New Strategies:
#1266 – Dynamic Factor Allocation via Momentum-Based Regime Switching
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: ETFs
Complexity: Complex
Backtest period: 1998-2025
Indicative performance: 12.98%
Estimated volatility: 19.54%
Source paper:
Tai, Jim and Leung, Stephanie and Jimenez, Justin: Dynamic Factor Allocation via Momentum-Based Regime Switching
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6224058
Abstract: We present a systematic framework for dynamic allocation across equity factors (Value, Size, Momentum, Quality, Growth) using a momentum-based regime switching model. Using a z-score normalization approach with only two hyperparameters, we identify Bull and Bear regimes for each factor through normalized trend signals. Our regime identification proves statistically significant across all factors, with Size and Growth showing strong significance (p <0.01). Cross-sectional validation confirms that Bull regimes exhibit systematic positive forward returns (t= 1.98,p= 0.047), while Bear regimes show no significant pattern, supporting asymmetric treatment in portfolio construction. The factor timing strategy achieves a Sharpe ratio of 0.66 compared to 0.59 for an equal-weighted benchmark, with annualized returns of 13.0% versus 11.3% over the 1998-2025 period. The strategy demonstrates consistent outperformance, with a 78.6% calendar year hit rate, while maintaining manageable turnover of approximately 3x annually. We validate practical implementability through ETF-based backtesting, finding only modest performance degradation from tracking error and expense ratios. Our results support the hypothesis that factor momentum exhibits sufficient persistence to be captured by simple trend-following signals.
#1267 – A Forecast Model For Crypto Currencies
Period of rebalancing: Daily
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Complex
Backtest period: 2016-2025
Indicative performance: 29.33%
Estimated volatility: 14.81%
Source paper:
Chen, Jaslyn and Chen, Kaston: A Forecast Model For Crypto Currencies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6300843
Abstract: This paper examines whether the well known equity momentum effect exists in cryptocurrency markets. We estimate daily cross-sectional regressions of forward returns on past returns with daily price data from Binance for about 50 most actively traded cryptocurrencies. The resulting time series of regression coefficients serves as a measure of the momentum effect’s strength and evolution over time. With the results, we propose a dollar-neutral trading strategy that goes long the top quartile and short the bottom quartile of cryptocurrencies ranked by their momentum signal. Our historical simulation shows the strategy has better sharpe ratio and drawdown profile than simply buying and holding Bitcoin alone.
#1268 – Nonlinear Time Series Momentum
Period of rebalancing: Daily
Markets traded: equities, currencies, commodities, bonds
Instruments used for trading: futures
Complexity: Very Complex
Backtest period: 1980-2024
Indicative performance: 8.4%
Estimated volatility: 10%
Source paper:
Moskowitz, Tobias J. and Sabbatucci, Riccardo and Tamoni, Andrea and Uhl, Björn: Nonlinear Time Series Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5933974
Abstract: We document a persistent nonlinear relationship between price trends and risk-adjusted returns across markets and asset classes that is consistent with asset pricing theory. Nonlinearities in time series momentum are consistent with past returns reflecting information about conditional expected returns, in line with investors using conditioning information to form efficient portfolios. Machine learning techniques are useful in uncovering these relationships and yield economically and statistically significant out-of-sample improvements in time series momentum strategies.
#1269 – Innovative Human Capital – Inventor Intensity Factor
Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex
Backtest period: 1986-2022
Indicative performance: 4.47%
Estimated volatility: 13.73%
Source paper:
Fang, Min and Hsu, Po-Hsuan and Jie, Hanni and Xu, Yan: Innovative Human Capital and Stock Returns: A Global Perspective
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5763883
Abstract: We analyze the asset pricing implications of innovative human capital (i.e., scientists and engineers) from both theoretical and empirical perspectives. Our model features a separation of R&D staff from regular manpower in the production of knowledge and the final good, and predicts expected stock returns to increase with firms’ investment in innovative human capital. Additionally, labor market frictions for talent strengthen the predictability of stock returns. We collect international firms’ patent records and show that firms with higher inventor-to-employee ratios subsequently experience higher stock returns. Moreover, such a relation is stronger in countries with stronger patent protection that prohibits inventor mobility and increases corresponding adjustment costs.
#1270 – Risk Premia in Diversified Energy Portfolios
Period of rebalancing: Daily
Markets traded: commodities
Instruments used for trading: futures
Complexity: Very Complex
Backtest period: 2005-2015
Indicative performance: 4%
Estimated volatility: 5%
Source paper:
Bogorad, Mark: Risk Premia in Diversified Energy Portfolios
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6374158
Abstract: We study the behavior of classical risk premia in energy futures, focusing on momentum, carry, value, and statistical arbitrage across a broad and updated commodity universe. We show that while some premia have weakened in traditional benchmark contracts, others persist or re-emerge in newly liquid markets and in signals that had been largely inactive in prior periods. At the portfolio level, low correlations across economically distinct strategies generate strong risk-adjusted performance under simple aggregation. We then examine the practical mechanics that govern real-world implementation, including roll dynamics, term-structure effects, and portfolio integration. Together, the results provide an implementation-aware perspective on how energy risk premia behave today and how they can be combined into robust systematic portfolios.
New research papers related to existing strategies:
#1206 – Revaluation Alpha
Beckmeyer, Heiner and Berg, Florian and Wiedemann, Timo and Wortmann, Jonas: Reviving Anomalies
https://ssrn.com/abstract=6468806
Abstract: Prior literature finds that most anomalies fail to deliver significant returns, especially when considering implementation costs. We develop a framework to revive anomalies by incorporating expected returns and transaction costs into portfolio formation. Our approach identifies implementable anomalies across different fund sizes by (i) using machine learning return forecasts and (ii) accounting for scale-dependent price impacts. A 1/N investment strategy in implementable anomalies yields out-of-sample Sharpe ratios between 0.64 and 1.01 with highly significant alphas across all fund sizes. We further document that mutual funds tilt their holdings toward implementable anomalies and that such tilts predict higher subsequent fund returns.
#33 – Post-Earnings Announcement Effect
Kim, Dongcheol and Roh, Tai-Yong and Min, Byoung-Kyu and Lee, Deok-Hyeon: Post-Earnings-Announcement Drift: Expected Growth Risk or Limits-to-Arbitrage?
https://ssrn.com/abstract=6459829
Abstract: To explain post-earnings-announcement drift (PEAD), we propose expected growth risk, measured as covariance between stock returns and expected future real GDP growth rates. We find that both expected growth rates and expected growth risk increase with standardized unexpected earnings, and that expected growth risk is significantly priced in the cross-section of returns. A model incorporating expected growth risk alone explains PEAD well. Moreover, after controlling for expected growth risk, the previously documented relation between PEAD and limits-to-arbitrage disappears. These results suggest that the empirical evidence supporting the mispricing hypothesis attributed to limits-to-arbitrage is a consequence of a failure to incorporate appropriate risk, and that the drift is a manifestation of expected growth risk.
#304 – Seasonality in Treasury Auctions Strategy
#995 – Pre-Refunding Announcement Gains in U.S. Treasuries
Fleming, Michael J. and Liu, Weiling and Nguyen, Giang: Intraday Price Pressure and Order Flow Around U.S. Treasury Auctions
https://ssrn.com/abstract=6447502
Abstract: Using 33 years of intraday Treasury data, we provide the first high-frequency evidence on auction-day price pressure: yields rise in the hours before auction and reverse afterward. This pressure strengthens when dealers face tighter risk-bearing constraints and weakens when investor demand is stronger or more elastic. More importantly, net order flow dominates in explaining the pressure, providing the first direct evidence that trading transmits dealer constraints into prices. Despite concerns about dealer capacity amid rapid federal debt growth, price pressure has not increased in recent years, partly because non-dealer participants now absorb more auction supply and ease dealers’ intermediation burden.
And several interesting free blog posts that have been published during the last month:
Trading as a Small Business: What Beginner Investors and Traders Usually Learn Too Late
Many beginners enter the markets with the same silent assumption: if they study hard enough, find the right indicators, or discover the right strategy, they should eventually be able to generate high returns with manageable risk. The market appears full of examples that seem to confirm this belief. Screenshots of triple digit gains are everywhere. Backtests often look smooth. Social media makes it feel as if exceptional performance is common.
The reality is much harsher.
One of the most valuable lessons for a beginner is not how to optimize entries, build indicators, or use the latest machine learning model. It is learning how to frame trading correctly from the start. For a small retail trader, trading should not be treated as a shortcut to wealth. It should be treated as a business. And like any business, it requires realistic expectations, risk control, patience, and a clear understanding of where a small player can actually compete.
That perspective matters because most of the mistakes beginners make do not stem from a lack of ability or effort. They arise from starting with the wrong mental model and unrealistic expectations about how markets actually work.
Exploiting Mean-Reversion in Decentralized Prediction Markets: Evidence from Polymarket Binary Contracts
This study examines the profitability of mean-reversion trading strategies applied to binary outcome contracts on Polymarket, the world’s largest decentralized prediction market platform. We analyze three distinct contracts representing varying risk profiles: a quasi-risk-free instrument (No to “Will Jesus Christ return in 2025?”) and two high-yield speculative contracts (No to “Will China invade Taiwan in 2025?” and “Will the US confirm that aliens exist in 2025?”). Using high-frequency price data sampled at 10-minute intervals over approximately one year, we implement a parameterized mean-reversion framework across twelve strategy variants, testing robustness under varying liquidity constraints and transaction cost assumptions. Our findings reveal that while mean-reversion signals generate substantial alpha under passive limit-order execution (zero-spread scenario), strategy performance degrades significantly when more aggressive market orders are accounted for.
The Tranching Dilemma
What if a meaningful part of a usual trading strategy’s performance has nothing to do with your signal—but simply when you rebalance? A recent paper written by Carlo Zarattini & Alberto Pagani highlights a largely underestimated risk in systematic investing: rebalance timing luck (RTL). For practitioners running rotation or factor strategies, this is not noise—it’s a structural source of dispersion. Using a concentrated U.S. equity momentum strategy, the authors show that identical portfolios differing only by rebalance day can diverge by as much as ~350 bps in annual returns, compounding into dramatically different terminal wealth outcomes.
How to Analyze Individual Equity Curves
One of the advantages of the Quantpedia Pro platform and its Portfolio Analysis toolkit is the ability to analyze not only multi-asset and multi-strategy portfolios but also individual equity curves. Users can upload virtually any return series or analyze assets already present in the database. The same analytical tools used for portfolio construction can therefore also be applied to single assets.
Given the current macro-driven environment, commodity markets—particularly crude oil—offer a relevant case study. The United States Oil Fund (USO) ETF serves as a practical proxy for oil price dynamics. By analyzing its equity curve through Quantpedia Pro, we can explore whether persistent patterns, behavioral effects, or structural inefficiencies exist and whether they can be transformed into systematic trading strategies.
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
1249 – Small Cap Stock Trading Strategies for Retail Traders
1253 – A Regime-Aware Market Capitalization Rotation Strategy
1259 – LPPLS for Bubbles in Speculative Markets
1261 – Geopolitical Risk in Currency Markets
