New strategies:
#570 – Corporate Bond Value Strategy
Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: bonds
Complexity: Moderately complex strategy
Backtest period: 2003-2020
Indicative performance: 4.94%
Estimated volatility: 5.81%
Source paper:
Bartram, Söhnke M. and Grinblatt, Mark and Nozawa, Yoshio: Book-to-Market, Mispricing, and the Cross-Section of Corporate Bond Returns
https://ssrn.com/abstract=3510630
Abstract:
We study the role played by “bond book-to-market” ratios in U.S. corporate bond pricing. Controlling for numerous risk factors tied to default and priced asset risk, including yield-to-maturity, we find that the ratio of a corporate bond’s book value to its market price strongly predicts the bond’s future return. The quintile of bonds with the highest bond book-to-market ratios outperforms the quintile with the lowest ratios by more than 3% per year, other things equal. Additional evidence on signal delay, scope of signal efficacy, and factor risk rejects the thesis that the corporate bond market is perfectly informationally efficient.
#571 – Post Seasoned Equity Offering Returns in China
Period of rebalancing: 6 Months
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2006-2014
Indicative performance: 12.36%
Estimated volatility: 32.67%
Source paper:
Huang, Yong and Uchida, Konari and Zah, Daolin, Market Timing of Seasoned Equity Offerings with Long Regulative Process
https://ssrn.com/abstract=2726227
Abstract:
A long regulative process exists between the initial announcement and execution of seasoned equity offerings (SEOs) in China. Although the initial announcement of an SEO is associated with a significant reduction in the stock price, the regulator (China Securities Regulatory Commission) finally approves it after a significant run up in the price of the stock. Chinese managers execute SEOs after additional stock price increases. As a result, the stock price at issuance is not significantly different from the price on announcement, and is significantly higher than the price three months before the announcement. We also find stock prices decline following the execution. These results suggest regulative screenings for market stabilization are beneficial for SEO market timing, and that Chinese managers successfully time the market, even with a prolonged regulative process.
#572 – The Low Volatility Anomaly in Equity Sectors
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 2011-2020
Indicative performance: 6.96%
Estimated volatility: 12.55
Source paper:
Benoit Bellone, Raul Leote de Carvalho: The Low Volatility Anomaly in Equity Sectors – 10 Years Later!
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3697914
Abstract:
Ten years after showing that the low volatility anomaly in the performance of stocks is a phenomenon that should be considered in each sector as opposed to on an absolute basis ignoring sectors, we present evidence that this observation has held up well, and that if anything, has become even more valid.
#573 – Momentum and High Accruals
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 1965-2008
Indicative performance: 11.22%
Estimated volatility: 15.03%
Source paper:
Ming Gu, Yangru Wu: Accruals and Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1785554
Abstract:
We establish a robust link between momentum and accruals. Momentum profitability is mostly concentrated in firms with high accruals. Cross-sectional characteristics of momentum previously documented do not subsume the effect of accruals on momentum. Loser stocks with high accruals experience significant decreases in industry-adjusted sales growth and the largest amount of income-decreasing special items in subsequent years. Most of momentum profit among high-accrual firms is attributable to the high discretionary accrual group. Our findings indicate that due to the joint force of earnings overestimation and earnings manipulation, the downward payoff of loser stocks with high accruals largely drives the accrual-based momentum profit.
#574 – Oil Volatility Affects Industry Momentum in China
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1996-2015
Indicative performance: 62.88%
Estimated volatility: 38.23%
Source paper:
Chun-Da Chen, Chiao-Ming Cheng, Riza Demirer: Oil and Stock Market Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3050712
Abstract:
This study provides a novel perspective to the oil-stock market nexus by examining the predictive ability of oil return and volatility on stock market momentum in China. We find that oil return volatility serves as a strong predictor of industry momentum, even after controlling for stock market state, volatility and key macroeconomic variables. We argue that the predictive ability of oil over momentum payoffs is driven by time-varying investor sentiment that relates to excess buying pressure on winner stocks during uncertain times, captured by oil return volatility. Our tests also show that an oil-based momentum strategy wherein the investor conditions the trade on the state of oil return volatility yields significant abnormal returns, more than double that could be obtained from the conventional momentum strategy. In short, the findings suggest that oil market dynamics can contribute to stock market inefficiencies in such a way that these inefficiencies create significant abnormal profits for active managers.
New research papers related to existing strategies:
#27 – Market Timing with Aggregate and Idiosyncratic Stock Volatilities
#77 – Betting Against Beta Factor in Stocks
Poon, Percy and Yao, Tong and Zhang, Andrew (Jianzhong), The Alphas of Beta and Idiosyncratic Volatility
https://ssrn.com/abstract=3706597
Abstract:
We study the relation between the idiosyncratic volatility (IVOL) anomaly and the beta anomaly at various prediction horizons. IVOL significantly negatively predicts stock returns at the short horizon of up to six months and beta does not predict stock returns at any horizon. However, both IVOL and beta significantly negatively predict alphas over horizons from a few months to beyond one year. At the short horizon, neither anomaly can fully explain the other. At long horizons of beyond six months, the IVOL-alpha relation becomes insignificant after controlling for the beta effect. A measure of idiosyncratic volatility over a long window, popularly used by the investments industry to construct low-volatility portfolios, is related to returns and alphas at various horizons in a way similar to beta, and its predictive power is mostly explained by beta. Overall, while IVOL and beta each has unique information about short-term alphas, at long horizons the two anomalies share the same origin.
#536– Machine Learning Stock Picking
Varaku, Kerda, Stock Price Forecasting and Hypothesis Testing Using Neural Networks
https://ssrn.com/abstract=3597684
Abstract:
In this work we use Recurrent Neural Networks and Multilayer Perceptrons, to predict NYSE, NASDAQ and AMEX stock prices from historical data. We experiment with different architectures and compare data normalization techniques. Then, we leverage those findings to question the efficient-market hypothesis through a formal statistical test.
#536– Machine Learning Stock Picking
Baba-Yara, Fahiz: Machine learning and return predictability across firms, time and portfolios
https://www.babayara.com/files/JobMarket_10.pdf
Abstract:
Previous research finds that machine learning methods predict short-term return variation in the cross-section of stocks, even when these methods do not impose strict economic restrictions. However, without such restrictions, the models’ predictions fail to generalize in a number of important ways, such as predicting time-series variation in returns to the market portfolio and long-short characteristic sorted portfolios. I show that this shortfall can be remedied by imposing restrictions, that reflect findings in the financial economics literature, in the architectural design of a neural network model and provide recommendations for using machine learning methods in asset pricing. Additionally, I study return predictability over multiple future horizons, thus shedding light on the dynamics of intermediate and long-run conditional expected returns.
#544 – Impact of intangible assets on B/M
Li, Feifei, Intangibles: The Missing Ingredient in Book Value
https://ssrn.com/abstract=3686595
Abstract:
We closely examine the impact of adding intangibles to traditional book equity as a more meaningful value measure. Our intangibles-adjusted value metric subsumes the traditional book-to-price metric in explaining cross-sectional equity returns and improves value factor performance across subsample periods and geographic regions. We find knowledge capital (capitalized R&D expenditures) plays a more important role than organization capital (capitalized partial SG&A expenditures). The improved value premium comes from both the long and short sides of intangibles-adjusted HML (iHML), which is good news for investors under a long-only constraint and provides useful information for investors who choose to short or underweight certain names.
#377 – Trading Futures Using Basis Indicator
Molyboga, Marat, Back to Basis: A Universal Return Predictor Across Asset Classes
https://ssrn.com/abstract=3690628
Abstract:
This paper shows analytically that the basis between spot and futures contracts contains information about future returns of securities across the asset classes of commodities, equity indices, fixed income and foreign exchange. The bases in commodities are positively correlated with a leading indicator of the business cycle whereas the bases in the financial assets are negatively related to the short-term rate. The return predictability of the basis can be captured with a simple multi-asset long-short strategy which produces an out-of-sample Sharpe ratio of 0.5 and an alpha of 2.5%-4.5% per annum with respect to commonly used asset pricing models. Specifically, the analysis includes five Fama-French Factors, a bond index and futures risk premia of multi-asset momentum, value, time-series momentum, and four asset-specific carry factors. The strategy performance is counter-cyclical and robust to transaction costs.
And three interesting free blog posts have been published during last 2 weeks:
Market Makers and Extreme Price Movements
Often, this blog provides novel research that may not include the straightforward trading strategy, yet it is an interesting insight for portfolio managers, risk managers, investors or traders. Novel research of Brogaard et al. (2020) examines the crucial role of market makers during extreme price movements. According to the authors and the past literature, there are two competing theories of how the extreme price movements end, and both are related to the market makers. It is the constrained liquidity provision theory and the strategic liquidity provision. This research tests and explains these competing theories, with findings that are in line with the strategic liquidity provision. The results can be found particularly interesting during extreme price movements because the paper has shown that firstly, liquidity providers scale back and only interfere later. Market makers utilize price pressures in stressful times in a profitable way, since they profit from subsequent reversals.
The Active vs Passive: Smart Factors, Market Portfolio or Both?
While there may be debates about passive and active investing, and even blogs about the numbers of active funds that were outperformed by the market, the history taught us that the outperformance of active or passive investing is cyclical. As a proxy for the active investing, the new Quantpedia’s research paper examines factor strategies and their smart allocation using fast or slow time-series momentum signals, the relative weights based on the strength of the signals and even blending the signals. While the performance can be significantly improved, using those smart approaches, the factors still got beaten by the market in both US and EAFE sample. However, the passive approach did not show to be superior. The factor strategies and market are significantly negatively correlated and impressively complement each other. The combined Smart Factors and market portfolio vastly outperforms both factors and market throughout the sample in both markets. With the combined approach, the ever-present market falls can be at least mitigated or profitable thanks to the factors.
Trading Index (TRIN) – Formula, Calculation & Trading Strategy in Python
Short-term mean reversion trading on equity indexes is a popular trading style. Often, price-based technical indicators like RSI, CCI are used to assess if the stock market is in overbought or oversold conditions. A new research article written by Chainika Thakar and Rekhit Pachanekar explores a different indicator – TRIN, which compares the number of advancing and declining stocks to the advancing and declining volume. TRIN’s advantage is that it’s cross-sectionally based and its calculation uses not only price but also volume information. Thakar& Pachanekar’s research paper is useful for fans of indicator’s based trading strategies and offers a short introduction to TRIN’s calculation together with an example of mean-reversion market timing strategy written in a python code.
Plus, the following six trading strategies have been backtested in QuantConnect in the previous two weeks:
#179 – Market Timing Using Sharpe Ratios
#197 – Default Risk Filter Applied on Momentum Effect within Stocks
#200 – Classical Equity Anomalies Combined with Trendfollowing Filter
#250 – Interest Rates Momentum Predicts FX Rates
#563 – Currency Factor Momentum
#567 – Low-risk Anomaly Index
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