Quantpedia Premium Update – October 7th

New Strategies:

#1191 – Relative Signed Jump Factor in Commodities

Period of rebalancing: Monthly
Markets traded: commodities
Instruments used for trading: futures
Complexity: Moderately Complex strategy
Backtest period: 1990-2024
Indicative performance: 6.29%
Estimated volatility: 14.83%

Source paper:

Kiss, Tamás and Ferreira Batista Martins, Igor: Good volatility, bad volatility and the cross section of commodity returns
https://ssrn.com/abstract=5390453
Abstract: This article studies whether asymmetries in volatility help explain the cross section of commodity returns. We decompose realized variance into upside and downside components and construct a normalized difference measure, the relative signed jump (RSJ), following Bollerslev et al. (2020). A trading strategy that goes long the top tercile of commodities with the highest RSJ and shorts the bottom tercile delivers a statistically and economically significant annualized excess return of -6.29%. Our tradable RSJ factor explains the cross section of commodity returns beyond well-established factors such as market, carry and momentum. Our results also show that the pricing ability of volatility asymmetries is distinct from other higher-order moments such as realized skewness.

#1192 – Defense First – A Multi-Asset Tactical Model for Adaptive Downside Protection

Period of rebalancing: Monthly
Markets traded: equities, commodities, bonds, currencies
Instruments used for trading: ETFs, CFDs, funds, futures
Complexity: Simple strategy
Backtest period: 1986-2025
Indicative performance: 10.87%
Estimated volatility: 8.5%

Source paper:

Carlson, Thomas: Defense First: A Multi-Asset Tactical Model for Adaptive Downside Protection
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5334772
Abstract: In times of economic uncertainty, traditional “safe haven” portfolios—those heavily invested in bonds or gold—have often failed to protect investors. This paper introduces Defense First, a simple but powerful strategy that adjusts monthly among four defensive assets: long-term U.S. Treasury bonds (TLT), gold (GLD), broad commodities (DBC), and the U.S. dollar (UUP). It uses a clear ranking system based on past performance to allocate to the strongest performers. When one or more of these defensive assets falter, the position slots U.S. stocks (SPY) as a fallback option. Using data from 1986 to 2025, Defense First delivers better risk-adjusted returns than the stock market, with lower volatility, smaller losses in downturns and low correlation to typical stock and bond portfolios. The approach relies only on publicly available data and uses liquid, easy-to-trade ETFs. This paper connects the strategy to academic research on momentum, crisis investing, defensive rotation, and diversified portfolio design.

#1193 – Grid Trading Strategy for BTC

Period of rebalancing: Intraday
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Very Complex strategy
Backtest period: 2021-2024
Indicative performance: 35%
Estimated volatility: –

Source paper:

Chen, Kai-Yuan and Chen, Kai-Hsin and Jang, Jyh-Shing Roger: Dynamic Grid Trading Strategy: From Zero Expectation to Market Outperformance
https://www.semanticscholar.org/paper/Dynamic-Grid-Trading-Strategy%3A-From-Zero-to-Market-Chen-Chen/252c87bb2b48d8a5ce0fbdbe97db543e02a9f868
Abstract: In this paper, we propose a profitable strategy for the cryptocurrency market through grid trading. We start by analyzing the expected value of the traditional grid trading strategy. Based on our findings, we introduce an improved approach, the Dynamic Grid-based Trading (DGT) strategy, which demonstrates superior performance compared to conventional methods.

New research papers related to existing strategies:

#1171 – Sunspot Wheat Timing Strategy

Warren, Wai Tong Kam: Solar Influences on Financial Markets: Toward Smarter Investment Decisions Across Indices
https://ssrn.com/abstract=5171811
Abstract: This dissertation explores the relationship between sunspot activity and the performance of major financial indices-the Hang Seng Index (HSI), S&P 500 ETF (SPY), NASDAQ Composite (IXIC), and Dow Jones Industrial Average (DJI)-to enhance investment decision-making. Utilizing Spearman’s rank correlation analysis, the study examines this relationship across yearly, monthly, and daily time scales from 2015 to 2024, incorporating lead-lag dynamics to assess temporal effects. The findings reveal consistent negative correlations between sunspot numbers and index performance, with technology-heavy indices like IXIC displaying the strongest associations (e.g., yearly ρ =-0.653 to-0.692, monthly ρ =-0.681 to-0.705, daily ρ =-0.715 to-0.738, p < 0.05), particularly when sunspot activity leads by 1-3 years or 1-12 months. Broader indices such as DJI and HSI exhibit weaker correlations (e.g., yearly ρ =-0.352 to-0.405 for HSI, p < 0.05), with effects diminishing on shorter time scales. These results suggest that solar activity, especially during solar maxima, may influence financial markets, potentially through physical disruptions (e.g., geomagnetic storms affecting technology) or behavioral shifts in investor sentiment. The study proposes that integrating solar cycle awareness into investment strategies could enhance long-term risk management, particularly for technology-focused portfolios, while acknowledging the limitations of correlation-based analysis in establishing causality. This interdisciplinary work bridges solar physics and financial economics, offering novel insights for investors and researchers.

#498 – Value in Anomalies

Arnott, Robert D. and Ehsani, Sina and Harvey, Campbell R. and Shakernia, Omid: Revaluation Alpha
https://ssrn.com/abstract=5451754
Abstract: We define revaluation return as the portion of a factor’s historical return that comes from changes in its valuation ratio. A factor may have impressive historical performance, but if the high return was driven by rising valuations, it is unwise to expect similar future returns because valuation ratios cannot rise indefinitely, and often mean-revert. We define structural return as the historical factor return net of the revaluation return. For decades, the factor literature has implicitly assumed that revaluation alpha is equally predictive to structural alpha. We challenge that assumption. Structural returns predict future returns better than raw historical returns and add incremental value beyond well-known timing signals such as momentum.

#887 – Pairs Trading in Cryptocurrencies

Palazzi, Rafael Baptista: Trading Games: Beating Passive Strategies in the Bullish Crypto Market
https://onlinelibrary.wiley.com/doi/10.1002/fut.70018
Abstract: This study examines the effectiveness of cointegrated pairs trading in cryptocurrency markets, introducing systematic parameter optimization within the trading framework. The analysis is conducted using a dataset comprising ten major cryptocurrencies, selected based on market capitalization and consensus mechanism, spanning the period from January 2019 to May 2024. The methodology incorporates dynamic risk management through adaptive trailing stop-loss and volatility filtering mechanisms. Empirical results demonstrate that the pairs trading strategy consistently outperforms conventional pairs trading and passive approaches, generating significant risk-adjusted excess returns, while maintaining low market exposure. These findings contribute to the literature on empirical finance and provide valuable insights into alternative investment strategies for emerging digital asset markets.

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

Cross-Sectional and Dollar Components of Currency Risk Premia

Currency strategies often appear simple on the surface – go long high-yielding currencies, short low-yielding ones, or take a position on the U.S. dollar. But these trades actually mix two distinct components: a Dollar component, which bets on broad movements of the U.S. dollar against all others, and a Cross-Sectional (CS) component, which exploits relative differences across countries. The question is, which of these components really drives currency risk premia? A new paper by Vahid Rostamkhani tackles this long-standing question by decomposing the predictive power of eleven macroeconomic fundamentals—such as interest rates, inflation, unemployment, and fiscal variables—into these two components across almost a century of data (1926-2023). This approach directly tests whether it is more rewarding to time the dollar itself or to focus on cross-country fundamental spreads.

Hedging Tail Risk with Robust VIXY Models

Extreme market events, once perceived as statistical outliers, have become a central concern for investors. The persistence of sharp drawdowns and volatility spikes demonstrates that the cost of ignoring tail risks is not tolerable for long-term portfolio resilience. While diversification can mitigate ordinary fluctuations, it often fails when markets move in unison under stress. This makes explicit protection against severe downside events not just desirable but necessary. Tail hedging addresses this need by providing a structured defense against the most damaging scenarios, ensuring that portfolios remain robust when traditional risk management tools fall short. Using VIXY ETF, we will present and test a range of hedging strategies designed to protect portfolios under stress. By applying robust testing frameworks, we aim to evaluate how different implementations of VIXY ETF-based tail hedges perform across a variety of market environments, highlighting both their strengths and inherent trade-offs.

Gold’s Rally and the Gold Mining Stocks Trap

Gold has been in the headlines lately as it climbs to new highs, prompting many investors to look for ways to benefit from the rally. However, many institutional investors – such as mutual funds and pension funds – face restrictions on buying physical gold or gold-backed ETFs. Instead, they often turn to gold mining stocks to gain indirect exposure to gold’s price. That approach seems logical on the surface: mining stocks typically offer leveraged exposure to gold’s movements. But as highlighted by Dirk G. Baur, Allan Trench, and Lichoo Tay in their recent study “Gold Shares Underperform Gold Bullion”, this strategy can be misleading. The authors demonstrate that, over the long run, gold mining shares structurally underperform physical gold itself.