New strategies:
#510– Factor Momentum
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1963-2016
Indicative performance: 10.49%
Estimated volatility: 15.28%
Source paper:
R. Arnott, M. Clements, V. Kalesnik, J. Linnainmaa: Factor Momentum https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3116974
Abstract:
Past industry returns predict the cross section of industry returns, and this predictability is at its strongest at the one-month horizon (Moskowitz and Grinblatt 1999). We show that the cross section of factor returns shares this property, and that industry momentum stems from factor momentum. Factor momentum is transmitted into the cross section of industry returns via variation in industries’ factor loadings. Momentum in industry-neutral factors spans industry momentum; industry momentum is therefore a by-product of factor momentum, not vice versa. Factor momentum is a pervasive property of all factors; we show that factor momentum can be captured by trading almost any set of factors.
#511 – Cheap Options Are Expensive
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: options
Complexity: Moderately complex strategy
Backtest period: 1996-2017
Indicative performance: 32.32%
Estimated volatility: 13.32%
Source paper:
Eisdorfer, Assaf and Goyal, Amit and Zhdanov, Alexei: Cheap Options Are Expensive
https://ssrn.com/abstract=3607030
Abstract:
We show that options written on stocks with low prices are over-priced. This effect is robust to a variety of tests, controlling for common stock- and option- risk characteristics, and to reasonable transaction costs. Natural experiments corroborate this finding; options tend to become relatively more expensive following stock splits; and options on mini-indices are overpriced relative to options written on otherwise identical regular-priced indices. Our evidence suggests that (less sophisticated) retail investors consider options with low underlying prices as good deals due to low prices of such options. Demand pressure from these investors leads to option overpricing.
#512 – Savings Rate Beta and Performance of Stocks
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1968-2018
Indicative performance: 7.96%
Estimated volatility: 7.19%
Source paper:
Ince, Baris: Is Personal Savings Rate Priced in the Cross-section of Stock Returns
https://ssrn.com/abstract=3524869
Abstract:
I investigate the importance of personal savings rate in the cross-sectional pricing of individual stocks. I estimate each stock’s monthly-varying sensitivity to the personal savings rate and show that stocks in the lowest savings rate beta quintile generate 6% more annualized risk-adjusted return compared to stocks in the highest savings beta quintile. I find that the savings premium is driven by the outperformance (underperformance) of stocks with negative (positive) savings rate beta. These results are robust to controls for various firm-specific characteristics and risk factors. Moreover, the alpha spread between the highest and the lowest savings rate beta stocks increases during high economic uncertainty, low credit availability, and high income risk periods. Finally, the results are consistent with the risk correction predictions of the Consumption-CAPM literature.
#513 – Predicting Intraday Returns with Machine Learning methods
Period of rebalancing: Intraday
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1993-2019
Indicative performance: 194.32%
Estimated volatility: 24.96%
Source paper:
Pushpendu Ghosh, Ariel Neufeld and Jajati Keshari Sahoo: Forecasting directional movements of stock prices for intraday trading using LSTM and random forests
https://www.ntu.edu.sg/home/ariel.neufeld/Pushpendu.pdf
Abstract:
We employ both random forests and LSTM networks (more precisely CuDNNLSTM) as training methodologies to analyze their effectiveness in forecasting out-of-sample directional movements of constituent stocks of the S&P 500 from January 1993 till December 2018 for intraday trading. We introduce a multi-feature setting consisting not only of the returns with respect to the closing prices, but also with respect to the opening prices and intraday returns. As trading strategy, we use Krauss et al. (2017) and Fischer & Krauss (2018) as benchmark and, on each trading day, buy the 10 stocks with the highest probability and sell short the 10 stocks with the lowest probability to outperform the market in terms of intraday returns – all with equal monetary weight. Our empirical results show that the multi-feature setting provides a daily return, prior to transaction costs, of 0.64% using LSTM networks, and 0.54% using random forests. Hence we outperform the single-feature setting in Fischer & Krauss (2018) and Krauss et al. (2017) consisting only of the daily returns with respect to the closing prices, having corresponding daily returns of 0.41% and of 0.39% with respect to LSTM and random forests, respectively.We employ both random forests and LSTM networks (more precisely CuDNNLSTM) as training methodologies to analyze their effectiveness in forecasting out-of-sample directional movements of constituent stocks of the S&P 500 from January 1993 till December 2018 for intraday trading. We introduce a multi-feature setting consisting not only of the returns with respect to the closing prices, but also with respect to the opening prices and intraday returns. As trading strategy, we use Krauss et al. (2017) and Fischer & Krauss (2018) as benchmark and, on each trading day, buy the 10 stocks with the highest probability and sell short the 10 stocks with the lowest probability to outperform the market in terms of intraday returns – all with equal monetary weight. Our empirical results show that the multi-feature setting provides a daily return, prior to transaction costs, of 0.64% using LSTM networks, and 0.54% using random forests. Hence we outperform the single-feature setting in Fischer & Krauss (2018) and Krauss et al. (2017) consisting only of the daily returns with respect to the closing prices, having corresponding daily returns of 0.41% and of 0.39% with respect to LSTM and random forests, respectively.
#514 – Retail Trading and Momentum Profitability
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period:
2004 – 2014
Indicative performance: 14.04%
Estimated volatility: 19.22%
Source paper:
Chung, Ling Tak Douglas: Retail Trading and Momentum Profitability https://ssrn.com/abstract=3486843
Abstract:
Monthly momentum returns increase monotonically across quintile portfolios of stocks sorted by retail trading participation with a top-minus-bottom spread of 1.42% (t-statistics = 3.46). Stocks that are heavily traded by retail investors exhibit lottery-like features such as low prices, high idiosyncratic volatilities/skewness, and high past maximum returns. Using lottery characteristics to proxy for the extent of retail trading, future momentum profits monotonically increase in the cross-sectional lotteryness of stocks over a 77-year back-testing period for which retail trading data is unavailable. Further analysis shows that lottery-like stocks exhibit stronger comovements that amplify momentum profits.
New research papers related to existing strategies:
#510 – Factor Momentum
Flögel, Volker and Schlag, Christian and Zunft, Claudia: Momentum-Managed Equity Factors
https://ssrn.com/abstract=3423287
Abstract:
Managed portfolios that exploit positive first-order autocorrelation in monthly excess returns of equity factor portfolios produce large alphas and gains in Sharpe ratios. We document this finding for factor portfolios formed on the broad market, size, value, momentum, investment, profitability, and volatility. The value-added induced by factor management via short-term momentum is a robust empirical phenomenon that survives transaction costs and carries over to multi-factor portfolios. The novel strategy established in this work compares favorably to well-known timing strategies that employ e.g. factor volatility or factor valuation.
Ehsani, Sina and Linnainmaa, Juhani T.: Time-Series Efficient Factors
https://ssrn.com/abstract=3555473
Abstract:
Factors in prominent asset pricing models are positively serially correlated. We derive the optimal allocation that transforms an auto-correlated factor to a “time-series efficient” factor. The key determinant of the value of factor timing is the ratio of a factor’s auto-correlation to its Sharpe ratio. Time-series efficient factors earn significantly higher Sharpe ratios than the original factors and contain all the information found in the original factors. Momentum strategies profit by timing auto-correlated factors; they pick up factor “inefficiencies.” We show that, rather than augmenting models with the momentum factor, each factor can instead be made time-series efficient. An asset pricing model with time-series efficient factors, such as an efficient Fama-French five-factor model, prices momentum. Time-series efficient factors also explain more of the co-variance structure of returns; they describe the cross section better than the standard factors and align more closely with the true SDF.
Yang, Hanlin: Decomposing Factor Momentum
https://ssrn.com/abstract=3517888
Abstract:
Factor momentum returns do not stem from momentum in factor returns. To study the source of returns, this paper decomposes the factor momentum portfolio into a factor timing portfolio and a static portfolio, where the former dynamically collects the return due to serial correlations of factor returns and the latter passively collects factor premiums. Evidence from 210 stock return factors reveals that the static portfolio robustly accounts for a dominant fraction of the factor momentum return and outperforms in risk-adjusted returns, whereas factor return predictability is empirically too weak to produce timing benefits. The static portfolio survives the post-publication decline of factor performance but the factor momentum portfolio does not.
#26 – Value (Book-to-Market) Factor
Maloney, Thomas and Moskowitz, Tobias J.: Value and Interest Rates: Are Rates to Blame for Value’s Torments?
https://ssrn.com/abstract=3608155
Abstract:
Value stocks sharply underperformed growth stocks from 2017 to early 2020, exacerbating a longer period of lackluster performance that dates back to the Global Financial Crisis for some value factors. Some have blamed the interest rate environment – the low level of interest rates, falling bond yields or the flattening yield curve. We examine these claims. Theory suggests the link between value and interest rates is ambiguous and complicated. Empirically, we find fairly modest links that change for different specifications. Evidence of a mild relationship between interest rate variables and value’s performance is found for some specifications, but not others. Despite some eye-catching patterns in recent data, particularly those related to changes in bond yields or the yield curve slope, the economic significance of any relationship is small and not robust in other samples. We conclude that the performance of value is not easily assessed based on the interest rate environment, and that factor timing strategies based on interest rate-related signals are likely to perform poorly.
#227 – Trend Factor within Stocks
Lin, Hai and Liu, Pengfei and Zhang, Cheng: The Trend Premium Around the World: Evidence from the Stock Market
https://ssrn.com/abstract=3587342
Abstract:
This paper studies the predictive power of the trend strategy in the international stock market. Using data from 49 markets, we find that a trend signal exploiting the short-, intermediate-, and long-term price information can predict stock returns cross-sectionally in the international market. The significance of the trend strategy is associated with market-level characteristics such as macroeconomic conditions, culture, and the information environment. The trend premium is more pronounced in markets with a more advanced macroeconomic status, a higher level of information uncertainty and individualism, and better accessibility to foreign investors. Nevertheless, the trend strategy only outperforms the momentum strategy in a relatively short horizon.
#461 – ESG Factor Momentum Strategy
Antoncic, Madelyn and Bekaert, Geert and Rothenberg, Richard V and Noguer, Miquel: Sustainable Investment – Exploring the Linkage between Alpha, ESG, and SDG’s
https://ssrn.com/abstract=3623459
Abstract:
Environmental, Social and Governance (ESG) investing has been one of the most important trends in the asset management industry over the past decade. Previously institutional asset owners believed that ESG issues, also known as nonfinancial risks and opportunities, were not relevant to portfolio value and therefore were nonessential, or even in conflict with their fiduciary duties to act in the best interest of their beneficiaries. In this paper, we analyze the relationship between alpha generation and ESG metrics. We also measure whether companies have an either positive or negative net influence on the U.N.’s Sustainable Development Goals (SDG´s) which are emerging as the new, broader standard to measure sustainability. First, we explore whether utilizing ESG factors can improve performance vis a vis the MSCI US index. By constructing a sector-neutral portfolio using MSCI ESG momentum scores from 2013 to 2018, we determine that it is feasible to generate positive alpha from an ESG momentum strategy. Second, we utilize structured and unstructured data to determine a company’s net influence on the SDGs, or what we call its SDG ‘footprint.’ Our research shows that an ESG momentum portfolio not only outperforms the MSCI US index but has a relatively better SDG footprint than that of the index. Third, we establish a positive contemporaneous connection between the sample portfolio’s ESG ratings change (its momentum) and its coinciding SDG footprint over the sample period. We conclude that a positive linkage exists between ESG, alpha, and the SDG’s.
And two interesting free blog posts have been published during last 2 weeks:
Transaction Costs Optimization for Currency Factor Strategies
A lot of backtests of systematic trading strategies omit transaction costs (in the form of spreads and fees). Simulation is then simpler, but resultant model portfolio and its performance can be misleading. In the case of currency factor investing, backtest without the tcosts simulation can pick currencies with wider spreads and higher volatilities. And in real trading, with real-world transaction costs, a strategy can, therefore, perform significantly worse than expected. A research paper written by Melvin, Pan, and Wikstrom offers an elegant optimization methodology to incorporate transaction costs into the backtesting process which allows strategies to retain their alpha …
YTD Performance of Crisis Hedge Strategies
After a month, we are back with a year-to-date performance analysis of a few selected trading strategies. In the previous article, we were writing about the performance of equity factors during the coronavirus crisis. Several readers asked us to take a look also on different types of trading strategies, so we are now expanding to other asset classes. We picked a subset of strategies that can be used as a hedge at the times of market stress (at least, that’s what the source academic research papers indicate) and checked how they fared.
Plus, the following eight trading strategies have been backtested in QuantConnect in the previous two weeks:
#329– Portfolio Hedging Using VIX Options
#360 – Trend Following Trading Strategies for Currencies
#361 – Idiosyncratic Commodity Momentum
#478 – Generalised Risk-Adjusted Momentum in Equity Indexes
#479 – Generalised Risk-Adjusted Momentum in Stocks
#476 – Speculator Spreading Pressure and the Commodity Futures Risk Premium
#450 – Unemployment Gap Factor in Fixed Income
#500 – Interest Rate Momentum in Global Yield Curves
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