{"id":36888,"date":"2024-10-25T23:55:53","date_gmt":"2024-10-25T21:55:53","guid":{"rendered":"https:\/\/quantpedia.com\/?p=36888"},"modified":"2024-12-14T11:05:56","modified_gmt":"2024-12-14T10:05:56","slug":"quantpedia-premium-update-october-25th","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-october-25th\/","title":{"rendered":"Quantpedia Premium Update – October 25th"},"content":{"rendered":"

New Strategies<\/h4>\n\n\n\n

#1060 – Stronger Momentum Strategy in Non-Dividend Stocks<\/a><\/strong><\/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> 1972-2021
Indicative performance:<\/strong> 18.39%
Estimated volatility:<\/strong> 14.75%<\/p>\n\n\n\n

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

Cannon, Brad and Lynch, John: Does Cross-Sectional Return Extrapolation Explain Anomalies?
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3816782<\/a>
We provide evidence that dividend-paying stocks are less exposed to return extrapolation than non-dividend-paying stocks (capital-gain stocks). In particular, social media sentiment and analyst price targets of capital-gain stocks are each significantly more sensitive to past returns. Consistent with models of return extrapolation, capital-gain stocks earn higher momentum and long-term reversal returns. The significant difference in returns is not explained by factors nor stock characteristics related to dividend status. The value premium, however, is similar among both groups. Collectively, our findings suggest that return extrapolation may be an important source of some anomaly returns.<\/p>\n\n\n\n

#1061 – Predicting Anomalies<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Quarterly
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1990-2019
Indicative performance:<\/strong> 3%
Estimated volatility:<\/strong> –<\/p>\n\n\n\n

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

Bowles, Boone and Reed, Adam V. and Ringgenberg, Matthew C. and Thornock, Jacob: Predicting Anomalies<\/strong>
https:\/\/ssrn.com\/abstract=4939779<\/a>
Abstract:
We show that stock returns follow pedictable patterns before the publication of anomaly trading signals. Moreover, anomaly trading signals derived from financial data are themselves predictable, making it possible to trade before financial statements are released. A trading strategy based on predicted signals earns an annualized return of 3.65% in the quarter before the signal is released. In recent periods this predictability is concentrated in signals that are harder to forecast and returns are increasingly earned several quarters before signals are released. Our findings suggest anomalies are more anomalous than previously recognized.<\/p>\n\n\n\n

#1062 – The Pre-Holiday Effect in Commodities<\/a><\/strong><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded:<\/strong> commodities
Instruments used for trading:<\/strong> CFDs, ETFs, futures
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2006-2024
Indicative performance:<\/strong> 10.14%
Estimated volatility:<\/strong> 11.25%<\/p>\n\n\n\n

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

Vojtko, Radovan and Dujava, Cyril: PRE-HOLIDAY EFFECT IN COMMODITIES<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4990978<\/a>
Abstract:
This paper investigates the presence of a pre-holiday effect in commodity prices, similar to the well-documented phenomenon observed in equity markets. We focus on the case of crude oil and gasoline, hypothesizing that increased demand for fuel around holidays leads to price appreciation. Using historical data, we employ a battery of statistical tests to assess the existence and signi\ufb01cance of pre-holiday effects in these commodity prices. Our \ufb01ndings reveal that a statistically signi\ufb01cant pre-holiday effect is present, particularly for gasoline prices. These results suggest potential opportunities for traders to exploit predictable holiday price movements.<\/p>\n\n\n\n

#1063 – Machine Learning Trading Strategies in FX Markets<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Quarterly
Markets traded:<\/strong> currencies
Instruments used for trading:<\/strong> CFDs, forwards, futures
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 1999-2022
Indicative performance:<\/strong> 12.9%
Estimated volatility:<\/strong> 8.81%<\/p>\n\n\n\n

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

Lin, Chien-Hsiu and Liu, Tao: Machine learning in enhancing the performance of prediction and trading strategies in FX markets<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4937353<\/a>
Abstract:
This research involves synthesizing empirical foreign exchange rate forecasting with the field of machine learning. Due to the limits of traditional econometric frameworks that cannot incorporate too many variables into the model, we aim to resolve the problem by employing machine learning methods in currency forecasting and portfolio investment. Our predicative performance results show that for both developed or emerging countries, machine learning models are able to forecast currency variations more precisely than either the random walk or recursive linear regression models. We conclude that currency characteristics, such as changes in local policy rates (PRate), local government debt as a percent of GDP (Public_debt), foreign reserves (FCR), and current account balance as a percentage of GDP (CAB), serve important roles in forecasting. Rather than using only macroeconomic variables, accounting for each currency\u2019s characteristics into prediction is critical. We also show that the strategies based on the forecasts of machine learning models can generate a higher Sharpe ratio than either recursive linear regressions or traditional strategies, such as carry trade, momentum, and value strategies. Our robustness results show that the machine learning models can successfully implement not only in forecasting three-month exchange rates, but also, mid- or long-term ones, such as six or twelve months. Moreover, we find that the U.S. inflation (INF) demonstrates greater importance in forecasting exchange rates as forecast horizon gets longer, consistent with the effectiveness of purchasing power parity model (PPP) in forecasting long term exchange rate.<\/p>\n\n\n\n

#1064 – Leveraged ETF Arbitrage Between SPY and TQQQ<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Intraday
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2022-2024
Indicative performance:<\/strong> 25.9%
Estimated volatility:<\/strong> 10.16%<\/p>\n\n\n\n

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

Li, Harry and Min, Zixiao and Shen, Kaiwen and Yang, Yufei and Zhao, Hang: Exploiting Price Discrepancies: A Comprehensive Study of Leveraged ETF Arbitrage Between SPY and TQQQ<\/strong>
https:\/\/ssrn.com\/abstract=4944736<\/a>
Abstract:
This paper investigates the efficiency of an ETF arbitrage strategy leveraging price discrepancies between the SPDR S&P 500 ETF Trust (SPY) and the ProShares UltraPro QQQ (TQQQ). Utilizing historical data from Yahoo Finance, we employ a systematic approach to identify trading signals based on the spread between the adjusted closing prices of these ETFs. Our strategy involves initiating a long position in one ETF and a short position in the other when specific threshold conditions are met, aiming to capitalize on the mean-reverting behaviour of the spread. The performance of the strategy is assessed through back-testing, incorporating bid-ask spread, transaction costs and management fees to evaluate its profitability and risk-adjusted returns. Results indicate a significant influence of transaction costs on profitability, highlighting the strategy\u2019s sensitivity to trading expenses and market conditions. The study underscores the potential of ETF arbitrage while acknowledging the limitations posed by market dynamics and competition. Code is available at GitHub.<\/p>\n\n\n\n

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

#822 – Negative ESG Premium in Chinese Stock Market<\/a><\/strong><\/p>\n\n\n\n

Tan, Yeng-May and Szulczyk, Kenneth and Sii, Yew-Hei: Performance of ESG-Integrated Smart Beta Strategies in Asia-Pacific Stock Markets<\/strong>
https:\/\/ssrn.com\/abstract=4807529<\/a>
Abstract:
Environmental, Social, and Governance (ESG) investing is about ethical investing. While ESG investing has garnered heightened attention, the research has not settled on whether ESG investing can \u201cdo well while doing good\u201d. Using a proprietary ESG rating database of monthly firm-specific data, we examine the performance of ESG-incorporated investing strategies in Australia, Mainland China, Hong Kong, Malaysia, and Singapore. Specifically, we combine positive screening and the smart beta approach to evaluate the performance of ESG-based and non-ESG-based (traditional) equity portfolios. Our key findings reveal that high-ESG-based portfolios do not offer superior risk-adjusted returns compared to the low-rated portfolios. While a high-ESG-rated portfolio generally outperforms the market index, the ESG and traditional smart beta alphas differ little. Our results also indicate that the minimum-volatility portfolio achieves the best performance of all the factors. We use data from Refinitiv ESG and Bloomberg ESG to substantiate and support the results. Our findings add to the growing ESG literature that answers whether investors risk sacrificing returns while investing ethically.<\/p>\n\n\n\n

#485 – Toxical Releases and Stock’s Performance<\/a><\/strong><\/p>\n\n\n\n

Derrien, Fran\u00e7ois and Krueger, Philipp and Landier, Augustin and Yao, Tianhao: ESG News, Future Cash Flows, and Firm Value<\/strong>
https:\/\/ssrn.com\/abstract=3903274<\/a>
Abstract:
We investigate the expected consequences of negative ESG news on firms’ future profits. After learning about negative ESG news, analysts significantly downgrade their forecasts at short and longer horizons. Negative ESG news affect forecasts more strongly at longer horizons than other types of negative corporate news. The negative revisions of earnings forecasts following negative ESG news reflect expectations of lower future sales (rather than higher future costs). Quantitatively, forecast revisions can explain most of the negative impacts of ESG news on firm value. Analysts are correct to revise forecasts downward following negative ESG news.<\/p>\n\n\n\n

#460 – ESG Level Factor Investing Strategy<\/a><\/strong><\/p>\n\n\n\n

Gao, Yumeng and Herbert, Benjamin and Melin, Lionel: The ESG Disclosure Premium<\/strong>
https:\/\/ssrn.com\/abstract=4935848<\/a>
Abstract:
Do firms benefit from reporting sustainability information? This paper finds that firms that initiate disclosure of ESG metrics enjoy higher equity valuation today. Disclosing on any of the eight key environmental and social quantitative measures that we identify in this paper lowers firms’ cost of equity. The positive disclosure premium has increased over time and even turned from negative to positive in North America and emerging markets in particular after the 2015 Paris Agreement. Differentiated rewards for disclosure initiation are identified across sectors, while current disclosure achievements -or lack thereof- are reported. This paper identifies a substantial room for progress in particular in emerging markets and less carbon-intensive sectors. This mapping points to areas of potentially fruitful engagement between firms and investors that would benefit all stakeholders.<\/p>\n\n\n\n

#1014 – Cross-Market Intraday Time-Series Momentum<\/a><\/strong><\/p>\n\n\n\n

Xu, Dezhong and Li, Bin and Singh, Tarlok and Li, Jinze: Cross-Market Intraday Time-Series Momentum<\/strong>
https:\/\/ssrn.com\/abstract=4978089<\/a>
Abstract:
We propose a new cross-market intraday momentum: the US stock market’s last half-hour return predicts the next day’s first half-hour stock returns in international markets. The corresponding cross-market intraday time-series momentum (CITSM) strategy shows economic significance in international stock markets investments. The CITSM strategy\u2019s profit remains positive with the consideration of transaction costs. The profitability of the CITSM strategy is driven by some specific market characteristics. The CITSM is strong when international market liquidity is low; international market information uncertainty is high; the US market information uncertainty is high; and the US market liquidity is at extreme levels.<\/p>\n\n\n\n

And several interesting free blog posts that have been published during the last 2 weeks:<\/h4>\n\n\n\n
How to Improve ETF Sector Momentum<\/a><\/h5>\n\n\n\n

In this article, we explore the historical performance of sector momentum strategies and examine how their alpha has diminished over time. By analyzing the underlying causes behind this decline, we identify key factors contributing to the underperformance. Most importantly, we introduce an enhanced approach to sector momentum, demonstrating how this solution significantly improves the performance of an ETF sector momentum strategy, making it once again an effective tool for systematic investors.<\/p>\n\n\n\n

Pre-Holiday Effect in Commodities<\/a><\/h5>\n\n\n\n

Our research will explore the intriguing phenomenon of the Pre-Holiday effect in commodities, particularly crude oil and gasoline. Historical data reveals a short-term price drift prior to major U.S. holidays, suggesting a trend in these markets. We hypothesize that this anomaly may be driven by increased demand for oil and its derivatives, such as gasoline, as people prepare for travel, often by car, during the holiday season. This seasonal behavior offers unique opportunities for market participants.<\/p>\n\n\n\n

Short Sellers: Informed Liquidity Suppliers<\/a><\/h5>\n\n\n\n

Short sellers often have a bad reputation, seen as market disruptors who profit from declining prices. Yet, they play a crucial role in making markets more efficient by identifying overvalued assets and correcting mispricings. A recent study uncovers another surprising aspect of their behavior: rather than just demanding liquidity, the most informed short sellers actually provide it. Using transaction-level data, the research shows that these traders supply liquidity, especially on news days and when trading on known anomalies, challenging the conventional view of short sellers as merely aggressive market participants.<\/p>\n\n\n\n

How to Build Mean Reversion Strategies in Currencies<\/a><\/h5>\n\n\n\n

Our article explores a simple mean reversion trading strategy applied to FX futures, focusing on identifying undervalued and overvalued currencies to generate returns. Using FX futures rather than spot rates allows for the inclusion of interest rate differentials, simplifying the analysis. The strategy employs two position-sizing methods\u2014linear and exponential\u2014both rebalanced monthly based on currency deviations from their mean. While the linear method offers stability, its returns are limited. In contrast, the exponential method, despite higher risk and deeper drawdowns, ultimately delivers stronger growth and better overall performance by leveraging the mean reversion tendencies of FX pairs.<\/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

1054 – Enhancing the High-Volume Return Premium<\/a>
1060 – Stronger Momentum Strategy in Non-Dividend Stocks<\/a>
1061 – Predicting Anomalies<\/a><\/strong><\/p>\n\n\n\n\n\n

 <\/p>","protected":false},"excerpt":{"rendered":"

Five new strategies have been added. Four new related research papers have been included into existing strategy reviews and four short free blog posts<\/strong><\/a> have 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-36888","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/36888","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=36888"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/36888\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=36888"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=36888"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=36888"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}