Quantpedia Premium Update – 16th July 2020

New strategies:

#515 – Bear Market Risk and Hedge Fund Returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: funds
Complexity: Complex strategy
Backtest period: 1997-2017
Indicative performance: 10.95%
Estimated volatility: 8.58%

Source paper:

Ho, Thang and Kagkadis, Anastasios and Wang, George Jiaguo: Bear Market Risk and the Cross-Section of Hedge Fund Returns
https://ssrn.com/abstract=3551136
Abstract:
We propose the bear beta, i.e. the sensitivity of hedge funds to a bear spread portfolio orthogonalized to the market, as a novel way of classifying funds as insurance buyers or sellers. We find that low bear beta funds (insurance sellers) outperform high bear beta funds (insurance buyers) by 0.58% per month on average. The negative relation between bear beta and future hedge fund returns is not subsumed by a large set of fund characteristics and risk exposures. Consistent with a risk-based explanation, this relation remains negative during market crashes but turns positive during periods of increasing bear market concerns.

#516 – Interest Rate Differentials and the Dynamic Asymmetry in USD/EUR pair

Period of rebalancing: Daily
Markets traded: currencies
Instruments used for trading: futures, CFDs
Complexity: Very complex strategy
Backtest period: 2014-2019
Indicative performance: 5.32%
Estimated volatility: 10%

Source paper:

Hambuckers, Julien and Ulm, Maren: Interest Rate Differentials and the Dynamic Asymmetry of Exchange Rates
https://ssrn.com/abstract=3541862
Abstract:
In this paper, we revisit the predictive content of interest rates for daily exchange rate returns using an improved econometric strategy. The novelty of our approach is to take into account dependencies of higher orders by allowing for a time-varying asymmetry in the distribution of exchange rates. Using data on USD/EUR currency pair over the period 1999-2019, we find the dynamic asymmetry component to be significant and driven by interest rate differentials, but also by general uncertainty and past unexpected shocks. In line with recent currency crash theories, our study suggests that the larger the difference between interest rates, the more likely the high yield currency is to appreciate but also to experience currency crashes. To assess the economic significance of our results, we introduce a directional forecasting approach derived from our model. We show that a trading rule based on these forecasts provides better in-sample and out-of-sample economic performance compared to benchmark models.

#517 – Predicting Bond Returns with Equity return

Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: bonds, futures
Complexity: Moderately complex strategy
Backtest period: 1950-2019
Indicative performance: 3.80%
Estimated volatility: 10%

Source paper:

Guido Baltussen, Martin Martens, Olaf Penninga: Predicting Bond Returns: 70 years of International Evidence
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3631109
Abstract:
We examine the predictability of government bond returns using a deep sample spanning 70 years of international data across the major bond markets. Using an economic, trading-based testing framework we find strong economic and statistical evidence of bond return predictability with a Sharpe ratio of 0.87 since 1950. This finding is robust over markets and time periods, including 30 years of out-of-sample data on international bond markets and a set of nine additional countries. Furthermore, the results are consistent over economic environments, including prolonged periods of rising or falling rates, and is exploitable after transaction costs. The predictability relates to predictability in inflation and economic growth. Overall, government bond premia display predictable dynamics and the timing of international bond market returns offers exploitable opportunities to investors.

#518 – Predicting Bond Returns with Commodity Index

Source paper:

#519 – Predicting Bond Returns with a Combined Model

Source paper:

New research papers related to existing strategies:

#389 – Cryptomarket Discounts

Crépelière, Tommy and Zeisberger, Stefan: Arbitrage in the Market for Cryptocurrencies
https://ssrn.com/abstract=3606053
Abstract:
Recent literature has documented substantial arbitrage opportunities in markets for cryptocurrencies across exchanges. This paper examines the price differences of four different cryptocurrencies on 21 stock exchanges. Our results provide information on the development of the pricing of digital currencies and thus the efficiency of the market. In particular the differences between trading within cryptocurrencies and trading from fiat currencies to cryptocurrencies are analyzed, and if these price inconsistencies effectively enable arbitrage. By testing an arbitrage strategy from a practical perspective, the potential and challenges of arbitrage in the cryptomarket are explored.

#169 – Exploiting Option Information in the Equity Market

Bali, Turan G. and Murray, Scott: In Search of a Factor Model for Optionable Stocks
https://ssrn.com/abstract=3487947
Abstract:
We propose the first factor model that explains cross-sectional variation in optionable stock returns. Our model includes new factors based on option-implied volatility minus realized volatility, the call minus put implied volatility spread, and the difference between changes in call and put implied volatilities, along with the market factor. The model outperforms previously-proposed factor models at explaining the performance of portfolios of optionable stocks formed by sorting on other option-based predictors, as well as other well-known stock return predictors. Our model provides a benchmark for assessing whether portfolios of optionable stocks generate returns that are not explained by previously-documented phenomena.

#14 – Momentum Factor Effect in Stocks

Parajuli, Bharat Raj: Does the Delay in Firm-Specific Information Cause Momentum?
https://ssrn.com/abstract=3576112
Abstract:
In this paper, I develop a medium-horizon firm-specific information delay (FSID) measure using the methodology introduced by Hou and Moskowitz (2005) (hereafter HM). Unlike the HM measure of the speed of diffusion of US market-specific information in the short horizon (four weeks), FSID measures the speed of diffusion of firm-specific information in the medium horizon (six months). Whereas previous studies including HM found no significant relation between momentum premium and the HM measure, I find that momentum ceases to exist in the cross section of firms after controlling for FSID. FSID has a symmetrical effect on both loser and winner firms: high-FSID loser firms lose more than low-FSID loser firms, while high-FSID winner firms win more than low-FSID winner firms. High-FSID firms are firms with greater uncertainties related to their fundamentals; these are slightly larger growth firms, have higher dispersion among analysts about their future earnings, pay low dividends, have higher costs of goods, have higher volatility around their profitability, and actively perform major corporate events.

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

Do Floor Traders Matter?

The pandemic of COVID-19 brought many changes for the whole humanity. The financial markets were no exception, but the trading has continued. Nowadays, the order can be placed from anywhere around the world and almost all stock exchanges are electronic and algorithmic. However, there still is one exchange where the floor trading exists – NYSE. During these tough times, NYSE was also purely electronic, the floor trading was closed, and human interaction was not possible. A novel study by Brogaard, Ringgenberg and Roesch examines the role of floor traders in the recent era driven by computers. The conclusion is clear: in the current digital age, floor traders still matter.

The Risk in Equity Risk Factors

The bear markets were and surely would be present in the equities in the future. While many fear them, experienced investors accept that the growth of the equity market cannot be constant and that inherent equity risk often manifests as a painful market drawdown. When someone designs a strategy, it is a general practice to check its performance during such downturns. Therefore, we can recommend an interesting novel research paper by Paul Geertsema and Helen Lu. The selected paper analyzes the risk of the most common equity factors and plots their over- or under-performance during multiple crisis periods since the Vietnam war until the COVID-19.

ESG Scores and Price Momentum Are More Than Compatible

What will happen if we mix ESG scoring with price momentum? Can we improve simple ESG investing strategy? The pure price momentum can be 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.

Plus, the following seven trading strategies have been backtested in QuantConnect in the previous two weeks:

#260 – Trend Following in Commodity Calendar Spreads
#332 – Contrast Effect During the Earnings Announcements
#333 – US Sector Rotation wiEarnings Announcement Seasonality Effect in Equities
#497 – Monetary (FOMC) Momentum in Stocks
#506 – Volatility Risk Premium in Commodities
#507 – Volatility Risk Premium in Currencies
#509 – S&P500 Futures Return During the EU-Open Period

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