Quantpedia Premium Update – September 24th

New Strategies:

#1174 – Buying the Worst-Performing S&P 500 Equities

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 2000-2024
Indicative performance: 26.93%
Estimated volatility: 30.26%

Source paper:

Teed, Louie: The SARM model: An Approach For Exploiting Temporary Performance Deviations of the Constituents of Standard & Poor’s 500 Index
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5366456
Abstract: My study introduces and examines the effectiveness of my S&P 500 Annual Reversion to the Mean (SARM) model, which employs a contrarian investment approach that systematically purchases the worst-performing S&P 500 equities each year, with position sizes proportional to the magnitude of their negative returns from the year prior. My findings culminate with statistically significant annual growth rate outperformance, with the SARM model generating an average annual return of 26.93% versus 9.36% for the S&P 500 benchmark. I attribute this outperformance to the interaction of behavioral biases and institutional constraints in contrarian investing, which prevent efficient exploitation of mean reversion opportunities in large-cap equity markets despite their theoretical predictability.

#1175 – Quantum Momentum Linkages

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex strategy
Backtest period: 2014-2024
Indicative performance: 13.5%
Estimated volatility: 10%

Source paper:

Samson, Ryan and Banner, Adrian and Candelori, Luca and Cottrell, Sebastien and Di Matteo, Tiziana and Duchnowski, Paul and Kirakosyan, Vahagn and Marques, Jose and Musaelian, Kharen and Pasquali, Stefano and Stever, Ryan and Villani, Dario: Supervised Similarity for Firm Linkages
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5286827
Abstract: We introduce a novel proxy for firm linkages, Characteristic Vector Linkages (CVLs). We use this concept to estimate firm linkages, first through Euclidean similarity, and then by applying Quantum Cognition Machine Learning (QCML) to similarity learning. We demonstrate that both methods can be used to construct profitable momentum spillover trading strategies, but QCML similarity outperforms the simpler Euclidean similarity.

#1176 – OHLCT Multi-Scale CNN Strategy

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex strategy
Backtest period: 2022-2024
Indicative performance: 43.16%
Estimated volatility: –

Source paper:

Pei, Zhiyuan and Yan, Jianqi and Yan, Jin and Yang, Bailing and Li, Ziyuan and Zhang, Lin and Liu, Xin; Zhang, Yang: A Stock Price Prediction Approach Based on Time Series Decomposition and Multi-Scale CNN using OHLCT Images
https://arxiv.org/abs/2410.19291
Abstract: Recently, deep learning in stock prediction has become an important branch. Image-based methods show potential by capturing complex visual patterns and spatial correlations, offering advantages in interpretability over time series models. However, image-based approaches are more prone to overfitting, hindering robust predictive performance. To improve accuracy, this paper proposes a novel method, named Sequence-based Multi-scale Fusion Regression Convolutional Neural Network (SMSFR-CNN), for predicting stock price movements in the China A-share market. Firstly, the historical opening, highest, lowest, closing price, and turnover rate (OHLCT) of stocks are converted into images, separated by weekends, with time information to help the CNN learn the impact of different trading periods. To reduce overfitting, long sequences of stock features are decomposed into multiple time periods, and OHLCT images at different time scales are utilized as inputs, significantly reducing overfitting. Thirdly, in order to overcome the problem that classification labels lose information about the magnitude of stock price changes, we introduce regression labels to help the model capture more latent features of stock price fluctuations. By utilizing CNN to learn sequential features and combining them with image features, we improve the accuracy of stock trend prediction on the A-share market stock dataset. This approach reduces the search space for image features, stabilizes and accelerates the training process. Extensive comparative experiments on 4,454 A-share stocks show that the model achieves 61.15% positive predictive value and 63.37% negative predictive value for the next 5 days, resulting in a total profit of 165.09%.

#1177 – Visual Firm Insight Strategy

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex strategy
Backtest period: 2014-2021
Indicative performance: 26%
Estimated volatility: 8.3%

Kaczmarek, Tomasz and Pukthuanthong, Kuntara: Just Look: Knowing Peers with Image Representation
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4934624
Abstract: We introduce Image-based Firm Similarity (IFS), a novel approach to industry classification that leverages machine learning and visual data. IFS leverages innate human visual processing capabilities to classify firms based on real-time investor perceptions. By analyzing over four million images—including visuals from Google, design patents, and 10-K filings—IFS captures firm similarities more dynamically and intuitively compared to traditional methods. It outperforms established classifications in pair trading, diversification, and industry momentum strategies, particularly in sectors with tangible products and rapid innovation. IFS aligns with investor perceptions, enhancing market applications by providing a more responsive and visually grounded understanding of firm relationships. IFS emphasizes vertical connections over horizontal ones.

#1178 – Analyst Network Momentum Strategy

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex strategy
Backtest period: 2006-2022
Indicative performance: 29.44%
Estimated volatility: 7.07%

Source paper:

Gorduza, Dragos and Kong, Yaxuan and Dong, Xiaowen and Zohren, Stefan: Extracting Alpha from Financial Analyst Networks
https://arxiv.org/abs/2410.20597
Abstract: We investigate the effectiveness of a momentum trading signal based on the coverage network of financial analysts. This signal builds on the key information-brokerage role financial sell-side analysts play in modern stock markets. The baskets of stocks covered by each analyst can be used to construct a network between firms whose edge weights represent the number of analysts jointly covering both firms. Although the link between financial analysts coverage and co-movement of firms’ stock prices has been investigated in the literature, little effort has been made to systematically learn the most effective combination of signals from firms covered jointly by analysts in order to benefit from any spillover effect. To fill this gap, we build a trading strategy which leverages the analyst coverage network using a graph attention network. More specifically, our model learns to aggregate information from individual firm features and signals from neighbouring firms in a node-level forecasting task. We develop a portfolio based on those predictions which we demonstrate to exhibit an annualized returns of 29.44% and a Sharpe ratio of 4.06 substantially outperforming market baselines and existing graph machine learning based frameworks. We further investigate the performance and robustness of this strategy through extensive empirical analysis. Our paper represents one of the first attempts in using graph machine learning to extract actionable knowledge from the analyst coverage network for practical financial applications.

#1179 – Narrative Momentum Investing

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex strategy
Backtest period: 2013-2023
Indicative performance: 8.84%
Estimated volatility: 10.71%

Source paper:

Lee, Hojoon and Lou, Xiaoxia and Ozik, Gideon and Sadka, Ronnie: Narrative Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4912496
Abstract: This paper advances that investors underreact to economic narratives. Using real-time-collected articles from thousands of digital sources for over a decade, coverage intensities of roughly 350 narratives are quantified, and corresponding narrative-mimicking, long-short portfolios are constructed using stock narrative betas. Narrative-mimicking portfolios of recently rising narrative intensities outperform those of declining intensities by about 8% annually, controlling for standard risk factors. Neither stock nor factor price momentum explains narrative momentum, which is stronger for slowly trending narratives. Furthermore, analysts tend to underreact to narrative-sensitive stocks. Additional results highlight the importance of considering the discourse among sources beyond traditional, general media.

#1180 – Hybrid LSTM-ARIMA Strategy

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: futures, CFDs, ETFs
Complexity: Very Complex strategy
Backtest period: 2005-2023
Indicative performance: 11.82%
Estimated volatility: 11.96%

Source paper:

Kashifa, Kamil and Ślepaczuk, Robert: LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies
https://arxiv.org/abs/2406.18206
Abstract: This study focuses on building an algorithmic investment strategy employing a hybrid approach that combines LSTM and ARIMA models referred to as LSTM-ARIMA. This unique algorithm uses LSTM to produce final predictions but boost results of this RNN by adding the residuals obtained from ARIMA predictions among other inputs. The algorithm is tested across three equity indices (S&P 500, FTSE 100, and CAC 40) using daily frequency data spanning from January, 2000 to August, 2023. The architecture of testing is based on the walk-forward procedure which is applied for hyperparameter tunning phase that uses using Random Search and backtesting the algorithms. The selection of the optimal model is determined based on adequately selected performance metrics combining focused on risk-adjusted return measures. We considered two strategies for each algorithm: Long-Only and Long-Short in order to present situation of two various groups of investors with different investment policy restrictions. For each strategy and equity index, we compute the performance metrics and visualize the equity curve to identify the best strategy with the highest modified information ratio (IR**). The findings conclude that the LSTM-ARIMA algorithm outperforms all the other algorithms across all the equity indices what confirms strong potential behind hybrid ML-TS (machine learning – time series) models in searching for the optimal algorithmic investment strategies.

#1181 – Path-Dependent Turnover Reduction Strategy

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex strategy
Backtest period: 1972-2020
Indicative performance: 11.9%
Estimated volatility: 4.4%

Source paper:

Chitsiripanicha, Soros and Paolella, Marc S. and Polak, Pawel and Walker, Patrick S.: Smoothing Out Momentum and Reversal
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4955388
Abstract: We introduce new path-dependent constraints within a sequential portfolio optimization framework designed to reduce turnover in frequently rebalanced investment strategies, such as momentum and short-term reversal. This method classifies individual assets into distinct groups based on their attractiveness from signal and rebalancing perspectives, effectively managing the trade-off between anomaly-based predictability and the required trading volume for exploitation. These constraints function independently from the ℓ1 portfolio turnover regularization, which manages reallocation at the aggregated portfolio level, proving more effective in enhancing net profitability. The combined turnover management mechanisms reduce the turnover of daily-rebalanced momentum and reversal portfolios by 95-99%, aligning closely with traditional monthly-rebalanced strategies. Furthermore, our method captures signals more promptly, resulting in more stable portfolios, a substantial reduction in maximum drawdown from 76-99% to 22-49%, and an improvement in risk-adjusted net returns by 38-149%, all under realistic transaction cost assumptions.

#1182 – Unsupervised Clustered Pairs Trading Strategy

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex strategy
Backtest period: 2000-2023
Indicative performance: 4.66%
Estimated volatility: 4.57%

Source paper:

Rotondi, Francesco and Russo, Federico: Machine Learning for Pairs Trading: a Clustering-based Approach
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5080998
Abstract: In this paper we employ unsupervised learning techniques to identify potential stocks for pairs trading using a clustering algorithm based on three distinct metrics: the Euclidean distance, a PCA-based Euclidean distance and a partial correlation-based distance, the latter representing a novel application in this context. Restricting only to the pairs identified by the clustering algorithm, we implement a straightforward pairs trading strategy that delivers statistically and economically significant excess returns, both in absolute terms and on a risk-adjusted basis, even after accounting for transaction costs. Specifically, focusing on stocks that are or have been constituents of the S&P 500 during the period 2000–2023, we find average monthly excess returns ranging from 36 to 41 basis points, with Sharpe ratios between 0.20 and nearly 0.30 (equivalent to annualized Sharpe ratios of 0.72 to almost 1). The excess returns are uncorrelated with the market or any traditional risk factor. Among the metrics analyzed, the partial correlation-based distance achieves the highest risk-adjusted performance, likely attributable to its superior clustering accuracy, as evidenced by a purity index based on major industry sector classifications. Robustness checks and sensitivity analyses further corroborate these results.

#1183 – Risk-Targeting Option Portfolio Strategy

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: options
Complexity: Very Complex strategy
Backtest period: 1997-2021
Indicative performance: 4.8%
Estimated volatility: 1.19%

Source paper:

Wu, Liuren and Xu, Yaofei: Cross-Sectional Variation of Risk-targeting Option Portfolios
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4922029
Abstract: Option contracts are listed on thousands of stocks with different number of contracts per each name. This paper proposes to construct four risk-targeting option portfolios to consolidate the information in all the option contracts on each stock at any given date along four risk dimensions. A cross-sectional regression identifies the market price of each risk dimension. The pricing estimates positively predict the excess returns of the corresponding option portfolio. Long-short portfolio of option portfolio construction along each risk dimension in proportion to the market price of risk estimates generates highly positive risk-adjusted excess returns across all four risk dimensions.

#1184 – Idiosyncratic Profitability Hedge Strategy

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex strategy
Backtest period: 1977-2023
Indicative performance: 8.21%
Estimated volatility: 20.11%

Source paper:

Han, Miaodi and Jackson, Andrew B. and Monroe, Gary S.: Returns on Idiosyncratic Profitability
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4890319
Abstract: This study investigates the influence of firm-idiosyncratic profitability on stock pricing decisions and whether a trading strategy based on idiosyncratic profitability can generate significant hedge portfolio abnormal returns. We disaggregate a firm’s profitability into three components – a market component, an industry component and a firm specific component, which we label as idiosyncratic profitability. We argue that the idiosyncratic component of profitability represents the outcome of a firm’s strategy and therefore reflects a fundamental aspect of firm performance. We document that an out-of-sample trading strategy based on idiosyncratic profitability generates significant hedge portfolio returns. Our results are robust in that our hedge portfolio returns still exist after controlling for known risk factors and previously documented anomalies.

#1185 – Predictability Clusters Strategy

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex strategy
Backtest period: 2003-2022
Indicative performance: 8.21%
Estimated volatility: 20.11%

Source paper:

Cong, Lin Wiliam and Feng, Guanhao and He, Jingyu and Wang, Yuanzhi: Mosaics of Predictability
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4853767
Abstract: We postulate that return predictability is an intrinsic and time-varying asset characteristic potentially related to the cross section of expected returns, instead of just an attribute of the chosen predictors or models. We develop a tree-based clustering method to gauge heterogeneous return predictability by grouping a panel of asset returns using high-dimensional asset characteristics and market-wide predictors. Our approach tells what types of assets exhibit greater return predictability under what market conditions, and empirically reveals substantial predictability heterogeneity in the U.S. equity market. Stocks with high earnings surprises, high earnings-to-price ratios, and low trading volumes exhibit the strongest predictability; predictability diminishes sharply with low market dividend yield but peaks with high dividend yield and low market liquidity. Out-of-sample, a new anomaly linked to investors’ model misspecification easily generates monthly excess alphas exceeding 1%, and investing in highly predictable clusters significantly outperforms conventional benchmarks with Sharpe ratios approaching 2.

#1186 – April Inflow Strategy

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: ETFs, futures, CFDs
Complexity: Simple strategy
Backtest period: 1965-2019
Indicative performance: 1.71%
Estimated volatility: 2.97%

Source paper:

Jalbert, Terrance: Is There an April Effect in Stock Returns?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4843739
Abstract: This paper examines the extent that retirement account inflows around the April 15th U.S. income tax filing deadline affect U.S. equity prices. Beginning in 1975, the U.S. Federal tax system allowed individuals to realize tax advantages by placing funds in specialized retirement accounts. Individuals can put money into these accounts until April 15th following the end of the tax year. Further, evidence suggests a disproportionate number of taxpayers file their returns near the April 15th deadline. This paper examines if money flowing into retirement accounts around the April 15th deadline produce a calendar-based stock-return pattern. This paper posits these market inflows result in higher average daily stock returns around April 15th. Results show large and significant April effects with event-window daily returns as much as eight times larger than daily returns for the rest of the year. Results hold for both U.S. and international stock indexes.

#1187 – Crisis Stock Picking Alpha Strategy

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex strategy
Backtest period: 1965-2019
Indicative performance: 35.32%
Estimated volatility: 19.96%

Source paper:

Khodayari Gharanchaei, Maysam and Babazadeh, Reza: Crisis Alpha: A High-Performance Trading Algorithm Tested in Market Downturns
https://arxiv.org/abs/2409.14510
Abstract: Forming quantitative portfolios using statistical risk models presents a significant challenge for hedge funds and portfolio managers. This research investigates three distinct statistical risk models to construct quantitative portfolios of 1,000 floating stocks in the US market. Utilizing five different investment strategies, these models are tested across four periods, encompassing the last three major financial crises: The Dot Com Bubble, Global Financial Crisis, and Covid-19 market downturn. Backtests leverage the CRSP dataset from January 1990 through December 2023. The results demonstrate that the proposed models consistently outperformed market excess returns across all periods. These findings suggest that the developed risk models can serve as valuable tools for asset managers, aiding in strategic decision-making and risk management in various economic conditions.

#1188 – Deep Learning Options Trader

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: options
Complexity: Very Complex strategy
Backtest period: 2010-2023
Indicative performance: 26.6%
Estimated volatility: 20%

Source paper:

Tan, Wee Ling and Roberts, Stephen and Zohren, Stefan: Deep Learning for Options Trading: An End-To-End Approach
https://arxiv.org/abs/2407.21791
Abstract: We introduce a novel approach to options trading strategies using a highly scalable and data-driven machine learning algorithm. In contrast to traditional approaches that often require specifications of underlying market dynamics or assumptions on an option pricing model, our models depart fundamentally from the need for these prerequisites, directly learning non-trivial mappings from market data to optimal trading signals. Backtesting on more than a decade of option contracts for equities listed on the S&P 100, we demonstrate that deep learning models trained according to our end-to-end approach exhibit significant improvements in risk-adjusted performance over existing rules-based trading strategies. We find that incorporating turnover regularization into the models leads to further performance enhancements at prohibitively high levels of transaction costs.

#1189 – Tube Oscillator Strategy

Period of rebalancing: Intraday
Markets traded: equities, currencies
Instruments used for trading: CFDs, futures, ETFs
Complexity: Very Complex strategy
Backtest period: 2019-2024
Indicative performance: 59.9%
Estimated volatility: 12.69%

Source paper:

Katic, Dragoljub and Richter, Stefan: Financial Market Geometry: The Tube Oscillator
https://arxiv.org/abs/2407.08036
Abstract: Based on geometrical considerations, we propose a new oscillator for technical market analysis, the tube oscillator. This oscillator measures the trending behavior of a fixed market instrument based on its past history. It is shown in an empirical analysis of the German DAX and the Forex EUR/USD exchange rate that a simple trading strategy based on this oscillator and fixed threshold leads to consistent positive monthly returns of average magnitude of 2% or more. The oscillator is derived from a broader understanding of the geometric behavior of prices throughout a fixed period, which we term financial market geometry. The remarkable profit results of the presented technique show that 1) prices of financial market instruments have a strong underlying deterministic component which can be detected and quantified with a matching approach and 2) financial market geometry is capable of providing such detectors.

#1190 – Analyst-Driven Skewness Strategy

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: options
Complexity: Very Complex strategy
Backtest period: 2007-2017
Indicative performance: 10.03%
Estimated volatility: 7.9%

Source paper:

Chen, Shuaiyu and Li, Shuaiqi and Yang, Yucheng: Supervised Similarity for Firm Linkages
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5158343
Abstract: We propose novel firm-level measures for subjective expectations on variance and skewness derived from analysts’ price forecast ranges in their research reports. We find that analyst expectations positively predict future variance and skewness of stock return, even after controlling for corresponding option-implied moments and past realized moments. Moreover, analyst variance (skewness) expectation positively predicts returns on straddle (skewness asset) and generates a profitable option strategy with an annualized Sharpe ratio of 0.93 (1.27). Using the same analyst’s expectations for return, variance, and skewness, we uncover a positive subjective risk-return trade-off and a negative skewness-return trade-off that are consistent with classical finance theories. To examine the formation of analyst expectations, we employ large language models to identify key topics from analysts’ discussions and apply machine learning techniques to quantify their impacts. Bankruptcy, government debt, and commodities play a crucial role in shaping analysts’ variance expectations, while earnings losses, bank loans, and business cycles are the dominant drivers of their skewness expectations. We find strong interaction effects between narratives and option-implied and realized moments in shaping analysts’ risk perceptions.

New research papers related to existing strategies:

#007 – Low Volatility Factor Effect in Stocks
#077 – Betting Against Beta Factor in Stocks

Xu, Mingyang: Low-Beta Puzzles: Implications for Factor Investing
https://ssrn.com/abstract=5387600
Abstract: The low-beta anomaly refers to the phenomenon that low-beta stocks earn higher returns than predicted by the CAPM, i.e. positive alphas, while high-beta stocks earn negative alphas. Even though it has been observed and studied for over 50 years, debate continues over whether this reflects a true anomaly or a risk premium. Consequently, various explanations for the low-beta anomaly have been proposed, either from a behavioral finance or risk perspective. However, why and how low-beta stocks outperform still remain inconclusive. In this paper, we propose a simple risk factor model to study lowbeta factor and explain associated low-beta puzzles. Using cross-sectional regression methods, factor portfolios are constructed analytically based on the risk factor model and their realized returns are then estimated. We find that the two types of low-beta factor-mimicking portfolios are essentially dollar neutral and beta neutral low-beta factor portfolio, respectively, and that the performances of all lowbeta factor strategies can be attributed to their exposures to these two factors. Our risk factor model also helps explain other low-beta puzzles, such as low beta high alpha, lower return with predicted beta, relationship with other risk factors, low beta and low vol connection, and so on. Because of its unique characteristics, the low-beta factor requires special consideration when combined with other factors in a multi-factor strategy. Based on the risk factor model, we propose an objective function for portfolio optimization that enables investors to take full advantage of low-beta anomaly by explicitly controlling exposure to the low-beta factor.

#5 – FX Carry Trade
#8 – Currency Momentum Factor
#9 – Currency Value Factor – PPP Strategy

Trabelsi Karoui, Ali and Kammoun, Aida: The FX Premium Puzzle Revisited: Global Imbalances and Liquidity as Key Risk Factors
https://ssrn.com/abstract=5357133
Abstract: We evaluate the performance and risk-return of the currency investment strategy, specifically carry, momentum , value and the new global imbalance using a novel approach that combines daily tradability with monthly return horizons. Portfolios are constructed using the daily data, with rebalancing opportunities available each day to calculate the return based on one-month forward contact. This strategy stands as closely simulating real-world trading behavior. Our methodology involves eliminating investment constraints to better assess how greater financial flexibility affects performance, particularly in strategies driven by capital flows. Our results show that the global imbalance portfolio has a high Sharpe ratio of 0.7, showing the cross-border financial flows and current account dynamics in delivering higher risk-adjusted returns without limits on money borrowing. Additionally, momentum and value strategies show varying levels of influence on excess returns, illustrating the complexities of currency trading techniques. This study provides a comprehensive analysis of currency market dynamics using daily excess return, incorporating realistic trading mechanics, emphasizing the role of liquidity and non-systematic risk in investment strategies, introducing global imbalance factor that capture macro-financial vulnerabilities. Our findings offer practical insights for investors seeking to optimize their currency portfolios under varying liquidity and risk environments.

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

What Drives the Excess Bond Premium?

The Excess Bond Premium (EBP – the portion of corporate bond spreads not explained by default risk), a key metric in quantitative finance for gauging credit spreads, has long been a subject of intense scrutiny. Recent research sheds new light on its dynamics, moving beyond traditional macroeconomic factors to explore the role of information flow. By analyzing news attention across 180 topics, a significant portion of the EBP’s variation can be explained, offering a novel lens to understand its fluctuations and predictive power.

Leveraged ETFs in Low-Volatility Environments

Leveraged ETFs (such as SPXL – (Direxion Daily S&P 500 Bull 3X Shares) offer amplified exposure to the S&P 500, promising high returns but exposing investors to volatility drag caused by daily rebalancing. This effect can significantly erode performance over longer horizons, particularly during periods of elevated market volatility. Inspired by recent research, The Volatility Edge, A Dual Approach For VIX ETNs Trading, focused on volatility-linked ETNs, we propose a volatility filter that adjusts ETF exposure based on the relationship between short-term realized volatility and implied volatility. By reducing exposure in high-volatility periods and maintaining it in calmer markets, this approach aims to harness leverage effectively while mitigating the most damaging drawdowns.