{"id":5726,"date":"2020-01-31T23:56:30","date_gmt":"2020-01-31T22:56:30","guid":{"rendered":"https:\/\/quantpedia.com\/?p=5726"},"modified":"2025-06-04T14:30:52","modified_gmt":"2025-06-04T12:30:52","slug":"quantpedia-premium-update-31st-january-2019","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-31st-january-2019\/","title":{"rendered":"Quantpedia Premium Update – 31st January 2020"},"content":{"rendered":"

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

#469 – Conglomerates Post-Earnings Announcement Drift<\/a> <\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1977-2010
Indicative performance:<\/strong> 16.40%
Estimated volatility:<\/strong> not stated<\/p>\n\n\n\n

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

Alexander Barinov: Firm Complexity and Post-Earnings-Announcement Drift<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2360338<\/a>
Abstracto:
We show that the post earnings announcement drift (PEAD) is stronger for conglomerates than single-segment firms. Conglomerates, on average, are larger than single segment firms, so it is unlikely that limits-to-arbitrage drive the difference in PEAD. Rather, we hypothesize that market participants find it more costly and difficult to understand firm-specific earnings information regarding conglomerates as they have more complicated business models than single-segment firms. This in turn slows information processing about them. In support of our hypothesis, we find that, compared to single-segment firms with similar firm characteristics, conglomerates have relatively low institutional ownership and short interest, are covered by fewer analysts, these analysts have less industry expertise and also make larger forecast errors. Finally, we find that an increase in organizational complexity leads to larger PEAD and document that more complicated conglomerates have even greater PEAD. Our results are robust to a long list of alternative explanations of PEAD as well as alternative measures of firm complexity.<\/p>\n\n\n\n

#470 – Macroeconomic Announcement Beta Strategy <\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1958 – 2018
Indicative performance:<\/strong> 10.41%
Estimated volatility:<\/strong> 8.46%<\/p>\n\n\n\n

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

Niu, Zilong and Zhang, Terry: Post Macroeconomic Announcement Reversal <\/strong>
https:\/\/ssrn.com\/abstract=3495741<\/a>
Abstracto:
We document that the positive slope of the security market line on macroeconomic announcement days is reversed on two days after the announcement. On post-announcement days, stocks in the top beta decile return -5.13 basis points per day, completely erasing their gains from the announcement day. We find similar post-announcement reversal in market returns. Moreover, the reversal is predictable. If the market response on the announcement day is negative (i.e. after bad macroeconomic news), the market continues to decline by another 14 basis points and high-beta stocks lose 31 basis points on the next day. These findings suggest that the market does not immediately absorb macroeconomic news upon release and announcement-day returns may overestimate macroeconomic risk premium. We develop a model in which investors process bad news slowly. When good news is reflected in stock prices faster, unconditional returns are higher close to the announcement and, subsequently, become negative as bad news begins to dominate. Our model successfully explains the stock market behaviour on both announcement and post-announcement days. <\/p>\n\n\n\n

#471 – Macroeconomic Announcement Beta Reversal<\/a> <\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1958 – 2018
Indicative performance:<\/strong> 6.46%
Estimated volatility:<\/strong> 8.61%<\/p>\n\n\n\n

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

Niu, Zilong and Zhang, Terry: Post Macroeconomic Announcement Reversal <\/strong>
https:\/\/ssrn.com\/abstract=3495741<\/a>
Abstracto:
We document that the positive slope of the security market line on macroeconomic announcement days is reversed on two days after the announcement. On post-announcement days, stocks in the top beta decile return -5.13 basis points per day, completely erasing their gains from the announcement day. We find similar post-announcement reversal in market returns. Moreover, the reversal is predictable. If the market response on the announcement day is negative (i.e. after bad macroeconomic news), the market continues to decline by another 14 basis points and high-beta stocks lose 31 basis points on the next day. These findings suggest that the market does not immediately absorb macroeconomic news upon release and announcement-day returns may overestimate macroeconomic risk premium. We develop a model in which investors process bad news slowly. When good news is reflected in stock prices faster, unconditional returns are higher close to the announcement and, subsequently, become negative as bad news begins to dominate. Our model successfully explains the stock market behaviour on both announcement and post-announcement days. <\/p>\n\n\n\n

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

#331 – Timing Betting-Against-Beta (BAB) Anomaly<\/a>
#456 – Timing High and Low Volatility Equity Factor Strategy<\/a><\/strong><\/p>\n\n\n\n

Ehsani: The Risk in Low-Variance Anomaly<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=348025<\/a>7<\/a>
Abstracto:
The low variance (LV) strategy always bets against the volatile leg of common factor-portfolios. Factor loadings of the strategy are thus perfectly predictable based on the status of factor portfolio variances during the formation period. I find that the strategy earns alpha only when traders have to bear major factor risk to arbitrage it away: LV is an anomaly only when it is expected to bet on factor risk. In other times\u2014when low variance means low factor risk\u2014alpha is exactly zero. My results are consistent with models that rationalize anomalies by arbitrageurs reluctance to eliminate mispricing due to factor risk aversion. I use the findings to develop a trading strategy that uses factor data to time LV. <\/p>\n\n\n\n

#466 – Trend-Following and Spillover E\ufb00ect<\/a><\/strong><\/p>\n\n\n\n

Zaremba, Bianchi, Long: Momentum Spillover from Government Bonds to Equity Markets<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3498785<\/a>
Abstracto:
We investigate the momentum spillover effect from government bonds to their respective equity markets. Using a unique long-run dataset of 61 countries for the years 1900\u20132019, we demonstrate that past bond yield changes positively predict future stock index returns in the cross-section. The quintile of countries with the largest decline (or smallest increase) in government bond yields outperforms the quintile of countries with the smallest decline (or largest increase) by 0.63% per month. The effect is robust to many considerations. Our findings support the hypothesis that investors underreact to changes in government bond yields. Finally, we show that global investors can employ this bond momentum spillover effect to enhance international asset allocation decisions.<\/p>\n\n\n\n

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

Why Do Top Hedge Funds Outperform?<\/a><\/strong><\/p>\n\n\n\n

Every hedge fund manager and every trader wants to know what \nstrategies are employed in a fund ran by his competition. The curiosity \nis even stronger if we want to see how strategies are mixed in the \nkitchen of the most successful hedge funds. Top performing funds are \nusually notoriously secretive about their portfolios. But we still can \nlearn something from the history of their monthly returns. One such \ninteresting methodology is described in a research paper written by \nCanepa, Gonzalez, and Skinner. Their analysis hints that the \ntop-performing hedge funds are usually successful because they are able \nto manage their factor exposure better. They are not dependent so much \non classical equity risk factors as average funds are. And if they are \nexposed to some risk factor, the top-performing hedge funds are able to \nclose underperforming factor strategy sooner than average funds.<\/strong><\/p>\n\n\n\n

Autores:<\/strong> Canepa, Gonzales, Skinner<\/p>\n\n\n\n

T\u00edtulo: <\/strong>Hedge Fund Strategies: A non-Parametric Analysis<\/p>\n\n\n\n

Pre-Election Drift in the Stock Market<\/a><\/strong><\/p>\n\n\n\n

There are many calendar \/ seasonal \nanomalies by which we can enhance our strategies to gain more return. \nOne of the least frequent but still very interesting anomalies is for \nsure the Pre-Election Drift in the stock market in the United States. \nThis year is the election year, and public discussion is getting more \nheated. The current president of the United States and candidate for \nre-election, Donald Trump, is a peculiar figure who split the population\n of the United States into two parts, ones who hate him and those who \nlove him. We can probably expect volatile market moves as we will move \ncloser to this year\u2019s presidential election. But this post will not be \nabout politics but about trading. In this post, we will try to uncover a\n pattern in historical data that shows significant market moves a few \ndays before elections\u2026<\/strong><\/p>\n\n\n\n

Autores:<\/strong> Vojtko, Cisar<\/p>\n\n\n\n

T\u00edtulo:<\/strong> Pre-Election Drift in the Stock Market<\/p>\n\n\n\n

\n\n\n\n

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\u00bfTe gustar\u00eda tener acceso gratuito a? nuestros servicios<\/a>? Entonces, Abre una cuenta con Lightspeed.<\/a> y disfrute de un a\u00f1o de Quantpedia Premium sin costo alguno.<\/strong><\/p>\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":"

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

Two new related research papers have been included into existing strategy reviews. And two short free blog posts<\/strong><\/a> have been published during last few weeks.<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-5726","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/5726","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\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=5726"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/5726\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=5726"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=5726"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=5726"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}