Quantpedia Premium Update – November 23rd

New Strategies:

#1071 – Interday Cross-Sectional Momentum

Period of rebalancing: Intraday
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2021-2023
Indicative performance: 5.7%
Estimated volatility: 2.53%

Source paper:

Schlie, Zhou: Interday Cross-Sectional Momentum: Global Evidence and Determinants
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4934543
Abstract:
We examine whether half-hour returns predict half-hour returns on subsequent days at the firm level, using a novel set of high-frequency data on firms that constitute the stock market indices of nine developed markets. We show that interday cross-sectional momentum (ICSM) exists in all markets of our sample. It is most pronounced during the last half hour of a trading day. In the previously studied U.S. market, ICSM has become weaker. Based on trading motives derived from the literature, we propose and test four hypotheses that link the strength of ICSM in the last half hour to firm-level market characteristics. We find that ICSM is stronger when volatility is low, and absolute overnight returns are small, while liquidity has no significant effect. Finally, we show that international investors can save transaction costs of economically significant size by strategically timing trades based on ICSM.

#1072 – Leveraging the Low-Volatility Effect

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1990-2023
Indicative performance: 1.9%
Estimated volatility: 11.2%

Source paper:

van der Linden, Lodewijk and Soebhag, Amar and van Vliet, Pim: Leveraging the Low-Volatility Effect
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4995381
Abstract:
Low-volatility has become a mainstream investment style over the past two decades, recognized for delivering high risk-adjusted returns. However, many investors fail to fully capitalize on this strategy due to benchmark constraints. Low-volatility stocks tend to lag during prolonged bull market, a challenge that can be addressed using leverage. This paper outlines five use cases to leverage upon the low-volatility effect, including an enhanced strategy, an alternative to the 60/40 asset allocation, and the use of long- and short-extension with stocks and market futures. These approaches help investors aiming to meet objectives ranging from stable performance, consistent outperformance, market-neutral returns, or as an alternative for put options, unlocking the full potential of this underutilized factor.

#1073 – How To Profitably Trade Bitcoin’s Overnight Sessions

Period of rebalancing: Daily
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Simple strategy
Backtest period: 2015-2024
Indicative performance: 68.38%
Estimated volatility: 28.43%

Source paper:

Vojtko, Radovan and Dujava, Cyril: How To Profitably Trade Bitcoin’s Overnight Sessions?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5021138
Abstract:
As interest in cryptocurrencies continues to surge, driven by each new price rally, crypto assets have solidified their position as one of the main asset classes in global markets. Unlike traditional assets, which primarily trade during standard working hours, cryptocurrencies trade 24/7, presenting a unique landscape of liquidity and volatility. This continuous trading environment has prompted us to investigate how Bitcoin, the flagship cryptocurrency, behaves across intraday and overnight periods. With Bitcoin’s growing availability to both retail and institutional investors through ETFs and other investment vehicles, we hypothesized that trading activity in these distinct timeframes could reveal patterns similar to those seen in traditional markets, where returns are often impacted by liquidity shifts during off-peak hours.

#1074 – Google Trends Predict Grain Prices

Period of rebalancing: Monthly
Markets traded: commodities
Instruments used for trading: CFDs, ETFs, futures
Complexity: Simple strategy
Backtest period: 2004-2019
Indicative performance: 14.6%
Estimated volatility: 44.24%

Source paper:

Gómez Martínez, Raúl and Orden Cruz, Carmen and Prado Román, Camilo: Google Trends as Predictor of Grain Prices
http://dx.doi.org/10.5937/ekoPolj2101203G
Abstract:
This paper examines the predictive power of Google trends on the grain’s futures price movement. The aim was to validate if an algorithmic trading system designed was profitable and able of beating the market. In the research was used data from soybean futures and corn futures, both contracts are listed in the Chicago Mercantile Exchange. The results of the research show that its forecasting power is high when predicting soybean futures and corn futures prices. According to the findings, the formulation of such predictive analysis is a good option for individual traders, investors, and commercial firms.

#1075 – Antifragile Asset Allocation

Period of rebalancing: Monthly
Markets traded: bonds, commodities, currencies, equities, REITs
Instruments used for trading: ETFs
Complexity: Moderately complex strategy
Backtest period: 2003-2019
Indicative performance: 18.57%
Estimated volatility: 7.7%

Source paper:

Giordano, Gioele: Antifragile Asset Allocation Model
https://www.naaim.org/wp-content/uploads/2019/05/00K_Antifragile-Asset-Allocation-Model_GioeleGiordano_1st-Place.pdf
Abstract:
Most of the active investment strategies focus on the constant excess returns generation over time, through a dynamic management of positions on the market. These positions are subject to possible Black Swans, events that are by definition unpredictable, destructive and only explainable afterwards. The conventional approach to risk management is to diversify investments across asset classes, however the crashes of 2001 (Dotcom bubble) and 2008 (Great Financial Crisis) questioned those portfolios so far considered well diversified. The risk of such events occurring is called tail risk. Over the last few years, many tail risk protection strategies have spread, often producing unsatisfactory results. This paper aims to demonstrate how the combination of an active quantitative investment model and an effective tail risk hedging strategy leads to the creation of an antifragile portfolio, immune to the black swans and able to exploit them to their advantage.

New research papers related to existing strategies:

#6 – Bond Carry Strategy

Min, David and Dong, Audrey: The Drivers of Global Government Bond Returns https://ssrn.com/abstract=4948827
Abstract:
This paper examines the relation between forward rates and expected returns of global government bonds. We find that current forward rates contain reliable information about differences in future realized returns across government bonds of different maturity ranges and currencies of issuance. Motivated by these findings, we examine US and global portfolios that vary their exposures to maturity and currency of issuance by systematically emphasizing bonds with higher forward rates. We find that such dynamic yield curve selection and positioning can add value for investors over the long run and in different interest rate environments.

#914 – Bitcoin Leads Altcoins on Intraday Basis

Belhoula, Mohamed Malek: Dynamic Causal Relationships between Bitcoin Trading Volume and Forks’ Performance: A Quantile-Based Analysis Across Market Conditions
https://ssrn.com/abstract=4993559
Abstract:
This study examines the cross-asset causal relationship between Bitcoin trading volume and the returns and volatility of its forks, using daily data from January 1, 2018, to August 7, 2024. We employ two advanced methodologies: the non-parametric causality in quantiles approach and the quantile connectedness framework. Our findings reveal that Bitcoin serves as a leading asset, with its trading volume exhibiting significant predictive power over the returns and volatility of several of its forks, including Bitcoin Cash (BCH), Bitcoin Gold (BTG), Bitcoin SV (BSV), Litecoin (LTC), and Bitcoin Diamond (BCD), particularly during extreme market conditions. Additionally, our results indicate that Bitcoin typically spearheads market movements, with its forks generally lagging and responding to changes in Bitcoin’s trading volume. This predictive power is not consistent across all market states; during bearish phases, Bitcoin’s trading volume is strongly associated with significant negative returns for its forks. Furthermore, most of forks exhibit strong net receiving behavior, where increases in Bitcoin trading volume exacerbate declines in their returns, suggesting potential contagion effects. This underscores the critical interconnectedness of these assets, particularly during periods of market stress. Investors and institutions can utilize these insights to enhance decision-making, mitigate risks, and seize opportunities arising from the dynamic relationship between Bitcoin trading volume and the performance of its forks, especially under volatile market conditions.

#485 – Toxical Releases and Stock’s Performance
#614 – Climate sentiment, carbon prices and Emission minus Clean Portfolio

Demirtas, K. Ozgur and Atilgan, Yigit and Gunaydin, A. Doruk: Pollution Premium: Further Evidence
https://ssrn.com/abstract=4986905
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 of 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 mispricing-based explanation. Finally, firms with higher emission intensities have lower institutional ownership by investment advisers.

#25 – Size Factor – Small Capitalization Stocks Premium

Gao, Cheng and Yuan, Peixuan: On the Nature of Size Effect: Biased Beliefs Behind Bad News Drift
https://ssrn.com/abstract=4958810
Abstract:
We attribute the seeming disappearance of the size effect to the impact of post-bad-news drift. Size effect is ever-present; however, it is repeatedly obscured by the sluggish response of small stocks to negative information. The real size effect remains robust and strong across various time periods, industries, and global markets, particularly manifesting itself upon the arrival of news. Our findings suggest that the size effect is primarily driven by mispricing due to biased expectations. Investors tend to oversell small firms with poor past performance and neglect those with high future potential, leading to the systematic undervaluation of small stocks.

#300 – Overnight Momentum Strategy

Perreten, Thomas and Wallmeier, Martin: Overnight Return Momentum and the Timing of Trading Volume
https://ssrn.com/abstract=5004991
Abstract:
We document that the overnight return anomaly for S&P 500 firms is related to the pattern of intraday trading volume: stocks with heavy trading near the open (U-shaped pattern) tend to have higher overnight returns than stocks with thin trading near the open (L-shaped pattern). This finding is inconsistent with explanations of the overnight anomaly based on distorted opening prices due to thin trading near the open. Our results suggest that over the sample period from October 2008 to December 2023, the overnight break played a crucial role in a gradual adjustment to higher price multiples, as evidenced by the increase in the Campbell-Shiller P/E ratio from 15 to 31. In this view, the overnight momentum in this period reflects the momentum in the Campbell-Shiller P/E ratio, moderated by differential but stable trade timing patterns.

#7 – Low Volatility Factor Effect in Stocks

Blitz, David and Howard, Clint and Huang, Danny and Jansen, Maarten: Low-Risk Alpha Without Low Beta
https://ssrn.com/abstract=5005746
Abstract:
We propose a risk-managed approach to capturing the low-volatility anomaly. Leveraging multifactor low-risk portfolios to a beta of 1.0 while controlling tracking error amplifies strategy returns and information ratios. Across developed and emerging markets, this levered low-risk strategy outperforms the market and traditional low-risk portfolios. Outperformance is driven by the strategy’s low-risk tilt rather than leverage effects. Our results suggest that investors who are able to overcome leverage constraints are able to harvest the low-volatility anomaly more efficiently.

#137 – Trend-following in Futures Markets
#1026 – Combining Systematic Trend-Following and Global Macro Strategies

Braun, Steven and Hoffstein, Corey and Jablecki, Juliusz, In Pursuit of Trend-Following Beta: The Promise and Pitfalls of Replication
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4990063
Abstract:
Against the background of a large body of research documenting the benefits of allocating to trend-following strategies, this paper identifies two related challenges facing potential allocators: mis-specifying and poorly executing a trend-following program oneself or selecting a manager who does the same. Both risks can overwhelm the diversification benefits of the strategy and so we examine whether they can be mitigated through replication of a broad index of trend-following funds. Using a series of numerical tests, we confirm that replication is indeed possible, both in an idealized case of a virtual fund of funds (via an ensemble of generic trend-following strategies) and in the more realistic scenario of an index composed of live funds (the BarclayHedge BTOP50 Index). However, we also show that simple regression-based replication introduces its own tracking risk driven by the trade-off between fee savings and skill leakage. We analyze the trade-off numerically and show that the effectiveness of replication can be improved through regularization techniques rendering it attractive as an active strategy that is able to generate structural excess returns versus the benchmark by avoiding fees.

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

How To Profitably Trade Bitcoin’s Overnight Sessions?

As interest in cryptocurrencies continues to surge, driven by each new price rally, crypto assets have solidified their position as one of the main asset classes in global markets. Unlike traditional assets, which primarily trade during standard working hours, cryptocurrencies trade 24/7, presenting a unique landscape of liquidity and volatility. This continuous trading environment has prompted us to investigate how Bitcoin, the flagship cryptocurrency, behaves across intraday and overnight periods. With Bitcoin’s growing availability to both retail and institutional investors through ETFs and other investment vehicles, we hypothesized that trading activity in these distinct timeframes could reveal patterns similar to those seen in traditional markets, where returns are often impacted by liquidity shifts during off-peak hours.

Can Twitter Images Predict Price Action During FED Announcements?

Do the quants possess a crystal ball? The recent research hints, that if we try to process the Twiter images, then we may get a small glimpse into the future. The Federal Open Market Committee (FOMC) meetings significantly influence financial markets, drawing global attention from traders and investors, especially regarding equity risk premia. Recent research indicates that combining sentiment analysis of Twitter images with text analysis can more accurately predict stock performance on FOMC days than text alone.

How Does the Passive Investing Impact Market Risk?

The rise of passive investing has been one of the most profound trends in the asset management industry in the past two decades. However, how does the popularity of passive funds impact market risk? We can rely on the data, and a recent research paper shows that the impact is significant, mainly through a substantial increase in stock correlations. As more investors flock to passive funds, which track indices, the prices of stocks within those indices tend to move more in tandem, increasing market-wide risk.