New Strategies:
#1241 – How to Identify Gamma Squeeze Stocks
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex
Backtest period: 2019-2023
Indicative performance: 69.48%
Estimated volatility: 37.25%
Source paper:
Baig, Ahmed and Strong, Cuyler and Zaynutdinova, Gulnara: Seeking Gamma: Lessons from the Meme Frenzy
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5959235
Abstract: There has been substantial debate about the existence and impact of a gamma squeeze during the GameStop price surge in January 2021. We provide novel empirical evidence confirming that a gamma squeeze indeed occurred and suggest that this squeeze started earlier than previously documented, in the Fall of 2020. We also identify other gamma squeeze episodes across a broader set of meme stocks during the same time period. Extending our analysis beyond meme stocks to all U.S. stocks, we systematically identify 669 potential gamma squeeze events from 2019 to 2023. These gamma squeezes result in economically significant price impacts, generating an average cumulative abnormal return of 5.13% in the month following their initiation. Our findings offer valuable insights for researchers, regulators, and market participants.
#1242 – Drift Regimes Unlock Hidden Cross-Sectional Predictability
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderate
Backtest period: 2004-2021
Indicative performance: 62%
Estimated volatility: 12.2%
Source paper:
Singha, Mainak: Discovery of a 13-Sharpe OOS Factor: Drift Regimes Unlock Hidden Cross-Sectional Predictability
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5754502
Abstract: We document an high cross-sectional equity factor achieving out-of-sample Sharpe ratios exceeding 13 through regime-conditional signal activation. By combining value and short-term reversal signals exclusively during stock-specific drift regimes—periods when individual stocks exhibit over 60% positive days in trailing 63-day windows—we generate annualized returns of 158.6% with 12.0% volatility and maximum drawdown of−11.9%. Through rigorous walk-forward validation spanning 20 years of S&P 500 data (2004–2024), we demonstrate performance approximately 13 times superior to market benchmarks on risk-adjusted basis, achieved entirely out-of-sample with frozen parameters. The strategy passes extensive robustness tests including 1,000 randomization trials yielding p-values below 0.001, maintains Sharpe ratios above 7 across 30% parameter variations, and exhibits virtually zero exposure to traditional risk factors with totalR 2 below 3%. We provide mechanistic evidence that drift regimes fundamentally alter market microstructure, creating unique conditions where behavioral biases amplify, liquidity dynamics shift, and cross-sectional price discovery mechanisms produce exploitable inefficiencies. Conservative capacity analysis suggests deployable capital of$100–500 million before material performance degradation.
#1243 – Expiration Seasonality in the Commodity Spreads
Period of rebalancing: Daily
Markets traded: commodities
Instruments used for trading: futures
Complexity: Moderate
Backtest period: 1990-2021
Indicative performance: 17.3%
Estimated volatility: 38.9%
Source paper:
Irwin, Scott H. and Sanders, Dwight R. and Smith, Aaron and Yan, Lei: Was Allen Paul Right? Liquidation Bias in Commodity Futures Markets
https://ssrn.com/abstract=6071584
Abstract: This study examines liquidation bias—the systematic rise in nearby commodity futures prices relative to deferred contracts before expiration—using 27 U.S. commodity futures contracts from 1990-2021. We find spreads increase 0.65% over the final 15 trading days, with strongest effects in grains (0.94%) and livestock (1.75%). The phenomenon persists across market conditions and changes in trading technology, suggesting it is driven by contract design. We argue that delivery options are the primary driver, particularly in markets with seller-only delivery initiation. These findings highlight important trade-offs in futures contract design and demonstrate how embedded options systematically affect commodity futures pricing, with implications for analyzing hedging effectiveness and market efficiency.
#1244 – Systematic FX Session Breakout Strategy
Period of rebalancing: Intraday
Markets traded: currencies
Instruments used for trading: CFDs, futures, forwards, swaps
Complexity: Complex
Backtest period: 2024-2026
Indicative performance: 20%
Estimated volatility: 10%
Source paper:
Rounce, Kelvin Adjei: Systematic FX Session Breakout Strategy: Regime-Aware Risk-Optimised Trading
https://ssrn.com/abstract=5805443
Abstract: I present a fully systematic foreign exchange trading framework that exploits intraday volatility expansion patterns occurring at the London market open following Asian session consolidation. The strategy employs a hypothesis-driven approach, identifying statistically significant directional continuation during the 07:00–11:00 UTC window after overnight range formation. Through the introduction of adaptive trailing stop logic and a conditional machine learning exit gate, I improved base strategy expectancy from +0.21R to +0.57R per trade (+171%). Monte Carlo simulation under prop firm risk constraints demonstrates a 98% pass probability with median time-to-target of approximately 12 trades. Comprehensive stress testing validates robustness against consecutive loss clusters, regime degradation, and black swan events. The complete system has been deployed for live paper trading with MetaTrader 5 integration and real-time Telegram alerting.
#1245 – Systematic FX Session Breakout Strategy
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex
Backtest period: 1968-2022
Indicative performance: 5.54%
Estimated volatility: 14.59%
Source paper:
Bali, Turan G. and Ince, Baris and Ozsoylev, Han N.: MAX on Steroids: A New Measure of Investor Attraction to Lottery Stocks
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6065166
Abstract: This paper introduces MAXβ, a refined measure of investor attraction to lottery stocks constructed by purging the systematic return component from MAX (the average of the five highest daily returns over a month) to better isolate lottery-seeking behavior. The value-weighted MAXβ-sorted long-short portfolio yields significant abnormal returns, robust to risk and mispricing factors, and independent of past extreme return persistence. The anomaly’s source differs by investor type: in retail-dominated stocks, it is driven by the underperformance of high- MAXβ stocks, whereas in institution-dominated stocks, it stems from the outperformance of low- MAXβ stocks. Our findings establish MAXβ as a distinct anomaly rooted in heterogeneous skewness preferences.
#1246 – Factor MAX Strategy
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex
Backtest period: 1963-2023
Indicative performance: 3.91%
Estimated volatility: 5.05%
Source paper:
Wang, Liyao and Zeng, Ming: Factor MAX and Predictable Factor Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6053114
Abstract: Extreme factor returns contain valuable information about future factor performance. Factors with the highest maximum daily returns in the past month outperform those with the lowest maximum returns by 0.32% per month (t = 5.89). The profitability of this factor MAX strategy is not subsumed by factor momentum or by a range of lottery or momentum-related anomalies at the stock-level. Our evidence suggests that investors’ limited attention and resulting underreaction to extreme factor-level news drive the predictive power of factor MAX. Consistent with this mechanism, the MAX strategy is more profitable for factors that receive low investor attention or whose extreme-return dates do not coincide with salient macroeconomic or firm-specific earnings announcements. Further evidence shows that investors appear to overreact to stock-level MAX events yet underreact to factor-level MAX events, which helps reconcile why factor MAX predicts returns in the opposite direction of stock MAX.
A new research paper related to existing strategies:
#136 – Residual Momentum Factor
Gao, Cheng and Yuan, Peixuan: The Unpriced Risk in Momentum Strategies
https://ssrn.com/abstract=6112846
Abstract: Standard momentum strategies bear substantial unpriced risk arising from time-varying exposures to common risk factors. We isolate this risk ex ante cross-sectionally and show that it drives momentum crashes. Volatility management succeeds precisely by mitigating this unpriced exposure. We demonstrate that ignoring these dynamics induces a severe spanning bias in unconditional tests. Once we control for unpriced risk, momentum delivers a more significant alpha and remains unexplained by factor momentum or other leading pricing models. Unpriced risk is therefore central to understanding the economic sources of momentum profits.
#663 – R&D Expenditures and Stock Returns
Sehgal, Abhishek: R&D Alpha: Investment Intensity and Long-Term Stock Returns
https://ssrn.com/abstract=6002295
Abstract: Objective. We test whether high research and development (R&D) intensity predicts higher subsequent equity returns in a large-cap U.S. universe using methodology designed for portfolio implementability. Method. Each year, we sort an S&P 500 large-cap universe into quintiles by R&D intensity (R&D expense divided by revenue) and evaluate subsequent July-June returns to mitigate look-ahead bias. The annual high-minus-low premium series (HML_RD, Q5-Q1) spans Jul1995–Jun1996 to Jul2024–Jun2025 (30 observations). Primary statistical inference uses monthly Fama-MacBeth cross-sectional regressions (360 months) and monthly factor spanning tests, which provide sufficient power to detect the premium. Results. The high-minus-low R&D factor (HML_RD) averages 3.73% per year (t = 1.10, p = 0.2793; Newey-West), positive in 17/30 years (57% win rate). At monthly frequency, factor spanning tests confirm a statistically distinct premium (FF5 α = 4.37%, p < 0.01). Monthly Fama-MacBeth regressions provide directionally consistent cross-sectional evidence after controlling for size and book-to-market (p = 0.0737). The investable RD20 strategy delivers 7.52% annual excess return versus SPY after transaction costs (Jul2001–Jun2025 backtest, N=24), retaining 99.6% of the gross premium. Implementation. We map the signal into a simple long-only portfolio that holds the top 20 stocks by R&D intensity (equal-weighted) and reconstitutes annually in July. Interpretation. Results are consistent with either mispricing of intangible assets or risk compensation for innovation exposure. We document sector tilts, factor exposures, and regime dependence without claiming to isolate a single mechanism. The analysis is associational rather than causal.
And several interesting free blog posts that have been published during the last month:
Combining Calendar Strategies into the Trading Portfolio
Calendar strategies are often viewed as weak when assessed individually. Their annualized returns tend to be low, market exposure is limited, and trading activity is sparse. Compared to trend following or swing strategies, which can remain invested for extended periods, calendar strategies may appear inefficient at first glance. This impression, however, largely stems from evaluating these strategies outside of their intended context. Calendar strategies are not designed to operate as standalone trading systems. Their primary role is within a portfolio, where their structural properties become relevant rather than their individual performance metrics.
Evaluating Reversal Potential in Niche Alternative ETFs
Alternative ETFs sit at an unusual intersection of public-market accessibility and hedge-fund-style investment techniques. They package managed futures, merger arbitrage, and option-based income strategies into exchange-traded products, yet they remain thinly traded and relatively niche compared to mainstream equity or bond ETFs. This combination makes them intriguing: they offer exposure to alternative risk premia, and their limited liquidity raises possibilities to build short-term reversal strategies.
Systematic Allocation in International Equity Regimes
This research examines the critical quantitative investment problem of systematic tactical allocation to international equity mandates—specifically Emerging Markets (EM) and Europe, Australasia, and the Far East (EAFE)—amidst conjectured macroeconomic regime transitions. The investigation is precipitated by observable deteriorations in USD hegemony, elevated geopolitical risk premiums, and protracted macroeconomic uncertainty. These factors collectively challenge the post-Global Financial Crisis paradigm of consistent US equity outperformance, suggesting a potential inflection point in relative returns and currency-adjusted Sharpe ratios.
The central research question is whether a statistically robust, signals-based framework can be engineered to systematically time exposure to EAFE equities, thereby capitalizing on these postulated regime shifts. We move beyond traditional, static mean-variance optimization by developing a dynamic model that integrates momentum variables to generate actionable, out-of-sample allocation signals.
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
1233 – Long-Run Interest Rate Differentials and Carry Trade
1234 – 10-Delta Risk Reversal factor in Currencies
1235 – Gold-Treasury Momentum Strategy
