New Strategies:
#1313 – Skewness Managed Portfolios
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex
Backtest period: 1963-2024
Indicative performance: 13.1%
Estimated volatility: 24.26%
Source paper:
Gong, Rui and Lynch, John and Ogden, Richard: Skewness Managed Portfolios
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6913978
Abstract: We show that the returns to many prominent cross-sectional anomalies are driven by a small number of extreme, positively skewed stock returns. Consistent with this, we propose a skewness-managed strategy that predicts ex-ante skewness using firm characteristics and modifies each anomaly portfolio by selecting high-skewness stocks in the long leg and low-skewness stocks in the short leg. Applied to 18 well-known anomalies, the strategy improves returns by an average of 5.45 percentage points, with gains concentrated in periods of economic and financial stress. Sharpe ratios also increase consistently, with an average gain of 0.12. Despite closely tracking the original factors, these skewness-managed portfolios deliver significant alphas even relative to models constructed from the same characteristics, suggesting that modern asset pricing models may systematically overlook higher-order moments.
#1314 – M-Score – A Measure of Accounting Quality
Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex
Backtest period: 2007-2024
Indicative performance: 4.61%
Estimated volatility: 7.73%
Source paper:
Wang, Richard Zhe: Measures of Accounting Quality and the Cross-Section of Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6881278
Abstract: Whether accounting quality still predicts the cross-section of stock returns, or has been arbitraged away as its canonical accruals proxies decay, is an open question. This paper tests six measures of accounting quality, drawn evenly from the accruals and misstatement-prediction traditions, in US data over the most recent two decades. Both Sloan’s total accruals and Dechow-Dichev accrual quality earn no significant alpha, though the broader RSST accruals measure retains a significant equal-weighted alpha. Surprisingly, the 1999-vintage M-score retains a value-weighted spread alpha of −4.61%/yr (t =−2.53), and an XGBoost-based accounting quality score (AQS) outperforms the classical measures in joint Fama-MacBeth cross-sectional regressions and bivariate factor-spanning tests. The results are robust to both theq5 and the Fama-French five-factor-plus-momentum benchmarks. The AQS premium demonstrates the Stambaugh-Yu-Yuan mispricing signature, as it concentrates in the short leg and amplifies in high-sentiment periods. Overall, accounting quality remains priced in the cross-section, but its predictive signals have migrated from the classical accruals proxies to the M-score and the machine-learning-based AQS.
#1315 – Intraday Volume Periodicity
Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex
Backtest period: 2007-2024
Indicative performance: 4.61%
Estimated volatility: 7.73%
Source paper:
Osuna, Rudy: Intraday Volume Periodicity
https://www.quantconnect.com/research/21066/intraday-volume-periodicity/p1
Abstract: Algorithmic execution leaves a fingerprint on the tape. When many participants slice parent orders on regular schedules, intraday trading volume develops persistent high-frequency periodicities that a spectral decomposition can measure. Wu, Zhang and Dai (2025) document these periodicities across the US and Chinese markets and show they carry information about price formation. This post builds a US Equity, long-only, cross-sectional strategy on that idea. Each week, the algorithm ranks the 100 most liquid US Equities by the strength of their intraday-volume periodicity, selects the top quintile of stocks, and tilts the weights toward the stocks with the strongest execution flow. These factors are provided by our new Spectral Tick-Flow Signal dataset. The results show that the algorithm outperformed the buy-and-hold benchmark over the last 5 years.
#1316 – Sizing Market Exposure With Aggregate Sales Growth
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: ETFs, futures, CFDs
Complexity: Complex
Backtest period: 2021-2026
Indicative performance: 20.48%
Estimated volatility: 25.6%
Source paper:
Melchin, Derek: Sizing Market Exposure With Aggregate Sales Growth
https://www.quantconnect.com/research/21132/sizing-market-exposure-with-aggregate-sales-growth/p1
Abstract: Aggregate sales growth (ASG) measures how fast revenue is expanding across the average US public company, weighted by size. Garfinkel, Hribar, and Hsiao (2025) document a negative relationship between this aggregate and future market excess returns, which they trace to intensified market competition that squeezes future earnings and to investors over-extrapolating the growth. This strategy turns that finding into a monthly market-timing rule that rotates between the SPY and the BIL, sizing the Equity leg by mean-variance optimization. The signal itself is rebuilt each month from the cross-section of US non-financial common stocks using Morningstar fundamentals. Over July 2021 to July 2026, the strategy earned a 0.753 Sharpe ratio, outperforming a buy-and-hold position in the SPY.
#1317 – Using Machine Learning to Trade The Implied Volatility Surface and the Cross-Section of Stocks
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: options
Complexity: Very Complex
Backtest period: 1996-2024
Indicative performance: 7.48%
Estimated volatility: 7.41%
Source paper:
Ye, Wuyi and Xiong, Yanqing and Yang, Jinting and Jiao, Shoukun: The Implied Volatility Surface and the Cross-Section of Stock Returns: Evidence from Machine Learning
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6890048
Abstract: This paper examines whether the implied volatility surface contains incremental predictive return information beyond conventional option-implied characteristics. We construct standardized delta–maturity grids of implied volatilities and apply machine learning methods to predict future stock excess returns. Over the 2011–2024 out-of-sample period, H–L portfolios formed on the resulting surface-based signals generate sizable Sharpe ratios and significant alphas relative to standard factor benchmarks, outperforming most hand-crafted option variables. Further tests show that nonlinear models exploit broader surface patterns and retain significant alphas after controlling for option-implied variables and factor-mimicking portfolios associated with economic channels. The evidence suggests that the implied volatility surface contains incremental predictive information that is not fully captured by existing low-dimensional option characteristics.
New research papers related to existing strategies:
#1147 – A Dual Approach For VIX ETNs Trading
Melchin, Derek: Harvesting the Volatility Risk Premium With a Dual VIX Signal
https://www.quantconnect.com/research/21143/harvesting-the-volatility-risk-premium-with-a-dual-vix-signal/p1
Abstract: The volatility risk premium is the tendency for Options to price in more volatility than the market actually delivers, which rewards investors who sell that volatility while markets stay calm. Zarattini, Mele, and Aziz (2025) turn that premium into a rule-based strategy that times exposure to VIX exchange-traded products using two signals, the expected volatility risk premium and the shape of the VIX term structure, and scales the position by the level of the VIX. The strategy in this research post trades that logic through the VIXY, shorting it to harvest the premium and buying it when the signals call for long volatility, with the VIX and VIX3M Indices supplying the signals. When it is short volatility, the capital not committed to that position sits in the SPY rather than in cash. Over January 2016 to July 2026, the strategy earned a 0.729 Sharpe ratio, outperforming a buy-and-hold position in the SPY.
#1060 – Stronger Momentum Strategy in Non-Dividend Stocks
Melchin, Derek: Momentum in Captial-Gain Stocks
https://www.quantconnect.com/research/21160/momentum-in-capital-gain-stocks/p1
Abstract: The literature increasingly attributes the momentum effect of stocks to return extrapolation, the tendency of investors to project past price changes into future expectations. Cannon and Lynch (2025) find that momentum returns are roughly twice as large among stocks that pay no dividends. Inspired by that result, this strategy builds a momentum portfolio from the most liquid US Equities that pay no dividends. Following the Fama-French convention, the strategy calculates its momentum threshold from the NYSE-listed stocks alone and only trades the stocks in its universe with a greater momentum than that threshold. The results show the strategy achieved greater risk-adjusted returns than a buy-and-hold position in the SPY over the last 10 years.
And several interesting free blog posts that have been published during the last month:
Sectoral Intramonth Momentum Cycle: Exploiting Turn-of-the-Month Patterns in Sector ETF Strategies
We document a persistent intramonth momentum cycle in U.S. sector ETFs that yields meaningful risk-adjusted returns when properly sequenced. Using the nine original Select Sector SPDR ETFs and SPY as the market benchmark from December 1998 through June 2026, we show that trailing 252-day sector momentum generates a positive spread on the first trading day of the month—and then sharply reverses on days two and three. A third, independent leg of the cycle emerges in the window from ten to five trading days before month-end, consistent with the intramonth momentum cycle recently documented at the single-stock level by Nathan, Suominen and Tasa (2026). Stitching the three legs together into a single composite strategy delivers 5.99% annualized return at a 0.55 Sharpe ratio for the long-short variant, and 3.77% at 0.54 for the market-neutral variant—all while being invested fewer than half the trading days each month. Our contribution is twofold: we extend the calendar-anomaly literature from individual equities to sector-level portfolios, and we provide practitioners with a transparent, low-turnover framework that translates these academic patterns into actionable trade schedules.
Quantpedia API for Peer Group Strategy Analysis
A single backtest can show that strategy was profitable, but it does not always show whether the strategy was competitive. This is especially true for systematic futures strategies. A trend-following strategy can have a positive Sharpe ratio, a long live-like performance history, and a reasonable drawdown, but those numbers are difficult to interpret without a relevant comparison group.
A broad equity index is often not the right benchmark for this type of strategy. A monthly rebalanced multi-asset futures strategy has a different objective, different risk profile, and different return drivers than a long-only stock index. A more useful question is whether the strategy performs well compared with other systematic trend-following futures strategies.
This case study shows how the Quantpedia API can be used to build a custom peer group benchmark for strategy evaluation. The workflow has two steps. First, Quantpedia strategy metadata is used to screen and define a comparable peer group. Second, the historical equity curves of the selected strategies are downloaded, converted into daily returns, and aggregated into an equal-weighted peer group benchmark.
Boundaries of Time Series Momentum
Time-series momentum stands as one of the most reliable and heavily backtested anomalies in quantitative finance, serving as a foundational alpha source for modern managed futures and trend-following strategies. However, a recent academic paper by Matti Suominen and Erik Hjalmarsson, titled “Boundaries of Time Series Momentum,” uncovers a structural vulnerability that every practitioner must account for. The authors demonstrate that while equity market trends persist reliably during normal business cycles, they systematically break down and aggressively reverse when market valuations reach historical extremes. This phenomenon establishes clear macro “boundaries” where chasing the trend shifts from a highly profitable strategy to a severe drawdown risk.
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
1099 – Margin Debt Levels Predict S&P 500 Returns
1296 – Composite Seasonal Equity Strategy
1303 – Dynamic Conversion Arbitrage on the Magnificent 7
1304 – Filing Language Stability as a Selection Signal
