New strategies:
#610 – Presidential Economic Approval Rating 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: 1983-2019
Indicative performance: 14.16%
Estimated volatility: 19.33%
Source paper:
Zilin Chen, Zhi Da, Dashan Huang, Liyao Wang: Another Presidential Puzzle? Presidential Economic Approval Rating and the Cross-Section of Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3805395
Abstract:
We construct a monthly Presidential Economic Approval Rating (PEAR) index from 1981 to 2019, by averaging ratings on president’s handling of the economy across various national polls. In the cross-section, stocks with high betas to changes in the PEAR index significantly under-perform those with low betas by 0.9% per month in the future, on a risk adjusted basis. The low-PEAR-beta premium persists up to one year, and is present in various sub-samples (based on industries, presidential cycles, transitions, and tenures) and even in other G7 countries. It is also robust to different risk adjustment models and controls for other related return predictors. Since the PEAR index is negatively correlated with measures of aggregate risk aversion, a simple risk model would predict the low PEAR-beta stocks to earn lower (not higher) expected returns. Contrary to the sentiment-induced overpricing, the premium does not come primarily from the short leg following high sentiment periods. Instead, the premium could be driven by a novel sentiment towards presidential alignment.
#611 – Idiosyncratic Volatility in China
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2000-2020
Indicative performance: 14.02%
Estimated volatility: 23.52%
Source paper:
Jansen, Maarten and Swinkels, Laurens and Zhou, Weili: Anomalies in the China A-share Market
https://ssrn.com/abstract=3810114
Abstract:
This paper sheds light on the similarities and differences with respect to the presence of anomalies in the China A-share market and other markets. To this end, we examine the existence of 32 anomalies in the China A-share market over the period 2000-2019. We find that value, risk, and trading anomalies carry over to China A-shares. Evidence for anomalies in the size, quality, and past return categories is substantially weaker, with the exception of a strong residual momentum and reversal effect. We document that most anomalies cannot be explained by industry composition, and are present among large, mid, and small capitalization stocks. We are the first to examine the existence of residual reversal, return seasonalities, and connected firm momentum for the China A-share market. We find strong out-of-sample evidence for the former two, but not the latter. Specific characteristics of the China A-share market, such as short-sale restrictions, the prevalence of state-owned enterprises, and the effect of stock market reforms, are examined in more detail. These features do not seem to be important drivers of our empirical findings.
#612 – Abnormal Turnover in China
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2000-2020
Indicative performance: 17.18%
Estimated volatility: 22.16%
Source paper:
Jansen, Maarten and Swinkels, Laurens and Zhou, Weili: Anomalies in the China A-share Market
https://ssrn.com/abstract=3810114
Abstract:
This paper sheds light on the similarities and differences with respect to the presence of anomalies in the China A-share market and other markets. To this end, we examine the existence of 32 anomalies in the China A-share market over the period 2000-2019. We find that value, risk, and trading anomalies carry over to China A-shares. Evidence for anomalies in the size, quality, and past return categories is substantially weaker, with the exception of a strong residual momentum and reversal effect. We document that most anomalies cannot be explained by industry composition, and are present among large, mid, and small capitalization stocks. We are the first to examine the existence of residual reversal, return seasonalities, and connected firm momentum for the China A-share market. We find strong out-of-sample evidence for the former two, but not the latter. Specific characteristics of the China A-share market, such as short-sale restrictions, the prevalence of state-owned enterprises, and the effect of stock market reforms, are examined in more detail. These features do not seem to be important drivers of our empirical findings.
#613 – Seasonal Difference in China
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2000-2020
Indicative performance: 11.35%
Estimated volatility: 17.06%
Source paper:
Jansen, Maarten and Swinkels, Laurens and Zhou, Weili: Anomalies in the China A-share Market
https://ssrn.com/abstract=3810114
Abstract:
This paper sheds light on the similarities and differences with respect to the presence of anomalies in the China A-share market and other markets. To this end, we examine the existence of 32 anomalies in the China A-share market over the period 2000-2019. We find that value, risk, and trading anomalies carry over to China A-shares. Evidence for anomalies in the size, quality, and past return categories is substantially weaker, with the exception of a strong residual momentum and reversal effect. We document that most anomalies cannot be explained by industry composition, and are present among large, mid, and small capitalization stocks. We are the first to examine the existence of residual reversal, return seasonalities, and connected firm momentum for the China A-share market. We find strong out-of-sample evidence for the former two, but not the latter. Specific characteristics of the China A-share market, such as short-sale restrictions, the prevalence of state-owned enterprises, and the effect of stock market reforms, are examined in more detail. These features do not seem to be important drivers of our empirical findings.
#614 – Climate sentiment, carbon prices and Emission minus Clean Portfolio
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2010-2020
Indicative performance: 9.36%
Estimated volatility: 12.71%
Source paper:
Santi, Caterina: Investors’ Climate Sentiment and Financial Markets
https://ssrn.com/abstract=3697581
Abstract:
We propose a measure of investors’ climate sentiment by performing sentiment analysis on StockTwits posts on climate change and global warming. We find that investors’ climate sentiment generates a mispricing in the Emission-minus-Clean (EMC) portfolio (Choi et al., 2020), the portfolio that invests in emission stocks and goes short on clean stocks. Specifically, when investors share a positive attitude towards climate change, they tend to overvalue the negative externalities produced by emission stocks. Moreover, we show that carbon prices are a successful incentive to reduce CO2 emissions. Finally, a portfolio strategy that uses information on investors’ climate sentiment and carbon prices generates a return of 9.77% annually.
New research papers related to existing strategies:
#581 – Equity Index and Risk-free Asset Allocation Using Neural Networks
#496 – Using Machine Learning to Pick the Right Combination of Risky and Risk-Free Asset
Ohana, Jean-Jacques and Ohana, Steve and Benhamou, Eric and Saltiel, David and Guez, Beatrice: Explainable AI Models of Stock Crashes: A Machine-Learning Explanation of the Covid March 2020 Equity Meltdown
https://ssrn.com/abstract=3809308
Abstract:
We consider a gradient boosting decision trees (GBDT) approach to predict large S&P 500 price drops from a set of 150 technical, fundamental and macroeconomic features. We report an improved accuracy of GBDT over other machine learning (ML) methods on the S&P 500 futures prices. We show that retaining fewer and carefully selected features provides improvements across all ML approaches. Shapley values have recently been introduced from game theory to the field of ML. They allow for a robust identification of the most important variables predicting stock market crises, and of a local explanation of the crisis probability at each date, through a consistent features attribution. We apply this methodology to analyze in detail the March 2020 financial meltdown, for which the model offered a timely out of sample prediction. This analysis unveils in particular the contrarian predictive role of the tech equity sector before and after the crash.
#461 – ESG Factor Momentum Strategy
Latino, Carmelo and Pelizzon, Loriana and Rzeźnik, Aleksandra, The Power of ESG Ratings on Stock Markets
https://ssrn.com/abstract=3801703
Abstract:
This paper studies the impact of environmental, social, and governance (ESG) ratings on investors’ preferences and stock prices. We exploit a change in ESG rating methodology that non-linearly shifted ESG ratings for firms as a natural experiment. We show that the ‘pseudo’-changes in the ESG ratings induced by the change in methodology are unrelated to potential fundamental changes in firm’s sustainability. Yet, we find that an exogenous change in a stock’s ESG rating exerts a transitory price pressure and alters the composition of stock ownership. Individual investors are especially sensitive to the ‘pseudo’-changes in the ESG ratings. They (dis)invest in stocks that they misconceive as ESG (down-) upgraded. Short sellers act as arbitrageurs and take the other side of retail investors’ trades. Overall, we find that a one standard deviation quasi-increase in the ESG ratings translates into 1pp drop in stock monthly abnormal return.
#27– Market Timing with Aggregate and Idiosyncratic Stock Volatilities
Gempesaw, David and Kassa, Haim and Zykaj, Blerina Bela: Does Idiosyncratic Volatility Proxy for a Missing Risk Factor? Evidence Using Portfolios as Test Assets
https://ssrn.com/abstract=3770317
Abstract:
One of the main explanations for the idiosyncratic volatility (IVOL) puzzle (i.e., the negative relation between lagged IVOL and returns) is a missing risk factor. We show analytically that if IVOL proxies for a missing risk factor, then the negative relation between IVOL and returns should persist at the portfolio level. Empirically, we find that the IVOL puzzle disappears when we use well-diversified portfolios as test assets. The IVOL puzzle also weakens after controlling for additional risk factors. Overall, our results suggest that both diversifiable (i.e., true idiosyncratic risk) and non-diversifiable risk play a role in explaining the IVOL puzzle.
#5 – FX Carry Trade
Kohlscheen, Emanuel and Avalos, Fernando Hugo and Schrimpf, Andreas: When the Walk is Not Random: Commodity Prices and Exchange Rates
https://ssrn.com/abstract=3782312
Abstract:
We show that there is a distinct commodity-related driver of exchange rate movements, even at fairly high frequencies. Commodity prices predict exchange rate movements of eleven commodity-exporting countries in an in-sample panel setting for horizons up to two months. We also find evidence of systematic (pseudo) out-of-sample predictability, overturning the results of Meese and Rogoff (1983): information embedded in our country-specific commodity price indexes clearly helps to improve upon the predictive accuracy of the random walk in the majority of countries. We further show that the link between commodity prices and exchange rates is not driven by changes in global risk appetite or carry.
#33 – Post-Earnings Announcement Effect
#94 – Trading on Earnings Announcements
#80 – Earnings Announcement Premium
Campbell, John L. and Zheng, Xin and Zhou, Dexin: Number of Numbers: Does Quantitative Disclosure Reduce Uncertainty in Quarterly Earnings Conference Calls?
https://ssrn.com/abstract=3775905
Abstract:
Theoretical research argues that numbers convey more precise information than words. Based on this work, we hypothesize that when managers provide disclosure with a greater proportion of quantitative information in an earnings conference call, investor uncertainty around the call will be lower and, thus, short-window returns around the call will be higher. We offer three main findings. First, we find a positive association between the extent of hard information (i.e., numerical disclosure) in earnings conference calls and short-window stock returns around the call. This result suggests that investor uncertainty is lower when managers provide greater numerical disclosure. Second, we find that this positive association is larger when firms are smaller and have larger stock volatility or analyst forecast dispersion. These results suggest that the effect of numerical disclosure in reducing investor uncertainty is greater when the firm’s information environment is otherwise more uncertain. Finally, we find that this positive association is larger when firms issue a negative earnings surprise. This result suggests that the effect of numerical disclosure in reducing investor uncertainty is greater when the uncertainty of a firm’s performance is greater. Overall, our results suggest that investors react to the extent of hard information (i.e., numerical disclosure) in earnings conference calls.
#436 – A Multi Strategy Approach to Trading Foreign Exchange Futures
Nucera, Federico and Sarno, Lucio and Zinna, Gabriele, Currency Risk Premia Redux
https://ssrn.com/abstract=3796290
Abstract:
We study a large currency cross section using recently developed asset pricing methods. First, we show that the implied pricing kernel includes three latent factors: a strong U.S. Dollar level factor, and two weak, high Sharpe ratio Carry and Momentum slope factors. The evidence for an additional Value factor is scant. Second, based on this pricing kernel, we obtain robust estimates of the risk premia of more than 100 non-tradable risk factors. Some of these factors — mostly relating to volatility, uncertainty and liquidity conditions in currency and other markets — are priced, disclosing a clear nexus across asset classes.
#506 – Volatility Risk Premium in Commodities
Jacobs, Kris and Li, Bingxin, Option Returns, Risk Premiums, and Demand Pressure in Energy Markets
https://ssrn.com/abstract=3773127
Abstract:
We study energy futures option returns for crude oil, natural gas, heating oil, and gasoline. Average call and put returns are negative at short maturities, more so for OTM options, and increase with maturity. Put returns are less negative than call returns, but this is not the case for delta-hedged returns, indicating that the aggregate risk of the underlying energy futures is priced in the raw option returns. Moneyness patterns in raw and delta-hedged returns are similar to patterns for index option returns. Significant differences between the four commodities remain after removing the effect of the underlying futures returns, with natural gas as the main outlier. Variance risk premiums are negative and explain some maturity patterns in returns, but they cannot account for return differences across markets. Energy producers are net short the underlying through their option positions, and speculators net long. The larger the net long position of the speculators, the lower the returns on call options, which suggests that demand from speculators may affect option returns in energy markets.
And two interesting free blog posts have been published during last 2 weeks:
An Analysis of Volatility Clustering of Equity Factor Strategies
Volatility clustering is a well-known effect in equity markets. In simple meaning, volatility clustering refers to a tendency of large changes in asset prices to follow large changes and small changes in asset prices to follow small changes. This interesting effect can be sometimes uncovered as one of the reasons for the functionality of some selected trading strategies. For example, low-volatility months in stock indexes (like the S&P 500 Index) are usually also months with higher performance. As volatility tends to cluster, a low volatility month in the present can signal a low volatility month with a better performance also in the future.
Based on this, we will be testing two hypotheses: (1) firstly, if there is a volatility clustering anomaly present in equity factor strategies; (2) secondly, if there is any performance pattern related to volatility.
Crowding in Commodity Factor Strategies
Nowadays, factor strategies are widely spread and used by practitioners, but this factor boom has given rise to some concerns. A key question is whether these strategies stay profitable once published and if they are not arbitraged away. Some strand of the literature suggests that there is a performance decay. A different view on performance decay is presented in the novel research of Kang et al. (2021), which indicates that the performance might be time-varying. Using the commodity market and premier anomalies such as momentum, basis, and value, the authors suggest a crowding in the factor strategies that predicts future performance. Crowded factors tend to underperform in future, and there is a significantly negative impact on the expected return. Moreover, the most substantial returns are connected with the least crowding activity. Therefore, the results are especially important for active factor traders.
Plus, the following six trading strategies have been backtested in QuantConnect in the previous two weeks:
#85 – Momentum in Mutual Fund Returns
#252 – Closed-End Fund Mean Reversion Trading
#264 – Dividend Risk Premium Strategy
#289 – Google Search Strategy Based on Limited Investor Attention
#606 – Climate Change Exposure and the Cross Section of Stock Returns
#609 – Intraday Reversal in US
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