Quantpedia Premium Update – 21st of July

New strategies:

#892 – Shorting Companies With the Most Overpaid CEOs

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 2015 – 2018
Indicative performance: 5.41%
Estimated volatility: 8.55%

Source paper:

Spencer Barnes: Shareholder implications of anti-ESG news: Evidence from the 100 most overpaid CEOs
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4474327
Abstract:
I examine how negative ESG news distorts shareholder outcomes. In 2015 As You Sow began the yearly release of “The 100 Most Overpaid CEOs” list, leading to negative ESG news for firms that make the list. Overpaying CEOs is a governance concern as it signals a failure in proper controls and a social concern as it creates a negative perception of the company concerning shareholder and employee treatment. An equal-weighted (value-weighted) portfolio of the listed firms earned an annual six-factor alpha of -3.60% (-2.64%). The underperformance is especially acute for firms that pay their CEOs above-average wages.

#893 – Intangibles-Adjusted Profitability Factor

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1976-2021
Indicative performance: 4.16%
Estimated volatility: 8.47%

Source paper:

Jagannathan, Ravi and Korajczyk, Robert A. and Wang, Kai: An Intangibles-Adjusted Profitability Factor
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4394069
Abstract:
We treat expenditures that create intangible assets as investments and instead of expensing them, we add them back to earnings when measuring the return on equity of firms while constructing the profitability factor in the Fama and French (2015) five factor model. The profitability factor we construct has significant alpha relative to many extant multi-factor asset-pricing models, including the standard Fama-French five factor model. When the profitability factor in the Fama and French (2015) five factor model is replaced with our intangibles adjusted profitability factor, the model performs better in explaining the cross section of stock returns, and several anomalies documented in the literature. Portfolios that exploit price momentum, earnings momentum, and operating leverage no longer have significant alphas. The improvement is consistent with the dividend discount model for equity valuation. Adjusted earnings constructed by treating expenditures that create intangible assets as investments help forecast the cross section of future cash dividends and operating cash flows on stocks better, especially at longer horizons. Adopting our adjustment when constructing the monthly rebalanced profitability factor in the Hou et al. (2015) four factor model improves its performance as well. Our intangible adjusted profitability factor has smaller left tail risk and co-tail risk with the market when compared to the standard profitability factor.

#894 – Term Spread and Term Premium Predict US Government Bonds Returns

Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: CFDs, ETFs, forwards, funds, futures, swaps
Complexity: Complex strategy
Backtest period: 1968-2021
Indicative performance: 8.08%
Estimated volatility: 7.45%

Source paper:

van Schaik, Luke and Flint, Emlyn and Chikurunhe, Florence: Trading the Term Premium
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4324696
Abstract:
The proliferation of factor investing strategies in recent years has highlighted the idea that a portfolio can harvest improved risk-adjusted returns through timed exposure to risk factors during times of elevated risk premia. While there is a large body of research on such risk factors and risk premia in equity markets, there has been relatively little research on the topic in fixed income markets.

One such fixed income risk factor that has begun to receive interest from practitioners and academics is the term premium, the risk premium associated with duration risk in bonds. Adrian et al. (ACM) (2013) introduced a sophisticated affine term structure model which is able to efficiently estimate the term premium using linear regressions. While the model appears to align with economic theory, little work has been done on investigating practical applications of the term premium in timing exposure to duration risk in a bond portfolio.

This report investigates the practical applicability of the ACM model in the South African and United States sovereign bond markets, finding that signals generated from the model are able to capture regimes of increased risk-adjusted returns. Using these signals in systematic strategies also generates promising results.

#895 – Listen Closely: Using Vocal Cues to Predict Future Earnings

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 2015-2020
Indicative performance: 17.03%
Estimated volatility: –

Source paper:

Ewertz, J. and Knickrehm, Ch. And Nienhaus, M, and Reichmann, D.: Listen Closely: Using Vocal Cues to Predict Future Earnings
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4307178
Abstract:
In this study, we aim to advance the prediction of firm earnings – an important task for many business applications. While existing earnings prediction models only rely on numerical financial data, we hypothesize and find that vocal cues from manager speech yield substantial predictive power: the area under the receiver operating characteristics curve ranges from 61.20% to 63.44%, significantly higher compared to models based on detailed financial data and textual inputs. We further analyze the models’ economic value to investment practitioners. We find that investors can use the models’ earnings forecasts to implement trading strategies that beat the market by 8.8% on average per year. Moreover, financial analysts can use vocal cues to improve their earnings forecast accuracy by more than 40%. Collectively, our results imply that managers’ vocal cues are important information signals for future earnings that investment practitioners currently overlook.

#896 – Expected Change in Liquidity Forecasts Stocks’ Returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1963-2022
Indicative performance: 9.64%
Estimated volatility: 14.20%

Source paper:

Schmitt, C. and Schuster, P.: Liquidity Forecasts and Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4389864
Abstract:
We suggest a procedure to predict individual stock liquidity and study the relation between stock liquidity forecasts and average stock returns. Our forecast model reduces the root-mean-squared error by 12% for the Amihud (2002) liquidity measure compared to realized stock liquidity in the previous month. Our liquidity forecasts capture economically large changes in liquidity and improve in accuracy over time. Whereas liquidity measures provide mixed empirical results in asset pricing tests, we find a strong relation between expected changes in liquidity and average stock returns. Sorting portfolios according to the expected change in illiquidity in the next period leads to a monthly excess return of the long-short portfolio of 1% for equally-weighted and 0.8% for value-weighted portfolios. Our results are robust to controlling for various predictors of stock returns in Fama and MacBeth (1973) cross-sectional regressions and to using the effective spread as an alternative liquidity measure. The large return premium of expected illiquidity changes can be explained with portfolio re-allocations of investors with heterogeneous investment horizons. Consistent with the clientele effect of Amihud and Mendelson (1986), mutual funds with short investment horizons sell (buy) stocks for which liquidity is expected to deteriorate (improve). Funds with longer horizons, the natural counterpart, do not react on expectations but base their portfolio decisions on realized illiquidity, leading to a temporal mismatch between supply and demand.

#897 – Using Wavelet Transformation to Predict S&P 500 Performance

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: CFDs, ETFs, funds, futures
Complexity: Very complex strategy
Backtest period: 1947-2019
Indicative performance: 3.82%
Estimated volatility: 18.17%

Source paper:

Kang, J. and Jiang, F. and Dai, Z.: Stock Return Predictability in Frequency Domain
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4266050
Abstract:
This paper investigates the role of time-frequency information in dimension reduction prediction of stock returns. Using the long-term wavelet component of monthly S&P500 excess returns as supervision, we employ a machine learning method to extract the common predictive factor from prevalent macroeconomic variables, and construct a new macroeconomic index aligned with stock return prediction. The macroeconomic index exhibits significant predictive power, both in- and out-of-sample, at the aggregate and portfolio levels. It outperforms all individual macroeconomic predictors and factors based on higher frequency information of realized returns. Our findings demonstrate substantial economic value of the new index in asset allocation. Moreover, we also observe complementary relation between macroeconomic index and investor sentiment. The predictive power is most pronounced during high economic uncertainty periods when investors are likely to underreact to fundamental signals and stems from cash flow predictability channel.

New research papers related to existing strategies:

#119 – Google Search Effect

Li, Jun and Liu, Xianwei and Ye, Qiang and Zhao, Feng and Zhao, Xiaofei: It Depends on When You Search
https://ssrn.com/abstract=4370525
Abstract:
Existing studies have found that online search is a revealed measure for investor attention and a useful predictor of stock returns. We study the heterogeneity in retail investor attention by comparing search conducted on weekdays vs. weekends and investigate the price pressure channel and information processing channel for stock return predictability. According to the information processing channel, weekends afford retail investors more time for the intensive cognitive analysis necessary to make better predictions. Alternatively, weekend search might better capture the price pressure from retail investors’ trading activities. We provide empirical results that support the information processing channel. We first show that weekend search, rather than weekday search, predicts large-cap stock returns in both the cross-section and time series. Additionally, our findings on retail trading activity contradict the price pressure channel in that weekday search, rather than weekend search, leads to a subsequent retail order imbalance. Overall, our study contributes to the literature on the predictive power of online search on stock returns, which has mainly focused on the price pressure channel, yielding significant results for small-cap stocks only.

#734 – Empirical Asset Pricing via Machine Learning

Cakici, Nusret and Fieberg, Christian and Metko, Daniel and Zaremba, Adam: Machine Learning Goes Global: Cross-Sectional Return Predictability in International Stock Markets
https://ssrn.com/abstract=4141663
Abstract:
We examine return predictability with machine learning in 46 stock markets around the world. We calculate 148 firm characteristics and use them to feed a repertoire of different models. The algorithms extract predictability mainly from simple yet popular factor types—such as momentum, reversal, value, and size. All individual models generate substantial economic gains; however, combining them proves particularly effective. Despite the overall robustness, the machine learning performance depends heavily on firm size and availability of recent information. Furthermore, it varies internationally along two critical dimensions: the number of listed firms in the market and the average idiosyncratic risk limiting arbitrage.

#606 – Climate Change Exposure and the Cross Section of Stock Returns

Rakyan, Mohit and Mohamed, Rehan: Does Climate VAR Add Financial Value: Some Empirical Evidence
https://ssrn.com/abstract=4163947
Abstract:
Increasing carbon-emissions and changes in the Earth’s surface temperature have brought about a large incidence of climate-related disasters over the past decade, with even worse outcomes expected in the near future. In this study, we attempt to de-risk a Russell 1000 index portfolio by utilizing physical Climate VaR (CVaR) metrics and excluding securities most exposed to such events. The exclusions are carried out based on sectors to avoid any biases from sectoral deviations. We find that the so-created CVaR portfolio often outperforms the benchmark with excluded stocks exhibiting much lower returns, a result which is further bolstered during climate disasters.

#378 – Time-Series Momentum Factor in Cryptocurrencies
#594 – Size in Cryptocurrencies

Ammann, Manuel and Burdorf, Tom and Liebi, Luca and Stöckl, Sebastian: Survivorship and Delisting Bias in Cryptocurrency Markets
https://ssrn.com/abstract=4287573
Abstract:
This study quantifies performance measure distortions in a cryptocurrency sample truncated by survivorship and delisting bias. Previous research shows that the attrition rate in cryptocurrency markets is high. However, the survivorship and delisting bias in cryptocurrencies lacks empirical research. Using data for 3’904 cryptocurrencies during the 2014-2021 period, we estimate an annualized bias of 0.93% (62.19%) for value-weighted (equal-weighted) portfolios. After controlling for survivorship and delisting bias, we revisit the relationship between average returns, size, past performance, market β, liquidity, and downside risk. Our results confirm the size effect, but the premium is overestimated by 50% in a survival-conditioned sample. In contrast, we find no evidence of a positive relationship between average returns, one-week momentum, market β, and downside risk. Our results suggest that the survivorship and delisting bias are important biases that ought to be omitted.

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

Beta-Adjusting Factor Returns

Beta-adjusted returns equity factors are considerably more stable, indicating that factor construction methodologies may be improved beyond dollar and size neutrality. Low-beta effect at the level of factors confirms the existence of seasonal and momentum effects in the cross-section of factor returns. Altogether, these insights deepen the understanding of factor behavior and can aid the development of more robust factor-based investment strategies.