Quantpedia Premium Update – April 25th

New Strategies:

#1119 – Informative Price Pressure During Pre-FOMC Days

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: futures, ETFs, funds CFDs
Complexity: Very Complex strategy
Backtest period: 1996-2021
Indicative performance: 15.48%
Estimated volatility: –

Source paper:

Arif, Salman and Da, Zhi and Lin, Wenwei: Informative Price Pressure
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5136393
Abstract:
Informed investors often hedge their stock bets right before FOMC meetings. The resulting price pressure, when aggregated across stocks, reveals their long-term view of the stock market (with a minus sign). Consistent with this, we find that the average stock market return on the day before recent FOMC meetings, while completely reverted the next day, strongly and negatively predicts stock market returns up to two years in the future. The market return predictability is robust to additional controls, various sample cuts and extends to other important macroeconomic announcements. The day before the FOMC meeting is associated with low informed trading intensity, which explains the decision of informed investors to hedge on that day. At the same time, the VIX index is higher on that day, resulting in detectable price pressure.

#1120 – Righ-Tail vs. Left Tail Stock Picking Strategy in China

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2006-2019
Indicative performance: 17.46%
Estimated volatility: 14.49%

Source paper:

Lin, Fengjiao and Qiu, Zhigang: Safety First or Lottery First: A Mental Accounting Based Right Tail Reversal in China
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5082965
Abstract: This paper explores how Chinese investors balance safety and lottery-like preferences within the framework of mental accounting. In China’s retail-investor-dominated market, we uncover a phenomenon termed right-tail reversal—a negative relationship between right-tail bonus (potential gains) and expected returns, which contrasts sharply with the left-tail momentum observed in the U.S. market, where a negative relationship exists between left-tail risk (potential losses) and expected returns. Importantly, the right-tail reversal persists even after controlling for left-tail risk, while a standard positive risk-return trade-off between left-tail risk and expected returns emerges (indicating no left-tail momentum) when controlling for right-tail bonus. These results suggest that, within the mental accounting framework, the pleasure derived from potential gains diminishes the pain of potential losses for Chinese investors, thereby reducing their perception of risk. This insight supports a profitable investment strategy: going long on stocks with low right-tail bonus and high left-tail risk, while shorting stocks with the opposite characteristics. The right-tail reversal effect is particularly pronounced in stocks that garner significant retail investor attention.

#1121 – Similar-Attention Stocks Effect in Chinese Stocks

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2000-2023
Indicative performance: 30.3%
Estimated volatility: 4.39%

Guo, Hongye and Lu, Fangzhou and Zheng, Hanyu: Memory-induced Cross-stock Extrapolation in China
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5129987
Abstract: We propose a similarity criterion for stocks based on their Euclidean distance over a set of attention-related variables to capture investors’ memory of stocks. We find that the average past return of similar-attention stocks exhibits a significantly strong, negative predictive power for the future return of a focal stock, suggesting a cross-stock reversal effect. A long-short strategy based on this cross-stock reversal generates a monthly return of 2.23% and an annualized Sharpe ratio of 1.63. This effect cannot be explained by common factors, including short-term reversal. We further demonstrate that the cross-stock reversal effect stems from investors’ memory-based belief and irrational cross-extrapolating trading. The predictability is related to the investor clientele since it appears in markets with more retail investors.

#1122 – Volatility-Scaled Momentum in Cryptocurrencies

Period of rebalancing: Weekly
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Complex strategy
Backtest period: 2016-2022
Indicative performance: 12.05%
Estimated volatility: 10%

Source paper:

Habeli, Seyed Mohammad and Barakchian, S. Mahdi and Motavasseli, Ali: Adaptive Risk Allocation in Crypto Markets: Evaluating Volatility-Scaled Portfolios
https://ssrn.com/abstract=5090097
Abstract: This study applies volatility scaling, an established timing strategy that dynamically adjusts portfolio risk exposure, to the cryptocurrency market. We scale the market portfolio and seven cryptocurrency strategies by their recent volatility to improve risk-adjusted returns. Volatility scaling increases Sharpe ratios and generates abnormal returns in certain strategies, particularly momentum-based strategies and the overall market portfolio. The strategy remains effective even under tight leverage constraints and its advantages become more evident as we extend the investment horizon. Our results are robust after accounting for common risk factors and limits to arbitrage. We confirm that similar to the equity market, volatility scaling does not systematically benefit all strategies, and the strongest effects occur in momentum portfolios, suggesting a robust and potentially universal pattern.

#1123 – Switching Between Momentum and Reversal Strategies in Equities

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1940-2023
Indicative performance: 13.08%
Estimated volatility: 23.36%

Source paper:

Rodon Comas, Arnau: Market-State Dependent Momentum Strategies: An Empirical Examination of Anomalies in Asset Pricing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5130289
Abstract: This study examines the profitability of a market-state dependent momentum-reversal strategy and its implications for asset pricing anomalies. Utilizing a dataset spanning 1940 to 2023 from the Center for Research in Security Prices (CRSP), we construct a dynamic trading strategy that switches between momentum and reversal regimes based on market conditions. The empirical results demonstrate that the strategy consistently generates significant abnormal returns, challenging the weak-form Efficient Market Hypothesis (EMH). Factor model regressions against the Fama-French five-factor model, along with additional momentum and long-term reversal factors, confirm that the strategy captures return patterns unexplained by conventional risk-based models. The findings align with behavioral finance theories, suggesting that investor biases and structural market constraints contribute to sustained price trends and reversals. While the study highlights the robustness of a conditional momentum strategy, limitations such as transaction costs and market frictions require further exploration. These results contribute to the broader discussion on market efficiency and the dynamic nature of asset pricing anomalies, offering practical insights for quantitative investors and portfolio managers.

#1124 – Seasonal Front-Running in Country ETFs

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: ETFs
Complexity: Moderately complex strategy
Backtest period: 2000-2025
Indicative performance: 9.92%
Estimated volatility: 19.18%

Source paper:

Beluská, Soňa and Vojtko, Radovan: Front Running in Country ETFs, or How to Spot and Leverage Seasonality
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5202070
Abstract: Understanding seasonality in financial markets requires recognizing how predictable return patterns can be influenced by investor behavior. One underexplored aspect of this is the impact of front-running—where traders anticipate seasonal trends and act early, shifting returns forward in time. We have already explored seasonality front-running in commodities, stock sectors, and crisis hedge portfolios. Our new research examines whether this phenomenon extends to country ETFs, an asset class where seasonality has been less studied. By applying a front-running strategy to a dataset of country ETFs, we identify opportunities to capitalize on seasonal effects before they fully materialize. Our findings indicate that pre-seasonality drift is strongest in commodities but remains present in country ETFs, offering a potential edge in portfolio construction. Ultimately, our study highlights how front-running seasonality can enhance ETF investing, providing an additional layer of market timing beyond traditional trend-following approaches.

New research papers related to existing strategies:

#224 – Profitability Factor Combined with Value Factor

Novy-Marx, Robert and Medhat, Mamdouh: Profitability Retrospective: What Have We Learned?
https://www.nber.org/papers/w33601
Abstract: A lot! Profitability subsumes all of “quality” investing, explaining both the performance of the strategies that industry markets and the factors that academics employ. It also has striking power pricing “defensive equity” strategies that overweight low-beta or low-volatility stocks. Profitability tilts explain all the abnormal performance of popular “alternative value” strategies, including those adjusted for “intangibles,” and half of value’s post-2007 underperformance. Profitability is crucial for pricing a wide array of seemingly unrelated anomalies, yielding a more parsimonious understanding of the cross section of expected returns.

#628 – Social Media Sentiment Factor

Cookson, J. Anthony and Lu, Runjing and Mullins, William and Niessner, Marina: Market Signals from Social Media
https://ssrn.com/abstract=5187350
Abstract: This paper develops daily market-wide sentiment and attention indexes derived from millions of posts across major investor social media platforms. We find that sentiment extrapolates from past market-wide returns and exhibits a strong reversal. In contrast, attention predicts negative returns as a continuation of previous trends. The two indexes have distinct predictions for aggregate trading: abnormal trading rises when sentiment is low and attention is high. To identify the drivers of attention and sentiment, we use a shock to data sharing networks: We find sentiment spreads through real firm connections while attention does not. Moreover, attention rises after abnormally high trading, while sentiment rises after abnormally high returns. This extrapolative return pattern is asymmetric, primarily driven by negative market jumps. These findings provide new evidence on the daily market dynamics of sentiment and attention.

#50 – FED Model

Van Sant, John D.: Superior Stock Market Returns Using a Shiller CAPE-Based Regression Model
https://ssrn.com/abstract=5180299
Abstract: Using a six-factor prediction model formed around a version of Shiller’s CAPE, I “forecast” retroactively yearly stock returns which are then compared with one year treasury note yields to construct stock/bond portfolios. Covering the span from 1970 through 2024 the basic model yields an annual return of 11.40%, a Sharpe Ratio of .68 with average drawdowns of -.51%. Squaring the stock/bond ratio increases the yearly return to 11.76% and produces a Sharpe Ratio pf .65 and average drawdowns of -.90%. These results compare with a buy-and-hold policy over the same period returning 12.49% yearly, a Sharpe Ratio pf .45 and average drawdowns of 14.42%. The returns from my prediction model are roughly comparable to returns achieved by Haghani and White (2022 and 2023) which are based on Merton’s risky asset-risk free rate allocation model.

#906 – Using ChatGPT to Forecast Stock Price Movements

Kirtac, Kemal and Germano, Guido: Enhanced Financial Sentiment Analysis and Trading Strategy Development Using Large Language Models
https://ssrn.com/abstract=5181105
Abstract: This study proposes a novel methodology for enhanced financial sentiment analysis and trading strategy development using large language models (LLMs) such as OPT, BERT, FinBERT, LLAMA 3 and RoBERTa. Utilizing a dataset of 965,375 U.S. financial news articles from 2010 to 2023, our research demonstrates that the GPT-3-based OPT model significantly out- performs other models, achieving a prediction accuracy of 74.4% for stock market returns. Our findings reveal that the advanced capabilities of LLMs, particularly OPT, surpass traditional sentiment analysis methods, such as the Loughran-McDonald dictionary model, in predicting and explaining stock returns. For instance, a self-financing strategy based on OPT scores achieves a Sharpe ratio of 3.05 over our sample period, compared to a Sharpe ratio of 1.23 for the strategy based on the dictionary model. This study highlights the superior performance of LLMs in financial sentiment analysis, encouraging further research into integrating artificial intelligence and LLMs in financial markets.

And several interesting free blog posts that have been published during the last 2 weeks:

Trump’s Executive Orders and Their Impact on Financial Markets

In recent months, financial markets have experienced heightened volatility as Donald Trump, in his second term as President of the United States, increasingly uses executive orders to steer economic policy. While he also made use of this presidential power during his first term (2017–2021), the volume and impact of executive actions have notably intensified. In this analysis, we explore how markets reacted to Trump’s executive orders in his first presidency compared to the current term, aiming to uncover patterns and draw meaningful conclusions from both periods.

Fear, Not Risk, Explains Asset Pricing

With financial markets increasingly whipsawed by geopolitical tensions and unpredictable policy shifts from the Trump administration—investors are once again questioning how to understand risk, fear, and the true drivers of returns. A recent and compelling paper dives into this debate with a provocative thesis: in “Fear, Not Risk, Explains Asset Pricing,” authors Rob Arnott and Edward McQuarrie argue that traditional models built on quantifiable risk have failed to explain real-world returns, and that fear—messy, emotional, and deeply human—is the missing piece.

Uncovering the Pre-ECB Drift and Its Trading Strategy Applications

As the world’s attention shifts from the US-centric equity markets to international equity markets (which strongly outperform on the YTD basis), we could review some interesting anomalies and patterns that exist outside of the United States. In the world of monetary policy, traders have long observed a notable positive drift in U.S. equities on days surrounding Federal Reserve (FOMC) meetings. Interestingly, a similar—but slightly shifted—pattern emerges in European markets around European Central Bank (ECB) press conferences. Our quantitative analysis reveals that European equity markets tend to exhibit a strong and consistent upward drift on the day before the ECB’s scheduled press conference. The reason for this timing difference lies in logistics: since the ECB typically speaks at 14:15 CET (8:15 a.m. EST), well before the major U.S. markets open, investors often front-run the potential market-friendly signals from the central bank. Rather than risk holding positions into the uncertainty of the announcement itself, market participants gradually build up exposure the day before, pricing in expectations of dovish or supportive policy moves.

Short-Term Correlated Stress Reversal Trading

Short-term reversal strategies in U.S. large-cap equity indexes, such as the S&P 500, are well-documented and widely followed. These reversals often occur in response to brief periods of market stress, where sharp declines are followed by quick recoveries (as we have experienced in the last few weeks). Traditional approaches typically identify such stress periods using only the price action of the equity index itself. In this research, however, we explore a broader perspective—one that leverages the behavior of other asset classes, including gold, oil, and intermediate-term U.S. Treasuries. We demonstrate that using signals from these correlated assets to detect stress events can enhance the timing and robustness of reversal trades in equities. Furthermore, we show that combining signals across multiple markets leads to a more effective and diversified reversal strategy.