{"id":7159,"date":"2020-06-01T14:17:12","date_gmt":"2020-06-01T12:17:12","guid":{"rendered":"https:\/\/quantpedia.com\/?p=7159"},"modified":"2025-06-04T14:21:34","modified_gmt":"2025-06-04T12:21:34","slug":"quantpedia-premium-update-31st-may-2020","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-31st-may-2020\/","title":{"rendered":"Quantpedia Premium Update \u2013 31st May 2020"},"content":{"rendered":"

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

#500 – Interest Rate Momentum in Global Yield Curves<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>bonds
Instruments used for trading:<\/strong> swaps, futures
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 1991-2019
Indicative performance:<\/strong> 4.83%
Estimated volatility:<\/strong> 8.36%<\/p>\n\n\n\n

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

Jonathan Hartley: Interest Rate Momentum Everywhere Across Global Yield Curves<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3542813<\/a>
Abstracto:
This paper explores time series momentum in interest rates across developed and emerging market countries and various interest rate maturities. With a one-year lookback window, almost all countries in our sample of G10 developed countries and 18 emerging market countries strategies have statistically significant positive time series momentum strategy returns. Shorter tenor (2-year) interest rate swaps have greater momentum returns compared to longer tenor (5-year and 10-year) interest rate swaps which arguably is a result of investor underreaction to monetary policy cycles. A significantly greater share of the positive momentum returns across all tenors comes from falling rate environments versus rising rate environments as a result of the secular decline in interest rates in recent decades. This suggests that if low interest rates continue to persist, future interest rate time series momentum strategy returns could be lower.<\/p>\n\n\n\n

#501 – Carry Factor in Emerging Market Debt<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>bonds
Instruments used for trading:<\/strong> swaps (CDS)
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 2004 \u2013 2018
Indicative performance:<\/strong> 3.80%
Estimated volatility:<\/strong> 11.20%<\/p>\n\n\n\n

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

J. Brooks, S. Richardson, Z. Xu: (Systematic) Investing in Emerging Market Debt<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3531590<\/a>
Abstracto:
We extend the analysis of systematic investment approaches to emerging market (EM) fixed income. We focus on hard currency bonds issued by emerging sovereign and quasi-sovereign entities. We find that systematic exposures linked to carry, defensive, momentum and valuation themes are well compensated and lowly correlated in EM markets. A transaction-cost and liquidity aware long-only portfolio generates an Information Ratio above 1. We further show that excess of benchmark returns for a broad set of EM managers are (i) largely explained by passive exposures to EM corporate credit excess returns and EM local currency returns, and (ii) have non-trivial macroeconomic exposures (growth, inflation, volatility and liquidity). A systematic approach to EM debt may be a powerful diversifier.<\/p>\n\n\n\n

#502 \u2013 Defensive Factor in Emerging Market Debt<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>bonds
Instruments used for trading:<\/strong> swaps (CDS)
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 2004-2018
Indicative performance:<\/strong> 3.60%
Estimated volatility:<\/strong> 10.50%<\/p>\n\n\n\n

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

J. Brooks, S. Richardson, Z. Xu: (Systematic) Investing in Emerging Market Debt<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3531590<\/a>
Abstracto:
We extend the analysis of systematic investment approaches to emerging market (EM) fixed income. We focus on hard currency bonds issued by emerging sovereign and quasi-sovereign entities. We find that systematic exposures linked to carry, defensive, momentum and valuation themes are well compensated and lowly correlated in EM markets. A transaction-cost and liquidity aware long-only portfolio generates an Information Ratio above 1. We further show that excess of benchmark returns for a broad set of EM managers are (i) largely explained by passive exposures to EM corporate credit excess returns and EM local currency returns, and (ii) have non-trivial macroeconomic exposures (growth, inflation, volatility and liquidity). A systematic approach to EM debt may be a powerful diversifier.<\/p>\n\n\n\n

#503 – Momentum Factor in Emerging Market Debt<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>bonds
Instruments used for trading:<\/strong> swaps (CDS)
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 2004 – 2018
Indicative performance:<\/strong> 5.40%
Estimated volatility:<\/strong> 8.50%<\/p>\n\n\n\n

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

J. Brooks, S. Richardson, Z. Xu: (Systematic) Investing in Emerging Market Debt<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3531590<\/a>
Abstracto:
We extend the analysis of systematic investment approaches to emerging market (EM) fixed income. We focus on hard currency bonds issued by emerging sovereign and quasi-sovereign entities. We find that systematic exposures linked to carry, defensive, momentum and valuation themes are well compensated and lowly correlated in EM markets. A transaction-cost and liquidity aware long-only portfolio generates an Information Ratio above 1. We further show that excess of benchmark returns for a broad set of EM managers are (i) largely explained by passive exposures to EM corporate credit excess returns and EM local currency returns, and (ii) have non-trivial macroeconomic exposures (growth, inflation, volatility and liquidity). A systematic approach to EM debt may be a powerful diversifier.<\/p>\n\n\n\n

#504 – Value Factor in Emerging Market Debt<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>bonds
Instruments used for trading:<\/strong> swaps (CDS)
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 2004 \u2013 2018
Indicative performance:<\/strong> 3.70%
Estimated volatility:<\/strong> 10.20%<\/p>\n\n\n\n

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

J. Brooks, S. Richardson, Z. Xu: (Systematic) Investing in Emerging Market Debt<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3531590<\/a>
Abstracto:
We extend the analysis of systematic investment approaches to emerging market (EM) fixed income. We focus on hard currency bonds issued by emerging sovereign and quasi-sovereign entities. We find that systematic exposures linked to carry, defensive, momentum and valuation themes are well compensated and lowly correlated in EM markets. A transaction-cost and liquidity aware long-only portfolio generates an Information Ratio above 1. We further show that excess of benchmark returns for a broad set of EM managers are (i) largely explained by passive exposures to EM corporate credit excess returns and EM local currency returns, and (ii) have non-trivial macroeconomic exposures (growth, inflation, volatility and liquidity). A systematic approach to EM debt may be a powerful diversifier.<\/p>\n\n\n\n

#505 –<\/strong> Systematic Investing in Emerging Market Debt<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>bonds
Instruments used for trading:<\/strong> bonds
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2004-2018
Indicative performance:<\/strong> 8.40%
Estimated volatility:<\/strong> 7.90%<\/p>\n\n\n\n

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

J. Brooks, S. Richardson, Z. Xu: (Systematic) Investing in Emerging Market Debt<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3531590<\/a>
Abstracto:
We extend the analysis of systematic investment approaches to emerging market (EM) fixed income. We focus on hard currency bonds issued by emerging sovereign and quasi-sovereign entities. We find that systematic exposures linked to carry, defensive, momentum and valuation themes are well compensated and lowly correlated in EM markets. A transaction-cost and liquidity aware long-only portfolio generates an Information Ratio above 1. We further show that excess of benchmark returns for a broad set of EM managers are (i) largely explained by passive exposures to EM corporate credit excess returns and EM local currency returns, and (ii) have non-trivial macroeconomic exposures (growth, inflation, volatility and liquidity). A systematic approach to EM debt may be a powerful diversifier.<\/p>\n\n\n\n

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

#118 – Time Series Momentum Effect<\/a><\/strong><\/p>\n\n\n\n

Liu, Lu, Wang: Asymmetry, Tail Risk and Time Series Momentum<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3573878<\/a>
Abstracto:
Similar to the cross-sectional momentum crashes, the time series momentum experiences deep and persistent drawdowns in the stressed time of slumps in the upward momentum, rebounds in the downward momentum, and long time sideways market. We measure the upside and downside risk using the upper and lower partial moments, which are derived from the individual asset\u2019s daily return. The time series momentum reversals are partly forecasted by the asymmetric structure of the tail-distributed upside and downside risk. An implementable systematic rule-based decision function is designed to manage the signals given by the time series momentum. Its empirical application on the Chinese commodity futures markets documents improvements in terms of both the Sharpe ratio and the Sortino ratio from 2008 to 2019. These results are robust across the time series momentum with different looking back windows.<\/p>\n\n\n\n

#442 – Intraday Momentum in Crude Oil ETF<\/a><\/strong><\/p>\n\n\n\n

Wen, Xu, Ma, Xu: Intraday Momentum and Return Predictability: Evidence from the Crude Oil Market<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3553682<\/a>
Abstracto:
Intraday return predictability has firstly been identified in the equity markets, and we extend the analysis to the crude oil market by using high-frequency United States Oil Fund data from 2006 to 2018. We find a different intraday prediction pattern in the oil market, where only the first half-hour returns positively predict the last half-hour returns. A market timing strategy based on the findings generates substantial profits. We further decompose the first half-hour return into the overnight and the open half-hour components, and find that the former contains more predictive information. Economic mechanisms of the infrequent portfolio rebalancing and the presence of late-informed investors explain our findings. Notably, unlike equity markets, the oil market exhibits a unique intraday trading volume pattern, caused by the release of two routine oil inventory announcements. However, the information contained in the inventory announcements does not offer predictability to the last half-hour returns.<\/p>\n\n\n\n

#178 – Abnormal Volume Effect in the Stock Market<\/a><\/strong><\/p>\n\n\n\n

Wang: The High-Volume Return Premium and Economic Fundamentals<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3571784<\/a>
Abstracto:
Extending Kaniel, Ozoguz, and Starks (2012, J. Financ. Econ.) and many others, we present first empirical evidence that indicates the high volume return premium is linked to economic fundamentals. The volume premium has strong predictive power for future industrial production growth and other macroeconomic indicators with or without controls for common equity pricing factors and business cycle variables. However, only a small portion of the volume premium can be attributed to its co-movement with equity return factors and economic risk factors. Mis-pricing-based factor models also fail to adequately explain the return anomaly.<\/p>\n\n\n\n

#42 – Alpha Cloning – Following 13F Fillings<\/a><\/strong><\/p>\n\n\n\n

Amir-Ghassemi, Papanicolaou, Perlow: Aggregate Alpha in the Hedge Fund Industry: A Further Look at Best Ideas<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3586138<\/a>
Abstracto:
Our paper addresses the portfolio selection of hedge fund firms as a measure of abnormal skill. It further decomposes this skill through the lens of canonical `Best Ideas’. Both are achieved through the application of regulatory mandated position-level data from the SEC, colloquially known as 13F data. The approach aims to reduce common biases associated with traditional return database analysis while unlocking position-level portfolio analysis. Across a composite of hedge fund managers and twenty years of analysis, we find historically abnormal excess return associated with their security selection. However, there has been a significant decline in this abnormal return after the 2008 financial crisis. We examine this out-performance through portions of each manager\u2019s portfolio, using ex ante methodologies to elicit Best Ideas. We find no significant difference in return characteristics between these Best Ideas relative to the aggregate portfolio positions of these hedge fund managers. These findings are broadly in contrast to similar studies conducted on mutual funds, demonstrating differences in portfolio behavior across the two classes of fund managers.<\/p>\n\n\n\n

#230 – Mean Variance Carry Trade Strategy<\/a><\/strong><\/p>\n\n\n\n

Rigamonti, Lucivjanska: Mean-Semivariance Portfolio Optimization Using Minimum Average Partial<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3542727<\/a>
Abstracto:
Mean-semivariance and minimum semivariance portfolios are a preferable alternative to mean-variance and minimum variance portfolios whenever the asset returns are not symmetrically distributed. However, similarly to other portfolios based on downside risk measures, they typically perform poorly in practice because the estimates of the necessary inputs are less reliable than the estimates of the full covariance matrix. We address this problem by performing PCA using the Minimum Average Partial on the downside correlation matrix in order to reduce the dimension of the problem and, with it, the estimation errors. We apply our strategy to several datasets and show that it consistently outperforms various existing downside risk-based asset allocation rules, largely closing the gap in out-of-sample performance with the strategies based on the covariance matrix.<\/p>\n\n\n\n

#169 – Exploiting Option Information in the Equity Market<\/a><\/strong><\/p>\n\n\n\n

Bergsma, Csapi, Diavatopoulos, Fodor: Show Me the Money: Option Moneyness Concentration and Future Stock Returns<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3507507<\/a>
Abstracto:
nformed traders often use options that are not in-the-money due to higher potential gains for a smaller upfront cost. Thus, trading activity by option moneyness should be a gauge of informed option trading. We construct a dollar volume-weighted average moneyness measure to capture option trading activity at different moneyness levels. Stock returns increase with this measure, suggesting more trading activity in options with higher leverage predicts future stock returns. Our results hold cross-sectionally and at the portfolio level yielding a Fama-French five-factor alpha of 12% per year for all stocks and 33% per year for high implied volatility stocks.<\/p>\n\n\n\n

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

Stocks Not For the Long Run?<\/a><\/strong><\/p>\n\n\n\n

Socially Responsible Investing (also called ESG Factor Investing) grows in popularity. More and more investors enter the stock market not just to There are very few observations of the attributes of financial markets that are considered by most of the investors as nearly permanent facts. One of the most often cited examples is that over the long interval stocks outperform bonds. But is it really such truth? Over how long interval? 10 years, 20 years, 30 years? As the new and better historical data are becoming available for analysis, they show interesting findings. Let\u2019s show one example. There exist one very long interval during which the return of stocks was nearly equal to bonds. What do you think is the length of such an interval in the case of the US? It\u2019s 150 years! Yes, that\u2019s correct, there was a one-and-half-century long period in the history of the United States when the performance of stocks and bonds was nearly identical. We do not imply that it will be the case in the 21st century. But an important research paper written by Edward McQuarrie shows that investors must prepare for even the most unexpected possibilities when they are making their asset allocation decisions.<\/p>\n\n\n\n

Long-Short vs Long-Only Implementation of Equity Factors<\/a><\/strong><\/p>\n\n\n\n

How should be equity factor strategies implemented? In a long-only (smart beta) way? As a long-short strategy, as most of the hedge funds usually do? Or in a partially-hedged fashion by going long equity factor and shorting market to offset some of the market risks? There is no one universal answer as it depends on the investment mandate and constraints of each fund manager contemplating to implement factor investing strategies. But recent academic paper written by Benaych-Georges, Bouchaud and Ciliberti suggests that it\u2019s a good idea to go in the direction of long-short implementation (if it\u2019s possible). Managing short book can be challenging; however, the added benefit of lower correlation among strategies gives resultant factor portfolio a significant boost in the return-to-risk ratio (even after accounting for realistic implementation and shorting costs).<\/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

#63 – Trendfollowing Combined with Volatility Premium<\/a>
#493 – Overlapping Momentum Portfolios<\/a>
#489 – Combining VIX Futures Term Structure Strategy and S&P500 Index<\/a>
#488 – Stock Picking of ETF Constituents<\/a>
#110 – Speculators\u2019 Effect in Commodities<\/a>
#111 – Hedgers\u2019 Effect in Commodities<\/a>
#120 – Speculators\u2019 Effect in FX<\/a>
#121 – Hedgers\u2019 Effect in FX<\/a><\/p>\n\n\n\n

\n\n\n\n

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\u00bfBuscas datos hist\u00f3ricos o plataformas de backtesting? Consulta nuestra lista de Descuentos en Algo Trading<\/a><\/strong>.<\/p>\n\n\n\n

\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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O s\u00edguenos en:<\/strong><\/p>\n\n\n\n

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

Six 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 May.<\/p>\n

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

Now we are bringing you our summary Quantpedia’s research again. Nine new Quantpedia Premium<\/strong><\/a> strategies have been added into our database, and nine new related research papers have been included in existing Premium strategies during last month.<\/p>\n

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

Additionally, we have produced 16 new backtests written in QuantConnect<\/strong><\/a> code. Our database currently contains over 290 strategies with out-of-sample backtests\/codes.<\/p>\n

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

Also, four new blog posts you may find interesting have been published on our Quantpedia blog<\/strong><\/a>in the previous two 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-7159","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/7159","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=7159"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/7159\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=7159"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=7159"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=7159"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}