Quantpedia Premium Update – 5th January 2023

#953 – Stock Momentum Volatility Switching Strategy

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1960-2021
Indicative performance: 13.83%
Estimated volatility: 21.09%

Cuevas Rodriguez, Gabriel and Mokanov, Denis and Zhang, Danyu: Earnings Expectations and Asset Prices
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4572199
Abstract:
This paper documents the following facts about equity analysts’ earnings expectations: (1) consensus earnings expectations underreact to news unconditionally, (2) the degree of underreaction declines during high-volatility periods, and (3) the degree of underreaction experiences a sustained decline over our sample. To account for these findings, we develop a simple model featuring endogenous inattention. We show that our model is able to account for the unconditional profitability of momentum, momentum crashes, the attenuation of momentum over time, and the enhanced profitability of volatility-managed momentum. Finally, we propose a real-time trading strategy that mixes short-run and long-run momentum strategies during high volatility episodes and show that the resultant trading strategy generates economically sizable gains relative to conventional momentum strategies.

#954 – Directional Momentum

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1932-2022
Indicative performance: 9.64%
Estimated volatility: 17.85%

Del Viva, Luca and Sala, Carlo and Souza, André B.M.: Directional Information in Equity Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4575793
We document the existence of sign predictability in equity returns. An investment strategy that buys stocks deemed most likely to have positive returns and sells stocks with the lowest probability of positive returns generates about 1% monthly alpha and is not explained by established asset pricing models. The proposed strategy has higher Sharpe ratios and exhibits fewer crashes than the renowned momentum strategy. We show that profits from exploiting directional information are driven by shifts in retail investors’ expectations after periods of excessive pessimism or optimism, rather than compensation for risk. We provide a simple model to motivate our findings.

#955 – Machine Forecast Disagreement

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1976-2022
Indicative performance: 14.98%
Estimated volatility: 16.97%

Source paper:

Bali, Turan G. and Kelly, Bryan T. and Moerke, Mathis and Rahman, Jamil A.: Machine Forecast Disagreement
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4537501
Abstract:
We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure disagreement as dispersion in forecasts across investor-models. Our measure aligns with extant measures of disagreement (e.g., analyst forecast dispersion), but is a significantly stronger predictor of future returns. We document a large, significant, and highly robust negative cross-sectional relation between belief disagreement and future returns. A decile spread portfolio that is short stocks with high forecast disagreement and long stocks with low disagreement earns a value-weighted alpha of 15% per year. A range of analyses suggest the alpha is mispricing induced by short-sale costs and limits-to-arbitrage.

#956 – News-Linked Momentum in China

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 2002-2021
Indicative performance: 17.26%
Estimated volatility: 15.49%

Source paper:

Wang, Huaixin: News Links and Predictable Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4458612
Abstract:
Exploiting financial news stories data, we construct news-implied linkages and document a strong lead-lag effect of firms with shared news coverage in China’s stock market. The news-link momentum strategy generates a monthly return of 1.33% and a four-factor alpha (Liu et al., 2019) of 1.43%. While prior evidence on the attention dynamics among firms with joint news coverage is limited, we show that the momentum spillover of news-linked firms is largely driven by investor underreaction. The return predictability from news links is also robust to controlling for alternative economic linkages. The findings suggest that information diffuses sluggishly among news-connected firms, thereby providing new evidence on the implication of media coverage for pricing efficiency.

#957 – Sparse Macroeconomic Risks and the Cross-Section of Stock Returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1980-2021
Indicative performance: 14.57%
Estimated volatility: 30.72%

Source paper:

Zhu, Lin and Jiang, Fuwei and Tang, Guohao and Jin, Fujing: The Sword of Damocles: Sparse Macroeconomic Risks and the Cross-section of Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4445765
Abstract:
Our study presents novel evidence of the pricing effectiveness of macroeconomic risks at the firm level. Specifically, we employ a sparse PCA approach to aggregate macroeconomic variables from the FRED-MD database and obtain the first 8 components, namely, inflation, housing, spreads, production, employment, personal income, yields and credit; then construct the corresponding firm-level macroeconomic risk exposures as the sensitivity of the individual stock returns to the sparse economic components. Our research yields three main findings: (i) these macro betas cannot be fully captured by common firm fundamentals, indicating the unique information in macro betas; (ii) the betas of inflation, production, personal income, yields and credit are strong predictors for future stock returns which are beyond other micro risks; (iii) behavioral mispricing theory especially arbitrage frictions and investor sentiment can help explain the macro beta premium.

#958 – Green Revenues Predict Stock Returns

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2016-2020
Indicative performance: 4.6%
Estimated volatility: 1.9%

Source paper:

Institute for Monetary and Financial Research, Hong Kong: Do the Markets Value the Green Revenues?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4310271
Abstract:
This working paper is written by Alexander Bassen (Hamburg University), Hao Shu (Central South University) and Weiqiang Tan (Education University of Hong Kong). This study provides cross-market evidence of the impacts of green revenues (GRs) on stock returns. Using 9,367 firm-year observations across 23 different markets in the period from 2016 to 2020, we find that firms with high proportions of GR earn higher returns than those with low proportions of GR. We also examine the role of national culture in the risk–return characteristics of portfolios with high-GRs minus low-GRs, showing that positive abnormal returns can be earned in markets with national cultural values that are higher (lower) in harmony (mastery) and egalitarianism (hierarchy).

New research papers related to existing strategies:

#470 – Macroeconomic Announcement Beta Strategy
#471 – Macroeconomic Announcement Beta Reversal

Chen, Jingjing and Jiang, George: Investor Risk Appetite and High-Beta Stock Valuation Around Macroeconomic Announcements
https://ssrn.com/abstract=4623331
Abstract:
We document a dramatic swing of high-beta stock returns around pre-scheduled macroeconomic announcements – from being negative on the day before, to positive on the day of, and negative again on the day after the announcement. A feasible long-short strategy of betting against beta (BAB) and betting on beta (BOB) yields annualized 25.28% return over the three-day announcement window. Trading activities show that some investors actively trade high-beta stocks to adjust risk exposure, reducing risk exposure on days before and after announcements but increasing risk exposure on announcement days. Options trading shows corroborating evidence that investors are more risk-averse on days before and after announcements than on announcement days. We show that the shifts in investor risk appetite have a significant explanatory power of the swing of high-beta stock returns. Our study sheds new light on the dynamic effect and the underlying mechanism of macroeconomic announcements on asset prices.

#40 – High-Frequency Arbitrage with ETF Twins
#182 – Dual Listed Stock Arbitrage

Poutré, Cédric and Dionne, Georges and Yergeau, Gabriel: The Profitability of Lead-Lag Arbitrage at High-Frequency
https://ssrn.com/abstract=4223813
Abstract:
Any lead-lag effect in an asset pair implies the future returns on the lagging asset have the potential to be predicted from past and present prices of the leader, thus creating statistical arbitrage opportunities. We utilize robust lead-lag indicators to uncover the origin of price discovery and we propose an econometric model exploiting that effect with level 1 data of limit order books (LOB). We also develop a high-frequency trading strategy based on the model predictions to capture arbitrage opportunities. The framework is then evaluated on six months of DAX 30 cross-listed stocks’ LOB data obtained from three European exchanges in 2013: Xetra, Chi-X, and BATS. We show that a high-frequency trader can profit from lead-lag relationships because of predictability, even when trading costs, latency, and execution-related risks are considered.

#571 – Post Seasoned Equity Offering Returns in China

Luu, Quang and Trang, Vo Thien and Trinh, Nguyen Thi Thu: Market Timing of CEOS and Foreign Investors’ Reaction
https://ssrn.com/abstract=4057711
Abstract:
Excess return, cumulative abnormal return, market to book ratios and liquidity risk are applied as proxies for evaluating the chief executive officer’s (CEO) market timing. The result indicates that managers have total succeeded in timing the market for SEO events. Firms implement seasonal equity offering (SEO) issuance after experiencing a strong increase of stock price, and then underwent a significant reversal of stock price. Besides that, CEO will time the market when they realize the liquidity risk of firms drop to the point where institutional investors have low consideration about risks. Foreign investors reacted strongly when the information about the SEO was announced, specifically, they changed their trading behavior from being a net buying to being a net selling or reducing buying. This reaction is especially strong in companies with low market liquidity. And as a result, foreign investors react more quickly to the information of new stock issuance, their stock return will increase sharply after SEO.

Evans, Kevin P. and Leung, Woon Sau and Li, Junqiu and Mazouz, Khelifa: ETF Ownership and Seasoned Equity Offerings
https://ssrn.com/abstract=4311787
Abstract:
This paper investigates the impact of ETF ownership on seasoned equity offerings (SEOs). We find that increases to firms’ ETF ownership is positively related to their propensity to conduct an SEO. ETF ownership is also associated with less negative SEO announcement returns, smaller discounts, and better long-run stock returns. Our evidence is consistent with equity issuance following investor demand for stocks driven by greater participation in ETFs, suggesting a possible alternative source of market timing opportunity.

#18 – Liquidity Effect in Stocks

de la Cruz, Elena Marquez and Martinez-Canete, Ana Rosa and Nieto, Belen: Illiquidity Linkages Between Individual Stocks and Corporate Bonds
https://ssrn.com/abstract=4281427
Abstract:
This paper evaluates the cross comovements of illiquidity between stocks and corporate bonds issued by the same firm employing individual corporate bonds information from TRACE from July 2002 to December 2014. We analyze these relations in both a time series and a cross-sectional framework, employing different statistical approaches. Our results consistently confirm a positive linkage between the liquidity of the two assets, except for bonds in the AAA rating category. Therefore, flight to liquidity seems to arise only for very low-risk corporate bonds. Additionally, we find that the stock–bond liquidity relation strengthens with firm risk.

#636 – Machine Learning and Stock Anomalies in China

Maasoumi, Esfandiar Essie and Wang, Jianqiu and Wang, Zhuo and Wu, Ke: Identifying Factors via Automatic Debiased Machine Learning
https://ssrn.com/abstract=4223091
Abstract:
Identifying risk factors that have significant explanatory power for the cross-sectional asset returns is fundamental in asset pricing. We adopt a novel automatic debiased machine learning (ADML) method proposed by Chernozhukov, Newey, and Singh (2022) to robustly estimate partial pricing effect of a certain factor controlling for a large number of confounding factors under a nonlinear Stochastic Discount Factor (SDF) assumption. The ADML resolves biased estimation, non-robustness, and overfitting issues that are common to traditional machine learning approaches. We find that the most significant factors selected by the ADML outperform the Fama-French sparse factors and factors identified via the double-selection LASSO method under a linear factor model assumption. Out of a high-dimensional zoo of US stock market factors commonly tested in the finance literature, we identify approximately 30 to 50 factors having significant but declining pricing power in explaining the cross-section of stock returns. Our findings are robust to hyperparameter settings and choices of test assets and machine learning methods.

#647 – Equity Duration

Montagna, Dennis Marco and Bianchi, Luca: Equity Duration: Theoretical and Practical Analysis
https://ssrn.com/abstract=4233635
Abstract:
Duration is an important parameter used by investors to choose between different investment opportunities in financial economics. While the concept of duration is usually associated with fixed-income assets, its expansion to the equity assets is becoming more relevant in the recent period, due to extraordinary measures by central banks. The Quantitative Easing and other related programs are relevant to risk-free rates and, consequently, discount factors and expected returns. The article aims to provide a first complete overview of Equity Duration, calculating and comparing different types, investigating the changes in prices and equity values, and identifying whether there is a relationship between duration fluctuations and enterprise values. The models are applied to the dataset covering roughly 30 years of data until 2021. The analysed equity indexes belong to the US market; four of them refer to the general US market, while the others refer to the first-level and second-level sectors, with the price of S&P500 used as the benchmark for beta computation in the CAPM discount factor formula. The analysis uses several methods to calculate the duration between 21/12/2007 and 01/10/2021, each one aiming to find the most accurate and consistent. The first method is the benchmark for the subsequent computations and uses the dividend discount model; the second method uses the discounted cash flow model over four years; the last one implements an H-Model to the discounted cash flow over nine years. Finally, we analyse the relationship between debt and duration fluctuations: data show a close relationship which could help investors’ decisions in asset allocation.

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

Top Ten Blog Posts on Quantpedia in 2023

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 over 75 short analyses of academic papers and our own research articles on this blog in 2023. We want to use this opportunity to summarize 10 of them, which were the most popular (based on the Google Analytics tool). The top 10 is really diverse; maybe you will be able to find something you have not read yet …

Why Do US Stocks Outperform EM and EAFE Regions?

Investing in emerging markets (EM) or developed markets (DM) outside of the United States tends to follow cyclical trends. At times, it becomes popular and crowded to focus solely on U.S. stocks, while in other periods, the trend shifts to favor everything except U.S. equities. This inclination often relies on historical and past performance data, although it doesn’t guarantee identical outcomes in the future. But what drives these periods of popularity? When do U.S. markets outperform Emerging Markets or other Developed Markets? When do large-cap stocks outperform small-cap stocks, and when do growth stocks outperform value stocks? Are those ebbs and flows in the performance of major thematic investments somehow interlinked, and can we uncover some insights into why this occurs? Those are the questions we will try to answer in the following analysis.