{"id":6920,"date":"2020-04-16T23:30:27","date_gmt":"2020-04-16T21:30:27","guid":{"rendered":"https:\/\/quantpedia.com\/?p=6920"},"modified":"2025-06-04T14:14:47","modified_gmt":"2025-06-04T12:14:47","slug":"quantpedia-premium-update-16th-april-2020","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-16th-april-2020\/","title":{"rendered":"Quantpedia Premium Update \u2013 16th April 2020"},"content":{"rendered":"

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

#486 – Credit Rating Announcements from Issuer- versus Investor-Paid Rating Agencies and 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> 1999-2011
Indicative performance:<\/strong> 22.04%
Estimated volatility:<\/strong> 13.20%<\/p>\n\n\n\n

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

Nguyen, Pham Minh Quan and Do, Hung Xuan and Molchanov, Alexander and Nguyen, Lily and Nguyen, Nhut (Nick) Hoang: Asymmetric Trading Responses to Credit Rating Announcements from Issuer- versus Investor-Paid Rating Agencies<\/strong>
https:\/\/ssrn.com\/abstract=3524019<\/a>
Abstracto:
Credit rating industry business model has traditionally been based on an \u2018issuer-pays\u2019 principle. Issuer-paid credit rating agencies (CRAs) have recently faced criticism regarding untimely releases of negative ratings adjustments, which is attributed to conflict of interest of their business model. A recent model based on \u2018investor-pays\u2019 principle is arguably free of such conflict. We examine how institutional investors respond to changes in credit ratings issued by these two types of CRAs. We find that investors react asymmetrically: they abnormally sell equity stakes around rating downgrades by investor-paid CRAs, while abnormally buying around rating upgrades by issuer-paid CRAs. Further, a dynamic trading strategy based on such trading behavior generates significant abnormal returns. Our study suggests that, through their trades, institutional investors capitalize on value-relevant information provided by both types of credit rating agencies.<\/p>\n\n\n\n

#487 – Mean-Variance Market Timing in the FX Market<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>currencies
Instruments used for trading:<\/strong> futures, forwards, CFDs, swaps
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 1977 – 2016
Indicative performance:<\/strong> 7.44%
Estimated volatility:<\/strong> 8.12%<\/p>\n\n\n\n

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

Maurer, Thomas Andreas and To, Thuy Duong and Tran, Ngoc-Khanh, Market Timing and Predictability in FX Markets<\/strong>
https:\/\/ssrn.com\/abstract=2797483<\/a>
Abstracto:
We construct mean-variance optimized currency portfolios and analyze the time- series variation of the conditional Sharpe ratio. Returns, volatility and skewness are predictable. Market timing \u2013 i.e., trading more (less) aggressively when the conditional risk-return trade-off is more (less) favorable \u2013 significantly increases the unconditional Sharpe ratio from 0.72 to 1.21, improves the skewness of the monthly return distribution from -0.79 to +0.89, and reduces the downside risk from 8.68% to 1.57% maximum loss per 1% expected excess return. Thus, restricting risk taking, i.e., prohibiting market timing, is costly. Understanding and quantifying these costs is important when considering constraints in asset allocations.<\/p>\n\n\n\n

#488 – Stock Picking of ETF Constituents<\/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> 2010-2017
Indicative performance:<\/strong> 18.00%
Estimated volatility:<\/strong> not stated<\/p>\n\n\n\n

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

Hailey Lynch et al.: The Revenge of the Stock Pickers<\/strong>
https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/0015198X.2019.1572358?needAccess=true<\/a>
Abstracto:
When an exchange-traded fund (ETF) trades heavily around a theme, correlations among its constituents increase significantly. Even some securities that have little or negative exposure to the theme itself begin to trade in lockstep with other ETF constituents. In other words, because ETF investors are agnostic to security-level information, they often \u201cthrow the baby out with the bathwater.\u201d As the prices of individual stocks get dragged up or down with ETFs, these mispricings can become significant, and the profits realized by taking advantage of them may present an opportunity for stock pickers.<\/p>\n\n\n\n

#489 – Combining VIX Futures Term Structure Strategy and S&P500 Index<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> futures, CFDs, ETFs
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 2007 – 2018
Indicative performance:<\/strong> 23.58%
Estimated volatility:<\/strong> 19.92%<\/p>\n\n\n\n

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

Jim Campasano: Portfolio Strategies for Volatility Investing<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3490978<\/a>
Abstracto:
The VIX premium has been shown to hold predictive power over volatility returns and investment risk. Applied within a portfolio construct, this study proposes a conditional strategy which allocates to market and volatility risk. While the strategy is predominantly short volatility, the strategy owns volatility during much of the financial crises. Both long and short volatility allocations prove profitable over the sample period, producing a portfolio more consistently profitable than the S\\&P 500 Index and related strategies.<\/p>\n\n\n\n

#490 \u2013 Lazy Stock Prices<\/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> 1995-2014
Indicative performance:<\/strong> 18.58%
Estimated volatility:<\/strong> 26.72%<\/p>\n\n\n\n

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

Cohen, Malloy, Nguyen: Lazy Prices<\/strong>
http:\/\/laurenhcohen.com\/wp-content\/uploads\/2017\/09\/lazyprices.pdf<\/a>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=1658471<\/a>
Abstracto:
We explore the implications of a subtle \u201cdefault\u201d choice that firms make in their regular reporting practices, namely that firms typically repeat what they most recently reported. Using the complete history of regular quarterly and annual filings by U.S. corporations from 1995-2014, we show that when firms make an active change in their reporting practices, this conveys an important signal about the firm. Changes to the language and construction of financial reports have strong implications for firms\u2019 future returns: a portfolio that shorts \u201cchangers\u201d and buys \u201cnon-changers\u201d earns up to 188 basis points per month (over 22% per year) in abnormal returns in the future. These reporting changes are concentrated in the management discussion (MD&A) section. Changes in language referring to the executive (CEO and CFO) team, or regarding litigation, are especially informative for future returns.<\/p>\n\n\n\n

#491 \u2013 Pre-Election Drift In the Stock Market<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> ETFs, futures, CFDs, funds
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 1950-2018
Indicative performance:<\/strong> 2.46%
Estimated volatility:<\/strong> not stated<\/p>\n\n\n\n

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

Radovan Vojtko, Dominik Cis\u00e1r: Pre-Election Drift in the Stock market<\/strong>
https:\/\/ssrn.com\/abstract=3531847<\/a>
Abstracto:
A particular event like elections are making lots of noise, but not only in our regular life where we should participate and so vote for our preferred candidate\/party. This process also impacts financial markets. The uncertainty, which implies from the result of the elections, affects the volatility of the financial markets, which can easily double. In this paper, we were focused only on one specific market where we were seeking any pattern which could be profitable by assembling an investment strategy on it. Analyzing the stock market of the United States, where the elections occur on the exact day every even year, we found a specific pattern in the days before elections. This positive drift starts as soon as the fifth day before the election\u2019s day and ends at the end of the election\u2019s day with almost 2,5 % performance on average. An additional advantage of this pre-election pattern is its independence from the elections results even it is tied to elections. Last years showed us bigger market moves around elections due to increased uncertainty caused by political reasons which aren\u2019t receding nowadays. Therefore, the period before elections could be more profitable regardless of the result of the elections.<\/p>\n\n\n\n

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

#360 – Trend Following Trading Strategies for Currencies <\/a><\/strong><\/p>\n\n\n\n

Deinwallner: Moving Average: How do the ANDOR and ANDAND Strategy Perform in Currency Markets<\/strong>
https:\/\/search.proquest.com\/openview\/b8ee8c65c9b3c6e50992db06cf6db6c7\/1.pdf?pq-origsite=gscholar&cbl=2046325<\/a>
Abstracto:
In this paper, I examine the profitability of a combined simple moving average (SMA) trading strategy named the ANDOR strategy and named the ANDAND strategy. The general problem was that it was unclear how the ANDOR strategy and the ANDAND strategy perform in currency markets. The purpose of this quantitative study was to conduct a comparison between the ANDOR, ANDAND, and SMA trading strategies for their profitability in currency markets, while controlling for three different time units weekly, daily, and hourly. For the methods and analysis the currency market returns, Sharpe ratios, standard deviation per return coefficients (S.R.C), and estimated costs were compared and a combined SMA was computed. A key result was that the ANDOR strategy was superior compared to the ANDAND and a SMA(S) strategy in the tested currency markets, with a average daily return of ( ANDOR = 0.58% per day) and a (Sharpe ratioANDOR = 1.15).<\/p>\n\n\n\n

#35 – Insiders Trading Effect in Stocks<\/a><\/strong>
#243 – Momentum Combined with Insider Trading<\/a><\/strong><\/p>\n\n\n\n

Anginer, Hoberg, Seyhun: Do Insiders Exploit Anomalies?
\nhttps:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2625614
\nAbstracto:
\nMany studies document predictable stock returns known as anomalies. We investigate whether insiders exploit anomalies and the consequences of mandatory disclosure using a large backward-extended insider trading database from 1975 to 2014. Our results suggest that all 13 anomalies we consider are driven by mispricing, which is corrected shortly after insider trading becomes public, but only when the direction of insider trading agrees with the anomaly. Anomaly returns vanish when insider trading disagrees with the anomaly. We conclude that insiders exploit anomalies likely due to mispricing, and required disclosures improve price efficiency while offering sophisticated investors higher anomaly alphas. <\/p>\n\n\n\n

#207 – Value Factor Effect within Countries<\/a><\/strong>
#247 – Value Effect within Countries v2<\/a><\/strong><\/p>\n\n\n\n

Kouwenberg, Salomons: Value investing in emerging markets<\/strong>
https:\/\/www.rug.nl\/research\/portal\/files\/31244639\/03e22.pdf<\/a>
Abstracto:
Our results confirm the profitability of value investing at the country level in emerging markets. A portfolio of countries with low price-to-book ratios significantly outperforms a portfolio of high price-to-book countries. Global risk factors cannot explain this outperformance. Next we measure a number of macroeconomic variables of the countries in the long and short value portfolios, as a proxy for local risk factors. We find that the countries in the low price-to-book portfolio on average have significantly lower economic growth, higher growth volatility, higher inflation, more overvalued currencies and more volatile currencies, compared to the high price-to-book portfolio. After portfolio formation, the difference in economic fundamentals between the high and low price-to-book portfolios decreases significantly, which indicates that investors might be extrapolating past economic trends too far into the future.<\/p>\n\n\n\n

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

How Do Investment Strategies Perform After Publication?<\/a><\/strong><\/p>\n\n\n\n

In many academic fields like physics, chemistry or natural sciences in general, laws do not change. While economics and theory of investing try to find rules that would be true and always applicable, it is not that simple, there is a \u201ccomplication\u201c \u2013 human. Psychology of humans is very complex. In the one hand, it creates anomalies in the market, that academics study and practitioners use. On the other hand, after an anomaly is discovered, often, the strategy becomes less profitable.<\/strong><\/p>\n\n\n\n

While for academics, it is just another research question, investors may be worried that the anomaly is arbitraged away, and it will become unprofitable in their portfolios. In this article, we will look deeper on whether the anomaly can be arbitraged away, if the profits are lower for the specific strategy once the strategy becomes well-known, and even if the strategies can be timed. Quantpedia\u2018s readers are often interested in these common topics, and we will try to shed some light on them.<\/strong><\/p>\n\n\n\n

Do Prediction Markets Predict Macroeconomic Risk?<\/a><\/strong><\/p>\n\n\n\n

The U.S. (and world too) economy is currently entering a recession. Right now, everybody can see it, the only question is how deep it will be. But is it possible in a real-time predict if the economy will enter a recession? And will that information help us to better set % allocation of equities in our portfolio? Most of the macroeconomic data shows recession in macroeconomic reports with a significant lag. There are multiple different forecasting models which we tries to predict recession or at least estimate the probability that we are entering into one. We are presenting one interesting research paper written by Jonathan Hartley which shows that prediction markets (betting markets created for the purpose of trading the outcome of events) can be successfully used as a complementary tool in various economic forecasting tools. Prediction markets can be used to measure risk in U.S. equities, credit spreads, the U.S. Treasury yield curve, and U.S. dollar foreign exchange rates.<\/strong><\/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":"

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

<\/strong><\/p>\n

Three 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>\n

<\/strong><\/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-6920","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/6920","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=6920"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/6920\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=6920"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=6920"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=6920"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}