{"id":18258,"date":"2022-04-04T16:13:35","date_gmt":"2022-04-04T14:13:35","guid":{"rendered":"https:\/\/quantpedia.com\/?p=18258"},"modified":"2022-04-07T07:00:17","modified_gmt":"2022-04-07T05:00:17","slug":"quantpedia-premium-update-1st-april","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-1st-april\/","title":{"rendered":"Quantpedia Premium Update \u2013 1st April"},"content":{"rendered":"

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

#733 \u2013 Hedging Pressure Predicts Commodity Option Returns<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>commodities
Instruments used for trading:<\/strong> options, futures
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1995-2018
Indicative performance:<\/strong> 124.27%
Estimated volatility:<\/strong> 92.59%<\/p>\n\n\n\n

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

Cheng, Ing-Haw and Tang, Ke and Yan, Lei, Hedging Pressure and Commodity Option Prices
<\/strong>https:\/\/ssrn.com\/abstract=3933070
<\/a>Abstracto:
A new measure of hedging pressure in commodity options markets\u2014commercial hedgers\u2019 net short option exposure\u2014predicts option returns and changes in the slope of implied volatility curves. Puts are more expensive, and calls are cheaper, when values of option hedging pressure are greater. This pattern is consistent with commercial traders\u2019 natural hedging motives. A strategy that provides liquidity to hedgers earns an average excess return of 6.4% per month before transaction costs and consideration of margin requirements. Overall, our results confirm the existence of hedging premiums, demand effects, and limits to arbitrage in commodity option markets.<\/p>\n\n\n\n

#734 \u2013Empirical Asset Pricing via Machine Learning<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> ETFs, futures
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 1995-2021
Indicative performance:<\/strong> 19.68%
Estimated volatility:<\/strong> 49.56%<\/p>\n\n\n\n

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

Cakici, Zaremba: Empirical Asset Pricing via Machine Learning: The Global Edition<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4028525<\/a>
Abstracto:
We examine the cross-section of international equity risk premia with machine learning methods. We identify, classify, and calculate 88 market characteristics and use them to forecast country returns with various machine learning techniques. While all algorithms produce substantial economic gains, neural networks prove particularly effective. The associated long-short portfolio yields 1.69% per month. Most models select a consistent group of leading predictors: long-run reversal, earnings yield, size, market breadth, and momentum. The return predictability is driven by mispricing rather than risk. In consequence, it is boosted by high limits to arbitrage but gradually diminishes over time as global markets mature.<\/p>\n\n\n\n

#735 \u2013 Butterfly implied 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> Complex strategy
Backtest period:<\/strong> 1996-2019
Indicative performance:<\/strong> 2.67%
Estimated volatility:<\/strong> 7.27%<\/p>\n\n\n\n

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

Di Wu: Butterfly Implied Returns<\/strong>
https:\/\/ssrn.com\/abstract=3880815<\/a>
Abstracto:
For each S&P 500 stock, I calculate the rolling correlation between the VIX and the premium of butterfly at different strikes. The butterfly that co-moves most positively with VIX reveals the expectation of the stock\u2019s return in the future market crash. I call this return the Butterfly Implied Return (BIR). I construct a new strategy by shorting the vulnerable stocks with low BIR and longing the resilient stocks with high BIR. Over the sample period from 1996 to 2019, this strategy earns a statistically significant alpha, ranging from 0.26% to 0.35% per month relative to various factor models. Building on BIR, I construct a value weighted average called the Butterfly Implied Return of the Market (BIRM) which measures the severity of the future market crash. I show that BIRM is an important determinant of the time varying equity risk premium.<\/p>\n\n\n\n

#736 \u2013 Expected profitability in UK Stocks<\/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> 1996-2017
Indicative performance:<\/strong> 3.49%
Estimated volatility:<\/strong> 2.58%<\/p>\n\n\n\n

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

Maher Khasawneh, David G. McMillan, Dimos S Kambouroudis, Expected Profitability, the 52-Week High and the Idiosyncratic Volatility Puzzle<\/strong><\/strong>
<\/strong><\/a>https:\/\/ssrn.com\/abstract=3954885<\/a>
<\/a>Abstracto:
We investigate the joint ability of fundamental-based and market-based news to explain the anomalous underperformance of the stocks with high idiosyncratic volatility (high IVOL). An out-of-sample prediction of future profitability is adopted as a proxy for the fundamental\u2013based news while market-based news is represented by the 52-week high price ratio. A sample of UK stocks over the period January 1996 to December 2017 is analysed. The empirical results indicate that both the fundamental-based projected profitability and the 52-week high price ratio are important in explaining the IVOL anomaly. Whereas individually nether variable fully accounts for the anomaly. This relation is more pronounced following a period of high sentiment and during an upmarket. Further results suggest that underreaction lies at the hearth of this explanation.

#737 \u2013When Hedge and Non-Hedge Funds Disagree<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Quarterly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Moderately complex strategy
Backtest period:<\/strong> 1994-2017
Indicative performance:<\/strong> 6.93%
Estimated volatility:<\/strong> 7.29%<\/p>\n\n\n\n

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

Caglayan, Mustafa Onur and Celiker, Umut and Sonaer, Gokhan, Disagreement between Hedge Funds and Other Institutional Investors and the Cross-Section of Expected Stock Returns<\/strong>
<\/strong><\/a>https:\/\/ssrn.com\/abstract=3705635<\/a>
Abstracto:
We find strong disagreements between hedge funds and other institutions in their common stock trades are twice as likely as agreements. Overall success of hedge funds\u2019 trades and poor performance of non-hedge funds\u2019 trades are both confined to disagreement stocks. While hedge funds are commonly positive feedback traders, they are neither positive nor negative feedback traders for stocks heavily sold by other institutions. Hedge funds also depend less on earnings news. Our findings highlight the importance of disagreement in studying the performance of institutional investors\u2019 trades and are consistent with the notion that skilled investors rely less on public information.<\/p>\n\n\n\n

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

#96 \u2013 Crude Oil Predicts Equity Returns<\/a><\/strong><\/p>\n\n\n\n

McMillan, David G. and Ziadat, Salem Adel: The Predictive Power of the Oil Variance Risk Premium
<\/strong>https:\/\/ssrn.com\/abstract=4052356
<\/a>Abstracto:
This paper examines the ability of the oil market variance risk premium (VRP) to predict both financial and key macroeconomic series. Interest in understanding movement in such variables increasingly considers measures of investor risk and the variance risk premium, which incorporates both implied and realised volatility, has recently come for the fore. It is well established that oil price movement impacts both the stock market and wider economy and thus, we examined whether this is also true of the oil VRP. Using monthly US data over the period from 2009 to 2021, we demonstrate the nature of oil VRP predictive power for oil and stock returns, as well as output growth, unemployment, and inflation. Of notable interest, while predictability from the oil VRP series dominates at the one-month horizon and (largely) wanes at over longer time periods, the reverse is found for the stock VRP. These results are robust to the inclusion of additional, established, predictor variables. This indicates that the impact of oil market risk has a more immediate effect on both the stock market and economy, with stock market risk reflecting longer term considerations.<\/p>\n\n\n\n

#96 \u2013 Crude Oil Predicts Equity Returns<\/a><\/strong>
#393 \u2013<\/strong> Oil Surprise Factor in Equities<\/a><\/strong><\/p>\n\n\n\n

Haykir, Ozkan and Yagli, Ibrahim and Aktekin-Gok, Emine Dilara and Budak, Hilal: Oil Price Explosivity and Stock Return: Do Sector and Firm Size Matter?<\/strong>
https:\/\/ssrn.com\/abstract=4060882<\/a>
Abstracto:
The paper aims to examine whether the oil price series contains price explosivity, and if exists whether this price explosivity offers excess return for oil-related, oil-substitute, and oil-user companies in US stock markets. Moreover, we examine whether the size effect moderates the relationship between oil price explosivity and stock returns. We use monthly West Texas Intermediate crude oil prices which spans between January 1986 and December 2019 and employ the Generalized Supremum Augmented Dickey-Fuller test to detect the price explosivity. The results indicate multiple episodes of price explosivity which mostly coincides with the 2008 financial crisis. The price explosivity leads to an excess return for oil-related companies; whereas, there is a negative impact of oil price explosivity on oil-substitute and oil-user firms. However, the effect of oil price explosivity on stock returns is heterogeneous across size groups. The results provide key insightful information to policymakers and investors. Policymakers should prevent the occurrence of price explosivity increasing the efficiency of an oil futures market. Given the diverse impact of oil price explosivity on the stock return across sectors and sub-size groups, investors should maximize their profits rebalancing their portfolio based on oil dependency and the size of the firm.<\/p>\n\n\n\n

#409 \u2013 Trading Volume in Cryptocurrency Markets and Reversals<\/a><\/strong>
#720 \u2013 Reversal in Small Cryptos<\/a><\/strong><\/p>\n\n\n\n

Farag, Hisham and Luo, Di and Yarovaya, Larisa and Zieba, Damian: Returns from Liquidity Provision in Cryptocurrency Market<\/strong>
https:\/\/ssrn.com\/abstract=4057510<\/a>
Abstracto:
We examine the liquidity provision premium in cryptocurrency markets using the returns from the short reversal strategy suggested by Nagel (2012). We show that the VIX index, economic policy uncertainty (EPU) index, crash risk, tail risk, and the innovations of Tether liquidity can predict the returns from liquidity provision. The forecast power is also significant in out-of-sample tests. Our results show that cryptocurrency market makers require compensation during periods of market uncertainty.<\/p>\n\n\n\n

#365 \u2013 Timing S&P500 Using a Large Set of Forecasting Variables<\/a><\/strong><\/p>\n\n\n\n

Hollstein, Fabian and Prokopczuk, Marcel: Managing the Market Portfolio<\/strong>
https:\/\/ssrn.com\/abstract=4014540<\/a>
financial, macroeconomic, and technical variables to time-series-manage the market portfolio. A combination of the out-of-sample market excess return forecasts of all variables yields a managed market portfolio that generates alphas relative to cross-sectional factor models that exceed 5% per annum. More broadly, the relation between time-series evaluation measures and (multifactor) alphas is weakly positive, but complex. The variables\u2019 predictability for future returns is more important than that for volatility. Finally, we document that managed market portfolios based on lagged factor realizations also perform well.<\/p>\n\n\n\n

#461 \u2013 ESG Factor Momentum Strategy<\/a><\/strong><\/p>\n\n\n\n

Ma, Yuanfang and McLoughlin, Nicholas: ESG Momentum in Regional Equity Markets<\/strong>
https:\/\/ssrn.com\/abstract=4032769<\/a>
This article investigates the use of ESG metrics for asset allocation decisions. We analyse a basic active allocation strategy within regional equity markets, assessing the usefulness of ESG information via two dimensions: the impact on active returns and the predictability of future ESG scores. Our results suggest tilting portfolios on the basis of ESG information can enhance both portfolio returns and future portfolio ESG scores.<\/p>\n\n\n\n

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

<\/a>What Can We Learn from Insider Trading in the 18th Century?<\/a><\/strong><\/p>\n\n\n\n

Directors, board members, and large shareholders are just some of those who might have non-public material information about their firm. Even though this information could be easily used to profit by trading their own stocks, this insider trading behavior is strictly prohibited. But how profitable can it be? We can study insider trading in the time when it wasn\u2019t regulated at all \u2013 in the early 1700s. The 300 hundred-year-old dataset consists of data recovered from original handwritten ledger books and transfer files of the three largest companies in the London stock market at the time. It gives us a glimpse into the evidence of how big the insider\u2019s advantage is, and the result is quite surprising \u2013 the authors calculated their outperformance to just 7% per year<\/p>\n\n\n\n

<\/a><\/a>Is There Any Hidden Information in Ann<\/a>ual Reports\u2019 Images?<\/a><\/strong><\/p>\n\n\n\n

Can the number or type of images in a firm\u2019s annual report tell us anything about the firm? Or is it just a marketing strategy that doesn\u2019t hold any further information? With the help of novel machine learning techniques, the authors Azi Ben-Rephael, Joshua Ronen, Tavy Ronen, and Mi Zhou study this problem in their paper \u201cDo Images Provide Relevant Information to Investors? An Exploratory Study\u201d. It seems that the proposed metrics help to forecast some of the firms\u2019 fundamental ratios.<\/a><\/p>\n\n\n\n

<\/a>Nuclear Threats and Factor Performance \u2013 Takeaway for Russia-Ukraine Conflict<\/a><\/strong><\/strong><\/p>\n\n\n\n

The Russian invasion of Ukraine and its repercussions continue to occupy front pages all around the world. The battle situation is very dynamic, but it seems that Ukraine holds ground very well and is even able to execute strong local counter-offensives against Russian forces. That\u2019s definitely not a situation that president Putin had expected when he started his \u201cspecial operation\u201d. Internal Russian politics is unforgiving, and Putin can\u2019t allow looking like a loser as then there is a high probability of an <\/strong>internal coup. So at home, in Russia, he is trying to find a way out by reframing the whole invasion as a fight for the liberation of the Donbas region. He put his fate (and probably his life) at stake on this \u201cliberation\u201d, but what if it goes bad for him? What if Ukraine is able to continue to resist, or with all of the weapons they receive, they will even start pushing him out even more? Unfortunately, he still has a few cards left up his sleeve.
<\/a><\/p>\n\n\n\n

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

#145 – Geographic Momentum in Stocks<\/a>
#148 – Value\/Growth Spread in<\/a>
#724 – Stocks of Underperforming Funds and Idiosyncratic Volatility<\/a>
#727 – VIX Put-Call Volume Ratio<\/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.
\nPlus, four 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-18258","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/18258","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=18258"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/18258\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=18258"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=18258"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=18258"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}