Quantpedia Premium Update – September 9th

New Strategies:

#1167 – Nearness to the 52-Week High in Cryptocurrencies

Period of rebalancing: Weekly
Markets traded: crypto
Instruments used for trading: crypto
Complexity: Moderately Complex strategy
Backtest period: 2014-2023
Indicative performance: 92.29%
Estimated volatility: 77.54%

Source paper:

Jia, Yuecheng and Simkins, Betty J. and Yan, Shu and Zhang, Vera and Zhao, Jiangyu: Psychological Anchoring Effect and Cross Section of Cryptocurrency Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5386180
Abstract: This paper investigates whether investors’ anchoring bias affects cryptocurrency returns. We use the nearness to the 52-week high (Nearness52) as a proxy for anchoring behavior and document a significant positive association between Nearness52 and subsequent cross-sectional cryptocurrency returns. The relationship remains robust after controlling for standard return predictors and employing alternative econometric specifications. A value-weighted spread portfolio, cANCHOR, which goes long on cryptocurrencies with high Nearness52 and short on those with low Nearness52, generates an average return of around 130 basis points per week. Additional analyses help rule out competing explanations based on risk exposure or market frictions. Augmenting the benchmark three-factor model of Liu, Tsyvinski, and Wu (2022) with our cANCHOR factor yields a novel four-factor model that better explains cross-sectional cryptocurrency returns and outperforms alternative approaches proposed in the literature.

#1168 – How to Trade LETFs

Period of rebalancing: Daily
Markets traded: commodities, equities, bonds
Instruments used for trading: ETFs
Complexity: Moderately Complex strategy
Backtest period: 2015-2025
Indicative performance: 58.99%
Estimated volatility: 101.7%

Source paper:

Bezdjian, Rob: Am I the Patsy? LETF Issuance Is Signal, Not Noise: How Trading LETFs a Day Late Can Make You a Dollar Richer
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5360727
Abstract: This paper introduces the “Day Late – Dollar Richer” (DLDR) strategy, a systematic, rules-based trading model that exploits predictable issuance and redemption behavior in Leveraged and Inverse Exchange-Traded Funds (LETFs). Building on the author’s prior forensic framework introduced in Deception by Design: Leveraged ETFs, Structural Fraud, and the Proof of Outperformance (Bezdjian, SSRN ID: 5347238), which quantified over $100 billion in net issuance profits for LETF sponsors, this model transforms those flows into a replicable, high-return signal—and, potentially, forensic proof of issuer-level structural fraud. By analyzing daily changes in shares outstanding and trading with a one-day lag, the strategy consistently generates profits across asset classes where traditional ETFs do not. Unlike prior academic literature that focuses primarily on volatility decay and compounding effects, this paper presents empirical evidence that share issuance behavior is itself predictive, a structural signal created by the ongoing exploitation of investors and now harnessed by this model. These findings raise urgent questions around product design, fiduciary responsibility, and regulatory oversight. LETFs are functionally zero-sum instruments. Every dollar of investor loss often translates to a corresponding gain for issuers or their counterparties—by design, not by chance. What was once described as “random decay” is now exposed as an arbitrage-able signal. For the first time, the public has a tool to reverse the transfer. Note on Scope and Data Access: To ensure clarity and accessibility, this paper presents a representative subset of LETFs. The full dataset and backtest output have been submitted to the U.S. Securities and Exchange Commission (SEC) under whistleblower protection. All methodology is fully disclosed to support independent validation, replication, and legal inquiry.

#1169 – Learning to Rank Momentum Strategies

Period of rebalancing: Daily
Markets traded: commodities, equities, bonds, currencies
Instruments used for trading: futures, CFDs, ETFs
Complexity: Very Complex strategy
Backtest period: 2000-2023
Indicative performance: 53%
Estimated volatility: 15%

Source paper:

Burdorf, Tom: Learning to Rank: Enhancing Momentum Strategies Across Asset Classes
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5255258
Abstract: Cross-sectional momentum trading strategies rely on accurate asset ranking for portfolio construction. Traditional methods use simple past performance or basic regression and classification models for ranking. However, these methods have proven suboptimal in other ranking-intensive fields, particularly information retrieval. I propose applying learning-to-rank algorithms to improve cross-sectional portfolio construction. These algorithms excel at ranking by learning the relative relationships between pairs or lists of assets rather than treating each asset in isolation. Testing this approach on cross-sectional momentum strategies across bond, equity, commodity, and foreign exchange markets reveals that learning-to-rank algorithms significantly enhance performance in several asset classes.

#1170 – Leveraged BTC Funding Carry Algorithm

Period of rebalancing: Intraday
Markets traded: crypto
Instruments used for trading: futures, crypto
Complexity: Very Complex strategy
Backtest period: 2020-2021
Indicative performance: 16.05%
Estimated volatility: 2.43%

Source paper:

Chan, Skyler: Leveraged BTC Funding Carry Algorithm: A Delta-Neutral Long-Spot/Short-Future Strategy
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5292305
Abstract: Bitcoin perpetual futures routinely pay funding fees, a yield most traditional trading desk leave on the table. Leveraging three years of exchange tick data, I built a fully automated 3x leveraged long-spot/short-perp-futures algorithm that captures this yield while remaining delta-neutral and margin-efficient. The strategy compounded at an annualized 16% with a Sharpe ratio of 6.1 and a max drawdown under 2%, driven by reinvestment of eight-hour funding inflows and precise hedge-resize logic. Since the core loop is exchange-agnostic, the system scales horizontally to multi-venue liquidity and creates a perfect opportunity for smaller funds to gain exposure to the crypto markets.

#1171 – Sunspot Wheat Timing Strategy

Period of rebalancing: Quarterly
Markets traded: commodities
Instruments used for trading: futures
Complexity: Simple strategy
Backtest period: 1979-2025
Indicative performance: 5.27%
Estimated volatility: 14.83%

Source paper:

Beluská, Soňa and Vojtko, Radovan: Sunspots as a Natural Signal for Trading Wheat Futures?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5375974 https://\/\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net/sunspots-as-a-natural-signal-for-trading-wheat-futures/
Abstract: Sunspots are temporary phenomena on the Sun’s surface that appear as dark spots due to lower temperatures compared to surrounding areas. They are associated with solar magnetic activity and follow an approximately 11-year cycle. While sunspots themselves may seem distant from everyday concerns, their influence on Earth’s climate – particularly through solar irradiance – has long been studied. This led us to explore whether solar activity could serve as a predictive indicator for agricultural commodity prices, such as wheat or corn. Periods of high sunspot activity are often associated with warmer and more variable weather patterns in the upper atmosphere, with limited but potentially relevant effects on Earth’s climate. Slight increases in air temperature may enhance crop yields, which could, in turn, place downward pressure on prices. However, we assume that the effect of sunspot counts is more likely to manifest over the coming years rather than in the short term. We propose to explore whether a structured investment strategy, such as tranche-based entry timing, could take advantage of this relationship. By monitoring sunspot activity and anticipating its potential impact on agricultural output, we aim to assess whether sunspot data can improve the timing and performance of commodity investments.

#1172 – Can We Finally Use ChatGPT As Quant Analyst?

Period of rebalancing: Daily
Markets traded: equities, commodities, bonds
Instruments used for trading: ETFs
Complexity: Simple strategy
Backtest period: 2015-2025
Indicative performance: 9.58%
Estimated volatility: 12.79%

Source paper:

Belobrad, David and Vojtko, Radovan: Can We Finally Use ChatGPT as a Quantitative Analyst?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5398597
Abstract: In two of our previous articles, we explored the idea of using artificial intelligence to backtest trading strategies. Since then, AI has continued to develop, with tools like ChatGPT evolving from simple Q&A assistants into more complex tools that may aid in developing and testing investment strategies-at least, according to some of the more optimistic voices in the field. Over a year has passed since our first experiments, and with all the current hype around the usefulness of large language models (LLMs), we believe it’s the right time to critically revisit this topic. Therefore, our goal is to evaluate how well today’s AI models can perform as quasi-junior quantitative analysts-highlighting not only the promising use cases but also the limitations that still remain.

#1173 – Crypto ETF Spread Mean-Reversion

Period of rebalancing: Monthly
Markets traded: crypto
Instruments used for trading: ETFs
Complexity: Moderately Complex strategy
Backtest period: 2021-2025
Indicative performance: 5.66%
Estimated volatility: 9.91%

Source paper:

Belobrad, David and Vojtko, Radovan: Trading the Spread: Bitcoin ETFs vs. Cryptocurrencies Infrastructure ETFs
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5398522
Abstract: This article investigates the use of simple spread trading strategies with Bitcoin ETFs and cryptocurrency infrastructure ETFs, two asset classes strongly correlated due to the broader influence of cryptocurrency market movements. The close relationship between these assets makes them ideal candidates for pair trading strategies based on mean reversion principles. Building on prior research on mean reversion strategies in currency markets, we adapt and extend these models to the cryptocurrency ETFs, showcasing their applicability beyond traditional markets. Specifically, we test two variations of mean reversion—linear and exponential—to demonstrate how traders can apply these techniques across different asset classes.

New research papers related to existing strategies:

#431 – Intraday Momentum in the Indian Equity Market
#621 – Cross-sectional Intraday Sector Momentum in India
#622 – Intraday Time-series Momentum in India

Wang, Chenxi and Gangwar, Siddhant: Optimizing Intraday Breakout Strategies on the NSE: A Block-Based Performance Evaluation
https://ssrn.com/abstract=5198458
Abstract: Intraday trading is playing a key role in today’s financial market in mitigating overnight risk and capturing short-run movements in prices in each trading day. In emerging economies such as India’s National Stock Exchange (NSE), two key intraday phenomena-high volatility at market opening and rising volumes towards market close-make for conditions favorable for Opening Range Breakout (ORB) strategies. The paper in hand considers alternative ORB setups (time frames of 5, 15, and 30 minutes, threshold surge volumes at 1.2× and 1.5×, and holding periods of N=2, 3, and 5) based on Tata Motors intraday prices. ORB daily returns are compared to a simple Buy & Hold (BH) strategy, in which BH is buying at daily open and selling at daily close. Our method is a direct ORB-BH daily difference, compared using a bootstrap method to obtain pvalues. ORB variants across the board have larger cumulativeturns than BH (often by significant margins), but p-values tend to be in the range 0.45-0.50, suggesting there is insufficient statistical evidence to claim ORB’s advantage is better than random luck. A block-by-block breakdown pseudo-out-of-sample split better illustrates ORB raw outperformance in multiple chunks but none produce lower p-values. Bull vs. Bearish breakout directions are compared as well. In each, outcomes have strong practical returns but are still statistically indecisive in a one-year sample for one security. We conclude ORB is operationally appealing but would need stricter data coverage, transactions cost modeling, and advanced significance testing to confirm or target in on apparent advantage in this strategy.

#569 – Intraday Time-series Momentum in Chinese Futures

Wang, Hanwen: Exploring Rest-of-Day Return Predictability in China’s CSI 800 Index
https://ssrn.com/abstract=5237483
Abstract: This paper investigates an intraday momentum trading strategy based on the Rest-of-Day (ROD) return within the Chinese CSI 800 index. Utilizing minute-level data from April 2024 to April 2025, the strategy is designed to go long or short based on the ROD return evaluated late in the trading day. Robustness tests show that the strategy’s performance is stable across different decision times, with Wednesdays and Fridays offering higher returns. Introducing a small threshold for the ROD signal decreases the number of trades without affecting gross returns. Practical implementation challenges, including transaction costs and T+0 trading restrictions, are acknowledged. Future research will extend the analysis to longer time horizons and alternative instruments such as index futures or ETFs.

#716 – Accruals Seasonality

Choy, Siu Kai and Lobo, Gerald J. and Tan, Yongxian: Do Investors Fully Understand the Seasonality in Accruals?
https://ssrn.com/abstract=5363581
Abstract: Seasonal fluctuations in a firm’s business activities can affect its balance sheet and give rise to seasonally predictable accruals. We find that seasonal patterns in accruals are associated with future stock returns. Specifically, we find that firms with historically lower (higher) accruals in a given fiscal quarter have higher (lower) stock returns in the months when those accruals are expected to be announced. Our results suggest that investors do not fully understand and price historical information on accruals seasonality. Additional analyses suggest that the emergence of this accruals seasonality anomaly is concentrated in the post-2001 period and driven by the effects of unsophisticated arbitrage against the accruals anomaly.

#281 – Skewness Effect in Commodities

Yang, Huan and Cai, Jun and Frijns, Bart and Webb, Robert I.: Expected Skewness, Forecast Combination, and Commodity Futures Returns
https://ssrn.com/abstract=5368383
Abstract: In this paper, we construct ex-ante measures of skewness from ten major commodity futures contract characteristics, including lagged skewness. We first employ monthly cross-sectional regressions of skewness on several lagged contract characteristics. Second, we follow a forecast combination approach and run monthly cross-sectional regressions of skewness on individual contract characteristics. Both approaches generate expected skewness that is significantly and negatively correlated with commodity futures contract returns, even when we construct expected skewness without using lagged skewness. Our empirical evidence, therefore, provides strong support for the key prediction of Barberis and Huang’s (2008) model relating asset return skewness to asset returns.

#007 – Low Volatility Factor Effect in Stocks

Carvalho, Raul Leote de and Andreis, Maxime and Laplenie, Olivier: The case for low-risk equity investing: evidence from 2011-2025
https://ssrn.com/abstract=5372550
Abstract: This paper investigates the performance of equity low-risk strategies since 2011, highlighting their ability to deliver strong risk-adjusted returns across diverse market conditions. We introduce a composite risk score that extends beyond volatility and demonstrate its effectiveness through empirical analysis. The study compares portfolio constructions, examines sector-level effects, and evaluates exposures to Fama-French factors. Results confirm the persistence of the low-risk anomaly and the presence of alpha unexplained by traditional risk premia, supporting the case for including low-risk strategies in long-term equity portfolios.

#608 – Intraday Reversal in China

Huang, Jing-Zhi Jay and Huang, Zhijian and Li, Zhuo and Wen, Fenghua: Selling at the Opening: The “T+1” Rule, Short-Term Speculation, and Stock Returns
https://ssrn.com/abstract=5376960
Abstract: Under a unique “T+1” trading rule, the next-day open is the first opportunity for short-term traders in China to sell their newly bought shares, which causes low stock overnight returns. We therefore propose close-to-open return (CTO) as an observable measure of new short-term speculators. We find that CTO positively predicts future stock returns in the cross section, supporting the idea that low CTO, as an indicator of the excess demand from short-term speculation, leads to lower subsequent returns. Our result is not driven by firm-specific news and alternative explanations based on risks, investor attention, or investor underreaction. Traders overpay for low-CTO stocks because of their inherent preference to this type of stock, rather than unwarranted optimism. Overall, this study provides a straightforward way to explore the pricing consequences of short-term traders without using account-level trading data.

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

The Best Strategies for FX Hedging

Foreign exchange (FX) markets are a cornerstone of global finance, offering investors and corporations opportunities to manage currency risk, enhance returns, and optimize portfolio performance. Among the most critical challenges in FX is the design of robust hedging strategiesto mitigate exposure to volatile currency movements. How does the financial industry deal with this task? We can draw inspiration from the paper written by Castro, Hamill, Harber, Harvey, and Van Hemert, which explores strategies such as dynamic hedging, trend-following, and momentum-based approaches, the concept of carry, and the interplay of these strategies with fundamental concepts like Purchasing Power Parity (PPP) and valuation metrics.

How Can We Explain the Low-Risk Anomaly?

The low-risk anomaly in financial markets has puzzled researchers and investors, challenging the traditional risk-return paradigm (higher risk->higher return). This phenomenon, where low-risk assets outperform their high-risk counterparts on a risk-adjusted basis, has been observed across various asset classes, including stocks and mutual funds. What may be the possible explanation? Pass-through mutual funds, which aim to replicate the performance of specific market indices, play a crucial role in this context by channeling investor flows and potentially influencing asset prices through demand pressure.

Bitcoin ETFs in Conventional Multi-Asset Portfolios

Understanding how Bitcoin-related instruments can fit into traditional portfolios is increasingly relevant for investors. Some risk-averse investors do not like to hold cryptocurrencies in their portfolios strategically; however, they may be open to investing in crypto-linked assets on a tactical level. In this context, our goal is to explore how we can provide short-term Bitcoin exposure while contributing to overall portfolio balance and potential downside protection.

Surprisingly Profitable Pre-Holiday Drift Signal for Bitcoin

Cryptocurrency markets have matured into a distinct asset class characterized by extreme volatility, deep liquidity pools, and worldwide retail participation. Traditional equity and commodity markets exhibit a well-documented pre-holiday effect, where returns on trading days immediately preceding public holidays tend to outperform other days. Given that Bitcoin is often described as the archetypal absolute risk asset, it is natural to hypothesize that any calendar-driven anomalies observed in equities should manifest—or even amplify—in crypto markets.

However, unlike equity markets, where institutional investors and marketing calendars drive collective behavior, crypto markets are more dispersed, retail-dominated, and influenced by nontraditional information flows. This article investigates whether the classic pre-holiday effect applies to Bitcoin and assesses the extent to which it can be amplified by an attention-grabbing momentum filter based on local price highs.