Quantpedia Premium Update – April 24th

New strategies:

#991 – Cryptocurrency Market Dynamics Around Bitcoin Futures Expiration Events

Period of rebalancing: Daily
Markets traded: cryptocurrencies
Instruments used for trading: CFDs, cryptos, ETFs, funds, futures
Complexity: Simple strategy
Backtest period: 2021-2024
Indicative performance: 22.51%
Estimated volatility: 13.87%

Source paper:

Vojtko, Radovan and Dujava, Cyril: Cryptocurrency Market Dynamics Around Bitcoin Futures Expiration Events
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4779669
Abstract:
In the rapidly evolving landscape of cryptocurrency markets, understanding the underlying dynamics that drive price movements and investor sentiment can be a matter of survival. However, there are myriad facets of trading reality, and the only thing that we can do is to slowly understand them one after another, one step at a time. This article picks one corner of the cryptocurrency market and sheds a little light on it. We have already written a few times about the importance of the introduction of Bitcoin futures and their impact on the Bitcoin price. Therefore, in this article, we will specifically examine Bitcoin’s behavior around the critical events when Bitcoin futures expire.

#992 – Buy the Dip after Slow Decrease from 52W High in S&P 500

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: CFDs, ETFs, funds, futures
Complexity: Simple strategy
Backtest period: 1980-2022
Indicative performance: 5.55%
Estimated volatility: –

Source paper:

Thrasher, Andrew: The 5% Canary
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4417394
Abstract:
This paper will show that the amount of time taken for the S&P 500 or Dow Jones Industrial Average to decline by 5% from a 52-week high illustrates significant insight to the subsequent trend in price. A study on price history for two major equity indices is conducted to show a relationship between duration of an initial decline and the potential for further material weakness or the opportunity for the index to reverse and move higher. With the examination of an 18th century mathematical challenge, a simple lens into the market is shown and evaluated, resulting in a tool that retail and professional investors may apply to markets in the pursuit of capital preservation and appreciation.

#993 – Actively Using Passive Sectors to Generate Alpha Using the VIX

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: ETFs, funds
Complexity: Moderately complex strategy
Backtest period: 1999-2019
Indicative performance: 12.04%
Estimated volatility: 16.96%

Source paper:

Gayed, Michael: Actively Using Passive Sectors to Generate Alpha Using the VIX
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3718824
Abstract:
A significant amount of academic research has documented momentum within and across broad sectors of the stock market as a means of generating alpha over a passive benchmark. However, few studies approach sector allocation from a mean reversion perspective using the Chicago Board of Exchange (CBOE) Volatility Index (VIX) as the trigger. We find that positioning into defensive sectors during periods of low volatility for the stock market, and into cyclical sectors during periods of high volatility produces significant long-term alpha. Using this framework, we back-test a dollar neutral strategy documenting return differentials, and create a modified S&P 500 Index that over-weights and underweights cyclical and defensive sectors systematically based on VIX levels. Absolute and relative returns for a sector allocation strategy that uses VIX levels significantly outperforms a passive buy and hold approach by using mean reversion to generate alpha. We postulate that the approach likely works because of behavioral biases related to loss aversion and the disposition effect creating mispricing that are repeatable and exploitable during periods of extreme market stress.

#994 – Trended Momentum

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1927-2020
Indicative performance: 11.34%
Estimated volatility: 13.48%

Source paper:

Cai, Charlie Xiaowu and L, Peng and Keasey, Kevin, Trended Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4740445
Abstract:
Our study demonstrates that a distinct price trend significantly enhances the momentum strategy, yielding an 11.34% annualized return in the highest trend clarity quintile portfolio—more than double that of a traditional momentum strategy. Clear trend momentum mitigates immediate post-formation reversals and postpones medium-term reversals by 15 months compared to less distinct trends. We find this trended momentum effect to be more pronounced in developed markets, which exhibit stronger, clearer price trends during the formation period. Our study highlights the importance of price path analysis and the role of momentum traders in asset pricing models and trading strategies.

New research papers related to existing strategies:

#470 – Macroeconomic Announcement Beta Strategy
#471 – Macroeconomic Announcement Beta Reversal

Chen, Jingjing and Jiang, George: Investor Risk Appetite and High-Beta Stock Valuation Around Macroeconomic Announcements
https://ssrn.com/abstract=4706283
Abstract:
We document a dramatic swing of high-beta stock returns around pre-scheduled macroeconomic announcements – from being negative on the day before, to positive on the day of, and negative again on the day after announcements. A feasible long-short strategy of betting against beta and betting on beta yields annualized 25.28% cumulative return over the three-day announcement window. Trading activities suggest that some (institutional) investors actively trade high-beta stocks to adjust risk exposure around the announcement. Options trading shows corroborating evidence that investors are averse to risk on days before and after announcements but willing to take risk on announcement days.

#101 – Earnings Revision Strategy
#239 – Large Price Changes combined with Analyst Revisions

McLean, R. David and Pontiff, Jeffrey and Reilly, Christopher: Retail Investors and Analysts
https://ssrn.com/abstract=4737062
Abstract:
Do retail investors respond to analysts’ revisions? We consider revisions in recommendations, price targets, and EPS forecasts. Revisions in recommendations and price targets portend greater retail trading in the direction of the revision. Revisions in EPS forecasts create more retail trading, regardless of the direction of the revision. The effects of recommendation revisions are stronger with All-Star Analysts. Retail investors trade in anticipation of revisions in price targets and recommendations. Retail trades earn higher returns when aligned with analysts’ revisions. Retail investors are one channel through which analysts’ information is transmitted into prices.

#832 – Presidential Fiscal News and Cross-section of Stock Returns

Venkataraman, Ajay: Finfluencer-in-Chief
https://ssrn.com/abstract=4736677
Abstract:
I investigate the relationship between sentiment expressed in White House presidential communications and expected returns across various asset classes in the post-war United States. I construct a novel text-based measure of political sentiment based on presidential correspondence. Exposure to this White House Sentiment factor carries an annualized negative risk premium of approximately 4%. The results further unveil a flight-to-safety phenomenon – while safe assets like treasuries and CDS earn positive risk compensation amid adverse sentiment, risky equities exhibit negative premiums. This effect concentrates in Republican administrations, suggesting that heightened risk aversion prevails during Republican eras, ensuing from the relatively more negative tone that prompts investor preference for safer securities, offering an explanation of the presidential puzzle.

#83 – Pre-Holiday Effect

Fleming, Grant Alan and Liu, Zhangxin (Frank) and Merrett, David and Ville, Simon: Are Investors Attentive Before a One-Off Holiday?
https://ssrn.com/abstract=4734139
Abstract:
We examine trading patterns around regular and one-off public holidays on the Sydney Stock Exchange using a novel dataset covering a fifty-year period. We find that trading volume was significantly lower in the day before a regular public holiday and higher the day after a regular public holiday, supporting the investor inattention hypothesis. However, the holiday effect is not evident for one-off (irregular) holidays associated with royal visits, deaths or military events. We conclude that the holiday effect only applies for regular (annual) holidays known well in advance, providing a fresh perspective on how predictability influences investor activity.

#614 – Climate sentiment, carbon prices and Emission minus Clean Portfolio
#679 – Carbon Emmision Intensity in Stocks
#707 – Benchmarks Portfolios with Decreasing Carbon Footprints

Li, Feng and Zheng, Xingjian: Carbon Awareness and Return Co-movement
https://ssrn.com/abstract=4719109
Abstract:
This paper documents a rise in investors’ carbon awareness in recent years with a novel model-free approach. We show that companies emitting carbon dioxide at similar scales experience a higher correlation in their stock returns. This emission-return co-movement only became significant after 2012 and has been steadily increasing ever since, whereas it was barely significant before 2012. We provide evidence showing that this co-movement is driven by investor flows as investors purchase green stocks and divest brown stocks, and is stronger when investors pay more attention to environmental news. To address the endogeneity issue, we adopt a state’s emission reduction initiatives that exogenously increase firms’ emission similarity. Overall, By examining the increasing significance of carbon risk pricing, our research offers fresh insights into the evolving preferences of investors and demonstrates an increasing market sensitivity to carbon risk.

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

FX Carry + Value + Momentum Strategies over Their 200+ Year History

We mentioned multiple times that we at Quantpedia love historical analysis that spans over a long period of time as it offers a unique glimpse into the different macro environments and periods of political and economic instabilities. These long-term studies help a lot in risk management, and they also help investors set the right expectations about the range of outcomes in the future. Historical analysis of equity and fixed-income markets is not rare, but currency markets are less explored. Therefore, we are happy to investigate a recent paper by Joseph Chen that analyzes carry, momentum, and value strategies in the currency markets over the 200-year history.

Impact of Business Cycles on Machine Learning Predictions

As an old investing adage goes, “Everybody’s a genius in a bull market.” It is easy to fall victim to the Dunning-Kruger effect, where attribution bias makes us mistake our luck for abilities. When the business cycles change, there are great problems with precise stock price predictability. And this is not the only problem for humans, who are baffled by many mental heuristics. Machine learning algorithms experience similar problems, too. What is happening, and why is it so? A new paper by Wang, Fu, and Fan gives an explanation and proposes some remedies …

Can Google Trends Sentiment Be Useful as a Predictor for Cryptocurrency Returns?

In the fast-paced world of cryptocurrencies, understanding market sentiment can provide a crucial edge. As investors and traders seek to anticipate the volatile movements of Bitcoin, innovative approaches are continuously explored. One such method involves leveraging Google Trends data to gauge public interest and sentiment towards Bitcoin. This approach assumes that search volume on Google not only reflects current interest but can also serve as a predictive tool for future price movements. This blog post delves into the intricacies of using Google Trends as a sentiment predictor, exploring its potential to forecast Bitcoin prices and discussing the broader implications of sentiment analysis in the financial market.