{"id":7221,"date":"2020-06-16T00:06:24","date_gmt":"2020-06-15T22:06:24","guid":{"rendered":"https:\/\/quantpedia.com\/?p=7221"},"modified":"2025-06-04T14:22:45","modified_gmt":"2025-06-04T12:22:45","slug":"quantpedia-premium-update-15th-june-2020","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-15th-june-2020\/","title":{"rendered":"Quantpedia Premium Update \u2013 15th June 2020"},"content":{"rendered":"

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

#506 – Volatility Risk Premium in Commodity Futures<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>commodities
Instruments used for trading:<\/strong> options, swaps
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 1994-2012
Indicative performance:<\/strong> 6.10%
Estimated volatility:<\/strong> 5.20%<\/p>\n\n\n\n

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

G. Renninson, N. Pedersen: The Volatility Risk Premium<\/strong>
https:\/\/www.pimco.com\/en-us\/insights\/viewpoints\/research\/the-volatility-risk-premium\/<\/a>
Abstracto:
Elevated global macroeconomic uncertainty and bouts of extreme market turbulence have recently plagued financial markets. This environment has prompted a search for diversifying investment opportunities that lie outside the space of traditional asset classes. This article examines the performance of options strategies that aim to capture a return premium over time as compensation for the risk of losses during sudden increases in market volatility. We show that these \u201cvolatility risk premium\u201d strategies deliver attractive risk-adjusted returns across 14 options markets from June 1994 to June 2012. Performance furthermore improves significantly after the crisis in 2008 (see Figure 1). We conclude that the risk-return tradeoff for volatility strategies compares favorably to those of traditional investments such as equities and bonds and that the strategies exhibit relatively low correlations to equity risk. Investors who want to diversify their portfolio\u2019s equity risk exposures should therefore consider making allocations to volatility risk premium strategies. However, successful implementation would require diversification across major options markets (equities, interest rates, currencies and commodities), active risk management and prudent scaling.<\/p>\n\n\n\n

#507 \u2013 Volatility Risk Premium in Currency Futures<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>currencies
Instruments used for trading:<\/strong> options, swaps
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 1994 \u2013 2012
Indicative performance:<\/strong> 1.20%
Estimated volatility:<\/strong> 1.70%<\/p>\n\n\n\n

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

G. Renninson, N. Pedersen: The Volatility Risk Premium<\/strong>
https:\/\/www.pimco.com\/en-us\/insights\/viewpoints\/research\/the-volatility-risk-premium\/<\/a>
Abstracto:
Elevated global macroeconomic uncertainty and bouts of extreme market turbulence have recently plagued financial markets. This environment has prompted a search for diversifying investment opportunities that lie outside the space of traditional asset classes. This article examines the performance of options strategies that aim to capture a return premium over time as compensation for the risk of losses during sudden increases in market volatility. We show that these \u201cvolatility risk premium\u201d strategies deliver attractive risk-adjusted returns across 14 options markets from June 1994 to June 2012. Performance furthermore improves significantly after the crisis in 2008 (see Figure 1). We conclude that the risk-return tradeoff for volatility strategies compares favorably to those of traditional investments such as equities and bonds and that the strategies exhibit relatively low correlations to equity risk. Investors who want to diversify their portfolio\u2019s equity risk exposures should therefore consider making allocations to volatility risk premium strategies. However, successful implementation would require diversification across major options markets (equities, interest rates, currencies and commodities), active risk management and prudent scaling.<\/p>\n\n\n\n

#508 \u2013 Exploring Core Earnings with Alternative Data<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Yearly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 1998-2017
Indicative performance:<\/strong> 7.51%
Estimated volatility:<\/strong> 12.64%<\/p>\n\n\n\n

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

Ethan Rouen, Eric So, Charles C.Y. Wang: Core Earnings: New Data and Evidence<\/strong>
https:\/\/www.hbs.edu\/faculty\/Publication%20Files\/20-047_34790b6b-02e8-4020-a064-462a10dbd192.pdf<\/a>
Abstracto:
Using a novel dataset that comprehensively classifies the quantitative financial disclosures in firms\u2019 10-Ks, including those hidden in the footnotes and the MD&A, we show that disclosures of non-operating and less persistent income-statement items are both frequent and economically significant, and increasingly so over time. Adjusting GAAP earnings to exclude these items creates a measure of core earnings that is highly persistent and that forecasts future performance. Street earnings for firms that meet or just beat analyst expectations are more likely to selectively exclude these items. Analysts and market participants also are slow to impound the implications of these items. Trading strategies that exploit cross-sectional differences in firms\u2019 transitory earnings produce abnormal returns of 7-to-10% per year.<\/p>\n\n\n\n

#509 – S&P500 Futures Return During the EU-Open Period<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Intraday
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> futures, CFDs
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2004-2018
Indicative performance:<\/strong> 2.58%
Estimated volatility:<\/strong> 4.69%<\/p>\n\n\n\n

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

Oleg Bondarenko, Dmitriy Muravyev: Market Return Around the Clock: A Puzzle<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3596245<\/a>
Abstracto:
We study how the excess market return depends on the time of the day using E-mini S&P 500 futures that are actively traded for almost 24 hours. Strikingly, four hours around Asian markets\u2019 close and European open account for the entire average market return. This period\u2019s Sharpe ratio is extremely high as overnight volatility is low. Its returns are positive in every year and survive transaction costs. Remarkably, average returns are zero during the remaining 20 hours and almost all sub-intervals. We attribute high returns around European open to the uncertainty resolution as European investors help process information accumulated during Asian trading hours. Consistent with this hypothesis, VIX future returns are positive during the Asian session and highly negative around European open.<\/p>\n\n\n\n

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

#297 \u2013 Combining Time-Series and Cross-Sectional Momentum<\/a><\/strong><\/p>\n\n\n\n

Zakamulin, Giner: Time Series Momentum in the US Stock Market: Empirical Evidence and Theoretical Implications<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3585714<\/a>
Abstracto:
We start this paper by presenting compelling evidence of short-term momentum in the excess returns on the S&P Composite stock price index. For the first time ever, we assume that the excess returns follow an autoregressive process of order p, AR(p), and evaluate the parameters of this process. Armed with a fairly accurate knowledge of the momentum generating process, we continue this paper by providing a number of important theoretical implications. First, we present analytical results on the profitability of long-only and long-short time-series momentum (TSMOM) strategies. Our results suggest that the long-only TSMOM strategy is profitable, while the long-short one is not. We find that over multiple periods the risk profile of the long-only TSMOM strategy resembles the risk profile of a portfolio insurance strategy. We estimate the power of the statistical test for superiority of the TSMOM strategy and find that the power is much below the acceptable level. Consequently, any empirical study tends not to reject the null hypothesis of no profitability of TSMOM strategy. Finally, we evaluate the precision of identification of the optimal number of lags in the TSMOM rule using a standard back-testing methodology and find that this precision is extremely poor. However, we demonstrate that the performance of the TSMOM rule is robust to the choice of the number of lags.<\/p>\n\n\n\n

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

Hasselgren: Herding, Hedge Funds, and the Carry Trade<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3544077<\/a>
Abstracto:
I study hedge fund herding patterns in currency futures contracts and find evidence of herding. High-interest (low-interest) currencies exhibit higher buy-side (sell-side) herding, consistent with carry trade positions. A strategy herding measure is then proposed that is used to track hedge fund herding in the carry trade strategy. I find that hedge fund carry trade strategy herding positively predicts future returns, a result that is robust to a host of other activity measures. I do not find hedge fund herding to be destabilizing, and investors can improve performance by following hedge fund herding behaviour in the carry trade.<\/p>\n\n\n\n

#5 \u2013 FX Carry<\/a><\/strong><\/strong>
#8 – Currency Momentum Factor<\/a><\/strong>
#9 – Currency Value Factor \u2013 PPP Strategy<\/a><\/strong>
#184 – Timing Carry Trade<\/a><\/strong>
#221 – Timing Carry Trade v2<\/a><\/strong><\/p>\n\n\n\n

Byrne, Ryuta: The Conditional Risk and Return Trade-Off on Currency Portfolios<\/strong>
https:\/\/mpra.ub.uni-muenchen.de\/99497\/1\/MPRA_paper_99497.pdf<\/a>
Abstracto:
If asset price risk-return relations vary over time based upon changing economic states, standard unconditional models may \u201cwash out\u201d state dependence and fail to identify that additional risk is contingently compensated with higher return. We address this matter by considering conditional risk-return relations for currency portfolios. Doing so within a data rich environment, we also develop broad based measures of investor risk. In general we find that agents require positive compensation for risks in some times and for some investment strategies. Our results identify that relations between currency returns and risk vary over time. Also we find that there are positive risk-return relations on momentum and value currency portfolios during the financial crisis. Furthermore, the risk-return relation on the momentum portfolio is counter-cyclical.<\/p>\n\n\n\n

#7 – Low Volatility Factor Effect in Stocks<\/a>
#14 – Momentum Factor Effect in Stocks<\/a>
#25 – Size Factor<\/a>
#26 – Value (Book-to-Market) Factor<\/a>
#130 – Investment Factor<\/a>
#229 – Earnings Quality Factor<\/a><\/strong><\/p>\n\n\n\n

Blitz, Baltussen, van Vliet: When Equity Factors Drop Their Shorts<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3493305<\/a>
Abstracto:
This paper makes a breakdown of common Fama-French style equity factor portfolios into their long and short legs. We find that factor premiums originate in both legs, but that (i) most added value tends to come from the long legs, (ii) the long legs of factors offer more diversification than the short legs, and (iii) the performance of the shorts is generally subsumed by the longs. These results hold across large and small caps, are robust over time, carry over to international equity markets, and cannot be attributed to differences in tail risk. Portfolio tests suggest that the short legs are of limited value to most investors, while the long legs in small caps are most attractive. We also examine recent claims that the value and low-risk factors are subsumed by the new Fama-French factors, and find that this does not hold for the long legs of these factors. Altogether, our findings show that decomposing canonical factors into their long and short legs is crucial for understanding factor premiums and building efficient factor portfolios.<\/p>\n\n\n\n

And two interesting free blog posts have been published during last 2 weeks:<\/strong><\/h4>\n\n\n\n

Embedded Leverage in High Beta Funds and Management Fees<\/a><\/strong><\/p>\n\n\n\n

Risk-averse investors want higher returns at any cost. If they are constrained and are not able to use leverage on their own, they will look for other ways to increase their performance. Recent academic paper written by Hitzemann, Sokolinski, Tai suggests, that such risk-seeking investor will search for a high-beta fund that will give them requested embedded leverage, even when that fund charge higher than average fees. Resultant net alpha of those high-beta funds is then negative, and this effect can explain the significant part of the underperformance of the overall mutual fund industry. And now, the logical question follows: As hedge funds have even higher fees than mutual funds, what is embedded in them, that constrained clients normally can\u2019t access? Higher leverage and access to option-like return distribution? Maybe.<\/p>\n\n\n\n

Trend Breaks in Trend-Following Strategies<\/a><\/strong><\/p>\n\n\n\n

Trend-following strategies are very effective when markets are cleanly trending, but they suffer when trends end too soon. How markets behaved during the last few years, were they prone to last-longing trends? Are we able to immunize trend-following to endure the negative impact of trend breaks better? A research paper written by Garg, Goulding, Harvey, and Mazzoleni finds a negative relationship between the number of turning points (a month in which slow 12-month and faster 2-month momentum signals differ in their indications to buy or sell) and risk-adjusted performance of a 12-month trend-following strategy. The average number of turning points experienced across assets has increased in recent years. But we can implement a \u201cdynamic\u201d trend-following strategy that adjusts the weight it assigns to slow and fast time-series momentum signals after observing market breaks to recover much of the losses experienced by static-window trend following\u2026<\/p>\n\n\n\n

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

#56 – COT Report Predicts Prices of Agricultural Commodities<\/a>
#81 – Combining Value Stocks with Momentum and Volume Factors<\/a>
#115 – Short-Term Residual Reversal<\/a>
#135 – Volatility Effect in Commodities<\/a>
#136 – Residual Momentum Factor<\/a>
#204 – Selling Options on Bond ETFs<\/a>
#343 – Impact of Macro News on PEAD Strategy<\/a>
#353 – US Sector Rotation with Five-Factor Fama-French Alphas<\/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":"

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

Four 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. Plus, eight trading strategies have been backtested in QuantConnect<\/strong><\/a> in the previous two 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-7221","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/7221","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=7221"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/7221\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=7221"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=7221"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=7221"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}