional Fama-French factors.
Our findings reveal a high correlation between AFE, SDS, and VDS-based portfolios and the market factor, underscoring the importance of market conditions in driving these returns. This research contributes to the literature on market efficiency and the predictive power of analyst forecasts, highlighting the critical role of uncertainty in asset pricing. Future work could extend this analysis to other regions and explore the impact of additional macroeconomic variables on the observed relationships.<\/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

The Impact of Methodological Choices on Machine Learning Portfolios<\/strong><\/a><\/p>\n\n\n\n

Studies using machine learning techniques for return forecasting have shown considerable promise. However, as in empirical asset pricing, researchers face numerous decisions around sampling methods and model estimation. This raises an important question: how do these methodological choices impact the performance of ML-driven trading strategies? Recent research by Vaibhav, Vedprakash, and Varun demonstrates that even small decisions can significantly affect overall performance. It appears that in machine learning, the old adage also holds true: the devil is in the details.<\/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

1062 – The Pre-Holiday Effect in Commodities<\/a>
1064 – Leveraged ETF Arbitrage Between SPY and TQQQ<\/a>
1065 – Enhanced ETF Sector Momentum Strategy<\/a>
1066 – Bitcoin as a Leading Indicator for Stock Market<\/a><\/p>\n\n\n\n\n\n\n\n\n\n\n\n

<\/p>\n\n\n\n\n\n

<\/p>\n\n\n\n

 <\/p>\n\n\n\n

\u00a0<\/p>\n

\u00a0<\/p>\n\n\n\n\n\n

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

Six new strategies have been added. Five new related research papers have been included into existing strategy reviews and one short free blog post<\/strong><\/a> has been published during last few weeks. Plus, four 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-37020","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/37020","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=37020"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/37020\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=37020"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=37020"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=37020"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}