Quantpedia Premium Update – 4th June

New strategies:

#751 – Consumer Spending and the Cross-Section of Stock Returns

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2013-2019
Indicative performance: 13.12%
Estimated volatility: 9.98 %

Source paper:

Gupta, Tarun and Leung, Edward and Roscovan, Viorel: Consumer Spending and The Cross-Section of Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3968780
Abstract:
Using a unique dataset of individual transactions-level data for a universe of U.S. consumer facing stocks, we examine the information content of consumer credit and debit card spending in explaining future stock returns. Our analysis shows that consumer spending data positively predict various measures of a company’s future earnings surprises up to three quarters in the future. This predictive power remains strong in both large- and small-cap universes of consumer discretionary firms in our sample and is robust to the type of transactions data considered (credit card, debit card, or both), although the relationship is stronger in the small-cap universe where informational asymmetries are more pronounced. Based on this empirical observation we build a simple long-short strategy that takes long/short positions in the top/bottom tercile of stocks ranked on our real-time sales signal. The strategy generates statistically and economically significant returns of 16% per annum net of transaction costs and after controlling for the common sources of systematic factor returns. A simple optimization exercise to form (tangency) mean-variance efficient portfolios of factors leads to an optimal factor allocation that assigns almost 50% weight to our long-short portfolio. Our results suggest that consumer transaction level data can serve as a more accurate and persistent signal of a firm’s growth potential and future returns.

#752 – Do Stock Returns Really Decrease With Default Risk?

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2000-2014
Indicative performance: 13.42%
Estimated volatility: 16.57%

Source paper:

Aretz, Florackis & Kostakis: Do Stock Returns Really Decrease with Default Risk? New International Evidence.
https://doi.org/10.1287/mnsc.2016.2712
Abstract:
This study constructs a novel dataset of bankruptcy filings for a large sample of non-US firms in 14 developed markets and sheds new light on the cross-sectional relation between default risk and stock returns. Using the reduced-form approach of Campbell et al. (2008) to estimate default probabilities, we offer conclusive evidence supporting the existence of a significant positive default risk premium in international markets. This finding is robust to different portfolio weighting schemes, data filters, risk-adjusting approaches and holding period definitions. Decomposing the default risk measure into its systematic and idiosyncratic components, we find that the former drives this positive relation. We also show that the default risk premium is more pronounced in countries where creditor protection is stronger and shareholder bargaining power is lower.

#753 – Intraday Seasonality in Bitcoin

Period of rebalancing: Intraday
Markets traded: cryptocurrencies
Instruments used for trading: cryptocurrencies
Complexity: Simple strategy
Backtest period: 2015-2021
Indicative performance: 33.0%
Estimated volatility: 20.93%

Source paper:

Padyšák, Matúš and Vojtko, Radovan: Seasonality, Trend-following, and Mean reversion in Bitcoin
https://ssrn.com/abstract=4081000
Abstract:
The cryptocurrency market is not negligible nor minor anymore. With the continuous development of the crypto market, researchers aimed to analyze novel cryptocurrencies thoroughly. An excellent starting point might be in other recognized effects from the developed asset classes. This research examines seasonality effects such as when the major NYSE opened or closed and their intraday, overnight, or daily components. Furthermore, we also examine the distribution of the daily returns and the returns that are significant. The results point to a simple seasonality strategy that is based on holding BTC only for two hours per day. The second aim is to examine trend-following and mean reversion strategies. The data suggests that BTC tends to trend when it is at its maximum and bounce back when at the minimum. These findings support the empirical observations that BTC tends to trend strongly and revert after drawdowns.

#754 – Betting Against Correlation in S&P500 Stocks

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2003-2021
Indicative performance: 7.32%
Estimated volatility: 27.48%

Source paper:

T. Pasetti, D.M. Montagna: The Low-Risk Effect, from Betting Against Beta to Betting Against Correlation
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3995496
Abstract
The aim of this work is to analyze the so-called “Low Risk Effect” and the evolution of the risk- reward relationship in time. Perhaps one of the milestones of the whole modern finance has been the investigation and the debate about the positive relationship between risk and reward in asset allocation, but are we sure that this theoretical paradigm is able to provide also empirical evidence? Starting form the “father” of Modern Portfolio Theory, passing through Sharpe’s CAPM and Black 1972, we analyzed in deep the so-called “Low Risk Effect” and the debate between leverage constraints and behavioural theories. We concluded our journey decomposing Betting Against Beta (Asness, Frazzini and Pedersen) and analysing their last contribution: Betting Against Correlation (BAC), a factor that goes long low correlation stocks and shorts high correlation ones. Starting from the BAC methodology framework, we decided to create some modifications in order to test the goodness of the model in terms of performance against the reference index. Finally we tried to implement a profitable strategy for S&P500 over the time interval 2003-2021, evidencing the phases of negative correlated stocks and arriving to define strategy’s sector composition. To conclude our work we performed a sectorial analysis in which we investigated the composition of Long/Short portfolios for our best strategy Correlation Weighted qBAC, trying to evidence the main drivers for strategy’s performance and critical issues in the last few years. To go further we built a “walking correlation analysis” that resulted useful to observe the dynamic evolution of stocks correlation in time both against the market and within the sector.

#755 – Mean-reversion and Trend-Following Based on MIN and MAX in BTC

Source paper:

New research papers related to existing strategies:

#7 – Low Volatility Factor Effect in Stocks

Hu, Guanglian: The Pricing of Realized, Implied, and Expected Market Volatilities in the Cross-Section of Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4039002
Abstract:
This paper studies the pricing of realized, option implied (i.e., expected risk neutral volatility), and expected (physical) market volatilities in the cross-section of stock returns. Consistent with the notion that volatility shocks are viewed as bad and investors pay a premium for hedging against increases in volatility, I find that stocks with high sensitivities to changes in realized volatility and expected volatility have significantly low average returns. On the other hand, implied volatility is not priced in the cross-section of stock returns. The differential pricing of market volatility risks is hard to reconcile with standard theories of volatility risk premium, but is potentially consistent with frictions between options and equity markets.

#460 – ESG Level Factor Investing Strategy

ten Bosch, Eline and Van Dijk, Mathijs A. and Schoenmaker, Dirk: Do the SDGs Affect Sovereign Bond Spreads? First Evidence
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4006375
Abstract:
We study the relation between a country’s performance on the United Nations’ Sustainable Development Goals (SDGs) and its sovereign bond spread. Using a novel country-level SDG measure for a global sample of countries, we find a significantly negative relation between SDG performance and credit default swap (CDS) spreads, while controlling for traditional macroeconomic factors. This effect is stronger for longer maturities, in line with the notion that the SDGs represent long-term objectives. The results are most consistent with perceived default risk driving this relation, rather than investor preferences. In sum, our initial evidence suggests that investing in the SDGs provides governments with financial benefits besides ecological and social welfare.

#582 – Carbon Risk in the Cross Section of Corporate Bond Returns

Ferriani, Fabrizio, Issuing Bonds during the COVID-19 Pandemic: Is There An ESG Premium?
https://ssrn.com/abstract=4042802
Abstract
We rely on the ESG ratings assigned by four distinct agencies (MSCI, Refinitiv, Robeco, and Sustainalytics) to study the link between ESG scores and firms’ cost of debt financing during the early stage of the COVID-19 pandemic. We document the existence of a statistically and economically significant ESG premium, i.e. better rated companies access debt at lower cost. Despite some heterogeneity across rating agencies, this result is robust to the inclusion of issuer’s credit standing as well as several bond and firms’ characteristics. We find that the effect is mainly driven by firms domiciled in advanced economies whereas creditworthiness considerations prevail for firms in emerging markets. Lastly, we show that the lower cost of capital for highly-rated ESG firms is explained by both risk-based considerations unrelated to corporate credit risk and by investors’ preference towards more sustainable assets.

#658 – Betting Against Uncertainty Beta in Australia
#677 – Betting Against Uncertainty Beta in US Hedge Funds

Caglayan, Mustafa Onur and Gong, Yuting and Xue, Wenjun: Investigation on the Effect of Global EPU Spillovers on Country-level Idiosyncratic Volatility
https://ssrn.com/abstract=3937378
Abstract:
Using the multivariate quantile model, this paper develops a global economic policy uncertainty (EPU) spillover measure for each country, and investigates the spillover effects of these country-specific global EPUs on the country-level idiosyncratic volatility across a sample of 23 economies. The regression results show that global EPU spillovers has a positive and significant effect on country-level idiosyncratic volatility. We find that that the effect of developed-markets-generated EPU spillovers on country-level idiosyncratic risk is noticeably larger compared to the effect of emerging-markets-generated EPU spillovers. The significant and positive effect of EPU spillovers on the country-level idiosyncratic volatility is also found to be robust to utilizing various economic, financial, and political risk factors as controls, as well as using alternative measures of idiosyncratic volatility as the dependent variable in our regression analyses.

#12 – Pairs Trading with Stocks
#670 – Machine Learning Pairs Trading Strategy

Elliott, Robert James and Bradrania, Reza: Estimating A Regime Switching Pairs Trading Model
https://ssrn.com/abstract=3928721
Abstract:
We consider a discrete time pairs trading model which includes regime changes in the dynamics. The prices of the pair of assets, and so their difference or spread, depend on the state of the market, which in turn is modelled by a finite state Markov chain. Different states of the chain give rise to different parameters in the dynamics of the spread. However, the state of the chain is not observed directly but only through the prices or spread. Based on observations of the spread this paper provides recursive estimates for both the state of the market and all coefficients in the model.

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

Best Performing Value Strategies – Part 1

Equity Value strategies have suffered hardly during years 2018, 2019 and also 2020. Due to the poor performance of Value during this period, many investors have abandoned the strategy, often expressing view that “Value strategy is not working anymore”. Nevertheless, equity Value strategies have managed a strong comeback recently, turning attention of investors and traders back to them. In our blog today, we will take a close look at many different equity Value strategies, their performance and how they behave.

Extending Historical Daily Commodities Data to 100 Years

Finding a high-quality data source is crucial for quantitative trading strategies. Also, having a long history is beneficial. Fama & French, for example, offer free historical data for stocks and a variety of factors. However, it is very hard to get good-quality and free data for other asset classes. For this reason, we have already examined how to extend historical daily bond data to 100 years.

For any event-driven analysis or to perform stress tests of various historical situations, long-enough data can only help. Whether one wants to analyze past market patterns, or simply examine the risk of their portfoliounder different historical scenarios, the use case for long data is pretty straightforward.

Following the theme of our previous article, we decided to extend historical data of another asset class, commodities. This article explains our commodity data methodology and introduces data sources, which helped us extend historical daily commodities data to 100 years.

100-Years of Multi-Asset Trend-Following

Trend-following strategies have gained extreme popularity in the recent decade. Almost every asset manager utilizes trend following, or momentum, in some form – whether consciously or subconsciously. We at Quantpedia are convinced that each and every strategy has to be scrutinized thoroughly before it’s put into use. This is one of our motivations why we will introduce to you our framework for building a 100-year daily history of a multi-asset trend-following strategy today.

Introduction and Examples of Monte Carlo Strategy Simulation

The Monte Carlo method (Monte Carlo simulations) is a class of algorithms that rely on a repeated random sampling to obtain various scenario results. Monte Carlo simulations are used to predict the probability of different outcomes when it would be difficult to use other approaches such as optimization. The main aim is to create alternative scenarios, which account for possible risk and help with decision making. The simulations are used in various fields, from finance and quantitative analysis to engineering or science. We plan to unveil our new “Monte Carlo” report for Quantpedia Pro clients in a next few days, and this article is our introduction to different methodologies that can be used for Monte Carlo calculation.