New strategies:
#557 – Profitability Factor in Chinese Equities
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2002-2014
Indicative performance: 39.45%
Estimated volatility: 18.24%
Source paper:
F. Jiang, X. Qi, G. Tang: Q-Theory, Mispricing, and Profitability Premium: Evidence from China
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2635345
Abstract:
This paper investigates whether rational risk or behavioral mispricing helps to explain the profitability premium in the Chinese stock market setting. We find that firms with high profitability generate substantially higher future stock returns than those with low profitability in China. This positive effect of profitability on returns is robust after controlling for alternative firm characteristics and risks, and is stronger among firms with large capitalization and high growth. We further show that profitability premium is stronger among firms with low investment frictions, consistent with the implications of investment-based q-theoretical asset pricing models. However, it is not stronger among firms with high limits to arbitrage, not consistent with the behavioral mispricing based explanations.
#558 – Quality Strategy in the Indian Market
Period of rebalancing: Quarterly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2010-2020
Indicative performance: 13.23%
Estimated volatility: 12.28%
Source paper:
Rajan Raju: Implementing a Systematic Long-only Quality Strategy in the Indian Market
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3490999
Abstract:
We believe investors should be willing to pay a higher price for higher quality companies. We build a composite quality score using ‘off -the-shelf’ criteria and publicly available financial data and show that a quarterly-rebalanced, long-only portfolio of 12 stocks selected using our score in India significantly outperforms the NIFTY 100 Index – both in terms of absolute returns (by 5.50% pa) and risk adjusted returns – while having an acceptable annual turnover (a modal turnover of 41.67%). We show that our quality score predicts the persistence of quality for up to 3 years and there is a weak relationship between the price multiple and the quality score. We show that ESG criteria can be incorporated into a quality measure. Furthermore, we demonstrate that quality needs to be reviewed regularly – so a buy-and-hold approach may not be an ideal strategy for an investor. In the absence of cheap ETFs to get systematic exposure to quality, the systematic long-only strategy using ‘off -the-shelf’ criteria provides a practical, executable systematic investment methodology that exposes an investor to quality in the Indian market.
#559 – Jump Risk in Commodities
Period of rebalancing: Monthly
Markets traded: commodities
Instruments used for trading: futures
Complexity: Very complex strategy
Backtest period: 1959-2015
Indicative performance: 4.63%
Estimated volatility: 14.89%
Source paper:
Hollstein, Fabian and Prokopczuk, Marcel and Tharann, Björn: Anomalies in Commodity Futures Markets: Risk or Mispricing?
https://ssrn.com/abstract=3567629
Abstract:
In recent years, commodity markets have become increasingly popular among financial investors. In contrast to traditional markets such as equities or bonds for which many studies have identified various profitable investment strategies, less is known for commodity markets. In this paper, we therefore examine prominent (anomaly) variables in commodity futures markets. We identify sizable premia for jump risk, momentum, and volatility-of-volatility. Other prominent variables, such as downside beta, idiosyncratic volatility, and MAX, are not priced in commodity futures markets. Based on the specific features of commodity futures we draw implications as to whether return premia are driven by behavioral distortions.
#560 – The Cross-Section of Non-Professional Analyst Skill
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2005-2017
Indicative performance: 12.42%
Estimated volatility: 18.69%
Source paper:
Farrell, Michael et al.: The Cross-Section of Non-Professional Analyst Skill Available
https://ssrn.com/abstract=3682490
Abstract:
We examine the cross-section of skill among non-professional analysts (NPAs) on Seeking Alpha, a prominent crowd-sourced investment research platform. We estimate that 60% of NPAs are skilled, and we document substantial dispersion in skill. Even after accounting for bid-ask spreads and allowing for a three-day investment delay, following NPAs in the top quintile of past skill earns annualized abnormal returns of 10%. In contrast, an unconditional strategy that follows all NPAs earns insignificant returns. An examination of retail and institutional order imbalances following NPA recommendations suggests that neither group recognizes the size-able differences in ability across NPAs.
#561 – Large cap US Corporate Bond Short Term Reversal
Period of rebalancing: Weekly
Markets traded: bonds
Instruments used for trading: bonds
Complexity: Simple strategy
Backtest period: 2007-2020
Indicative performance: 12.18%
Estimated volatility: 3.7%
Source paper:
Verma, Himanshu and Ahmad, Ajakh and Liew, Jim Kyung-Soo: Cross-sectional Examination of the Corporate Bond Market Performance – The Rise of the Momentum and Contrarian Unidentified Factor Mimicking Corporate Bond Portfolios!
https://ssrn.com/abstract=3689223
Abstract:
We examine momentum and reversal anomalies in corporate bond returns at the company-level employing a novel dataset, SoKat Credit, comprising bonds of 323 of the largest and liquid companies over the period from 2002 to 2020. Our study documents significant short-term reversal in the cross-sectional of corporate bond returns concentrated at the one week interval with annualized returns on the zero investment long-short portfolio of 9.9%. We also document the company-level momentum-spill-over effect into bond returns when sorting on past equity returns, that is, our “bond-stock” strategy, which delivers annualized returns of 5.0% are statistically significant and robust baring the usual suspects of caveats.
#562 – Betting On and Against the Right Semibetas
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1993-2014
Indicative performance: 9.76%
Estimated volatility: 9.3%
Source paper:
Bollerslev, Tim and Patton, Andrew J. and Quaedvlieg, Rogier, Realized Semibetas: Signs of Things to Come
https://ssrn.com/abstract=3528276
Abstract:
We propose a new decomposition of the traditional market beta into four semibetas depending on the signed covariation between the market and individual asset returns. Consistent with the pricing implications from a mean-semivariance framework, we show that higher semibetas defined by negative market and negative (positive) asset return covariation predict significantly higher (lower) future returns, while the other two semibetas do not appear to be priced. The results are robust to an array of alternative test specifications and additional controls. Rather than betting on or against beta, we conclude that it is better to bet on and against the “right” semibetas.
New research papers related to existing strategies:
#12 – Pairs Trading with Stocks
Miroslav Fil: Gold Standard Pairs Trading Rules: Are They Valid?
https://arxiv.org/pdf/2010.01157.pdf
Abstract:
Pairs trading is a strategy based on exploiting mean reversion in prices of securities. It has been shown to generate significant excess returns, but its profitability has dropped significantly in recent periods. We employ the most common distance and cointegration methods on US equities from 1990 to 2020 including the Covid-19 crisis. The strategy overall fails to outperform the market benchmark even with hyperparameter tuning, but it performs very strongly during bear markets. Furthermore, we demonstrate that market factors have a strong relationship with the optimal parametrization for the strategy, and adjustments are appropriate for modern market conditions.
#26– Value (Book-to-Market) Factor
Wakil, Gulraze, Levels of Total Book Assets and Future Stock Returns: Risk or Mispricing?
https://ssrn.com/abstract=3654554
Abstract:
This study investigates whether increases in future stock returns related to levels of total book assets (TBA), after controlling for market value (MV), are due to risk or stock mispricing. Based on a 30 year sample from 1987 to 2016, the findings reveal statistically significant average annual abnormal returns in the range of 5.5 and 10 percent using the Carhart (1997) four-factor model when going from the lowest to the highest TBA quintile portfolio, after sorting stocks into market value quintiles. Abnormal portfolio returns are even higher in the latter seven years of the sample period. These abnormal portfolio returns are supported by firm-level pooled regressions that include a battery of control variables known to be related to future stock returns. Moreover, these abnormal returns are not related to the accruals or asset growth anomalies. However, the evidence lends support to the behavioral explanation of investors not fully incorporating current investments and prior poor performance of firms into stock prices. Taken together, these findings suggest potentially significant abnormal returns for investors and provide support for standard setters who want more fair values in accounting assets.
#447 – Logistic Regression and Momentum-Based Trading Strategy
#367 – Timing S&P500 Using Full vs. Partial Employment
Mateusz Kijewski and Robert Ślepaczuk: Predicting prices of S&P500 index using classical methods and recurrent neural networks
https://www.wne.uw.edu.pl/files/6215/9765/7140/WNE_WP333.pdf
Abstract:
This study implements algorithmic investment strategies with buy/sell signals based on classical methods and recurrent neural network model (LSTM). The research compares the performance of investment algorithms on time series of S&P500 index covering 20 years of data from 2000 to 2020. This paper presents an approach for dynamic optimization of parameters during backtesting process by using rolling training-testing window. Every method was tested in terms of robustness to changes in parameters and evaluated by appropriate performance statistics e.g. Information Ratio, Maximum Drawdown, etc. Combination of signals from different methods was stable and outperformed benchmark of Buy & Hold strategy doubling its returns on the same level of risk. Detailed sensitivity analysis revealed that classical methods which used rolling training-testing window were significantly more robust to changes in parameters than LSTM model in which hyperparameters were selected heuristically.
#14 – Momentum Factor Effect in Stocks
Theissen, Erik and Yilanci, Can, Momentum? What Momentum?
https://ssrn.com/abstract=3710496
Abstract:
Risk-adjusted momentum returns are usually estimated by constructing momentum portfolios and then running a full-sample regression of their returns on a set of factors (portfolio-level risk adjustment). This approach implicitly assumes constant factor exposure of the momentum portfolio. However, momentum portfolios are characterized by strong turnover and time-varying factor exposure. We propose to estimate the risk exposure at the stock-level. The risk-adjusted return of the momentum portfolio in month t then is the actual return minus the weighted average of the expected returns of the component stocks (stock-level risk adjustment). Based on evidence from the universe of CRSP stocks, from sub-periods and size-based sub-samples, from volatility-scaled momentum strategies (Barroso and Santa-Clara 2015) and from an international sample covering 22 developed countries we conclude that the momentum effect may be much weaker than previously thought.
#536 – Machine Learning Stock Picking
Guo, Li and Yao, WeiLiang, Sparse Signals in Market Anomalies
https://ssrn.com/abstract=3683288
Abstract:
With a machine learning method, this study optimally combines the market anomalies to forecast cross sectional stock returns. A long-short portfolio achieves 5.20% (equal weight) Fama-french five-factor adjusted monthly return. The trading strategy outperforms all the individual anomalies, as well as beating the equal weight of anomaly combination strategy. The results are robust to the inclusion of transaction cost, representative anomaly predictors, small and large stocks, across recession and expansion periods, pre- and post- anomaly publication periods. It is noteworthy that the strategy is mainly driven by the long leg instead of short leg and performs consistently well regardless of high and low sentiment periods. Overall, the findings of this study suggest that machine learning method can efficiently combine anomaly information, and its return predictability is primarily driven by investors’ un-recognition of undervalued stocks, thereby showing important economic importance to the investors.
#465 – Equity Momentum Leads Corporate Bonds
Verma, Himanshu and Ahmad, Ajakh and Liew, Jim Kyung-Soo: Cross-sectional Examination of the Corporate Bond Market Performance – The Rise of the Momentum and Contrarian Unidentified Factor Mimicking Corporate Bond Portfolios!
https://ssrn.com/abstract=3689223
Abstract:
We examine momentum and reversal anomalies in corporate bond returns at the company-level employing a novel dataset, SoKat Credit, comprising bonds of 323 of the largest and liquid companies over the period from 2002 to 2020. Our study documents significant short-term reversal in the cross-sectional of corporate bond returns concentrated at the one week interval with annualized returns on the zero investment long-short portfolio of 9.9%. We also document the company-level momentum-spill-over effect into bond returns when sorting on past equity returns, that is, our “bond-stock” strategy, which delivers annualized returns of 5.0% are statistically significant and robust baring the usual suspects of caveats.
And two interesting free blog posts have been published during last 2 weeks:
Corporate bonds and equities of the same firm should share the same fundamentals, but does this preposition hold for the ESG scores and their implications? In the equity market, there is convincing literature that states that ESG scores lower risks or even can improve the performance of portfolios. However, it was shown that the ESG implications could not be universally applied to all countries and their markets. Novel research by Slimane et al. (2020) examines the role of the ESG in the fixed market. The paper shows that the fixed income market is probably some years behind the equity market, but the ESG is also emerging in the fixed income. The performance of ESG outperformers compared to underperformers is continually rising. In Europe, the difference is already economically significant; the rest of the world seems to lag a little. Therefore, the ESG might have a bright future also in the corporate bond market. So far, the results are promising…
Can Analysts Predict Performance of the US and International Stocks?
Analysts recommendations are quite puzzling topic among both practitioners and academics as well. The most important question related to the analysts is straightforward: what is the value of their recommendations? The research paper of Azvedo and Müller (2020) brings light on this topic, but also explores the relation of analysts recommendations and market anomalies. In line with other literature, it seems that the recommendations are significantly more valuable in international markets compared to the US market. While the prediction ability of analysts is not present in the US market, less developed markets and markets with higher limits-to-arbitrage are connected with valuable recommendations. Secondly, using around 200 cross-sectional anomalies, authors show that analysts are more lined up with anomaly-based composite mispricing measures in international markets. Therefore, there is not a bias from analysts to recommend overvalued stocks in global markets compared to the well-developed US market. We highlight several results and tables, but the paper is full of impressive results, ideas and tables. Therefore, we invite you to read this blog post as well as the source research paper.
Plus, the following five trading strategies have been backtested in QuantConnect in the previous two weeks:
#201 – Instititutional Ownership Effect During Earnings Announcements
#356 – The Dollar Ahead of FOMC Target Rate Changes
#381 – Blended Factors in Cryptocurrencies
#544 – Impact of intangible assets on B/M
#546 – Implied Volatility Spreads and Expected Market Returns in S&P500
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