New Strategies:
#1247 – Momentum-Driven EAFE Tactical Allocation
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: ETFs, CFDs
Complexity: Simple
Backtest period: 1969-2025
Indicative performance: 0.8%
Estimated volatility: 2.58%
Source paper:
Vojtko, Radovan and Dujava, Cyril: Systematic Allocation in International Equity Regimes
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6308640
Abstract: 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.
#1248 – FOMC Cycle 2-year Treasury Momentum Strategy
Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: futures, swaps, forwards
Complexity: Simple
Backtest period: 1990-2026
Indicative performance: 0.65%
Estimated volatility: 0.79%
Source paper:
Vojtko, Radovan and Dujava, Cyril: 2-Year Notes Momentum: Extracting Term Structure Anomalies from FOMC Cycles
https://papers.ssrn.com/abstract=6344979
Abstract: For many investors, short-term interest rates are often treated as something the market “discovers.” In reality, the Federal Reserve has enormous control over how the front end of the yield curve evolves. While textbooks often portray the Fed’s policy rate as a flexible tool that reacts quickly to economic data, the actual behavior of the Federal Open Market Committee (FOMC) looks very different. In practice, monetary policy tends to move in long, persistent cycles. The Fed spends years hiking rates, or years cutting them, and only rarely reverses direction quickly. For anyone trading rates, bonds, or rate-sensitive assets, this persistence matters. It means that the path of short-term interest rates over the next one to two years is often largely shaped by the Fed’s policy trajectory rather than by constantly shifting market expectations. This observation has an important implication: the short end of the Treasury curve often behaves less like a forecasting market and more like a gradual reflection of the Fed’s policy cycle. When the Fed enters a tightening or easing phase, that trend tends to propagate through Treasury yields from one month out to roughly two years. In this article, we show that these policy-driven trends can be measured and used. By identifying whether the Fed is in a tightening, easing, or neutral phase, investors can improve their expectations about the near-term evolution of the yield curve. For fixed-income portfolio managers and macro traders, recognizing these policy regimes can help sharpen rate forecasts, improve duration positioning, and better manage risks tied to interest-rate movements.
#1249 – Small-Cap Stock Trading Strategies for Retail Traders
Period of rebalancing: Weekly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex
Backtest period: 2022-2024
Indicative performance: 12.4%
Estimated volatility: 15.12%
Source paper:
Poudel, Sandip: Small-Cap Stock Trading Strategies for Retail Traders: A Data-Driven, Statistically Validated, and Reproducible Study of Strategy Design, Risk Management, and Technical-Pattern-Based Signals
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5921742
Abstract: Small-cap equities (market capitalization $300 million to $2 billion) present unique opportunities and challenges for retail traders due to their high volatility, liquidity constraints, and information asymmetries. This dissertation addresses five core research questions spanning strategy design, validation, risk management, automation, and practical deployment constraints. We develop and rigorously validate six distinct strategy families: (1) volatility-scaled momentum with liquidity filters, (2) gap-and-go opening range breakout variants, (3) mean reversion after extreme intraday moves, (4) breakout-retest pattern recognition, (5) event-driven volatility strategies with timestamp discipline, and (6) market regime-filtered composite strategies. Each strategy is specified with mathematical rigor, implemented in vectorized and event-driven Python code, and validated using walk-forward analysis with nested cross-validation, point-in-time data handling, and multiple-hypothesis testing corrections. This work demonstrates that small-cap trading strategies can achieve risk-adjusted returns exceeding 0.8 Sharpe ratio in out-of-sample periods when properly designed with realistic transaction costs ($0.01–$0.05 spreads), commissions ($0–$1.00 per share), and liquidity constraints (minimum average volume $500K daily). Critical contributions include: (1) a reproducible bias-control framework preventing survivorship bias, look-ahead bias, and data-snooping bias; (2) stress tests across market regimes (normal, crisis, regime shifts); (3) operational safeguards for retail broker APIs (Alpaca, Interactive Brokers, Tradier) including order reconciliation and kill-switch logic; and (4) an ethics framework addressing pump-and-dump risk and market manipulation. This dissertation is intended for graduate-level committees in Computer Science, quantitative finance practitioners, and retail traders seeking statistically rigorous, reproducible implementations of small-cap trading strategies.
#1250 – Intraday Volatility Surface Geometry and Rebalancing Premia in Options
Period of rebalancing: Intraday
Markets traded: equities
Instruments used for trading: options
Complexity: Very Complex
Backtest period: 2012-2023
Indicative performance: 6.65%
Estimated volatility: 1.49%
Source paper:
Gerchik, Daniil: Intraday Volatility Surface Geometry and Rebalancing Premia in Options
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5893362
Abstract: This paper investigates whether intraday distortions in the cross-strike geometry of the implied volatility surface forecast short-horizon option premia. Using data on equity options, I construct measures of the local level, skew, curvature, and cross-strike dislocations of the surface and relate them to subsequent 30-minute option returns. Options with high implied volatility level or rich at-the-money points earn lower subsequent returns, while those associated with high curvature and large dislocations earn higher returns, generating economically meaningful premia at very short horizons. These premia are absent in underlying stock returns, remain robust to standard liquidity controls, and are strongest near the close of trading when dealers rebalance positions. The evidence suggests an intraday rebalancing premium in options tied to the correction of cross-strike inventory imbalances rather than to directional equity risk.
#1251 – Structured Event Representation and Stock Return Predictability
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex
Backtest period: 2008-2022
Indicative performance: 10.9%
Estimated volatility: 13.97%
Source paper:
Li, Gang and Qiao, Dandan and Zheng, Mingxuan: Structured Event Representation and Stock Return Predictability
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5952915
Abstract: We find that event features extracted by large language models (LLMs) are effective for text-based stock return prediction. Using a pre-trained LLM to extract event features from news articles, we propose a novel deep learning model based on structured event representation (SER) and attention mechanisms to predict stock returns in the cross-section. Our SER-based model provides superior performance compared with other existing text-driven models to forecast stock returns out of sample and offers highly interpretable feature structures to examine the mechanisms underlying the stock return predictability. We further provide various implications based on SER and highlight the crucial benefit of structured model inputs in stock return predictability.
New research papers related to existing strategies:
#3 – Sector Momentum – Rotational System
#67 – Industry Momentum – Riding Industry Bubbles
#124 – Industry Momentum Combined with Reversal
#324 – Risk-Managed Industry Momentum
Dikhit, Dibyam: The Informational Role of the Most Recent Month in Industry-Level Momentum Strategies
https://ssrn.com/abstract=6009657
Abstract: This paper investigates the impact of information from the most recent month, commonly excluded from momentum portfolio construction, on subsequent industry-level momentum performance. Using the Fama-French 48 industry portfolios from 1975 to 2024, I compare standard 12-1 momentum strategies with 12-0 variants using a long-only approach, conditioning performance on prior-month returns and volatility. While conventional momentum strategies simply remove the most recent month to mitigate documented short-term reversal effects in stock returns, my results show that the behavior of the recent month contains economically meaningful information that helps explain differences in momentum performance across different market regimes in the Fama-French 48 industry portfolios. These findings indicate that the most recent month provides meaningful information for momentum strategies, rather than serving just as a mechanical function in portfolio construction.
#160 – Long-Term PE Ratio Effect in Stocks
Krishnan, C. N. V. and Tang, Peiwen: Cross-Sectional Analysis of Investor Exuberance in Equities
https://ssrn.com/abstract=6232138
Abstract: The Cyclically Adjusted Price-to-Earnings (CAPE) ratio is an equity market-valuation measure, and, arguably, a measure of investor exuberance. We find that, in general, over the 20-year period 2003 to 2023, S&P 500-firm-level CAPE ratio significantly drops during recessions, but goes up steadily during expansionary periods. After sorting firms based on size, book-to-market ratio, and return on equity, separately, we find that large cap, growth, and profitable firms tend to have higher CAPE ratios. We observe a dramatic rise in CAPE ratios of health-related companies during COVID period. We examine the major factors influencing CAPE movements for individual S&P 500 stocks. Principal Component Analysis based regression, LASSO regression, and Random Forest regression analyses show that investor exuberance is significantly associated with Consumer sentiment index, interest rates (that can affect the cost of funds of firms), and gold price (that may be considered a safe-haven in periods of stress).
#887 – Pairs Trading in Cryptocurrencies
Stoikov, Sasha and Xu, Dora and Shao, Shijie and Wang, Yourui and Zhang, Tongshu and Hu, Jinxuan and Cardoso, Federico: Pairs Trading in Crypto
https://ssrn.com/abstract=6188418
Abstract: We develop an automated framework for cryptocurrency pairs trading that identifies stable relationships across assets and executes mean reversion strategies in real time. Using 2 years of hourly data for more than 500 coins, we construct a multi-stage selection process that combines correlation clustering, structural metadata, and stability diagnostics to isolate pairs with persistent relative value behavior. We implement these signals through the Hummingbot platform, where we fine tune entry thresholds, holding rules, and risk controls before deploying the strategy in live markets. Through extensive backtesting and live trading on selected pairs, we show that our funnel improves signal quality, enhances stability across regimes, and produces consistent market neutral returns while revealing important challenges related to liquidity, convergence reliability, and execution costs.
And several interesting free blog posts that have been published during the last month:
2-Year Notes Momentum: Extracting Term Structure Anomalies from FOMC Cycles
For many investors, short-term interest rates are often treated as something the market “discovers.” In reality, the Federal Reserve has enormous control over how the front end of the yield curve evolves. While textbooks often portray the Fed’s policy rate as a flexible tool that reacts quickly to economic data, the actual behavior of the Federal Open Market Committee (FOMC) looks very different. In practice, monetary policy tends to move in long, persistent cycles. The Fed spends years hiking rates, or years cutting them, and only rarely reverses direction quickly. For anyone trading rates, bonds, or rate-sensitive assets, this persistence matters. It means that the path of short-term interest rates over the next one to two years is often largely shaped by the Fed’s policy trajectory rather than by constantly shifting market expectations.
This observation has an important implication: the short end of the Treasury curve often behaves less like a forecasting market and more like a gradual reflection of the Fed’s policy cycle. When the Fed enters a tightening or easing phase, that trend tends to propagate through Treasury yields from one month out to roughly two years. In this article, we show that these policy-driven trends can be measured and used. By identifying whether the Fed is in a tightening, easing, or neutral phase, investors can improve their expectations about the near-term evolution of the yield curve. For fixed-income portfolio managers and macro traders, recognizing these policy regimes can help sharpen rate forecasts, improve duration positioning, and better manage risks tied to interest-rate movements.
Finding and Integrating Crisis Hedge Strategies: Improving Equity Portfolio Resilience
Most systematic trading strategies are pro cyclical by nature. They perform best when markets trend higher and volatility remains contained. During broad market expansions, equity risk premia, momentum and trend following approaches tend to generate stable positive returns.
However, during market crises or extended bear markets, many of these strategies become synchronized. Correlations increase, volatility spikes and traditional diversification weakens. In such environments, portfolios built primarily from pro cyclical strategies may experience simultaneous drawdowns. This creates a structural need for strategies that behave differently during stress periods.
Crisis hedge strategies represent such a subset. They are designed to deliver diversification benefits specifically when equity markets decline. Because of their specialized behavior, they represent only a small fraction of the overall strategy universe.
This analysis demonstrates how crisis hedge strategies can be identified, evaluated and integrated into a model portfolio using the Quantpedia Pro framework.
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
1236 – Structural Convexity Index
1237 – Stock Trading Variability and Option Returns
1245 – MAX on Steroids A New Measure of Investor Attraction to Lottery Stocks
1246 – Factor MAX Strategy
