Quantpedia Premium Update – 4th July

New strategies:

#759 – Cross-Stock Return Predictability

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2001-2019
Indicative performance: 4.16%
Estimated volatility: 2.28 %

Source paper:

Feng, Jian and Huo, Xiaolin and Liu, Xin and Mao, Yifei: Economic Links from Bonds and Cross-Stock Return Predictability
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4047776
Abstract:
Identifying firms linked economically through the comovement of the credit rating of their corporate bonds, we find that a long-short strategy for stocks based on the link generates a risk-adjusted alpha of 0.62 percent per month, which cannot be explained by industry, customer-supplier, single- to multi-segment, foreign, technology, geographic, or shared analyst coverage links documented in the literature. The cross-return predictability is not significant in the bond market, and is mitigated in the presence of cross-holding investors. Furthermore, analysts are slow to update their forecasts in response to news regarding bond-linked peer firms. Overall, our results are consistent with limited investor attention due to market segmentation between the equity and bond markets.

#760 – The U.S. Dollar and Variance Risk Premia Imbalances

Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: futures
Complexity: Very Complex Strategy
Backtest period: 2005-2019
Indicative performance: 4.1%
Estimated volatility: 8.2%

Source paper:

M.M. Kjaer, A.M. Posselt: The U.S. Dollar and Variance Risk Premia Imbalances
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4078021
Abstract:
This paper provides empirical evidence for the difference in variance risk premium in the U.S. against other economies (VPI) having significant predictive power on monthly U.S. Dollar movements. The predictive power of VPI is rationalized by the variance risk premium’s economic interpretation and the asset market view of exchange rates. We show that VPI is nonredudant relative to traditional predictors and the predictive evidence has significant economic value for investors.

#761 – Gold to Oil Ratio Predicts Aggregate Stock Returns

Period of rebalancing: Monthly
Markets traded: equities, bonds
Instruments used for trading: ETFs, futures, bonds
Complexity: Moderately complex strategy
Backtest period: 1975-2020
Indicative performance: 7.02%
Estimated volatility: 9.70%

Source paper:

Fang, Tong, Gold price ratios and aggregate stock returns
https://ssrn.com/abstract=3950940
Abstract:
We show that most gold price ratios, which represent the relative valuations of gold, positively and significantly predict aggregate stock returns. These ratios fail to generate significant predictive ability after controlling for a series of return predictors described in Welch and Goyal (2008) or to display significant out-of-sample forecasting performance, except for the gold -oil price ratio (GO). GO is the most powerful predictor. A one-standard-deviation increase is associated with a 6.60% increase in the annual excess return for the next month. GO generates the most sizable out-of-sample R^2 and utility gains for a mean-variance investor. We find that the economic source of GO’s predictive ability originates from the cash flow channel using stock return decomposition and positive predictive power on economic conditions. The effect of GO is likely to be reversed only in periods when gold is valuable relative to oil, but the reversal is found to be insignificant. Return predictability from gold price ratios provides us with a new perspective for understanding gold price dynamics.

#762 – Oil Beta Uncertainty and Global Stock Returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: ETFs, stocks, funds
Complexity: Complex strategy
Backtest period: 1975-2020
Indicative performance: 10.17%
Estimated volatility: 15.38%

Source paper:

Chen, Chun-Da and Demirer, Riza, Oil Beta Uncertainty and Global Stock Returns
https://ssrn.com/abstract=3996492
Abstract:
This paper documents an economically and statistically significant positive premium for oil beta uncertainty in the cross-section of global equity returns. Using a battery of market and portfolio level tests, we show that oil beta uncertainty, measured by the total range spanned by the 95% confidence interval for estimated oil betas, carries a significant risk premium as high as 9.72% on an annual basis, even after controlling for global systematic risk factors. While most developed stock markets including Australia, Canada, Switzerland, the US and the UK consistently place in the low oil beta uncertainty portfolio, emerging stock markets including Vietnam, Egypt, China, Turkey and Pakistan are found to be consistently exposed to higher oil beta uncertainty. We show that the risk premium associated with oil beta uncertainty cannot be explained by a stock market’s exposure to market beta, oil beta or idiosyncratic volatility and is stronger during high volatility periods, bullish market states as well as periods of favorable economic conditions. Further analysis suggests that aggregate oil beta uncertainty also captures significant predictive information regarding future world market excess returns, particularly at the medium and longer forecast horizons. We argue that oil beta uncertainty serves as a proxy for disagreement (or ambiguity) on the sensitivity of global stock markets’ response to global economic conditions captured by oil market exposures, which in turn contributes to a risk premium associated with oil beta uncertainty. The findings present a new, behavioral channel in which oil market uncertainty drives the cross-section of global stock market returns.

#763 – Conditional FX Correlation Risk

Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: forwards
Complexity: Moderately Complex Strategy
Backtest period: 1983-2017
Indicative performance: 2.91%
Estimated volatility: 8.32%

Source paper:

Nakagawa & Sakemoto: Dynamic Allocations for Currency Investment Strategies.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4073980
Abstract:
This study conducts out-of-sample tests for returns on individual currency investment strategies and the weights on the universe of these strategies. We focus upon five investment strategies: carry, momentum, value, dollar carry, and conditional FX correlation risk. The performances of our predictive models are evaluated using both statistical and economic measures. Within a dynamic asset allocation framework, an investor adjusts investment strategy weights based upon results of the prediction models. We find that our predictive model outperforms our benchmark, which uses historical average information in terms of statistical and economic measures. When the Sharpe ratio of the benchmark model is 0.52, our predictive model generates economic gain of approximately 1.16% per annum over the benchmark. These findings are robust to the changes in investors’ risk aversion and target volatility for portfolio optimization.

New research papers related to existing strategies:

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

Huynh, Toan Luu Duc and Ridder, Nikolaus and Wang, Mei: Beyond the Shades: The Impact of Credit Rating and Greenness on the Green Bond Premium
https://ssrn.com/abstract=4038882
Abstract:
Green bonds are an innovative and rapidly growing fixed-income asset class that have significant potential in channelling funds towards climate and environmentally friendly investment projects, catalysing the transition to a more sustainable economy. This paper revisits the green bond premium and evaluates its determinants. For this purpose, 161 green bonds are matched with 322 conventional bonds from the same issuer that are also identical in terms of currency, rating, bond structure, seniority, collateral, and coupon type. Subsequently, yield differentials between the green and synthetic bonds are examined based on 71,440 daily observations from January 2016 to March 2021. The results provide significant evidence of an overall negative green bond premium, suggesting that green bonds experience lower yields than otherwise equivalent conventional bonds. The premium is significantly below zero across all continents observed and for both the financial and the government sector. On a cross-sectional average, the green bond premium equals -3.1 bps. The negative premium is more pronounced for green bonds with a lower credit rating. It is also stronger in the presence of an ESG rating and for bonds with a higher shade of green. Significant interaction effects between external review and rating provide evidence that the relationship between the green bond premium and the external review status depends on the bond’s credit rating, i.e., the external review status is more decisive for the green bond premium for bonds with a lower credit rating. The likelihood of obtaining such an external review, i.e., a second-party opinion, a third-party assurance, a green bond rating, or a certification mark, is positively correlated with proxies for social and environmental responsibility.

Janda, Karel and Kortusova, Anna and Zhang, Binyi: Estimation of green bond premiums in the Chinese secondary market
https://ssrn.com/abstract=4113580
Abstract:
Green bonds have gained prominence in China’s capital market as tools that help to fuel the transition to a climate-resilient economy. Although the issuance volume in the Chinese green bond market has been growing rapidly in recent years, the impact of the green label on bond pricing has not been adequately studied. Therefore, this paper investigates whether the newly developed financial instrument offers investors in China an attractive yield compared to other equivalent conventional bonds. By matching green bonds with their conventional counterparts and subsequently applying a fixed-effects estimation, our empirical results reveal a significant negative green bond yield premium of -1.8 bps on average in the Chinese secondary market. In addition, the yield premium is found to vary across issuers’ business sectors mainly due to the public reputation of bond issuers. Moreover, our empirical results reveal an insignificant relationship between the green certification and the yield premium, possibly reflecting inconsistent green bond standards in the Chinese market. Our results point to some practical implications for policymakers and investors.

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

Lee, Kiryoung and Cho, Juik: Measuring Chinese Climate Uncertainty
https://ssrn.com/abstract=4123659
Abstract:
We develop indices of Twitter-based Chinese Climate Uncertainty (TC-CU) and Climate Policy Uncertainty (TC-CPU). Our indices spike with climate change-related president announcements, UN climate change conferences, climate change-related flood deaths, warning about melting glaciers in China’s Qilian mountains, and rising climate concerns regarding bitcoin mining, among many others. We find that TC-CPU is associated with future US climate uncertainty and attention measures while US measures do not predict Chinese climate change measures. Moreover, TC-CU (TC-CPU) negatively predicts equity returns of small (value) firms. Finally, we show that CO2 emissions from oil decline in response to shocks to TC-CU.

#408 – Cointegrated Cryptocurrency Portfolios

Nakagawa, Kei and Sakemoto, Ryuta: Market Uncertainty and Correlation Between Bitcoin and Ether
https://ssrn.com/abstract=4076411
Abstract:
This study investigates whether market states impact the Bitcoin-Ether correlation. We observe an increase in the average correlation due to a rise in popularity of Ether. We also find that an increase in uncertainty leads to the low Bitcoin-Ether correlation, suggesting that investors focus on the different functionalities for the two cryptocurrencies and revise the valuation during high market uncertainty periods. The relationship between the Bitcoin-Ether correlation and uncertainty is nonlinear, and this pattern is clearer when uncertainty is estimated by the gold market than by the stock market.

#467 – Bitcoin Intraday Momentum

Wen, Zhuzhu and Bouri, Elie and Xu, Yahua and Zhao, Yang: Intraday Return Predictability in the Cryptocurrency Markets: Momentum, Reversal, or Both
https://ssrn.com/abstract=4080253
Abstract:
This paper reports evidence of intraday return predictability, consisting of both intraday momentum and reversal, in the cryptocurrency market. Using high-frequency price data on Bitcoin from March 3, 2013, to May 31, 2020, it shows that the patterns of intraday return predictability change in the presence of large intraday price jumps, FOMC announcement release, liquidity levels, and the outbreak of the COVID-19. Intraday return predictability is also found in other actively traded cryptocurrencies such as Ethereum, Litecoin, and Ripple. Further analysis shows that the timing strategy based on the intraday predictors produces higher economic value than the benchmark strategy such as the always-long or the buy-and-hold. Evidence of intraday momentum can be explained in light of the theory of late-informed investors, whereas evidence of intraday reversal, which is unique to the cryptocurrency market, can be related to investors’ overreaction to non-fundamental information and overconfidence bias.

#758 – Credit Risk Factor in Bonds

Dienemann, Fabian: A New Perspective on the Corporate Bond Liquidity Factor
https://ssrn.com/abstract=4093552
Abstract:
This study documents properties of market-wide corporate bond liquidity and demonstrates that liquidity risk is an important determinant of returns. During market downturns, transaction costs rise for sellers and fall for buyers. The negative relation between buyer and seller liquidity motivates a new across-measure liquidity factor that incorporates an asymmetric liquidity component. Shocks to market-wide liquidity explain a large fraction of bond return variation in the time series. Primarily driven by the asymmetric component, the liquidity factor attracts a cross-sectional risk premium that is robust to controls for credit, equity, and interest rate factors as well as the illiquidity level.

#28 – Value and Momentum Factors across Asset Classes
#208 – Switching between Value and Momentum in Stocks

Polk, Christopher and Vayanos, Dimitri and Woolley, Paul: Long-Horizon Investing in a Non-CAPM World
https://ssrn.com/abstract=4096829
Abstract:
We study dynamic portfolio choice in a calibrated equilibrium model where value and momentum anomalies arise because capital moves slowly from under- to over-performing market segments. Over short horizons, momentum’s Sharpe ratio exceeds value’s, the value-momentum correlation is negative, and the conditional value-momentum correlation predicts positively Sharpe ratios of value and momentum. Over long horizons instead, value’s Sharpe ratio can exceed momentum’s, the value-momentum correlation turns positive, and the value spread becomes a better predictor of Sharpe ratios. Momentum’s optimal portfolio weight relative to value’s declines significantly as horizon increases. We provide empirical evidence supporting our model’s predictions.

#117 – Lottery Effect in Stocks

Fang, Jing: Financial distress and stock comovement: Evidence, explanation, and implication
https://ssrn.com/abstract=4088989
Abstract:
Contrary to the financial distress premium notion, the stocks of financially distressed firms comove least. Financially distressed firms are characterized by high valuation uncertainty and information and arbitrage frictions. Therefore, their stocks are prone to mispricing and their stock price movements are idiosyncratic, which drives the low comovement between their stocks. Moreover, financially distressed firms are much more likely to yield both positive and negative extreme returns. Acknowledging all this helps unravel asset pricing puzzles about financial distress including the high profitability of anomaly-based trading strategies for financially distressed firms and the negative relation between financial distress and realized return.

#738 – Mean Variance Factor Timing

Zeissler, Tom Oskar Karl: Forecasting Long-Horizon Factor Volatility
https://ssrn.com/abstract=4092032
Abstract:
This paper investigates forecasts of long-term volatility for the fast-growing field of long-short factor strategies in an extensive in- and out-of-sample framework. More in detail, the study follows previous authors by empirically comparing various forecast configurations to provide guidance to academics and practitioners on how to accurately predict future volatility for a broad set of factor strategies. The data set spans various well-known factors over multiple asset classes, factor styles, and a long historical data period. As the in-sample results suggest, forecast accuracy is higher for longer historical lookback periods and forecasting windows, both indicating notable mean reversion effects on average across strategies. Furthermore, the evidence supports previous researchers who reported low forecast accuracy of the common approach of merely extrapolating past realized volatility. In contrast, fitted models that consider short-term volatility clustering and additionally exploit external predictors motivated by the asset-pricing literature perform remarkably better. Specifically, the study provides further evidence on the relevance of macro- and market-based state variables, such as fiscal balance, inflation, or term spread indicators, as determinants of the long-term risk of aggregated future asset prices. However, the subsequent out-of-sample analysis shows that most of these findings would have been hard to identify and exploit for investors. Especially the ‘industry standard’ approach of simply extrapolating historic volatility proves its right of existence by representing a tough benchmark to beat out-of-sample. Nevertheless, some patterns of the in-sample results remain intact after the out-of-sample analysis, for instance suggesting notoriously higher accuracy when using longer forecast windows or focusing on carry-styled factor strategies.

#650 – Volatility Effect in Cryptos

Wang, Jiqian and Bouri, Elie and Ma, Feng: Which Factors Drive Bitcoin Volatility: Macroeconomic, Technical, or Both?
https://ssrn.com/abstract=4080107
Abstract:
Academic research relies heavily on exogenous drivers to improve the forecasting accuracy of Bitcoin volatility. The present study provides additional insight into the role of macroeconomic and technical indicators in forecasting the realized volatility of Bitcoin. Using 17 famous macroeconomic variables and 18 technical indicators between December 2011 and April 2021, study results reveal that the shrinkage methods, including elastic net and LASSO, can powerfully extract predictive information from macroeconomic and technical indicators. We further investigate the forecasting power of macroeconomic factors and technical indicators in terms of variable selection, business cycle, and volatility levels, and the results show strong evidence that the macroeconomic factors (namely, S&P 500 realized volatility, global real economic activity index, and trade-weighted USD index return) are the most frequently selected by shrinkage method, suggesting that macroeconomic indicators can more strongly forecast Bitcoin volatility than technical indicators. However, technical indicators are more powerful in forecasting Bitcoin volatility during the low volatility level.

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

Skewness/Lottery Trading Strategy in Cryptocurrencies

A recent spring 2022 crisis in the cryptocurrency market emphasized the importance of market-neutral crypto trading strategies. It’s not enough just to HODL crypto market and hope for the everlasting bull market. Therefore, we continue our series of research articles about the cryptocurrency market and offer an analysis of the skewness anomaly. So after our description of the skewness effect in commodities, an article about the multi-asset skewness strategy, and observation of the skewness/lottery effect in ETFs, we have one more asset class, where we can find lottery/skewness anomaly – in cryptocurrencies.

Investor Sentiment and the Eurovision Song Contest

The summer is slowly approaching; therefore, our new article will be on a little lighter tone. We will examine a research paper on a periodic event with sentiment implications. The authors (Abudy, Mugerman, Shust) focused on a specific song competition – the Eurovision Song Contest, an international song competition organized annually. They examined a positive swing in investor mood in the winning country the day after the Eurovision Song Contest and documented an average abnormal return of 0.381%. On the contrary, they did not find any negative sentiment in other participating countries.