New Strategies:
#1234 – 10-Delta Risk Reversal factor in Currencies
Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: forwards, swaps, futures
Complexity: Very Complex
Backtest period: 2002-2020
Indicative performance: 4.08%
Estimated volatility: 2.29%
Source paper:
Londono, Juan M. and Yamani, Ehab: Global currency volatility and depreciation factors
https://ssrn.com/abstract=6018720
Abstract: We use currency option-implied moments to calculate two global risk factors that are priced in the cross section of currency returns. The U.S. dollar volatility and depreciation factors are calculated as the difference in the return of currencies sorted on, respectively, at-the-money option-implied volatilities and 10-delta risk reversals. We document that both factors are useful at pricing not only the cross-section of currency returns sorted by the factors themselves but also at pricing the cross section of currencies associated with two traditional puzzles, namely the return of carry and currency momentum portfolios. Moreover, these factors contain information that can be exploited to generate profitable currency investment strategies that beat the return of random walk strategies.
#1235 – Gold-Treasury Momentum Strategy
Period of rebalancing: Monthly
Markets traded: commodities, bonds
Instruments used for trading: ETFs, futures, CFDs
Complexity: Simple
Backtest period: 1969-2025
Indicative performance: 11.38%
Estimated volatility: 13.98%
Source paper:
Vojtko, Radovan and Dujava, Cyril: Cross-Asset Price-Based Regimes for Gold
https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=2610529
Abstract: This article develops a price-based macro–financial model of gold that formally links its medium-horizon return dynamics to cross-asset risk-premium configurations. Although gold has traditionally been conceptualized as a non-yielding inflation hedge or safe-haven asset, contemporary empirical evidence reveals a substantially more intricate structure: gold’s forward returns are systematically conditioned by the joint momentum of (i) gold itself and (ii) long-duration U.S. Treasury total-return indices. The alignment of these two signals appears to encode macroeconomic information—specifically the direction of real interest rates, the stance and expected trajectory of Federal Reserve policy, and the prevailing global risk-appetite regime.
#1236 – Structural Convexity Index
Period of rebalancing: Monthly
Markets traded: commodities, equities, bonds, REITs
Instruments used for trading: ETFs
Complexity: Simple
Backtest period: 2007-2025
Indicative performance: 7.8%
Estimated volatility: –
Source paper:
Dimitriadis; Paul: Structural Convexity Index Capturing Flight-to-Quality with an Always-on Long/Short Crisis Strategy
https://ssrn.com/abstract=6101507
Abstract: This paper introduces the Structural Convexity Index (SCI), a systematic long/short strategy engineered to deliver crisis alpha during material equity drawdowns while maintaining positive long-term drift. Unlike defensive approaches that depend on timing signals or market path-such as trend-following and volatility trading-SCI is “alwayson,” seeking immediate responsiveness in fast, V-shaped declines by holding permanent durable-minus-fragile spreads. The strategy targets a single, persistent mechanismflight-to-quality-expressed across three uncorrelated sectors through factor-aligned pairs: Gold (long physical gold via GLD; short junior miners via GDXJ), Energy (long integrated majors via XLE; short oilfield services via IEZ), and Credit (long intermediate U.S. Treasuries via IEI; short mortgage REITs via REM). SCI is constructed at 200% long and 100% short (100% net long) and is rebalanced monthly to maintain fixed exposures. Over the 2007-2025 simulation period, SCI generated a hypothetical 7.8% CAGR with-84% downside capture relative to the S&P 500. The strategy simulated positive returns in every major equity drawdown exceeding 15%, including a hypothetical +24.2% return during the 2022 inflation shock when stock-bond diversification failed. These results suggest SCI’s convexity is regime-independent, rooted in structural fragility and liquidity-driven repricing rather than falling interest rates. Finally, we argue the strategy’s persistence is supported by a behavioral moat: benchmark-constrained capital struggles to tolerate the tracking error inherent in structural crisis protection. SCI is a benchmark index for research and measurement purposes only and cannot be invested in directly. Any implementation would require an investment vehicle, the performance of which would differ from the index due to fees, financing/borrow costs, taxes, execution frictions, and operational constraints. Research updates are published at RheonResearch.com.
#1237 – Stock Trading Variability and Option Returns
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks, options
Complexity: Very Complex
Backtest period: 1997-2022
Indicative performance: 14.57%
Estimated volatility: 4.82%
Source paper:
Wiest, Tobias: Stock Trading Variability and Option Returns
https://ssrn.com/abstract=6128266
Abstract: I document a strong negative relationship between the variability of underlying share turnover and the cross-section of future delta-hedged equity option returns. Sorting options on the coefficient of variation of turnover yields significant return spreads for both call and put portfolios. This novel long-short option factor is not spanned by prominent multi-factor models and persists across time and alternative portfolio constructions. Turnover variability is largely uncorrelated with the variability of conventional liquidity measures and instead consolidates several documented risk proxies relevant for option market makers, including jump risk and volatility risk. The effect is most pronounced following upside trading spikes and survives controls for earnings announcements, news dissemination, and investor disagreement.
#1238 – Large Language Models and Futures Price Factors in China
Period of rebalancing: Daily
Markets traded: commodities
Instruments used for trading: futures
Complexity: Very Complex
Backtest period: 2018-2023
Indicative performance: 59.71%
Estimated volatility: 24.84%
Source paper:
Cheng, Yuhan and Liu, Yanchu and Zhou, Heyang: Large Language Models and Futures Price Factors in China
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5539218
Abstract: We leverage the capacity of large language models such as Generative Pre-trained Transformer (GPT) in constructing factor models for Chinese futures markets. We successfully obtain 40 factors to design single-factor and multi-factor portfolios through long-short and long-only strategies, conducting back-tests during the in-sample and out-of-sample period. Comprehensive empirical analysis reveals that GPT-generated factors deliver remarkable Sharpe ratios and annualized returns while maintaining acceptable maximum drawdowns. Notably, the GPT-based factor models also achieve significant alphas over the IPCA benchmark. Moreover, these factors demonstrate significant performance across extensive robustness tests, particularly excelling after the cutoff date of GPT’s training data.
#1239 – Forecasting Cryptocurrency Markets with Reddit
Period of rebalancing: Daily
Markets traded: crypto
Instruments used for trading: crypto
Complexity: Very Complex
Backtest period: 2018-2024
Indicative performance: 63.76%
Estimated volatility: 6.26%
Source paper:
Noguchi, Rentaro and Mahadevu, Ashrita and Aggarwal, Aariv and Qin, Huifang: Forecasting Cryptocurrency Markets: An Algorithmic Approach with Reddit-Based Relative Volume and Sentiment
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5589290
Abstract: Cryptocurrency markets are highly volatile, making short-term price prediction challenging due to rapid, unpredictable shifts. Because cryptocurrency prices are largely driven by supply and demand, prior work has explored using social media sentiment to forecast price changes, typically finding only weak correlations. Reported successes usually come from richer frameworks such as combining sentiment with traditional financial indicators and historical price data, suggesting that gains may be driven by non-sentiment inputs and not sentiment alone. Studies are also often limited to short time windows, demonstrating success in specific market conditions and failing to account for year-to-year changes in market behavior. We developed a rule-based sentiment-informed trading algorithm using signals extracted from Reddit comments on six major cryptocurrencies between 2018 and 2024. Our algorithm, CARVS (Cryptocurrency Algorithm using Relative Volume Sentiment) significantly outperformed a baseline buy-and-hold method from 2018 to 2024, with annual returns improved by 1325%, 3%, 88%, 4150%, 295%, 23%, and 123% over seven years, while maintaining near-equal risk across most years. Notably, the algorithm generated profits even in the declining markets of 2018 and 2022 when the buy-and-hold method lost the majority of its initial investment. Methods used to achieve these results include employing post-processing techniques to reduce noise in sentiment signals and creating a hybrid metric that combines comment volume and sentiment polarity to improve forecasting performance. The annual consistency of results over seven years suggests that social media sentiment, when properly handled, can independently guide profitable trading in volatile cryptocurrency markets.
#1240 – Populism Factor
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex
Backtest period: 1986-2024
Indicative performance: 7.7%
Estimated volatility: 11.47%
Source paper:
Colacito, Ric and Ding, Hui and Jiang, Fuwei and Qian, Yan: The POP Premium Populism and the Cross-Section of Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5763042
Abstract: We construct a Populism Index (POP) from over one million Wall Street Journal articles from 1984 to 2024 using large language models to quantify populism-related sentiment in U.S. media. Firms’ exposures to changes in populist sentiment is a priced risk in the cross-section of equity returns: stocks with low POP betas earn significantly higher returns, yielding an annualized excess return of about 8%, unexplained by standard risk factors. The premium exists before the 2016 surge in populism, persists under both Republican and Democratic administrations, and is absent in earlier bag-of-words indices, highlighting that populism represents a distinct, state-dependent source of systematic risk in equity markets.
A new research paper related to existing strategies:
#5 – FX Carry Trade
#8 – Currency Momentum Factor
#9 – Currency Value Factor – PPP Strategy
#47 – Changes in Long Term Rates Predict FX Rates
#48 – Term Spread Predicts FX Rates
#120 – Speculators’ Effect in FX
#121 – Hedgers’ Effect in FX
Della Corte, Pasquale and Riddiough, Steven: Currency Speculation
https://ssrn.com/abstract=5756369
Abstract: Over the past two decades, a growing body of research has documented profitable currency investment strategies. Beginning with the carry trade, the literature has expanded in multiple directions, uncovering diverse sources of excess return predictability linked to prices, trading volumes, and economic fundamentals. This article first reviews this burgeoning literature and provides a framework to organize its key strands for both academics and practitioners. We then turn to the often-overlooked issue of sample selection. Much of the existing work adopts an ad hoc approach to defining the currency universe. We show that strategy performance varies considerably across different currency subsamples. In particular, restricting the sample to countries with open capital accounts and liquid, floating currencies produces substantially weaker returns than when all currencies are included. Nonetheless, we find that combining strategies—regardless of the sample—yields improved and broadly comparable investment performance.
#1015 – Senators’ Disclosure and Stock Returns
Fischer, Leah and Sacerdote, Bruce: Taking Stock of Democracy: Abnormal Returns of High-Profile Members of Congress
https://ssrn.com/abstract=5960854
Abstract: As public trust in Congress declines, Congressional stock trading has once again become the subject of public policy recommendations and news headlines. This paper tracks Congressional returns on the stock market over time, focusing on the returns of the top 15 stock traders by volume. In particular, we isolate 2020 as a benchmark for the evaluation of buy-and-hold abnormal returns. In January of 2020, four Senators were publicly involved in insider trading scandals preceding the onset of the COVID-19 pandemic, but all investigations were dropped by the Department of Justice. We hypothesize that the existing legal framework’s deterrent effect may have weakened following these events. This would predict decreased risk aversion and higher Congressional returns on the stock market, especially for high-volume traders. We find that while Congress does not significantly outperform the market at any point in time, mean buy-and-hold abnormal returns for both Congress writ large and high-volume traders have increased at the 20-, 60-, and 127-day benchmarks. Additionally, the standard deviations of both of the aforementioned categories have grown over time, demonstrating wider variance in stock market performance for members of Congress from 2020-2023 relative to 2012-2020.
#944 – Overnight-Intraday Reversal in Futures
#1025 – QuantConnect Mean Reversion Intraday Strategy
Chitturi, Nikhil: Testing a Contrarian Hypothesis: The Predictive Power of Overnight News Sentiment on S&P 500 Intraday Returns
https://ssrn.com/abstract=5723163
Abstract: This study tests a common contrarian hypothesis: that aggregated overnight financial news sentiment moves opposite to the subsequent day’s market performance. We developed a novel “Market Sentiment Score” (MSS), a 10-point bearishness scale, to quantify news from 6:00 PM to 8:00 AM EST, and analyzed 20 trading days leading to November 5, 2025. We find the data refutes the contrarian hypothesis. We document a strong inverse statistical correlation of -0.88 between our adjusted sentiment score and the full-day (9:30 AM-4:00 PM) S&P 500 E-mini futures return. A linear regression model (Return % = -0.19 * Sentiment + 1.03) quantifies this relationship. This strong negative correlation shows a congruent practical outcome: as overnight sentiment becomes more bearish (score increases), the subsequent market return becomes more negative (return decreases).
#149 – FX Volatility Effect
#150 – FX Volatility Effect ver. 2
Londono, Juan M. and Yamani, Ehab Abdel-Tawab: Global currency volatility and depreciation factors
https://ssrn.com/abstract=6018720
Abstract: We use currency option-implied moments to calculate two global risk factors that are priced in the cross section of currency returns. The U.S. dollar volatility and depreciation factors are calculated as the difference in the return of currencies sorted on, respectively, at-the-money option-implied volatilities and 10-delta risk reversals. We document that both factors are useful at pricing not only the cross-section of currency returns sorted by the factors themselves but also at pricing the cross section of currencies associated with two traditional puzzles, namely the return of carry and currency momentum portfolios. Moreover, these factors contain information that can be exploited to generate profitable currency investment strategies that beat the return of random walk strategies.
And several interesting free blog posts that have been published during the last month:
Do S&P500 0DTEs Options Increase Market Volatility?
Recent market action has once again underscored how rapidly volatility can surface across asset classes, as evidenced by pronounced price swings in gold, silver, and cryptocurrency markets. Such episodes routinely revive debate within the quantitative community about structural drivers of intraday instability, with particular attention paid to the growing prominence of S&P 500 zero-days-to-expiration (0DTE) options. The rapid proliferation of these ultra-short-dated contracts has fueled concerns among practitioners, regulators, and exchange operators that concentrated option activity may transmit destabilizing hedging flows into the cash equity market. At the same time, the paper under review challenges this prevailing spillover hypothesis, suggesting that the availability of 0DTE options systematically alters market-makers’ hedging exposures in a way that may dampen, rather than amplify, realized index volatility. So, do 0DTE options truly increase market volatility?
Pragmatic Asset Allocation Across Market Cycles
Pragmatic Asset Allocation is a systematic, multi-asset investment strategy designed to adapt dynamically to evolving market conditions. Rather than maintaining a static equity exposure, the model actively allocates capital across a diversified set of asset classes—including equities, bonds, commodities, gold, and cash-like instruments—using momentum-based signals and disciplined periodic rebalancing. The strategy’s primary objective is to deliver attractive long-term returns while materially reducing drawdowns during adverse market environments.
It has now been two highly volatile years since we first published our paper on PAA, making this an opportune moment to review the strategy’s performance over the past year.
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
1220 – Low-volatility strategies with stop-losses for highly liquid cryptocurrencies
1230 – Intraday First Hour Reversal
1231 – Magic Formula in India
1232 – Defensive Absolute Return Portfolio
