{"id":16476,"date":"2022-01-03T00:27:38","date_gmt":"2022-01-02T23:27:38","guid":{"rendered":"https:\/\/quantpedia.com\/?p=16476"},"modified":"2022-01-03T07:01:13","modified_gmt":"2022-01-03T06:01:13","slug":"quantpedia-premium-update-31th-december-2021","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-31th-december-2021\/","title":{"rendered":"Quantpedia Premium Update \u2013 31th December 2021"},"content":{"rendered":"

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

#702 \u2013Not-sold Insider Holdings Effect<\/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 strategy
Backtest period:<\/strong> 1997-2020
Indicative performance:<\/strong> 5.41%
Estimated volatility:<\/strong> 10.55%<\/p>\n\n\n\n

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

Luke DeVault, Scott Cederburg, Kainan Wang: Is \u2018Not Trading\u2019 Informative? Evidence from Corporate Insiders\u2019 Portfolios 
<\/strong>https:\/\/ssrn.com\/abstract=3964861
<\/a>Abstracto:
Some individuals, e.g., those holding multiple directorships, are insiders at multiple firms. When they execute an insider trade at one firm, they may reveal information about the value of all\u2014both the traded insider position and not-traded insider position(s)\u2014the securities held in their “insider portfolio.” We find that insider “not-sold” stocks outperform “not-bought” stocks. Implementable trading strategies that buy not-sold stocks following the disclosure of a sale earn alphas up to 4.8% per year after trading costs. The results suggest that even insider sales that are motivated by liquidity and diversification needs can provide value-relevant information about insider holdings.<\/p>\n\n\n\n

#703 \u2013Machine Learning in News Articles Predicts Stock Returns<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 1989-2020
Indicative performance:<\/strong> 9%
Estimated volatility:<\/strong> 7.26%<\/p>\n\n\n\n

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

Zheng Tracy Ke, Bryan Kelly and Dacheng Xiu: Predicting Returns with Text Data
<\/strong>https:\/\/par.nsf.gov\/servlets\/purl\/10289824
<\/a>Abstracto:
We introduce a new text-mining methodology that extracts information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionary-based methods), our supervised learning framework constructs a score that is specifically adapted to the problem of return prediction. Our method proceeds in three steps: 1) isolating a list of terms via predictive screening, 2) assigning prediction weights to these words via topic modeling, and 3) aggregating terms into an articlelevel predictive score via penalized likelihood. We derive theoretical guarantees on the accuracy of estimates from our model with minimal assumptions. In our empirical analysis, we study one of the most actively monitored streams of news articles in the financial system\u2014the Dow Jones Newswires\u2014and show that our supervised text model excels at extracting return-predictive signals in this context. Information in newswires is assimilated into prices with an inefficient delay that is broadly consistent with limits-to-arbitrage (i.e., more severe for smaller and more volatile firms) yet can be exploited in a real-time trading strategy with reasonable turnover and net of transaction costs.<\/p>\n\n\n\n

#704 \u2013Debt-Equity Spread in Equities<\/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> 1980-2020
Indicative performance:<\/strong> 5.39%
Estimated volatility:<\/strong> 10%<\/p>\n\n\n\n

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

Chen, Hui and Chen, Zhiyao and Li, Jun: The Debt-Equity Spread
<\/strong>https:\/\/ssrn.com\/abstract=3944082
<\/a>Abstracto:
We propose the Debt-Equity Spread (DES), the difference between the actual and equity-implied credit spreads, as a measure of the valuation gap between debt and equity at the firm and bond level. DES strongly predicts stock and bond returns in opposite directions. A strategy that takes a long position in firms with low DES (indicating that stocks are cheap relative to bonds) and a short position in those with high DES generates an average stock return of 5.39% and bond return of -5.38% per annum. The return predictability is consistently significant over subsamples and is stronger among smaller, less liquid, and more difficult-to-short stocks and bonds. In addition, firms with higher DES tend to have more negative revisions in long-term growth forecasts, issue equity and retire debt more aggressively, and their insiders are more likely to sell their stocks. Together, these findings support DES being a measure of relative mispricing between debt and equity.<\/p>\n\n\n\n

#705 \u2013Debt-Equity Spread in Bonds<\/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> 1980-2020
Indicative performance:<\/strong> 5.38%
Estimated volatility:<\/strong> 5.69%<\/p>\n\n\n\n

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

Chen, Hui and Chen, Zhiyao and Li, Jun: The Debt-Equity Spread
<\/strong>https:\/\/ssrn.com\/abstract=3944082
<\/a>Abstracto:
We propose the Debt-Equity Spread (DES), the difference between the actual and equity-implied credit spreads, as a measure of the valuation gap between debt and equity at the firm and bond level. DES strongly predicts stock and bond returns in opposite directions. A strategy that takes a long position in firms with low DES (indicating that stocks are cheap relative to bonds) and a short position in those with high DES generates an average stock return of 5.39% and bond return of -5.38% per annum. The return predictability is consistently significant over subsamples and is stronger among smaller, less liquid, and more difficult-to-short stocks and bonds. In addition, firms with higher DES tend to have more negative revisions in long-term growth forecasts, issue equity and retire debt more aggressively, and their insiders are more likely to sell their stocks. Together, these findings support DES being a measure of relative mispricing between debt and equity.<\/p>\n\n\n\n

#706 \u2013Using Machine Learning to Identify Mispricing in European Stock Markets<\/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> 1993-2019
Indicative performance:<\/strong> 7.44%
Estimated volatility:<\/strong> 10.61%<\/p>\n\n\n\n

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

Hanauer, Matthias Xaver and Kononova, Marina and Rapp, Marc Steffen: Boosting Agnostic Fundamental Analysis: Using Machine Learning to Identify Mispricing in European Stock Markets
<\/strong>https:\/\/ssrn.com\/abstract=3977872
<\/a>Abstracto:
Interested in fundamental analysis and inspired by Bartram and Grinblatt (2018 & 2021), we apply linear regression (LR) and tree-based machine learning (ML) methods to estimate monthly peer-implied fair values of European stocks from 21 accounting variables. Comparing LR and ML models, we document substantial heterogeneity in the importance of predictors as measured by SHAP values. Examining trading strategies based on deviations from fair values, we find ML-strategies earn substantially higher risk-adjusted returns (\u201calpha\u201d) than their LR-counterparts (48\u201366 vs. 11\u201336 bp per month for value-weighted portfolios). Our findings document the importance of allowing for non-linearities and interactions in fundamental analysis, as well as substantial non-na\u00efve market inefficiencies.<\/p>\n\n\n\n

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

#460 \u2013 ESG Level Factor Investing Strategy<\/a><\/strong>
#461 \u2013 ESG Factor Momentum Strategy<\/a><\/strong>
#522 \u2013 ESG, Price Momentum and Stochastic Optimization<\/a><\/strong><\/p>\n\n\n\n

Larcker, David F. and Tayan, Brian and Watts, Edward: Seven Myths of ESG
<\/strong>https:\/\/ssrn.com\/abstract=3956044
<\/a>Abstracto:
The trend to incorporate Environmental, Social, and Governance (ESG) matters into corporate boardrooms and capital markets is pervasive. Nevertheless, considerable uncertainty exists over what ESG is, how it should be implemented, and its financial and nonfinancial impacts on corporate outcomes and fund performance. In this Closer Look, we explore seven commonly accepted myths surrounding ESG, many of which are not supported by empirical evidence.
We ask:
\u2022 What is ESG expected to solve: short-termism by corporate managers or a deeper problem of corporations profiting at the expense of stakeholders?
\u2022 Does ESG increase corporate value, or does it represent an incremental cost incurred for society?
\u2022 How much ESG investment is new (incremental) investment, and how much repackaging of existing spending?
\u2022 Why is governance included as the G in ESG?
\u2022 Is it possible to develop a reliable measure of ESG quality?
\u2022 Can standardized ESG reporting be done in an informative and cost-effective manner?<\/p>\n\n\n\n

#117 \u2013 Lottery Effect in Stocks<\/a><\/strong><\/p>\n\n\n\n

Hu, Conghui and Lin, Ji-Chai and Liu, Yu-Jane: What Are Benefits of Attracting Gambling Investors? Evidence from Stock Splits in China
<\/strong>https:\/\/ssrn.com\/abstract=3922178
<\/a>Abstracto:
Analyzing a sample of Chinese firms splitting their stocks via stock dividends and using proprietary trading data to measure investors\u2019 gambling preference, we find that stock splits raise the stocks\u2019 lottery characteristics, making them attractive to gambling investors, who willingly pay higher prices for skewed securities. Split firms also become more risk-taking. Furthermore, their cost of equity declines, largely due to increased gambling investors\u2019 pricing influence. Our findings suggest that firms with weak lottery characteristics and those with inefficient risk sharing, can use stock splits to attract gambling investors to improve risk sharing and to lower their cost of equity.<\/p>\n\n\n\n

#578 \u2013 Combining Smart Factors Momentum and Market Portfolio<\/a><\/strong><\/p>\n\n\n\n

Favero, Carlo A. and Melone, Alessandro and Tamoni, Andrea: Macro Trends and Factor Timing
<\/strong>https:\/\/ssrn.com\/abstract=3940452
<\/a>Abstracto:
We find that the value of well-known systematic (characteristics-based) risk factors, like SMB and HML, is anchored to macroeconomic trends related to inflation and real economic activity. Exploiting the cointegration logic, when the price of a factor is greater than the long-term value implied by the macro trends, expected returns should be lower over the next period. We provide strong supporting evidence for this intuition: deviations of factor prices from their value implied by macroeconomic conditions predict factor returns both in- and out-of-sample, translating into significant economic gains from the perspective of a mean-variance investor. Finally, our approach leads to an estimated SDF that displays sizable variation over time when benchmarked against standard long-run risk or habit models.<\/p>\n\n\n\n

#571 \u2013 Post Seasoned Equity Offering Returns in China<\/a><\/strong><\/p>\n\n\n\n

Giannone, Danilo Antonino: The Effect of Unscheduled News on Systematic Risk
<\/strong>https:\/\/ssrn.com\/abstract=3962137
<\/a>Abstracto:
I use intraday prices to explore the time-varying characteristic of the systematic risk around unscheduled firm-level news writing about secondary equity offering (SEO) programs. I show that, around this information flow, the beta drops by a statistically significant and economically important amount. Firm-level news about an SEO program leads to a sharp decrease in the company’s systematic risk of 33.4% on the day that the news is reported. These results are consistent with investors’ rationality and managers’ signals about company valuation. That is, investors sell overvalued firms in favour of more fairly valued companies. Further, the results show that the sentiment of news does not explain the change in the systematic risk. So, investors should closely monitor news taxonomy to understand their risk exposure around information flow. Finally, I show that, through the systematic risk variation documented in this paper, it is possible to explain more than 50% of the negative abnormal return observed on the SEO announcement date.<\/p>\n\n\n\n

#69 \u2013 Post-Earnings Announcement Drift Combined with Strong Momentum<\/a><\/strong><\/p>\n\n\n\n

Pazaj, Elisa: Transitory Earnings Shocks, Financial Constraints and Price Momentum
<\/strong>https:\/\/ssrn.com\/abstract=3947005
<\/a>Abstracto:
I examine the separate roles of persistent and transitory earnings shocks in explaining price momentum. I find that transitory shocks have significant explanatory power, suggesting financial constraints may be important for momentum firms. In a liquidity management model that accounts for both types of shocks, the most constrained firms end up in the extreme past performance portfolios. High expected cash-flow growth carries a positive risk for constrained firms, driving the difference in expected returns between winners and losers. The model reproduces the average momentum premium and its dissipation one year after formation observed in the data. Empirical proxies for financial constraints and expected growth confirm model predictions.<\/p>\n\n\n\n

#505 \u2013 Systematic Investing in Emerging Market Debt<\/a><\/strong><\/p>\n\n\n\n

Sol\u00eds, Pavel: Term Premia and Credit Risk in Emerging Markets: The Role of U.S. Monetary Policy
<\/strong>https:\/\/ssrn.com\/abstract=3973655
<\/a>Abstracto:
This paper documents the channels through which U.S. monetary policy impacts the sovereign bond yields of emerging markets. Traditional decompositions of sovereign yields are not suitable for emerging markets because they rely on a default-free assumption. Instead, I decompose the yields of 15 emerging markets into average expected future short-term interest rates, a (`clean’) term premium and compensation for credit risk. I use these decompositions to analyze the transmission channels of U.S. monetary policy surprises identified with intraday data. I find that the responses of emerging market yields to target, forward guidance and asset purchase surprises are sluggish, and amplify over time. The yield decompositions show that U.S. monetary policy transmits to emerging market yields through their different components. Unanticipated Fed’s policy decisions lead to a reassessment of policy rate expectations and a repricing of interest and credit risks in emerging markets. The effects vary along their yield curves.<\/p>\n\n\n\n

#537 \u2013 The Positive Similarity of Company Filings and Stock Returns<\/a><\/strong><\/p>\n\n\n\n

Dyer, Travis and Roulstone, Darren T. and Van Buskirk, Andrew: Disclosure Similarity and Future Stock Return Comovement
<\/strong>https:\/\/ssrn.com\/abstract=3975547
<\/a>Abstracto:
Existing research often assumes that firms\u2019 financial reporting choices influence their return comovement with other firms. We examine the validity of that assumption. First, we provide initial evidence suggesting that similarity in two firms\u2019 disclosures not only predicts, but influences, future return comovement between those two firms. Second, we show that this predictive ability aggregates to the market level; disclosure similarity can be used to estimate more accurate forward-looking market betas. Taken together, these two results imply that managers can influence their firms\u2019 betas by altering their firms\u2019 disclosures \u2013 a prominent assumption in existing research, but one with little empirical support until now.<\/p>\n\n\n\n

#459 \u2013 Editor Preference and Stock Returns<\/a><\/strong><\/p>\n\n\n\n

Schwenkler, Gustavo and Zheng, Hannan: Time-Varying Editor Preferences, Attention Constraints, and Asset Prices<\/strong>
https:\/\/ssrn.com\/abstract=3977285
<\/a>Abstracto:
We show that financial news editors have time-varying reporting preferences that signal risky assets to attention-constrained investors. Using monthly New York Times data and natural language processing techniques, we estimate the loadings of news coverage on common firm features and extract dynamic editor preferences for firms. We find that firms with high editor preference earn higher subsequent returns than those with low editor preference. An associated long-short strategy has an annualized alpha of 15% in excess of standard risk factors. Our empirical findings support recent theories that posit that, when investors face attention constraints and delegate their information collection to news publishers, editorial reporting choices signal to investors which assets carry high risk premia.<\/p>\n\n\n\n

#614 \u2013 Climate sentiment, carbon prices and Emission minus Clean Portfolio<\/a><\/strong>
#679 \u2013 Carbon Emmision Intensity in Stocks<\/a><\/strong><\/p>\n\n\n\n

Bessec, Marie and Fouquau, Julien: A Green Wave in Media, a Change of Tack in Stock Markets
<\/strong>https:\/\/ssrn.com\/abstract=3924837
<\/a>Abstracto:
This paper explores the impact of green sentiment in US media on financial markets. Using textual analysis with a dictionary-based approach, we retrieve several scores of attention, tonality and uncertainty in the coverage of environmental news of four major US newspapers. We consider various weighting schemes to account for the visibility and relevance of the text sources and several sets of newspapers to measure the possible impact of their editorial line. Our results establish that greater attention to environmental news in US media reduced the excess returns of carbon-intensive stocks and increased their volatility over the last decade, especially when the coverage was negative or uncertain. The opposite result holds for the most virtuous green assets. Restricting the corpus of texts to conservative newspapers mitigates the impact of the coverage. Overall, our findings illustrate how rising environmental concerns lead investors to shift their asset allocation.<\/p>\n\n\n\n

#614 \u2013 Climate sentiment, carbon prices and Emission minus Clean Portfolio<\/a><\/strong>
#679 \u2013 Carbon Emmision Intensity in Stocks<\/a><\/strong><\/p>\n\n\n\n

Blitz, David and Hoogteijling, Tobias: Carbon-Tax-Adjusted Value
<\/strong>https:\/\/ssrn.com\/abstract=3974773
<\/a>Abstracto:
We examine the effects of incorporating a potential tax on carbon emissions into a value investment strategy. We show that in a portfolio optimization problem, a carbon tax at the stock level is mathematically equivalent to a carbon constraint at the portfolio level. Using this insight we derive a value-carbon efficient frontier that reflects the trade-off between a high value exposure and a low carbon footprint. Empirically we find that carbon taxes up to $100, corresponding to a portfolio carbon footprint reduction of about 50%, have little effect on the characteristics and the performance of the long side of a value strategy. Much more aggressive footprint reduction levels seem unreasonable, as they correspond to extremely high carbon tax levels and performance starts to decay.<\/p>\n\n\n\n

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

A Primer on Grid Trading Strategy<\/a><\/strong><\/p>\n\n\n\n

Grid trading is an automated currency trading strategy where an investor creates a so-called \u201cprice grid\u201d. The basic idea of the strategy is to repeatedly buy at the pre-specified price and then wait for the price to rise above that level and then sell the position (and vice versa with shorting and covering). We will explore the basics and show favorable and unfavorable scenarios in the first article about this trading style. Later articles will dig deeper and investigate how Grid trading is related to other systematic trading strategies.<\/p>\n\n\n\n

<\/a>Top Ten Blog Posts on Quantpedia in 2021<\/a><\/strong><\/p>\n\n\n\n

As usual, at this time of the year, let us do a short recapitulation of posts on our blog in the previous 12 months. We have published nearly 70 short analyses of academic papers and our own research articles on this blog in 2021. We want to use this opportunity to summarize 10 of them, which were the most popular (based on the Google Analytics tool). Maybe you will be able to find something you have not read yet \u2026<\/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

#169 – Exploiting Option Information in the Equity Market<\/a>
#414 – Patent-to-Market Equity Factor<\/a>
#683 – Overvalued Stocks in China<\/a>
#692 – Employee Satisfaction Factor<\/a>
#693 – Quarterly Investment Spikes Predict Stock Returns<\/a><\/p>","protected":false},"excerpt":{"rendered":"

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

Ten 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.
\nPlus, five trading strategies have been backtested in QuantConnect<\/strong><\/a> 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-16476","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/16476","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=16476"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/16476\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=16476"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=16476"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=16476"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}