New strategies:
#520 – Lottery Stocks and Past Performance
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 1967-2017
Indicative performance: 28.78%
Estimated volatility: 23.38%
Source paper:
Peixuan Yuan : Gambling Preferences for Loser Stocks
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3632599
Abstract:
I discover that investors’ preferences for gambling mainly involve stocks that have performed poorly in the past three months, as lottery-like stocks with poor performance are much more likely to generate large payo s than those with good performance (61.53% vs. 40.17%). Furthermore, lotto investors tend to believe that lottery-like stocks with poor performance may have a vigorous rebound shortly, while those with good performance may be less likely to produce a highly positive return given their high prices. Therefore, lottery-like stocks with poor performance have a highly e ective lottery-like look, and thus they attract lotto investors. On the other hand, loser stocks without lottery-like features may continue to perform poorly. Overly optimistic (pessimistic) beliefs about stocks with (without) lottery-like features result in a pronounced lottery premium among loser stocks.
#521 – Global Dollar Risk Strategy
Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: futures, CFDs
Complexity: Very complex strategy
Backtest period: 1999-2017
Indicative performance: 4.26%
Estimated volatility: 7.69%
Source paper:
Ingomar Krohn: Time-Varying Global Dollar Risk in Currency Markets
https://ssrn.com/abstract=3524869
Abstract:
This paper documents that the price of dollar risk exhibits significant time variation, switching sign after large realized dollar fluctuations, when global dollar demand is high and funding constraints are tight. To exploit this feature of dollar risk, I propose a novel currency investment strategy which is effectively short the dollar in normal states, but long the dollar after large dollar movements. The proposed strategy is not exposed to standard risk factors, yields an annualized return exceeding 4%, and has an annualized Sharpe ratio of 0.34, significantly higher than that of well-known currency strategies. Furthermore, I show that currencies other than the dollar do not exhibit the same sign-switching pattern in their price of risk, consistent with the view that the dollar is special.
#522 – ESG, Price Momentum and Stochastic Optimization
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 2010-2019
Indicative performance: 17.47%
Estimated volatility: 13.49%
Source paper:
Padyšák, Matúš: ESG scores and price Momentum are more than compatible
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3650163
Abstract:
While price momentum is a stable part of financial markets, ESG scores are emerging more and more. However, there is an ongoing debate on the social responsibility of firms and the relationship with the performance. Literature offers mixed results whether the ESG enhances the performance of a stock, does not influence performance at all or even hampers the performance. In this paper, the pure price momentum is combined with ESG scores using a knapsack algorithm. Knapsack algorithm is a well-known mathematical problem of optimization, and in the case of momentum and ESG, can be used to make the momentum portfolios significantly more responsible, with lower volatility and better risk-adjusted return. The second option is to make the ESG portfolio substantially more profitable by using Knapsack algorithm to construct high ESG portfolio with large momentum. The approach resulted in a strategy with high ESG score and compared to pure momentum or momentum-ESG strategy, with significantly reduced volatility. Therefore, the ESG-momentum strategy has the best risk-adjusted return, the lowest drawdown, the lowest volatility and the most consistent returns.
#523 – DRIPs and Falling Stock Prices
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2007-2018
Indicative performance: 27.01%
Estimated volatility: 34.14%
Source paper:
Tze Chuan ‘Chewie’ Ang, Xin ‘Simba’ Chang, Xiaoxiong Hu, Patrick Verwijmeren: Equity Financing, Equity Lending, and Price Pressure: The Case of DRIP Arbitrage
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3552416
Abstract:
Dividend reinvestment plans (DRIPs) with discount offer shareholders the choice between receiving cash dividends or additional shares at a discount. We provide evidence on DRIP arbitrage where DRIP arbitrageurs extract the DRIP discount through short-term equity borrowing. We show the relation between equity lending, equity financing, and stock returns through DRIP arbitrage. DRIP arbitrage increases search costs in the equity lending market and creates negative price pressure in the stock market around dividend dates. Restrictions on equity lending impede DRIP arbitrage and negatively affect equity financing. Our results are more pronounced when the demand for DRIP arbitrage is higher.
#524 – Deviations of Fundamentals and Machine Learning
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1987-2017
Indicative performance: 9.87%
Estimated volatility: 12.45%
Source paper:
Avramov, Kaplanski, Subrahmanyam: Post-Fundamentals Drift in Stock Prices: A Machine Learning Approach
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3507512
Abstract:
Deviations of accounting fundamentals from their preceding moving averages forecast drifts in stock prices. Comprehensive machine-learning measures based on such deviations yield annualized alphas that exceed 18% (8%) for equal- (value-) weighted portfolios. The return predictability goes beyond momentum, 52-week highs, profitability, and other prominent anomalies. The profitability applies strongly to the long-leg and survives value-weighting and excluding microcaps. We provide evidence that the predictability arises because investors underreact to deviations from prevailing fundamental anchors.
New research papers related to existing strategies:
#167 – Idiosyncratic Momentum in Stocks
Hovmark, Simon: Idiosyncratic Momentum and the Importance of the Asset Pricing Model
https://ssrn.com/abstract=3633108
Abstract:
Using four different asset pricing models to estimate the residual returns, I show empirically that there are no material differences in the statistical and economic significance between idiosyncratic momentum strategies based on different asset-pricing models. I also show that idiosyncratic momentum is priced in the cross-section of returns, but spanned by a combination of risk factors when the combination includes price momentum. Despite being explained by common risk factors, the results suggest that idiosyncratic momentum is a stronger factor than price momentum and has a lower exposure to earnings momentum than price momentum.
#330 – Pre-Earnings Announcement Drift
Gao, Chao and Hu, Grace Xing and Zhang, Xiaoyan: Uncertainty Resolution Before Earnings Announcements
https://ssrn.com/abstract=3595953
Abstract:
Using four different asset pricing models to estimate the residual returns, I show empirically that there are no material differences in the statistical and economic significance between idiosyncratic momentum strategies based on different asset-pricing models. I also show that idiosyncratic momentum is priced in the cross-section of returns, but spanned by a combination of risk factors when the combination includes price momentum. Despite being explained by common risk factors, the results suggest that idiosyncratic momentum is a stronger factor than price momentum and has a lower exposure to earnings momentum than price momentum.
#14 – Momentum Factor Effect in Stocks
#213 – Long-Term Reversal Combined with a Momentum Effect #167 – Idiosyncratic Momentum in Stocks
Kelly, Bryan T. and Moskowitz, Tobias J. and Pruitt, Seth: Understanding Momentum and Reversals
https://ssrn.com/abstract=3610814
Abstract:
Stock momentum, long-term reversal, and other past return characteristics that predict future returns also predict future realized betas, suggesting these characteristics capture time-varying risk compensation. We formalize this argument with a conditional factor pricing model. Using instrumented principal components analysis, we estimate latent factors with time-varying factor loadings that depend on observable firm characteristics. We show that factor loadings vary significantly over time, even at short horizons over which the momentum phenomenon operates (one year), and that this variation captures reliable conditional risk premia missed by other factor models commonly used in the literature. Our estimates of conditional risk exposure can ex- plain a sizeable fraction of momentum and long-term reversal returns and can be used to generate even stronger return predictions.
#522 – ESG, Price Momentum and Stochastic Optimization
Lars Kaiser: ESG Integration: Value, Growth and Momentum
http://wp.lancs.ac.uk/fofi2018/files/2018/03/FoFI-2018-0140-Lars-Kaiser.pdf
This study provides finer-grained results about the financial effectiveness of ESG integration when combined with mainstream active investment styles. Specifically, we demonstrate that U.S. and European value, growth and momentum investors can raise their portfolio’s sustainability performance without sacrificing financial performance. By constructing size and industry-adjusted sustainability ratings, we provide the basis for a successful ESG integration and contribute to the evidence on ESG materiality from a risk perspective. Findings add to the growing demand for sustainable products in the traditional investment industry and overcome the notion of sustainability being a burden to classical investment practices.
And two interesting free blog posts have been published during last 2 weeks:
Cryptocurrency Volatility Index
Whenever traders want to assess the stock market’s mood, there is one really popular and useful index the most of them turn to. Yes, you guessed it right, it’s CBOE’s VIX Index. And which index can we use if we want to determine the mood of the cryptocurrencies? We can turn to a paper written by Fabian Woebbeking, which offers the methodology to compute two cryptocurrency volatility indexes (CVX & CVX76). The CVX and CVX76 Indexes also extract the market’s expectation of future volatility from option prices, but from options on the Bitcoin. The research suggests that the cryptocurrency option market has finally reached a sufficient market size to extract stable cryptocurrency volatility information.
The Effectivity of Selected Crisis Hedge Strategies
During past months we made a set of articles analyzing the performance of equity factors and selected systematic strategies during coronavirus crisis. These articles were short-ranged with data only from the start of the year 2020, which is enough for the purpose of the quick blog posts, but very short-sighted to see the nature of these strategies. Therefore, we expanded the time range by 20 years. For a better understanding of hedge possibilities of these strategies, we have added a comparison to essential safe-haven assets, not only to equities.
Plus, the following eight trading strategies have been backtested in QuantConnect in the previous two weeks:
#29 – Market Timing S&P 500 with VIX and COT Report
#30 – Timing Commodities and S&P500 with COT Report
#49 – S&P 500 Index Addition Effect
#59 – Open Interest in Futures Predicts Commodity Returns
#223 – Realized Skewness Predicts Equity Returns
#517 – Predicting Bond Returns with Equity Returns
#518 – Predicting Bond Returns with Commodity Index
#519 – Predicting Bond Returns with a Combined Model
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