Quantpedia Premium Update – 29th October 2021

New strategies:

#679 –Carbon Emmision Intensity in Stocks

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2010-2020
Indicative performance: 9.1%
Estimated volatility: not stated

Source paper:

Joshua Kazdin, Katharina Schwaiger, Viktoria-Sophie Wendt, Andrew Ang, Climate Alpha with Predictors also Improving Firm Efficiency
https://ssrn.com/abstract=3889640
Abstract:
Characteristics of companies associated with climate change predict excess equity returns. We show that firms with lower carbon emission intensities—with carbon emissions being a key component of the Paris Accord—have high excess returns. We present evidence that firms with lower carbon emissions have higher productivity, and that the lower carbon intensities may reflect greater firm efficiencies. A portfolio of firms with a higher proportion of LEED certified buildings also exhibits high excess returns. Such companies also contemporaneously exhibit higher return on assets. Portfolios constructed with the carbon emission intensities and the LEED certified buildings signals are only weakly correlated to a traditional quality factor. We discuss how climate change themed measures of firm efficiency may drive value for sustainably focused investors.

#680 – Emotion Beta and US Equities

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1995-2018
Indicative performance: 6.68%
Estimated volatility: 13.06%

Source paper:

Hasan, Shehub and Kumar, Alok and Taffler, Richard, Anxiety, Excitement, and Asset Prices
https://ssrn.com/abstract=3902654
Abstract:
This study examines the impact of integral emotions on portfolio decisions and asset prices. Using a new dictionary of anxiety- and excitement-related keywords, we measure the emotional state of the market and compute firm-level sensitivity to changes in market-level emotions (i.e., emotion beta). We find that stocks with high emotion betas outperform low emotion beta firms and this performance differential is corrected in about four months. During the 1990-2018 sample period, a Long-Short investment strategy with high-emotion beta stocks in the Long portfolio and low-emotion beta stocks in the Short generates an alpha of 4.92%. This evidence of emotion-based predictability is distinct from the known pricing effects of mood, sentiment, economic and policy uncertainty, and tone. Collectively, our findings show that emotional connections between investors and firms are priced.

#681 – Predictable Price Pressure Caused by Option Makers and Leveraged ETFs

Period of rebalancing: Intraday
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 2012-2019
Indicative performance: 6.21%
Estimated volatility: 1.98%

Source paper:

Barbon, Andrea and Beckmeyer, Heiner and Buraschi, Andrea and Moerke, Mathis: The Role of Leveraged ETFs and Option Market Imbalances on End-of-Day Price Dynamics
https://ssrn.com/abstract=3925725
Abstract:
Leveraged ETFs and market makers who are active in option markets must adjust imbalances arising from market movements. Establishing delta-neutrality may cause either return momentum or reversal depending on the sign and size of the imbalance vis-a-vis market prevailing liquidity. We ﬁnd that a large and negative (positive) aggregated gamma imbalance, relative to the average dollar volume, gives rise to an economically and statistically signiﬁcant end-of-day momentum (reversal). We compare this channel to the rebalancing of leveraged ETFs and ﬁnd that the eﬀect generated by leveraged ETFs is economically larger. Consistent with the notion of temporary price pressure, the documented eﬀects quickly revert at the next day’s open. Information-based explanations are unlikely to cause the results, suggesting a non-informational channel through which leveraged ETFs and option markets aﬀect underlying stocks towards the market close.

#682 – Undervalued Stocks in China

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2000-2021
Indicative performance: 11.48%
Estimated volatility: 20.94%

Source paper:

Lian, Xiangbin and Liu, Yangyi and Shi, Chuan, A Composite Four-Factor Model in China
https://ssrn.com/abstract=3928587
Abstract:
We investigate investors’ overreaction and underreaction and their implications to asset pricing in China stock market. The study first picks anomaly variables representing investors’ overreaction and underreaction and then measures these two effects quantitatively. Both of them deliver significant excess returns, both statistically and economically, in China stock market. We then equip these two effects with the market and the size factor to construct a composite four-factor model and study how they price other assets. Extensive empirical analysis shows that this new model is suitable for China stock market. The maximum annual Sharpe ratio spanned by the four factors is 2.02, which is one time higher than those spanned by similar models such as Stambaugh and Yuan (2017) and Daniel, Hirshleifer and Sun (2020). In addition, using 149 anomaly candidates as test assets, the composite four-factor model exhibit good pricing capability, as there is only one test asset whose abnormal return given the model exceeds the 3.0 t-statistic threshold.

#683 – Overvalued Stocks in China

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2000-2021
Indicative performance: 15.94%
Estimated volatility: 21.3%

Source paper:

New research papers related to existing strategies:

#212 – Scheduled Economic Announcements Effect in Stocks
#470 – Macroeconomic Announcement Beta Strategy

Chen, Jingjing: Asset Pricing Around Anticipated Announcements: A (Mood) Swing of Three Days
https://ssrn.com/abstract=3690077
Abstract:
Prior literature documents significantly positive market excess returns implied from CAPM (i.e., the coefficient of market beta) and significantly positive realized market excess returns on scheduled macroeconomic announcement days. In this study, I find that market excess return swings from negative on the day before, to positive on the day of, and negative again on the day after announcements. The average market excess returns, both implied and realized, over the three-day announcement window are insignificant. I show that market excess returns around macroeconomic announcements are primarily driven by a mood swing, i.e., changes of investor appetite toward risk. Specifically, investors become highly risk-averse prior to announcement but are much less so on the announcement day. I also show that uncertainty resolution at best partially accounts for the swing of market excess returns.

#67 – Industry Momentum – Riding Industry Bubbles
#3 – Sector Momentum – Rotational System

Arnott, Robert D. and Clements, Mark and Kalesnik, Vitali and Linnainmaa, Juhani T.: Factor Momentum
https://ssrn.com/abstract=3116974
Abstract:
Past industry returns predict future industry returns, and this predictability is at its strongest at the one-month horizon. We show that the cross section of factor returns shares this property and that industry momentum stems from factor momentum. Factor momentum transmits into the cross section of industry returns through variation in industries’ factor loadings. We show that momentum in “systematic industries,” mimicking portfolios built from factors, subsumes industry momentum as does momentum in industry-neutral factors. Industry momentum is therefore a byproduct of factor momentum, not vice versa. Momentum concentrates in its entirety in the first few highest-eigenvalue factors.

#681 – Predictable Price Pressure Caused by Option Makers and Leveraged ETFs
#538 – Oil Intraday Momentum
#564 – Intraday Momentum in Fixed Income
#442 – Intraday Momentum in Crude Oil ETF
#251 – Intraday Momentum in Equities

Baltussen, Guido and Da, Zhi and Lammers, Sten and Martens, Martin: Hedging Demand and Market Intraday Momentum
https://ssrn.com/abstract=3760365
Abstract:
Hedging short gamma exposure requires trading in the direction of price movements,thereby creating price momentum. Using intraday returns on over 60 futures on equities,bonds, commodities, and currencies between 1974 and 2020, we document strong “marketintraday momentum” everywhere. The return during the last 30 minutes before the marketclose is positively predicted by the return during the rest of the day (from previous marketclose to the last 30 minutes). The predictive power is economically and statistically highlysignificant, and reverts over the next days. We provide novel evidence that links marketintraday momentum to the gamma hedging demand from market participants such as marketmakers of options and leveraged ETFs.

#20 – Volatility Risk Premium Effect

Guo, Ivan and Loeper, Gregoire: The Volatility Risk Premium: An Empirical Study on the S&P 500 Index
https://ssrn.com/abstract=3739933
Abstract:
We perform an empirical analysis of trading strategies based on the systematic selling of delta hedged options, aiming at capturing the so-called volatility risk premium. We compare the performance across different strikes and maturities, and perform a breakdown of the drivers of performance. We also examine how such strategies can be combined to extract other premia related to the profile of the volatility surface, e.g. the skew and the term structure. In this first paper we focus on the S&P 500 index over the period 2010–2018.

#52 – Asset Growth Effect

He, Miao and Kapadia, Nishad and Tice, Sheri: Can the Representativeness Heuristic Explain the Asset Growth Anomaly?
https://ssrn.com/abstract=3739972
Abstract:
We find that the low average returns to firms with high asset growth are consistent with two key implications of models of diagnostic investor expectations (e.g., Bordalo, Gennaioli, La Porta, and Shleifer, 2019) that formalize the representativeness heuristic of Kahneman and Tversky (1972). These models predict that investors overestimate the subjective probability of states that are more representative of a firm’s type and also neglect risk after a string of good news. We construct a measure of how representative the stereotype the ‘next Google’ is of high asset growth in the recent past. We show that this measure predicts the returns of CMA, the asset growth factor in the five factor model of Fama and French (2015). Returns to CMA are 17.5% over the 3 years after months with high representativeness and only 5.4% following low representativeness months. In the cross-section, we find evidence consistent with investors neglecting the risk of high asset growth firms. The asset growth effect is not present in portfolios with low distress risk and the interaction between distress and asset growth, rather than asset growth by itself, predicts low returns in Fama and MacBeth (1973) regressions and portfolio sorts. We also find that analysts and markets do not appreciate the importance of the interaction between asset growth and distress and are sluggish in responding to news for the ‘interaction’ portfolio. Finally, we show that our measure of representativeness predicts the returns of the interaction portfolio.

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

How to Faster Enhance Strategic Asset Allocation with Tactical Models

Each change in a strategic asset allocation of a professionally managed portfolio comes only after meticulous analysis. Firstly, we must understand the current status of the portfolio – how it behaved in the past, the strong and weak points of current allocation, and the main risk factor exposures. Then we can think about the future. We can decide how active we want to be, how large a risk budget we have at our disposal, and what asset classes we want to continue to focus on in our tactical models. Afterward comes the time for creativity – we can analyze opportunities and look for ideas for new models that complement what we already have. That’s time for Quantpedia Pro, and we will use this short case study and walk you through the few features that simplify the process of finding new ideas for trading strategies that fit your individual case.

On the Creativity, Collecting and Design of Systematic Trading Strategies

Quantpedia started ten years ago in October 2011, and it was built out of necessity. The following article is a short recollection of the journey it has traveled. It’s a thought on the connection between creativity and our need to collect, analyze and categorize abstract ideas and how does it all help in the design of systematic trading strategies.

Six Examples of Trading Strategies That Use Alternative Data

Why has been alternative data recently so much popular? The answer most of the time hovers around the notion of “seeking the new alpha sources”. First, the hunt for alpha is huge due to the low yield world and is getting only bigger. Secondly, some of the more popular strategies can become crowded, leading to diminishing alpha or the risk of a sudden reversal in performance (all of us remember this year’s growth vs. value switch).

We at Quantpedia don’t create nor manage any alternative data sets. But we are aware of this trend, and we strive hard to find new alpha opportunities which may lie in these new data sources. From the database of almost 700 quantitative investment strategies Quantpedia has gathered, almost 100 strategies are based on alternative datasets. Today, we picked just 6 of them to give you a little taste of how these alternative strategies may look like, what kind of datasets they utilize and how they perform.

An Introduction to Value at Risk Methodologies

Understanding the risks of any quantitative trading strategy is one of the pillars of successful portfolio management. Of course, we can hope for good future performance, but to survive market whipsaws, we must have tools for sound risk management. The “Value at Risk” measure is such a standard tool used to assess the riskiness of trading and investment strategies over time. We plan to unveil our new “Value at Risk” report for Quantpedia Pro clients next week, and this article is our introduction to different methodologies that can be used for VaR calculation.