{"id":39079,"date":"2025-03-10T22:31:15","date_gmt":"2025-03-10T21:31:15","guid":{"rendered":"https:\/\/quantpedia.com\/?p=39079"},"modified":"2025-03-19T11:38:26","modified_gmt":"2025-03-19T10:38:26","slug":"quantpedia-premium-update-march-6th","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-march-6th\/","title":{"rendered":"Quantpedia Premium Update – March 6th"},"content":{"rendered":"

New strategies:<\/h4>\n\n\n\n

#1103 – Dangers of Relying on OHLC Prices \u2013 the Case of Overnight Drift in GDX ETF<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong>  Intraday
Markets traded:<\/strong> commodities, equities
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2006-2025
Indicative performance:<\/strong> 8.58%
Estimated volatility:<\/strong> 16.9%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Vojtko, Radovan and Dujava, Cyril: Dangers of Relying on OHLC Prices \u2013 the Case of Overnight Drift in GDX ETF<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5139307<\/a>
Abstract:
The overnight effect, a phenomenon where stocks deliver all their returns when the market is closed and no returns during the trading day, has been observed in various financial instruments, particularly in exchange-traded funds (ETFs). This study investigates the overnight drift in the VanEck Vectors Gold Miners ETF (GDX), a widely traded ETF that seeks to replicate the performance of the NYSE Arca Gold Miners Index. We examine the discrepancies between the ETF\u2019s price movements and the underlying index, focusing on the market opening and closing periods.

Our research utilizes high-frequency trading data and advanced statistical methods to analyze the intraday and overnight price patterns of GDX. We explore potential explanations for the observed overnight drift, including the impact of asynchronous trading of international holdings, order imbalances at market open, and the behavior of day traders and high-frequency market makers. Additionally, we investigate the role of ETF creation\/redemption mechanisms and their influence on price discrepancies between the ETF and its underlying assets.

The findings of this study contribute to the growing body of literature on market anomalies and ETF pricing efficiency. By providing insights into the overnight drift phenomenon in GDX, we aim to enhance understanding of ETF behavior and inform trading strategies for institutional and retail investors. Furthermore, our results have implications for market microstructure research and regulatory considerations in the rapidly evolving ETF landscape.<\/p>\n\n\n\n

#1104 – Idiosyncratic Reversal Effect Strategy<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1966-2019
Indicative performance:<\/strong> 9.81%
Estimated volatility:<\/strong> 11.15%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Schmid, Markus and Graef, Frank and Hoechle, Daniel: Firm-specific versus systematic momentum<\/strong>
https:\/\/ssrn.com\/abstract=5053270<\/a>
Abstract:
We decompose stock returns into a systematic and a firm-specific component and show that the dynamics of the firm-specific return component drives the wellknown stock momentum anomaly. Our results are robust to the use of a variety of prominent factor models for return decomposition. Furthermore, we find that momentum profits are largely unaffected when the investment universe is restricted to stocks with inconspicuous factor loadings. Our empirical findings call into question the transmission mechanism from factor momentum to stock momentum proposed in recent research.<\/p>\n\n\n\n

#1105 – Diversified Portfolio Protection Strategy<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded:<\/strong> bonds, commodities, equities
Instruments used for trading:<\/strong> CFDs, futures, options, stocks
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2000-2025
Indicative performance:<\/strong> 1.7%
Estimated volatility:<\/strong> –<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Horrex, James and Martinec, Sophie: The Importance of Diversification in Portfolio Protection Strategies<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5020625<\/a>
Abstract:
Given the difficulty in timing significant market drawdown\/risk-off events, we propose investors should consider portfolio protection strategies as an ‘always on’ or strategic allocation. \u2022 Portfolio protection implemented via listed equity index options, as is common, may come with a material ‘cost of carry’ that needs to be assessed against the efficacy of the protection.<\/p>\n\n\n\n

#1106 – Using Inflation Data for Systematic Gold and Treasury Investment Strategies<\/a><\/strong><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded:<\/strong> bonds, commodities
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 1981-2024
Indicative performance:<\/strong> 8.23%
Estimated volatility:<\/strong> 8.4%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Vojtko, Radovan and Dujava, Cyril: Using Inflation Data for Systematic Gold and Treasury Investment Strategies<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5151557<\/a>
Abstract:
This study investigates the intricate relationship between inflationary pressures and the valuation of key financial assets, specifically gold and treasury bonds. While the role of gold as a conventional inflation hedge is widely acknowledged, the impact of inflation on treasury yields and prices presents a more complex dynamic influenced by monetary policy responses and investor expectations. We delve into the theoretical underpinnings of these relationships, considering the interplay of real interest rates, inflation risk premia, and market sentiment. The primary objective of this research is to empirically assess whether the well-documented theoretical linkages between inflation and these asset classes can be systematically capitalized upon to generate positive risk-adjusted returns within a rigorous quantitative framework, drawing inspiration from established methodologies and findings in the extant literature, including practical implementations discussed in resources such as Quantpedia. Employing a comprehensive historical dataset encompassing inflation indicators and the price evolution of gold and treasury futures, we develop and back-test a suite of novel, data-driven trading strategies predicated on analyzing inflation data releases and their impact on market dynamics. Our empirical findings robustly demonstrate the existence of statistically significant and economically meaningful opportunities for systematic alpha generation by strategically positioning in gold and treasury markets based on inflation signals. These results contribute significantly to the growing knowledge of macro-driven investment strategies. They offer practical insights for quantitative portfolio managers seeking to incorporate inflation expectations into their asset allocation decisions, thereby enhancing portfolio performance and providing a systematic avenue for exploiting the intricate interplay between macroeconomic variables and financial asset prices.<\/p>\n\n\n\n

#1107 – Market Making in Crypto<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Intraday
Markets traded:<\/strong> cryptos
Instruments used for trading:<\/strong> cryptos
Complexity:<\/strong> Very Complex strategy
Backtest period:<\/strong> 2024-2024
Indicative performance:<\/strong> 45.84%
Estimated volatility:<\/strong> 58.77%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Stoikov, Sasha and Zhuang, Elina and Chen, Hudson and Zhang, Qirong and Wang, Shun and Li, Shilong and Shan, Chengxi: Market Making in Crypto<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5066176<\/a>
Abstract:
We develop automated market-making algorithms for cryptocurrency perpetual contracts, which provide liquidity while managing risk and maximizing returns. Using historical candlestick data, we develop an alpha signal we call the Bar Portion (BP), which is robust across cryptocurrencies. We then use the Hummingbot platform, an open-source framework for algorithm development, to fine tune risk management parameters before live trading. By live trading on the SOL-USDT, DOGE-USDT, and GALA- USDT trading pairs over a 24-hour period, we show that BP outperforms a baseline MACD signal.<\/p>\n\n\n\n

#1108 – The Aggregated Equity Risk Premium<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded:<\/strong> bonds, equities
Instruments used for trading:<\/strong> CFDs, ETFs, funds, futures
Complexity:<\/strong> Very Complex strategy
Backtest period:<\/strong> 2000-2021
Indicative performance:<\/strong> 6.93%
Estimated volatility:<\/strong> 16.12%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Azevedo, Vitor and Riedersberger, Christoph and Velikov, Mihail: The Aggregated Equity Risk Premium<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5091837<\/a>
Abstract:
We propose a new approach for predicting the equity risk premium (ERP) that first estimates expected returns on individual stock before aggregating them to the market level. Our deep learning combination forecast aggregates firm-level return predictions from neural networks of varying complexity, trained on a comprehensive two-dimensional feature set of post-publication firm-level characteristics and aggregate macroeconomic variables. Using this aggregation method, we achieve an out-of-sample R2 of 2.74% in a sample from 2000 to 2021. The forecasts demonstrate strong economic significance in trading strategies even with transaction costs. While the market generated a return of 376% over this period, a simple market-timing strategy based on our model\u2019s forecast signs yields a net cumulative return of approximately 768%. Our results show that aggregating firm-level predictions can lead to profitable market timing signals, challenging the conventional wisdom that the ERP is unpredictable out-of-sample and suggesting that valuable market-wide information can be extracted from the cross-section of individual stocks.<\/p>\n\n\n\n

New research papers related to existing strategies:<\/h4>\n\n\n\n

#7 – Low Volatility Factor Effect in Stocks<\/a><\/strong><\/p>\n\n\n\n

Cirulli, Antonello and De Nard, Gianluca and Traut, Joshua and Walker, Patrick S.: Low Risk, High Variability: Practical Guide for Portfolio Construction<\/strong>
https:\/\/ssrn.com\/abstract=5105457<\/a>
Abstract:
The low-risk anomaly challenges traditional financial theory by stating that less volatile stocks generate higher risk-adjusted returns. This paper explores how various portfolio construction choices influence the performance of low-risk portfolios. We show that methodological decisions critically influence portfolio outcomes, causing substantial dispersion in performance metrics across weighting schemes and risk estimators. This can only be marginally mitigated by incorporating constraints such as short-sale restrictions and size or price filters. Our analysis reveals that volatility-based estimators yield the most favorable performance distribution, outperforming beta-based approaches. Transaction costs are found to significantly affect performance and are vitally important in identifying the most attractive portfolios, highlighting the importance of realistic implementation constraints. Through rigorous empirical analysis, this study bridges the gap between theoretical insights and practical applications, offering actionable guidance to investors. The findings advocate for a cautious approach to nonstandard errors in portfolio modeling and emphasize the necessity of robust strategies in low-risk investing.<\/p>\n\n\n\n

#25 – Size Factor \u2013 Small Capitalization Stocks Premium<\/a><\/strong>
#26 – Value (Book-to-Market) Factor<\/a><\/strong><\/p>\n\n\n\n

McQuarrie, Edward F.: Do Factor Strategies Beat the Market? Sometimes Yes. Sometimes No<\/strong>
https:\/\/ssrn.com\/abstract=5098799<\/a>
Abstract:
Following two decades of skepticism and doubt, combined with worries about a replication crisis in finance, factors such as size and value have re-emerged as statistically robust effects, verified by multiple author teams using larger and more comprehensive datasets than heretofore available. Historical evidence simultaneously shows that even well-attested factors, when implemented as long-only portfolios in the world, have repeatedly underperformed the market for periods lasting a decade or more. This paper counterposes the historical and statistical evidence and suggests an integration.<\/p>\n\n\n\n

#654 – Momentum without the Crash Component<\/a><\/strong>
#951 – Hedging Momentum Crashes<\/a><\/strong><\/p>\n\n\n\n

Zhou, Hanchen: Are Upside and Downside Momentum Symmetrical?<\/strong>
https:\/\/ssrn.com\/abstract=5033710<\/a>
Abstract:
How can we construct an optimal dynamic low-net momentum factor portfolio based on our knowledge of how upside and downside momentum performs?<\/p>\n\n\n\n

#645 – Statistical Arbitrage With CNN and Transformer Networks<\/a><\/strong>
#670 – Machine Learning Pairs Trading Strategy<\/a><\/strong>
#997 – Clustering Based Multi-Pairs Trading<\/a><\/strong><\/p>\n\n\n\n

Rotondi, Francesco and Russo, Federico: Machine Learning for Pairs Trading: a Clustering-based Approach<\/strong>
https:\/\/ssrn.com\/abstract=5080998<\/a>
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.<\/p>\n\n\n\n

And one interesting free blog posts that has been published during the last 2 weeks:<\/strong><\/p>\n\n\n\n

Can Margin Debt Help Predict SPY\u2019s Growth & Bear Markets?<\/a><\/h5>\n\n\n\n

Navigating the financial markets requires a keen understanding of risk sentiment, and one often-overlooked dataset that provides valuable insights is FINRA\u2019s margin debt statistics. Reported monthly, these figures track the total debit balances in customers\u2019 securities margin accounts\u2014a key proxy for speculative activity in the market. Since margin accounts are heavily used for leveraged trades, shifts in margin debt levels can signal changes in overall risk appetite. Our research explores how this dataset can be leveraged as a market timing tool for US stock indexes, enhancing traditional trend-following strategies that rely solely on price action. Given the current uncertainty surrounding Trump\u2019s presidency, margin debt data could serve as a warning system, helping investors distinguish between market corrections and deeper bear markets.<\/p>\n\n\n\n


Plus, the following trading strategies have been backtested in QuantConnect<\/a> in the previous two weeks:<\/h4>\n\n\n\n

799 – Machine Learning Volatility Targeting of Equity Indices<\/a>
1103 – Dangers of Relying on OHLC Prices \u2013 the Case of Overnight Drift in GDX ETF<\/a>
1106 – Using Inflation Data for Systematic Gold and Treasury Investment Strategies<\/a><\/strong><\/p>","protected":false},"excerpt":{"rendered":"

Six new strategies have been added. Four new related research papers have been included into existing strategy reviews and one short free blog post<\/strong><\/a> has been published during last few weeks. Plus, three trading strategies have been backtested in QuantConnect<\/strong><\/a> in the previous two weeks.<\/p>","protected":false},"author":25721,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-39079","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/39079","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/users\/25721"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=39079"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/39079\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=39079"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=39079"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=39079"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}