{"id":7029,"date":"2020-05-01T16:45:00","date_gmt":"2020-05-01T14:45:00","guid":{"rendered":"https:\/\/quantpedia.com\/?p=7029"},"modified":"2025-06-04T14:19:46","modified_gmt":"2025-06-04T12:19:46","slug":"quantpedia-premium-update-30th-april-2020","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-30th-april-2020\/","title":{"rendered":"Quantpedia Premium Update \u2013 30th April 2020"},"content":{"rendered":"

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

#492 – The Impact of Linkedin Data about Employees on 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> 2010-2016
Indicative performance:<\/strong> 5.41%
Estimated volatility:<\/strong> 4.88%<\/p>\n\n\n\n

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

Ashwini Agrawal, Isaac Hacamo and Zhongchen Hu: Information Dispersion Across Employees and Stock Returns<\/strong>
http:\/\/www.lse.ac.uk\/fmg\/assets\/documents\/papers\/discussion-papers\/DP792.pdf<\/a>
Abstracto:
Rank-and-file employees are becoming increasingly critical for many firms, yet we know little about how their employment dynamics matter for stock prices. We analyze new data from the individual CV\u2019s of public company employees, and find that rank-andfile labor flows can be used to predict abnormal stock returns. Accounting data and survey evidence indicate that workers\u2019 labor market decisions reflect information about future corporate earnings. Investors, however, do not appear to fully incorporate this
information into their earnings expectations. The findings support the hypothesis that rank-and-file employees\u2019 entry and exit decisions convey valuable insight into their employers\u2019 future stock performance.<\/p>\n\n\n\n

#493 – Overlapping Momentum Portfolios<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Yearly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1926 \u2013 2018
Indicative performance:<\/strong> 7.47%
Estimated volatility:<\/strong> 17.73%<\/p>\n\n\n\n

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

Blanco, Iv\u00e1n and De Jesus, Miguel and Remesal, Alvaro: Overlapping Momentum Portfolios<\/strong>
https:\/\/ssrn.com\/abstract=3553765<\/a>
Abstracto:
Different momentum investors use different time horizons, or formation periods, to evaluate prior stock performance. We show that this heterogeneity has important consequences on asset returns. We provide evidence that heightened trading pressure due to the concurrence of the heterogeneous momentum investors contributes to enhanced return autocorrelation. In particular, U.S. “overlapping” momentum stocks – stocks that are in the intersection of the 6-month and 12- month momentum portfolios – display superior medium-term returns to the momentum strategy that neglects the overlap. The differential returns of the “overlapping” momentum portfolio are robust to the inclusion of a broad set of risk factors and interpretations. The results provide insights on the underlying mechanisms of under and over-reaction in financial markets as the result of limited information investment rules and slow information diffusion.<\/p>\n\n\n\n

#494 – Pro-Cyclical Stocks and Expected Future Economic Conditions<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1997-2013
Indicative performance:<\/strong> 24.32%
Estimated volatility:<\/strong> 47.44%<\/p>\n\n\n\n

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

Sheng Zhu, Jun Gao, Meadhbh Sher<\/strong>man: The Role of Future Economic Conditions in the Cross-section of Stock Returns: Evidence from the US and UK<\/strong>
Abstracto:
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3530363<\/a>
Our study investigates the explanatory power of future economic conditions on individual stock returns in the US and UK equity markets. We analyse a new trading strategy that is based on rational forecasts of future real activity. In addition, we specifically examine the performance of this trading strategy applied to two different classifications of stocks \u2013 procyclical stocks and countercyclical stocks. Our findings indicate a strong persistence in the relationship between returns on pro-cyclical stocks and the business cycle. However, such persistence is not present when moving to counter-cyclical stocks in the US and the UK. From this we suggest that US and UK equity investors who predict future real activity accurately can improve their investment profitability by longing pro-cyclical stocks when they expect future economic conditions to be above the long-run trend and shorting those stocks when future activity is anticipated to be below the steady state.<\/p>\n\n\n\n

#495 – Option-to-Futures Volume in Commodities<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Weekly
Markets traded: <\/strong>commodities
Instruments used for trading:<\/strong> futures, CFDs
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1994 \u2013 2018
Indicative performance:<\/strong> 15.29%
Estimated volatility:<\/strong> not stated<\/p>\n\n\n\n

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

Zhang, Tianyang: Trading Activity in Commodity Futures and Options Markets<\/strong>
https:\/\/ssrn.com\/abstract=3542967<\/a>
http:\/\/dx.doi.org\/10.2139\/ssrn.3542967<\/a>
Abstracto:
Little is known about trading activity in commodity options market. We study the information content of commodity futures and options trading volume. Time-series tests indicate that futures contracts in a portfolio with the lowest option-to-futures volume ratio (O\/F) outperform those in a portfolio with the highest ratio by 0.3% per week. Cross-sectional tests show that O\/F has higher predictive power for futures returns than such traditional risk factors as the carry, momentum, and liquidity factors. O\/F has longer predictive horizon for post-announcement returns than the information contained in the monthly World Agricultural Supply and Demand Estimates (WASDE) reports. The analysis of the weekly Commitments of Traders (COT) reports indicates that commercials (hedgers) provide liquidity to non-commercials (speculators) in short term in commodity options market.<\/p>\n\n\n\n

#496 – Using Machine Learning to Pick the Right Combination of Risky and Risk-Free Asset<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities, bonds
Instruments used for trading:<\/strong> ETFs, futures, funds, CFDs
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2011-2017
Indicative performance:<\/strong> 18.05%
Estimated volatility:<\/strong> 11.61%<\/p>\n\n\n\n

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

Michael Pinelis and David Ruppert: Machine Learning Portfolio Allocation<\/strong>
https:\/\/arxiv.org\/pdf\/2003.00656.pdf<\/a>
Abstracto:
We find economically and statistically significant gains from using machine
learning to dynamically allocate between the market index and the risk-free asset. We model the market price of risk as a function of lagged dividend yields and volatilities to determine the optimal weights in the portfolio: reward-risk market timing. This involves forecasting the direction of next month\u2019s excess return, which gives the reward, and constructing a dynamic volatility estimator that is optimized with a machine learning model, which gives the risk. Reward-risk timing with machine learning provides substantial improvements over the market index in investor utility, alphas, Sharpe ratios, and maximum drawdowns, after accounting
for transaction costs, leverage constraints, and on a new out-of-sample set of returns. This paper provides a unifying framework for machine learning applied to both return- and volatility-timing.<\/p>\n\n\n\n

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

#339 \u2013 Expected Investment Growth within the Cross-section of Stocks Returns<\/a><\/strong><\/p>\n\n\n\n

Hou, Mo, Xue, Zhang: An Augmented q-Factor Model with Expected Growth<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3525435<\/a>
Abstracto:
In the investment theory, firms with high expected investment growth earn higher expected returns than firms with low expected investment growth, holding investment and expected profitability constant. Building on cross-sectional growth forecasts with Tobin\u2019s q, operating cash flows, and change in return on equity as predictors, an expected growth factor earns an average premium of 0.84% per month (t = 10.27) in the 1967\u20132018 sample. The q5 model, which augments the Hou-Xue-Zhang (2015) q-factor model with the expected growth factor, shows strong explanatory power in the cross section and outperforms the Fama-French (2018) 6-factor model.<\/p>\n\n\n\n

#26 – Value (Book-to-Market) Factor<\/a><\/strong><\/p>\n\n\n\n

Israel, Laursen, Richardson: Is (Systematic) Value Investing Dead?<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3554267<\/a>
Abstracto:
Value investing is the age-old investment strategy that involves buying securities that appear cheap relative to some fundamental anchor. For equity investors that anchor is typically a measure of intrinsic value linked to financial statement variables. Recently, there has been much written about the death of value investing. While undoubtedly many systematic approaches to value investing have suffered recently, we find the suggestion that value investing is dead to be premature. Both from a theoretical and empirical perspective, expectations of fundamental information have been and continue to be an important driver of security returns. We also address a series of critiques levelled at value investing and find them generally lacking in substance.<\/p>\n\n\n\n

#355 – The Crisis Alpha Portfolio<\/a><\/strong><\/p>\n\n\n\n

Durham: Momentum and the Term Structure of Interest Rates<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2377379<\/a>
Abstracto:
A vast literature reports excess returns to momentum strategies across many financial asset classes. However, no study examines trading rules based on price history along individual government-bond term structures \u2014 that is, with respect to duration buckets across the curve \u2014 as opposed to across sovereign markets or individual term structures as a whole over time. Under duration-neutral and long-only constraints as well as low trading costs, this paper reports excess annualized returns of up to 120 basis points and information ratios as high as 0.79 using U.S. Treasury total return data from December 1996 through July 2013. Given a corresponding long-short strategy with no absolute duration risk, excess returns and information ratios are up to 207 basis points and 1.01, respectively. Unlike momentum strategies in some other asset classes, the excess return distributions are positively skewed, and momentum loads, if in any way, favorably on broad risk factors. Returns correlate to a degree with portfolios based on instantaneous forward term premium estimates, in turn derived from a set of Gaussian arbitrage-free affine term structure models. However, substantial variance remains unexplained, the betas are less than one, and the alphas are meaningfully positive. A caveat is that underlying behavioral explanations for momentum are lacking in the context of the U.S. Treasury market.<\/p>\n\n\n\n

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

A Link Between Investment Biases and Cortisol and Testosterone Levels<\/a><\/strong><\/p>\n\n\n\n

Financial markets are full of pricing anomalies, and their existence is often explained by human behavior. Behavioral finance postulates that cognitive irrationality is manifested in biases like the disposition effect (the tendency of people to sell assets that have increased in value, but keeping assets that have dropped in value in portfolio) or overconfidence bias (the tendency of people to be more confident in their own abilities). There are some papers which directly link investment decision making caused by these biases to actual physiology of investors (for example, a known impact of testosterone on investment performance). A new research paper written by Nofsinger, Patterson, and Shank examines not only testosterone but also cortisol levels of testing subjects and then compares their performance in a mock investment contest. Both hormones are strongly related to higher portfolio turnover and inability to accept losses, with cortisol levels even more significant than testosterone<\/strong><\/strong>.<\/strong><\/p>\n\n\n\n

Secular Decline in Yields around FOMC Meetings<\/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 dFederal Open Market Committee meetings (aka FED meetings) have a significant influence on the number of different assets (see for example our article related to drift in equities during FED meetings). The main channel which FED uses to influence the US economy is the level of short term interest rates. Therefore, it\u2019s not a surprise that FED meetings have influence also on long-term interest rates. But just how big? Bigger than most people think. We are presenting one interesting research paper written by Sebastian Hillenbrand, which shows that the whole secular decline in equity yields and long-term interest rates since 1980 was realized entirely in a 3-day window around FOMC meetings. Now, that\u2019s called the influence \u2026<\/strong><\/p>\n\n\n\n

Working with High-Frequency Tick Data \u2013 Cleaning the Data<\/a><\/strong><\/p>\n\n\n\n

Tick data is the most granular high-frequency data available, and so is the most useful in market microstructure analysis. Unfortunately, tick data is also the most susceptible to data corruption and so must be cleaned and conditioned prior to being used for any type of analysis.<\/strong><\/p>\n\n\n\n

This article, written by Ryan Maxwell, examines how to handle and identify corrupt tick data (for analysts unfamiliar with tick data, please try an intro to tick data first).<\/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":"

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

Three new related research papers have been included into existing strategy reviews. And three 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-7029","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/7029","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=7029"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/7029\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=7029"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=7029"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=7029"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}