Quantpedia Premium Update – 18th May

New strategies:

#747 – Market Uncertainty Resolution Following the Unemployment Announcements

Period of rebalancing: Daily
Markets traded: bonds
Instruments used for trading: futures
Complexity: Simple strategy
Backtest period: 2014-2018
Indicative performance: 2.85%
Estimated volatility: 0.75 %

Source paper:

Gu, Chen and Chen, Denghui and Stan, Raluca> Resolution of Financial Market Uncertainty Around the Release of Unemployment Rate Announcements
https://ssrn.com/abstract=4060445
Abstract:
We provide evidence that the release of the unemployment rate announcement unconditionally leads to financial market uncertainty resolution in the stock, treasury, commodity, and foreign currency markets. The finding is economically valuable. A simple daily strategy of selling the 10-year Treasury Note Volatility Index futures before the unemployment rate announcement and closing the position after the announcement generates an annualized Sharpe ratio of 3.79, while a similar intraday strategy using VIX futures generates an annualized Sharpe ratio of 3.98. Although this resolution is not conditional of the value of the unemployment rate surprise, we also find that larger (lower) than expected unemployment can weaken (strengthen) the uncertainty resolution process.

#748 – Bear Beta or Speculative Beta?—Reconciling the Evidence on Downside Risk Premium

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 1996-2017
Indicative performance: 12.55%
Estimated volatility: 23.57%

Source paper:

Wang, Tong: Bear Beta or Speculative Beta?—Reconciling the Evidence on Downside Risk Premium
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4027995
Abstract:
This paper develops a new approach to explain why risk factors constructed from option returns are priced in the stock market. We decompose an option- based factor into three main components and identify the one responsible for the beta-return relationship. Applying this method to the bear risk factor proposed by Lu and Murray (2019) reveals that the negative correlation between bear betas and stock returns does not reflect systematic risk premia. Instead, it represents an anomaly closely related to the betting-against-beta puzzle. We trace the root of this anomaly to disagreement concerning the aggregate stock market. Our work reconciles the conflicting evidence concerning downside risk by showing that it is not priced in the stock market while making a methodological contribution that helps accurately interpret option-based risk factors.

#749 – Climate Policy Uncertainty and the Cross-Section of Stock Returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2005-2020
Indicative performance: 7.04%
Estimated volatility: 9.3%

Source paper:

Chan, Malik: Climate Policy Uncertainty and the Cross-Section of Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4075528
Abstract
We study whether climate policy uncertainty (CPU) is priced cross-sectionally in individual stocks and find a significant negative relation. On average, the risk-adjusted annual future returns of stocks with low exposure to CPU are 6.5%–7.7% greater than the returns of stocks with high exposure. Low CPU-beta firms are value, green stocks with low crash risk, and they are more closely aligned with Democrats. Conversely, high CPU-beta firms are growth, brown stocks with high crash risk, and they lean toward Republicans. Consistent with the intertemporal capital asset pricing model of Merton (1973), our finding suggests that investors that are looking to hedge against uncertainty in climate policy are willing to pay higher prices and accept lower future returns for CPU-sensitive stocks.

#750 – Return range predicts stock returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 1973-2015
Indicative performance: 61.40%
Estimated volatility: 40.17%

Source paper:

Umutlu, Mehmet and Bengitoz, Pelin: Return range and the cross-section of expected index returns in international stock markets
https://ssrn.com/abstract=3859175
Abstract:
This study examines the cross-sectional relation between return range and future returns for the first time in literature. We show that the return range can serve as a very practical measure of total volatility instead of standard deviation due to the range’s high correlation with standard deviation and strong predictive ability. Range, standard deviation, and idiosyncratic volatility are cross-sectionally linked to future returns on indexes of small size, while earnings-to-price ratio and net share issuance predict returns of mid-cap and large-cap indexes, respectively. Maximum and minimum return effects along with the momentum effect are prevalent in returns of indexes of any size but stronger for small-cap indexes.

New research papers related to existing strategies:

182 – Dual Listed Stock Arbitrage

Dionne, Poutre, Yergeau: International High-Frequency Arbitrage for Cross-Listed Stocks
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4066962
Abstract:
We explore arbitrage activities for cross-listed stocks and develop a methodology to study the effect of information latency in high-frequency trading. The strategy is a hybrid between triangular arbitrage and pairs trading. The strategy can be generalized to multiple cross-listed stocks environments. Market frictions such as trade costs, inventory control, and arbitrage risks are considered. We test the strategy with cross-listed stocks involving three exchanges in Canada and the United States in 2019. The annual net profit with the limit order strategy is around US$6 million. International latency arbitrage with market orders is not profitable with our data.

125 – 12 Month Cycle in Cross-Section of Stocks Returns

Atilgan, Demitras, Gunaydin, Kirli: Mood Seasonality Around The Globe
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4021479
Abstract:
This paper examines the existence of mood seasonality, documented by Hirshleifer et al. (2020, JFE) for the cross-section of US equity returns, in an international setting. First, we confirm the results of the original study. Next, we extend these findings to non-US markets and show that a stock’s relative historical seasonal returns are positively correlated with its relative future seasonal returns during similar or congruent mood periods and negatively related with its relative future seasonal returns during dissimilar or non-congruent mood periods. Moreover, both regression and portfolio analyses indicate that mood beta, the sensitivity of equity returns to aggregate investor mood, helps explain these mood seasonality effects.

460 – ESG Level Factor Investing Strategy

Martin, Pramov, Huwyler: Seeking Financial Performance by avoiding ESG Risks: Sustainable investing in the world of equities
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4075534
Abstract:
The present study investigates the integration of environmental, social, and governance (ESG) scores constructed from company misconduct and incident data into the systematic investment process for equities. These ESG scores are used to set up various ESG investment strategies: From best-in-class and exclusionary screening to sustainability themed investing. The performance of these strategies is evaluated in a realistic investment setting in the United States, European, and Swiss markets and empirical results show that these scores, reflecting how companies are facing ESG-related risk, are a key factor of distinguishing financial performance. On a risk-adjusted basis, portfolios consisting of top ESG scorers consistently outperformed the benchmark whereas portfolios built from selecting bottom ESG scorers consistently underperformed it across all investigated universes.

203 – Value Premium in Large Cap Stocks

Vogel: Long-Only Value Investing: Does Size Matter?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4078256
Abstract:
The academic value factor (long cheap stocks, short expensive stocks) earns higher returns among small-cap stocks. However, when viewed through the lens of a long-only value investor, size is a less important factor. For example, equal-weight large-cap value portfolios historically earned similar returns as small-cap value portfolios. This finding is robust to different value measures and markets. Despite realized returns being statistically similar, the liquidity profile of the two value portfolios is dramatically different: Equal-weight large-cap value portfolios have approximately eleven times (or more) the liquidity of small-cap value portfolios.

207 – Value Factor – CAPE Effect within Countries

Boucher, Jasinski, Tokpavi: Conditional Mean Reversion of Financial Ratios and the Predictability of Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3983094
Abstract:
While traditional predictive regressions for stock returns using financial ratios are empirically proven to be valuable at long-term horizons, evidence of predictability at few-month horizons is still weak. In this paper, based on the empirical regularity of a typical dynamic of stock returns following the occurrence of a mean reversion in the US Shiller CAPE ratio when the latter is high, we propose a new predictive regression model that exploits this stylized fact. In-sample regressions approximating the occurrence of mean reversion by the smoothed probability from a regime-switching model show superior predictive powers of the new specification at few-month horizons. These results also hold out-of-sample, exploiting the link between the term spread as business cycle variable and the occurrence of mean reversion in the US Shiller CAPE ratio. For instance, the out-of-sample R-squared of the new predictive regression model is ten (four) times bigger than that of the traditional predictive model at a 6 (12) month horizon. Our results are robust with respect to the choice of the valuation ratio (CAPE, excess CAPE or dividend yield), and countries (Canada, France, Germany and the UK). We also conduct a mean-variance asset allocation exercise which confirms the superiority of the new predictive regression in terms of utility gain.

88 – 52-Weeks High Effect in Stocks

Vedova, Grant, Westerholm: Liquidity and Price Impact at the 52 Week High
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3925756
Abstract:
A stock’s 52 week high price represents an extremely salient anchor in an investor’s tradingdecision. Using trade and quote data, we document that stocks near the 52 week high priceexhibit significantly higher levels of liquidity than stocks far from the 52 week high. Thisincrease in liquidity is concentrated on the supply side, with depth on the ask side essentiallydoubling relative to a normal day. Price impact declines by as much as two-thirds on the52 week high day. We argue that the abnormal increase in liquidity is driven by dispositionand anchoring effects. These findings help explain the role of the 52 week high as a barrierto information discovery and a potential driver of long run momentum.

58 – VIX Predicts Stock Index Returns

Bangsgaard, Kokholm: The Lead-Lag Relation between VIX Futures and SPX Futures
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4003464
Abstract:
We analyze the lead-lag relationship between VIX futures and SPX futures on a sample of high-frequency data. We find that the two futures markets are weakly connected when market volatility is low. In contrast, when volatility is high, their prices are highly negatively correlated and with the VIX futures leading the SPX futures. We study the determinants of the lead-lag relation, and find that an improvement in the relative liquidity of one market strengthens the lead of that market. In addition, we compute a measure of cross-market activity and find that days of high activity are associated with a strengthened VIX futures leadership. The results provide some indication that VIX futures hedging activities of dealers impact the lead-lag relation. We also document that, when dealers at an aggregate level are in a negative gamma position, an increase in SPX option dealers’ rebalancing activities further strengthens an existing VIX futures leadership.

280 – Trading the VIX Futures Roll and Volatility Premiums with VIX Options
306 – Trading VIX ETFs v2

O’Neill, Whaley: Effects of Nondiscretionary Trading on Futures Prices
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4028483
Abstract:
This paper examines the effects of the nondiscretionary trading demands of VIX exchange-traded products (ETPs) issuers on the prices and volumes in the VIX futures. We find that the ETPs’ information-less, mechanical rebalancing of futures positions to maintain the constant maturity of the index and the promised leverage ratios of the VIX ETPs have significantly positive predictive power for end-of-day futures returns. We also show that the impact on price has diminished through time as a result of the increased liquidity provided by hedge funds, and the “natural” hedging of the issuers’ inverse products.

645 – Statistical Arbitrage With CNN and Transformer Networks
657 – Portfolio Optimization with Nonlinear Risk Budgeting using Neural Network

Pacreau, Lezmi, Xu: Graph Neural Networks for Asset Management
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3976168
Abstract:
It is impossible to analyze an asset taken in isolation, without taking into account the wider picture of the market. This fact is behind the extensive use of copulas or vector autoregressive models in finance, which allow to model dependencies between assets. In this paper, we look at the graph-based method to model inter-asset behavior. Graphs are ubiquitous when representing relationships, whether to model social network interactions, disease spread, traffic, or supply chain information. It allows for a very intuitive representation of market interconnections. We show how several types of market information can be translated into graphs and show some graph-based tools for market analysis. Furthermore, neural convolution layers have been developed which allow for more expressive neural models. Just like Euclidean convolution layers on images, they promise to contextualise each individual asset during prediction. We show the effect of three graph neural layers on the stock return forecasting problem. Using these forecasts, we build portfolios and show that graph layers act as a stabilizer to classical methods like LSTM, reducing transaction costs and filtering out high-frequency signals. We also study the effect of different graph-based information on the forecast and observe that in 2021, supply chain information becomes much more informative than sectoral or correlation-based graphs.

567 – Low-risk Anomaly Index

Blitz, Swinkels, Usaite, van Vliet: Shrinking Beta
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4031825
Abstract:
Betas are used in many applications ranging from asset pricing tests, cost of capital estimation, investment management and risk management. Beta needs to be estimated, and to reduce estimation error, shrinkage to its cross-sectional average value of one is often applied. Since beta is the product of the return correlation of a security with the market and its relative return volatility to that of the market, we shrink correlation and volatility separately and evaluate its predictive power. We find economically and statistically significant gains from shrinking correlations more than volatilities.

And several interesting free blog posts have been published during last 2 weeks:

Carbon Futures – Emerging Asset with Hedging Benefits

Diversification is one way of minimizing the risk of a portfolio. A well-diversified portfolio contains numerous assets from various sectors, each having a small weight so that a crash of any individual security does not hit the entire portfolio. The authors behind this paper came up with a new idea for diversifying stock portfolios. They examined new emerging asset, carbon futures.

How Often Should We Rebalance Equity Factor Portfolios?

Quantpedia has already covered a countless number of factor investing strategies and articles, from strategies in our Screener to multiple blog posts. Therefore, we can confidently say that we do like factor investing. However, there is always new research with a unique point of view. For example, we recently found a paper focused on the decay of the factor exposures of equity factor strategies. The study examines five factors: Value, Momentum, Quality, Investment, and Low Volatility, across 12 developed and emerging markets over a 20-year period. This research aims to find out how long it takes for a factor to decay after the portfolio is assembled. In other words, how often should the portfolio be rebalanced?

Grading and Merging ESG Scores from Multiple Providers

Socially responsible investing, also known as ESG investing, is a recent trend in the world of portfolio management. More and more investors have started to look into the Environmental, Social, and Governance scores of the companies they invest in. However, one major problem with ESG scoring is that there is not one universal scoring system. Many companies sell ESG data, but the scores are not comparable, and additionally, the ESG data providers are not very transparent about how they create the ratings. These problems with ESG data mean we need to have a method to grade and merge the information from multiple providers.

Extending Historical Daily Bond Data to 100 Years

Finding a good data source with quality data and long history is one of the greatest challenges in quantitative trading. There definitely are some data sources with very long histories. However, they tend to be on the more expensive side. On the other hand, cheap or free data usually lacks quality and/or has shorter time frames.This article explains how to combine multiple data sources to create a 100-year daily data history for US 10-year bonds. Having a 100-year history of daily data can be very beneficial to understanding the market patterns and analyzing history and extending backtests to arrive at a new source of out-of-sample data.