{"id":16172,"date":"2021-12-01T22:52:27","date_gmt":"2021-12-01T21:52:27","guid":{"rendered":"https:\/\/quantpedia.com\/?p=16172"},"modified":"2021-12-03T07:14:56","modified_gmt":"2021-12-03T06:14:56","slug":"quantpedia-premium-update-30th-november-2021","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-30th-november-2021\/","title":{"rendered":"Quantpedia Premium Update \u2013 30th November 2021"},"content":{"rendered":"

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

#689 \u2013Short Sellers and Cross-section of Country Indexes<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Weekly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> futures
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2006-2016
Indicative performance:<\/strong> 13.22%
Estimated volatility:<\/strong> 32.75%<\/p>\n\n\n\n

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

Arseny Gorbenko: Do Short Sellers Anticipate Future Market Returns? International Evidence
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3923111
<\/a>Abstracto:
I show that short interest is a strong negative predictor of stock market returns internationally. Short interest significantly and negatively predicts returns in 24 out of 32 countries examined; this predictability survives out-of-sample tests, persists outside recessions, and is not subsumed by other well-known return predictors. The predictive power of short interest varies across regions and increases when short selling is constrained by local short sale regulations or the availability of shares in the equity lending market. I construct a trading strategy that exploits the predictive ability of short interest for market returns via index futures and find that it generates returns and Sharpe ratios comparable to cross-sectional strategies exploiting a similar predictability pattern in stocks.<\/p>\n\n\n\n

#690 \u2013 Implied Asset Return Factor<\/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> 2002-2018
Indicative performance:<\/strong> 5.91%
Estimated volatility:<\/strong> 6.68%<\/p>\n\n\n\n

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

Lee, Jongsub and Naranjo, Andy and Sirmans, Stace: Implied Asset Return Profiles, Firm Fundamentals, and Stock Returns
<\/strong>https:\/\/ssrn.com\/abstract=3795783
<\/a>Abstracto:
We introduce a novel approach to ascertain firms\u2019 unobserved asset return distribution implied by the joint pricing of equity and credit securities within a structural framework. Motivated by Q-theory, we propose a two-factor model that captures asset growth and risk-shifting effects on stock returns. We show that strong asset returns representing systematic growth options predict higher stock returns, whereas shifting risk from equity to credit forecasts lower stock returns. We also find that the performance of many popular stock market factors (that overlook the optionality of equity) are significantly improved after controlling for asset-level risk-shifting exposure.<\/p>\n\n\n\n

#691 \u2013 Implied Asset Volatility Factor<\/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> 2002-2018
Indicative performance:<\/strong> 5.91%
Estimated volatility:<\/strong> 4.86%<\/p>\n\n\n\n

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

Lee, Jongsub and Naranjo, Andy and Sirmans, Stace: Implied Asset Return Profiles, Firm Fundamentals, and Stock Returns
<\/strong>https:\/\/ssrn.com\/abstract=3795783
<\/a>Abstracto:
We introduce a novel approach to ascertain firms\u2019 unobserved asset return distribution implied by the joint pricing of equity and credit securities within a structural framework. Motivated by Q-theory, we propose a two-factor model that captures asset growth and risk-shifting effects on stock returns. We show that strong asset returns representing systematic growth options predict higher stock returns, whereas shifting risk from equity to credit forecasts lower stock returns. We also find that the performance of many popular stock market factors (that overlook the optionality of equity) are significantly improved after controlling for asset-level risk-shifting exposure.<\/p>\n\n\n\n

#692 \u2013Employee Satisfaction Factor<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Yearly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 1984 – 2020
Indicative performance:<\/strong> 16.07%
Estimated volatility:<\/strong> 17.49%<\/p>\n\n\n\n

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

Hamid Boustanifar, Young Dae Kang, Employee Satisfaction and Long-run Stock Returns<\/strong>
https:\/\/ssrn.com\/abstract=3933687
<\/a>Abstracto:
Economic theory predicts that, in the absence of mispricing, investing in socially responsible businesses should have lower expected returns in equilibrium. In contrast, in an influential paper, Edmans (2011) shows that a portfolio of the \u201c100 Best Companies to Work For in America\u201d (BCs) earns a positive and significant Carhart four-factor alpha. More than one decade later, we ask whether this result (a) holds out-of-sample, (b) is driven by exposure to newly discovered factors and characteristics, and (c) depends on the state of the economy. We find that up to 22% of the documented BC portfolio\u2019s Carhart alpha could be attributed to exposures to more recently discovered factors such as investment, profitability, and quality. Nevertheless, using the state-of-art factor models and a sample from the period 1984 to 2020, an equal-weighted BC portfolio earns an abnormal return of 2% to 2.7% per year. The abnormal returns are not driven by firm characteristics, industry composition, or micro-cap stocks. The estimated alphas are positive in almost all periods within our sample (with no upward or downward trend) and are particularly large in \u201cbad\u201d times such as in the crisis periods of 2000-2002 and 2008-2009. Overall, our results suggest that the stock market still undervalues employee satisfaction, which seems to have the greatest value in \u201cbad\u201d times. We conclude with proposing potential reasons behind the (surprising) persistent outperformance of BCs.<\/p>\n\n\n\n

#693 \u2013 Quarterly Investment Spikes Predict 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> Moderately complex strategy
Backtest period:<\/strong> 1986-2019
Indicative performance:<\/strong> 3.91%
Estimated volatility:<\/strong> 7.91%<\/p>\n\n\n\n

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

Altieri, Michela and Schnitzler, Jan, Quarterly Investment Spikes, Stock Returns and the Investment Factor
<\/strong>https:\/\/ssrn.com\/abstract=3942428
<\/a>Abstracto:
We propose abnormal fourth quarter capital expenditures as a proxy for managerial agency conflicts in investment decisions. In line with this interpretation, we document a negative link with future stock returns. Interestingly, the performance of the resulting zero-investment portfolio closely resembles the investment factor, which has become part of standard asset pricing models. Cross-sectional tests show that high cash flows, low dividend yields, and low debt levels aggravate the reported effect. Our analysis provides new evidence in support of an agency interpretation for the investment factor.<\/p>\n\n\n\n

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

#550 \u2013 Carry Commodity Premia in China<\/a><\/strong>
#551 \u2013 Momentum Commodity Premia in China<\/a><\/strong><\/p>\n\n\n\n

Fan, John Hua and Zhang, Tingxi: The Untold Story of Commodity Futures in China
<\/strong>https:\/\/ssrn.com\/abstract=3124223
<\/a>Abstracto:
We investigate the behavior of commodity futures risk premia in China. In the presence of retail-dominance and barriers-to-entry, the term structure and momentum premia remain persistent, whereas hedging pressure, skewness, volatility and liquidity premia are distorted by time-varying margins and strict position limits. Furthermore, open interest, currency and inflation premia are sensitive to institutional settings. The observed premia cannot be attributed to common risks, sentiment, transactions costs or data-snooping, but are related to liquidity, anchoring, and regulation-induced limits-to-arbitrage. We highlight the distinctive features of Chinese futures markets and assess the challenges posed to theories of commodity risk premia.<\/p>\n\n\n\n

#287 \u2013 The FOMC Cycle Effect<\/a><\/strong>
#533 \u2013 FOMC Cycle and Credit Risk<\/a><\/strong><\/p>\n\n\n\n

Li, Xinyang: Bond Implied Risks Around Macroeconomic Announcements
<\/strong>https:\/\/ssrn.com\/abstract=3816926
<\/a>Abstracto:
Using a large panel of Treasury futures and options, I construct model-free measures of bond uncertainty and tail risks across different tenors from 2000 to 2020. I find that bond tail risk 1) negatively correlates with stock tail risk in general, but the correlation turns positive prior to and around three financial crises; 2) It increased dramatically before the 2008 Financial Crisis and in March 2020 foreshadowing the extreme challenges in the Treasury bond markets; 3) and has significantly decreased in recent years under zero-lower-bound and forward guidance. I then study the behavior of bond tail and uncertainty risk measures around FOMC announcements and document three novel findings: First, bond uncertainty increases three to two days prior to the announcements and reverts back upon release, due to an increase in call option prices rather than puts. Second, yields of 5, 10, and 30 year Treasuries decline by 1bps on the day before the announcement. Third, uncertainty risk cannot help explain the pre-FOMC announcement drift.<\/p>\n\n\n\n

#345 \u2013 Betting Against Correlation Effect<\/a><\/strong><\/p>\n\n\n\n

Douglas Castilho, Tharsis T. P. Souza, Soong Moon Kang, Jo\u00e3o Gama, Andr\u00e9 C. P. L. F. de Carvalho: Forecasting Financial Market Structure from Network Features using Machine Learning
<\/strong>https:\/\/arxiv.org\/abs\/2110.11751
<\/a>Abstracto:
We propose a model that forecasts market correlation structure from link- and node-based financial network features using machine learning. For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement of asset price returns across company constituents of major global market indices. We provide empirical evidence using three different network filtering methods to estimate market structure, namely Dynamic Asset Graph (DAG), Dynamic Minimal Spanning Tree (DMST) and Dynamic Threshold Networks (DTN). Experimental results show that the proposed model can forecast market structure with high predictive performance with up to 40% improvement over a time-invariant correlation-based benchmark. Non-pair-wise correlation features showed to be important compared to traditionally used pair-wise correlation measures for all markets studied, particularly in the long-term forecasting of stock market structure. Evidence is provided for stock constituents of the DAX30, EUROSTOXX50, FTSE100, HANGSENG50, NASDAQ100 and NIFTY50 market indices. Findings can be useful to improve portfolio selection and risk management methods, which commonly rely on a backward-looking covariance matrix to estimate portfolio risk.<\/p>\n\n\n\n

#614 \u2013 Climate sentiment, carbon prices and Emission minus Clean Portfolio<\/a><\/strong><\/p>\n\n\n\n

Huij, Joop and Laurs, Dries and Stork, Philip A. and Zwinkels, Remco C.J.: Carbon Beta: A Market-Based Measure of Climate Risk
<\/strong>https:\/\/ssrn.com\/abstract=3957900
<\/a>Abstracto:
Despite sustainable investments standing at record highs, it remains a challenge to quantify climate risk. We propose a proxy for a climate risk factor, the pollutive-minus-clean (PMC) portfolio, which captures differences in returns to firms that have high versus low corporate emissions. By regressing individual stock returns on the PMC factor, we obtain estimates of asset-level climate risk exposure: ‘carbon beta’. Validation of carbon betas confirms that variation in climate risk exposures aligns with our prior expectations. Our measure has desirable properties regarding availability, coverage, and informativeness compared to conventional climate risk measures. We study the interaction of carbon betas with several proxies for realisations in climate risk. Returns to stocks with high carbon betas are lower during months in which climate change is more frequently discussed in the news, during months in which temperatures are abnormally high, and during exceptionally dry months. Unlike firm emissions and intensities, variation in carbon betas correlates with green patent issuance and forward-looking measures of climate risk.<\/p>\n\n\n\n

#460 \u2013 ESG Level Factor Investing Strategy<\/a><\/strong><\/p>\n\n\n\n

Berg, Florian and K\u00f6lbel, Julian and Pavlova, Anna and Rigobon, Roberto: ESG Confusion and Stock Returns: Tackling the Problem of Noise
<\/strong>https:\/\/ssrn.com\/abstract=3941514
<\/a>Abstracto:
How strongly does ESG (environmental, social and governance) performance affect stock returns? Answering this question is difficult because existing measures of performance, ESG ratings, are noisy. To tackle the bias, we propose a noise-correction procedure, in which we instrument ESG ratings with ratings of other ESG rating agencies, as in the classical errors-in-variables problem. The corrected estimates demonstrate that the effect of ESG performance on stock returns is stronger than previously estimated; the standard regression estimates of ESG ratings’ impact on stock returns are biased downward by about 60%. Our dataset includes scores of eight ESG rating agencies for firms located in North America, Europe, and Japan. We determine which agencies\u2019 scores are valid instruments (not all of them are) and estimate the noise-to-signal ratio for each ESG rating agency (some of which are very large). Overall, our results suggest that it is advantageous to rely on several complementary ratings. In our sample, stocks with higher ESG performance have higher expected returns. Our model provides several explanations for this finding.<\/p>\n\n\n\n

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

Community Alpha of QuantConnect \u2013 Part 4: Composite Social Trading Multi-Factor Strategy<\/a><\/strong><\/p>\n\n\n\n

This blog post is the continuation (and finale) of series about Quantconnect\u2019s AlphaMarket<\/a> strategies. This part is related to the multi-factor strategies  notoriously known from the majority of asset classes. We continue in the examination of factor strategies built on top of social trading strategies, but the investment universe is reduced based on the insights of the previous part<\/a>. So, without further ado, we continue where we have left last time.<\/p>\n\n\n\n

The Quant Cycle \u2013 The Time Variation in Factor Returns<\/a><\/strong><\/p>\n\n\n\n

Although the factors in asset pricing models<\/a> offer a premium in the long run, they are undergoing bull and bear market cycles in the short term. One would expect that it is due to their connection to the business cycles as the factor premium represents a reward for bearing the macroeconomic risks. A novel study by Blitz (2021) finds that traditional business cycle indicators can\u2019t explain much of the time variation of factor returns as the factors are a behavioral phenomenon<\/a> driven by investor sentiment. To capture the large factor cyclical variation, the author proposes a quant cycle that is defined by the peaks and troughs in the factor returns corresponding to the bull and bear markets.<\/p>\n\n\n\n

Out-of-sample Dataset Before the \u201cSample\u201d: Pervasive Anomalies Before 1926<\/a><\/p>\n\n\n\n

Data are the key to \u00a0<\/strong>systematic investing\/trading strategies<\/a>. The hypotheses testing, risk or return evaluations, correlations, and factor loadings rely on past data and backtests. With an increasing speed of publication in finance, critiques of quantitative strategies have emerged. Strategies seem to decay in alpha, \u00a0<\/strong>post-publication returns tend to be lower<\/a>, and many strategies become insignificant once rigorously tested (in or out-of-sample). Moreover, some might even appear profitable purely by chance and the repetitive examination of the same dataset, such as CRSP stocks after 1963.\u00a0<\/p>\n\n\n\n

Is there any solution to overcome these limitations? Partially, the design of the novel machine learning strategies consisting of training, validation, and testing sets might help. Perhaps the most crucial part of such a scheme is the usage of the purely out-of-sample dataset. In this regard, the novel research by Baltussen et al. (2021) provides several valuable findings for the most recognized factors. The authors constructed a database of U.S. stocks, including dividends and market caps for 1488 major stocks from 1866 to 1926. The sample can be described as the pre-CRSP period, including independent, pre-publication, and \u201cout-of-sample\u201d data that can be a perfect test for the factors utilized today. <\/p>\n\n\n\n

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

#336 – Cross-Sectional Six-Month Equity ATM Straddle Trading Strategy<\/a>
#337 – Cross-Sectional Six- Minus One-Month Equity ATM Straddle Calendar Trading Strategy<\/a>
#402 – International Volatility Arbitrage<\/a>
#672 – Growth Potential and Options Returns<\/a>
#673 – Mispricing and Idiosyncratic Volatility Effect in Stocks<\/a><\/p>","protected":false},"excerpt":{"rendered":"

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

Five 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. Plus, five trading strategies have been backtested in QuantConnect in the previous two weeks.<\/p>","protected":false},"author":21001,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-16172","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/16172","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\/21001"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=16172"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/16172\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=16172"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=16172"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=16172"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}