Quantpedia Premium Update – 6th January

#817 – Text-Based Recession Detection Strategy

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: CFDs, ETFs, funds, futures
Complexity: Complex strategy
Backtest period: 2000-2020
Indicative performance: 8.78%
Estimated volatility: 13.52%

Source paper:

Baz, Salim and Cathcart, Lara and Michaelides, Alexander: What is the Value of Financial News?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4251414
Abstract:
We construct empirical measures of U.S. business-cycle activity based on media mentions of the word “recession” in financial newspapers. The MRIs (media recession indicators) are useful predictors of U.S. economic activity and stock returns, both in-sample and out-of-sample. Moreover, they compare favourably with existing business-cycle predictors (term premium and default spread, uncertainty and big data indicators). The MRIs can also predict the probability of a U.S. recession six months in advance. Using this information, we show that simple market- timing investment strategies substantially outperform the stock market index (S&P500). We conclude that reading the financial press can generate financial value.

#818 – ETF Flows Predict Subsequent ETF Performance

Period of rebalancing: Intraday
Markets traded: bonds, commodities, currencies, equities, REITs
Instruments used for trading: ETFs
Complexity: Very complex strategy
Backtest period: 2012-2016
Indicative performance: 9.58%
Estimated volatility: 8.48%

Source paper:

Xu, Liao and Yin, Xiangkang and Zhao, Jing: Are the Flows of Exchange-Traded Funds Informative?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3221852
Abstract:
This paper provides novel evidence of information asymmetry in Exchange-Traded Fund (ETF) markets. By decomposing daily ETF flows, we find that the unexpected flow component, orthogonal to the components driven by market-making and arbitraging, wields substantial power in predicting next day’s ETF returns. Informed traders are able to exploit their information advantage to realize an annualized open-to-close return of 19.16% or close-to-close return of 22.42%. The informativeness of the unexpected ETF component is further confirmed by its strong power of predicting next day’s macro news while the demand- and arbitrage-driven components are not closely related to forthcoming news.

#819 – Arbitrage Comovement Effect in ETFs

Period of rebalancing: Monthly
Markets traded: bonds, commodities, currencies, equities, REITs
Instruments used for trading: ETFs
Complexity: Very complex strategy
Backtest period: 2008-2017
Indicative performance: 8.07%
Estimated volatility: 8.8%

Source paper:

John J. Shim: Arbitrage Comovement
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3287912
Abstract:
I argue that arbitrage mistranslates factor information from ETFs to constituent securities and distorts comovement. The intuition behind this distortion is arbitrageurs trade constituent securities not based on their fundamental exposures but by their portfolio weights, causing securities to comove with the ETF based on a measure I call arbitrage sensitivity — a combination of portfolio weight and price impact sensitivity — rather than fundamental exposures. Arbitrage sensitivity predicts comovement between stock and ETF returns, especially in periods of high ETF volume and volatility, but not before 2008 when ETFs were not as heavily traded. Arbitrage-induced comovement leads to over-reaction to ETF returns for stocks more sensitive to arbitrage and under-reaction for those less sensitive. A long-short portfolio constructed based on arbitrage sensitivity generates an alpha of around 7.5% per year. Unlike most anomalies, arbitrage comovement is strongest in large-cap stocks, which are held by the most actively traded ETFs. Arbitrage comovement implies observed factor loadings are less reliable for assessing risk since they are at least partially driven by mechanical arbitrage trading instead of fundamental exposures.

#820 – Enhanced Betting Against Beta with Stochastic Dominance

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1926 – 2020
Indicative performance: 9.01%
Estimated volatility: 10.57%

Source paper:

Kolokolova, Olga and Xu, Xia: Enhancing Betting Against Beta with Stochastic Dominance
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4260904
Abstract:
The performance of the widely used betting-against-beta (BAB) investment strategy is improved by controlling for the stochastic dominance (SD) relation between individual stocks and the market portfolio. Dominating stocks, preferred by all risk-averse and prudent investors, are excluded from the short leg of the BAB strategy. Stocks that are dominated by the market are excluded from the long leg of the strategy. This prefiltering significantly enhances a wide range of performance and risk measures including abnormal returns relative to various factor models. The improvements are especially pronounced for the third-order SD, are robust to transaction costs and different market conditions.

New research papers related to existing strategies:

#99 – FX Carry Trade Combined with PPP (Value) Strategy

Rubaszek, Michał and Beckmann, Joscha and Ca’ Zorzi, Michele and Kwas, Marek: Boosting Carry with Equilibrium Exchange Rate Estimates
https://ssrn.com/abstract=4230681
Abstract:
We build currency portfolios based on the paradigm that exchange rates slowly converge to their equilibrium to highlight three results. First, this property can be exploited to build profitable portfolios. Second, the slow pace of convergence at short-horizons is consistent with the evidence of profitable carry trade strategies, i.e. the common practice of borrowing in low-yield currencies and investing in high-yield currencies. Third, the predictive power of equilibrium exchange rates may boost the performance of carry trade strategies.

#4 – Overnight Anomaly
#530 – Jump Risk in Stocks

Perras, Patrizia J. and Wagner, Niklas F.: Mind the Gap: Is There a Trading Break Equity Premium?
https://ssrn.com/abstract=4284444
Abstract:
This paper investigates the intertemporal relation between expected aggregate stock market returns and conditional variance considering periodic trading breaks. We propose a modified version of Merton’s intertemporal asset pricing model that merges two different processes driving asset prices, (i) a continuous process modeling diffusive risk during the trading day and, (ii) a discontinuous process modeling overnight price changes of random magnitude. Relying on high-frequency data, we estimate distinct premia for diffusive trading volatility and volatility induced by overnight jumps. While diffusive trading volatility plays a minor role in explaining the expected market risk premium, overnight jumps carry a significant risk premium and establish a positive risk-return trade-off. Our study thereby contributes to the ongoing debate on the sign of the intertemporal risk-return relation.

#630 – Machine Learning in Commodities

Huang, Ke and Zhang, Zuominyang and Li, Qiumei and Yin, Luo: Predicting Chinese Commodity Futures Price: An EEMD-Hurst-LSTM Hybrid Approach
https://ssrn.com/abstract=4290257
Abstract:
This paper proposes an EEMD-Hurst-LSTM prediction method based on the ensemble learning framework, which is applied to the prediction of typical commodities in China’s commodity futures market. This method performs ensemble empirical mode decomposition (EEMD) on commodity futures prices, and incorporates the components obtained by EEMD decomposition and the adaptive fractal Hurst index calculated by using intraday high-frequency data as new features into the LSTM model to decompose its correlation with the external market to detect changes in market conditions. The results show that the EEMD-Hurst-LSTM method has better predictive performance compared to other horizontal single models and longitudinal deep learning combined models. Meanwhile, the trading strategy designed according to this ensemble model can obtain more returns than other trading strategies and have the best risk control level. The research of this paper provides important implications for the trend following of commodity markets and the investment risk management of statistical arbitrage strategies.

#650 – Volatility Effect in Cryptos

Dobrynskaya, Victoria: Is Downside Risk Priced in Cryptocurrency Market?
https://ssrn.com/abstract=4289180
Abstract:
I look at the cryptocurrency market through the prism of standard multifactor asset-pricing models with particular attention to the downside market risk. The analysis for 1,700 coins reveals that there is a significant heterogeneity in the exposure to the downside market risk, and that a higher downside risk exposure is associated with higher average returns. The extra downside risk is priced with a statistically significant premium in cross-sectional regressions. Adding the downside risk component to the CAPM and the 3-factor model for cryptocurrencies improves the explanatory power of the models significantly. The downside risk is orthogonal to the size and momentum risks and constitutes an important forth component in the multifactor cryptocurrency pricing model.

#670 – Machine Learning Pairs Trading Strategy

Findsen, Frederik and Pedersen, Jens: A Stochastic Spread & Co-integration Approach to Pairs Trading in a Regime-Switching Environment
https://ssrn.com/abstract=4128453
Abstract:
This paper applies two statistical arbitrage algorithms on the U.S. equities market, using daily historical prices from January 2005 to March 2012. The algorithms construct portfolios using two different frameworks, namely, the Vasicek model and co-integration approach, with a Markov regime-switching component. The empirical results show that the strategies deliver annualised returns of 12 and 10 per cent and Sharpe ratios of 0.61 and 1.49, which is found to be superior to a comparable benchmark, i.e., the broad U.S. stock index Russell 3000. Our findings corroborate previous literature on the topic of pairs trading, and in addition to this, extends the literature by introducing a regime-switching component in the co-integration framework for pairs trading.

#810 – Integrating ESG into Fixed Income Portfolios

Lian, Yonghui and Ye, Tao and Zhang, Yiyang and Zhang, Lin: How Does Corporate ESG Performance Affect Bond Credit Spreads: Empirical Evidence from China
https://ssrn.com/abstract=4265558
Abstract:
In recent years, as the concept of sustainable development has grown in popularity, ESG has attracted widespread interest from people of all backgrounds. This paper empirically investigates the impact of corporate ESG performance on bond credit spreads using a sample of 988 bonds issued by 443 A-share listed companies from the first quarter of 2009 to the fourth quarter of 2020. Results indicate that bond credit spreads are lower for listed companies with higher ESG performance. Good ESG performance decreases bond credit spreads by decreasing corporate financial risk, enhancing corporate transparency, and decreasing debt agency costs. The effect of ESG performance on bond credit spreads is more pronounced for non-state enterprises, enterprises in poor macroeconomic environments, and enterprises in regions with a higher degree of marketization. This study provides evidence for the positive economic consequences of ESG performance from the perspective of bond financing, with implications for firms to improve ESG performance and bond investors to optimize investment decisions.

#311 – Currency Option Delta-Hedging Strategy
#507 – Volatility Risk Premium in Currencies
#539 – Historical and Implied Volatility in FX Options

Bakshi, Gurdip S. and Londono-Yarce, Juan-Miguel: King U.S. Dollar, Global Risks, and Currency Option Premiums
https://ssrn.com/abstract=4098947
Abstract:
Does the primacy of the U.S. dollar affect the pricing of risks in the currency options market? Our findings rely on a daily option panel of 15 currencies. This analysis reveals that (i) put risk premiums are negative, implying across-the-board interest in hedging foreign currency depreciations; (ii) call risk premiums are of variable sign and not as pronounced as for puts; (iii) volatility risk premiums are small or insignificant; and (iv) put (call) risk premiums are more (less) negative for the portfolio of investment versus funding currencies. We formalize a theory to understand the properties of currency option risk premiums.

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

A Balanced Portfolio and Trend-Following During Different Market States

What’s the performance of a balanced portfolio during rising rates? How does it behave when inflation is high? What about a combination of these market states? And how do trend-following strategies fare in such an environment? These and even more questions we will attempt to resolve in our today’s article. We will be looking at different market cycles and how a balanced portfolio and a typical trend-following strategy perform over these different market states.

Factor’s Performance During Various Market Cycles

Today, we analyze how all the factors we use in our Multi-Factor Regression Model performed during various Market Cycles (in sample), including the Bull/ Bear market, the High/ Low inflation, and the Rising/ Falling interest rates. Further, we also examine the performance of a Balanced Portfolio ETF – AOR, over past 100 years. This is done by creating the Factor AOR, which we constructed using our Multi-Factor Regression Model from AOR ETF. In addition to a chart comparison of equity curves, we also compare the performance of factor AOR to that of all the factors by means of risk/return tables, i.e. quantitatively. All the tables are sorted based on the Sharpe ratio from the best (at the top) to the worst (at the bottom).

Top Ten Blog Posts on Quantpedia in 2022

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 a record 80 short analyses of academic papers and our own research articles on this blog in 2022. 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 …

Defining Market Cycles Out of Sample

We have already published a few articles about how the different market cycles affect the performance of your portfolio and performance of market factors. So far, these states of the market were identified in-sample, with the benefit of hindsight. The full methodology of how we defined bull/ bear market, low/ high inflation, and rising/ falling interest rates is described in this article.

Today, we are going to define the same market states out-of-sample. We will describe our methodology and the thinking behind it all in this article. Both in sample and out of sample market cycle analysis may be useful for making investment decisions. It’s crucial to understand the differences and how to use this kind of analysis to your benefit.