{"id":10891,"date":"2021-02-02T21:34:26","date_gmt":"2021-02-02T20:34:26","guid":{"rendered":"https:\/\/quantpedia.com\/?p=10891"},"modified":"2025-06-04T14:08:18","modified_gmt":"2025-06-04T12:08:18","slug":"quantpedia-premium-update-2nd-february-2021","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-2nd-february-2021\/","title":{"rendered":"Quantpedia Premium Update \u2013 2nd February 2021"},"content":{"rendered":"

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

#586 \u2013 Genetic Programming Predicts Stock Returns<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2004-2019
Indicative performance:<\/strong> 8.99%
Estimated volatility:<\/strong> 11.29%<\/p>\n\n\n\n

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

Liu, Yang and Zhou, Guofu and Zhu, Yingzi: Maximizing the Sharpe Ratio: A Genetic Programming Approach
<\/strong>https:\/\/ssrn.com\/abstract=3726609
<\/a>Abstracto:
While common machine learning algorithms focus on minimizing the mean-square errors of model fit, we show that genetic programming, GP, is well-suited to maximize an economic objective, the Sharpe ratio of the usual spread portfolio in the cross-section of expected stock returns. In contrast to popular regression-based learning tools and the neural network, GP can double their performance in the US, and outperform them internationally. We find that, while the economic objective plays a role, GP captures nonlinearity in comparison with methods like the Lasso, and it requires smaller sample size than the neural network.<\/p>\n\n\n\n

#587 \u2013 Investor Meetings in China Predicts Stock Returns<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Moderately complex strategy
Backtest period:<\/strong> 2012-2019
Indicative performance:<\/strong> 8.08%
Estimated volatility:<\/strong> 7.07%<\/p>\n\n\n\n

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

Erik C. So, Rongfei Wang, Ran Zhang: Meet Markets: Investor Meetings and Expected Returns
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3751468
<\/a>Abstracto:
We show meetings of investors and firms convey information about expected returns. Investors frequently travel to meet in-person with firms before investing, and we show firms with abnormally frequent meetings predictably outperform firms with abnormally infrequent meetings by roughly 70-to-100 basis points per month. Abnormally frequent meetings also predict improvements in firms\u2019 fundamental performance, suggesting our results stem from investors allocating time and attention to meetings with management from underpriced firms. Together, our findings highlight the usefulness of investors\u2019 resource allocation decisions in expected return estimations, and provide insight into the multi-stage process investors undertake when forming portfolios.<\/p>\n\n\n\n

#588 \u2013 Cross-Border M&A and Currency Returns<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>currencies
Instruments used for trading:<\/strong> futures, CFDs
Complexity:<\/strong> Moderately complex strategy
Backtest period:<\/strong> 1994-2018
Indicative performance:<\/strong> 4.59%
Estimated volatility:<\/strong> 5.43%<\/p>\n\n\n\n

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

Steven Riddiough, Huizhong Zhang: Cross-Border M&A and Currency Returns
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3711715
<\/a>Abstracto:
We uncover a novel source of currency return predictability stemming from cross-border merger and acquisition (M&A) activity: abnormally large M&A inflows lead exchange rate appreciations, while depreciations follow unusually large M&A outflows. We show that a simple cross-sectional currency strategy exploiting this predictability generates a Sharpe ratio of over 0.70 and is orthogonal to existing currency strategies. The portfolio weights are found to coincide with local extremes in macroeconomic fundamentals: countries experiencing the largest abnormal M&A outflows are growing most above their economic growth trend – a pattern that reverses following portfolio formation – while the opposite reversal in macroeconomic fundamentals is observed in countries experiencing unusually large M&A inflows.<\/p>\n\n\n\n

#589 \u2013 Order Imbalances at Closing Auctions<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Intraday
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2010-2018
Indicative performance:<\/strong> 121.18%
Estimated volatility:<\/strong> 15.93%<\/p>\n\n\n\n

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

Yanbin Wu and Narasimhan Jegadeesh: Closing Auctions: Information Content and Timeliness of Price Reaction
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3732955
<\/a>Abstracto:
Closing auction volume currently accounts for about 11% of the total trading volume. ETF arbitrage trades significantly contribute to this growth, but these trades likely constitute less than 15% of the closing auction volume. The to-close return for the decile of stocks with the largest buy order imbalances in closing auctions is 32 basis points greater than that for the decile with the largest sell order imbalances. About 83% of the return difference reverses over the next 3\u20135 days. Trading strategies that exploit this phenomenon are significantly profitable.<\/p>\n\n\n\n

#590 \u2013 Order Imbalances at Closing Auctions and Subsequent Reversals<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Weekly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity: <\/strong>Simple strategy
Backtest period:<\/strong> 2010-2018
Indicative performance:<\/strong> 15.55%
Estimated volatility:<\/strong> 3.14%<\/p>\n\n\n\n

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

Yanbin Wu and Narasimhan Jegadeesh: Closing Auctions: Information Content and Timeliness of Price Reaction
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3732955
<\/a>Abstracto:
Closing auction volume currently accounts for about 11% of the total trading volume. ETF arbitrage trades significantly contribute to this growth, but these trades likely constitute less than 15% of the closing auction volume. The to-close return for the decile of stocks with the largest buy order imbalances in closing auctions is 32 basis points greater than that for the decile with the largest sell order imbalances. About 83% of the return difference reverses over the next 3\u20135 days. Trading strategies that exploit this phenomenon are significantly profitable.<\/p>\n\n\n\n

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

#1 \u2013 Asset Class Trend-Following<\/a><\/strong>
#210 \u2013 Adaptive Asset Allocation<\/a><\/strong><\/p>\n\n\n\n

Keller, Wouter J., Lazy Momentum with Growth-Trend timing: Resilient Asset Allocation (RAA)
<\/strong>https:\/\/ssrn.com\/abstract=3752294
<\/a>Abstracto:
Resilient Asset Allocation (RAA) is a more aggressive version of our Lethargic Asset Allocation (LAA) strategy. It combines a more robust \u201cAll Weather\u201d portfolio with even slower growth-trend (GT) filter and a faster market crash-protection. GT timing goes risk-off only when both the US unemployment (UE) and the US capital markets are bearish. To arrive at RAA, we adapt LAA in three steps. First, the (risky, near-static) portfolio is changed to an even more robust and more diversified \u201call-weather\u201d portfolio, now with five (instead of four) equal weighted assets and with only bonds as risk-off assets (\u201ccash\u201d). Second, the \u201ccanary\u201d technology from our Defensive Asset Allocation (DAA) paper is used for determining the market trend with a faster filter. Third, we change the unemployment trend filter to a slower one, where we simply compare the recent unemployment rate with that of one year ago. As a result, RAA is more aggressive and more robust than LAA, while at the same time nearly as \u201clazy\u201d with respect to trading and turnover (on average one trading month per year).<\/p>\n\n\n\n

#536 \u2013 Machine Learning Stock Picking<\/a><\/strong><\/p>\n\n\n\n

Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa and Rajiv Ratn Shah:Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations
<\/strong>https:\/\/www.aclweb.org\/anthology\/2020.emnlp-main.676.pdf
<\/a>Abstracto:
In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock forecasting is complex, given the stochastic dynamics and non-stationary behavior of the market. Stock movements are influenced by varied factors beyond the conventionally studied historical prices, such as social media and correlations among stocks. The rising ubiquity of online content and knowledge mandates an exploration of models that factor in such multimodal signals for accurate stock forecasting. We introduce an architecture that achieves a potent blend of chaotic temporal signals from financial data, social media, and inter-stock relationships via a graph neural network in a hierarchical temporal fashion. Through experiments on real-world S&P 500 index data and English tweets, we show the practical applicability of our model as a tool for investment decision making and trading.<\/p>\n\n\n\n

#49 \u2013 S&P 500 Index Addition Effect<\/a><\/strong><\/p>\n\n\n\n

Hollstein, Fabian and Wese Simen, Chardin, The Index Effect: Evidence from the Option Market
<\/strong>https:\/\/ssrn.com\/abstract=3762051
<\/a>Abstracto:
We document a significantly positive response of delta-hedged option positions on companies entering or leaving the S&P 500 index. Our findings (i) hold for both call and put options, (ii) are robust to placebo- and risk-adjustments, and (iii) are stronger for companies that are likely subject to more demand pressure from stock index investors. The inclusion effect is permanent, while the exclusion effect is transitory. We explore various mechanisms to explain these results, including leading theories of benchmarking, investor recognition, noise trading, and dispersion trading. We find that these explanations cannot individually account for all our novel results.<\/p>\n\n\n\n

#5 \u2013 FX Carry Trade<\/a><\/strong><\/p>\n\n\n\n

Panayotov, George, Currency Puzzles and the Oil Connection
<\/strong>https:\/\/ssrn.com\/abstract=3737495
<\/a>Abstracto:
Recent studies document the reversal of several long-standing puzzles in the currency market, including the forward premium puzzle, carry trade profitability and the exchange rate “disconnect”. We provide a common framework for understanding these reversals, consistent with the structural breaks that we find in currency returns at the end of 2005. These reversals can be explained by a shift in the relative importance of the global and local risk factors that drive currency returns. We link this shift to developments in the U.S. oil sector, and more generally in the U.S. real economy, that originate around 2005.<\/p>\n\n\n\n

#544 \u2013 Impact of intangible assets on B\/M<\/a><\/strong><\/p>\n\n\n\n

Eisfeldt, Andrea L. and Kim, Edward and Papanikolaou, Dimitris, Intangible Value
<\/strong>https:\/\/ssrn.com\/abstract=3720983
<\/a>Abstracto:
Intangible assets are absent from traditional measures of value, despite their very large (and growing) importance in firms’ capital stocks. As a result, the fundamental anchor for value that uses book assets is mismeasured. We propose a simple improvement to the classic value factor (HML^FF) proposed by Fama and French (1992, 1993). Our intangible value factor, HML^INT, prices assets as well as or better than the traditional value factor but yields substantially higher returns. This outperformance holds over the entire sample, as well as in more recent decades in which value has underperformed. We show that this is likely due to the intangible value factor sorting more effectively on productivity, profitability, financial soundness, and on other valuation ratios such as price to earnings or price to sales.<\/p>\n\n\n\n

And two interesting free blog posts have been published during last 2 weeks:<\/strong><\/p>\n\n\n\n

Probability Distributions of Bull and Bear Market States<\/a><\/strong><\/p>\n\n\n\n

Numerous academic papers have shown that the options<\/a> markets are not only the place where the supply and demand for options meets. For example, they might point out to the smart money<\/a> positioning, help to assess risk in the form of implied volatility, or be base of the well-known fear index VIX<\/a>. Novel research of Bhansali and Holdom (2021), uses information embedded in options markets to construct a probability-weighted mixture of two distributions of bull and bear market states for the S&P 500 index. The results show that the implied return distribution drastically change switching from normal to stressed market states and vice versa. Moreover, the uncertainty in both distributions changes in the same fashion.<\/p>\n\n\n\n

An excellent example is the shift of distribution before and after the recent US presidential election<\/a>, which can be found below. Many have feared that if the democrat candidate Biden wins the elections, it would be a bad signal for the markets. However, after the uncertainty has passed, the fear has seemed to disappear. Additionally, the paper also shows how to use the bimodality in return distributions for the asset allocation using various utility functions. Allocations are made using a risky asset, risk-free and even options. Indeed, this research is worth reading. <\/p>\n\n\n\n


Macro Factor Risk Parity<\/a><\/strong><\/p>\n\n\n\n

Risk and diversification are critical interests of every investor, especially when things go south since the correlations across assets tend to rise during stressful times<\/a>. Therefore, in the asset allocation, the risk parity allocation<\/a> is one of the key topics. Factors are commonly known as underlying sources of both risk and returns, and it is assumed that they can be utilized to achieve superior risk-adjusted returns and diversification. However, there seems to be a lack of research that would be related to the macro factors. This gap is quite striking since there is a general consent that macro factors (for example, inflation) largely influence the broad set of assets. Amato and Lohre (2020) research paper fills the gap and studies the usage of macro factors as diversifiers in asset allocation. <\/p>\n\n\n\n

The authors divide the macro factors to two groups, where the first consists of TERM, MARKET, USD, OIL and DEF (default risk), and the second group consists of CLI (a measure of output by OECD), G7.INFLATION, G7.Short.Rate and VIX. The research shows, that when the diversification matters the most<\/a>, only the second group improves both the risk and returns, acting as a successful diversification during various economic regimes and particularly, during high economic uncertainty. Overall, the paper offers exciting insights into diversification and macro factors, accompanied by more complex mathematical models definitely worth looking into. <\/p>\n\n\n\n

Plus, the following six trading strategies have been backtested in\u00a0QuantConnect<\/a>\u00a0in the previous two weeks:<\/strong><\/h4>\n\n\n\n

#27 – Market Timing with Aggregate and Idiosyncratic Stock Volatilities<\/a>
#573 – Momentum and High Accruals<\/a>
#574 – Oil Volatility Affects Industry Momentum in China<\/a>
#575 – Momentum and Low Risk Effects in India<\/a>
#580 – Capturing Energy Risk Premia<\/a>
#584 – Return Seasonality and Information Cycle<\/a><\/p>\n\n\n\n

\n\n\n\n

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Facebook Grupo<\/a>, Facebook P\u00e1gina<\/a>, Gorjeo<\/a>, LinkedIn<\/a>, Medio<\/a> o YouTube<\/a><\/strong><\/p>","protected":false},"excerpt":{"rendered":"

Five new strategies have been added.<\/p>\n

Five new related research papers have been included into existing strategy reviews and two short free blog posts have been published during last few weeks. Plus, six trading strategies have been backtested in QuantConnect<\/strong><\/a> in the previous two weeks.<\/p>","protected":false},"author":21001,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-10891","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/10891","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\/21001"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=10891"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/10891\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=10891"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=10891"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=10891"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}