Quantpedia Premium Update – April 8th

New strategies:

#984 – Systematic Hedging of the Cryptocurrency Portfolio

Period of rebalancing: Daily
Markets traded: cryptocurrencies
Instruments used for trading: CFDs, cryptos, ETFs, futures
Complexity: Complex
Backtest period: 2019-2023
Indicative performance: 46.37%
Estimated volatility: 34.31%

Source paper:

Vojtko, Radovan and Dujava, Cyril, Systematic Hedging of the Cryptocurrency Portfolio
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4762733
Abstract:
The article “Systematic Hedging of the Cryptocurrency Portfolio” on QuantPedia discusses a strategy for hedging a cryptocurrency portfolio that is stored in cold storage The article suggests that while cold storage has advantages, such as protection against hacking, it also exposes the holder to the price swings of the cryptocurrency market.

The article proposes a hypothetical market capitalization-weighted Top 5 cryptocurrency index portfolio (T5) as a proxy for a portfolio that hardcore HODLers may hold. The rule for inclusion in the index is simple: each year, on the first day of the year, select the top 5 coins ranked by market cap for a yearly holding period. Stablecoins are excluded from the index.

The article then explores a hedging strategy through BTC derivatives to minimize crypto market beta exposure risk. One approach introduced is a 1:1 (Proportional) Hedge, where the exact amount of $ value corresponding to Bitcoin (BTC) is shorted. The article suggests that this strategy can help mitigate the risk of price swings in the cryptocurrency market, especially when the market is at an all-time high.

Please note that this is a high-level summary and for a detailed understanding, you should read the full article.

#985 – Timing Convertible Bonds

Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: ETFs, funds
Complexity: Simple strategy
Backtest period: 1993-2022
Indicative performance: 4.1%
Estimated volatility: 8.3%

Source paper:

Rubin, Mirco and Schweigl, Paul: Do Volatility-Managed Portfolios Work Better for Convertible Bonds?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4592894
Abstract:
We extend the literature on volatility-managed portfolios to convertible bonds. Our volatility managed convertible bond strategies take less risk when volatility is high, entail economic utility gains of up to 53%, increase Sharpe Ratios and returns, and substantially reduce tail risk over their unmanaged equivalents, out-of-sample and including transaction costs. The strategies exploit the mispricing of long-term options that results from the overextrapolation of short-term realized volatility. Their outperformance is concentrated in high-sentiment periods during which they reduce exposure to sentiment-induced low market prices of risk, and produce significant alphas and higher Sharpe Ratios. As convertible bonds outperform equities in high-sentiment periods they provide a valuable supplement to conventional portfolios. Combining volatility-managing with Markowitz (1952)’s optimal portfolio allocation, out-of-sample, expands the mean-variance frontier. An E-GARCH model enhances economic performance for volatility-managed convertible bond strategies compared to standard volatility-forecasting models.

#986 – Opening Range Breakout Strategy in Individual Stocks

Period of rebalancing: Intraday
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2016-2023
Indicative performance: 41.6%
Estimated volatility: 14.8%

Source paper:

Zarattini, Carlo and Barbon, Andrea and Aziz, Andrew, A: Profitable Day Trading Strategy For The U.S. Equity Market
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4729284
Abstract:
The validity of day trading as a long-term consistent and uncorrelated source of income for traders and investors is a matter of debate. In this paper, we endeavored to answer this question by conducting a thorough analysis of the profitability of Opening Range Breakout (ORB) strategies, with a particular focus on the 5-minute ORB. Using a large dataset that covered more than 7,000 US stocks traded from 2016 to 2023, the research aimed to assess how eﬀective this strategy was in producing consistent and uncorrelated returns. A new aspect of our study was the focus on Stocks in Play, which are stocks that show higher than normal trading activity on a specific day, mostly because of fundamental news about the company. Our results showed a significant benefit in limiting day trading only to those Stocks in Play (even after considering transaction costs). A portfolio that consisted of the top 20 Stocks in Play achieved a total net performance of over 1,600%, with a Sharpe ratio of 2.81, and an annualized alpha of 36%. Passive exposure in the S&P 500 would have achieved a total return of 198% during the same period. Furthermore, this paper expanded the analysis to compare the return profile of the ORB strategy applied to diﬀerent time frames, such as 15, 30, and 60 minutes. In the last part of the paper, we presented detailed stock-specific statistics for the 25 best and worst performers of an ORB strategy over all the time frames. To the best of our knowledge, this is the first public paper with such intraday granularity and comprehensive stock-level database.

#987 – Economic Trend in Futures

Period of rebalancing: Monthly
Markets traded: bonds, commodities, currencies, equities
Instruments used for trading: CFDs, futures
Complexity: Very complex strategy
Backtest period: 1970-2022
Indicative performance: 13.3%
Estimated volatility: 12.2%

Source paper:

Brooks, Jordan and Feilbogen, Noah and Ooi, Yao Hua and Akant, Adam: Economic Trend
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4710868
Abstract:
In this paper we present a novel approach to investing across equity, bond, currency and commodity markets. “Economic trend” capitalizes on the tendency for new information to have a persistent impact on asset prices by positioning in each market on the basis of trends in macroeconomic fundamentals. The strategy has realized consistently attractive risk-adjusted returns over a 50+ year sample, and performance is pervasive across both markets and measures. It exhibits low correlation to traditional risk premia on average and tends to perform exceptionally well during drawdown periods for traditional asset classes. While economic trend is a close relative of price trend-following—both approaches aim to capitalize on the tendency of markets to systematically under-react to news—the two strategies are highly complementary. Combining the two leads to improved risk-adjusted performance and more robust drawdown protection than price trend-following alone.

#988 – Media Emotion Intensity and Commodity Futures Pricing

Period of rebalancing: Monthly
Markets traded: commodities
Instruments used for trading: CFDs, futures
Complexity: Complex strategy
Backtest period: 1998-2020
Indicative performance: 13.51%
Estimated volatility: 21.44%

Source paper:

Vu, Thanh and Chi, Yeguang and El-Jahel, Lina: Media Emotion Intensity and Commodity Futures Pricing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4757379
Abstract:
This study investigates the impact of media emotion intensity on commodities futures returns. Emotion intensity measures the proportion of emotional content relative to factual content in media news. This new factor is distinct from news sentiment and media coverage and exhibits weak cross-sectional correlations with both of them. The media emotion intensity factor exhibits an annual premium of around 14.40% and is more pronounced for commodities with low media coverage, high momentum, high basis-momentum, high hedging pressure, and backwardation. Emotion intensity significantly predicts the trading tendencies of both commercial and non-commercial traders and the cross-section of commodity futures returns at both portfolio and individual levels. Further, other commonly considered risk sources cannot subsume the predictability of the media emotion intensity factor.

#989 – Diversified Multi-Asset Multi-Factor Strategies through the Cycles

Period of rebalancing: Monthly
Markets traded: bonds, commodities, currencies, equities
Instruments used for trading: CFDs, futures
Complexity: Very complex strategy
Backtest period: 1924-2021
Indicative performance: 23.39%
Estimated volatility: 9.3%

Source paper:

Swade, Alexander and Lohre, Harald and Nolte (Lechner), Sandra and Shackleton, Mark B. and Swinkels, Laurens: A Century of Macro Factor Investing – Diversified Multi-Asset Multi-Factor Strategies through the Cycles
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4699060
Abstract:
We diversify an investment portfolio across macroeconomic factors that are mimicked by investable asset classes and style factors. Using a century of global data we analyze the resulting macro factor portfolio’s sensitivities to different macroeconomic scenarios and highlight the relevance of navigating time variation in macroeconomic risk premia. Specifically, we adapt the portfolio allocation to align with the identified macro environment as predicted by a forward-looking business cycle model. A Black-Litterman framework is used to thus improve upon a diversified macro factor allocation and to further tap into predictive asset class and style factor signals.

#990 – Market-Neutral Multi-Asset Carry Strategies

Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: CFDs, forwards, futures, swaps
Complexity: Very complex strategy
Backtest period: 1990-2018
Indicative performance: 5.6%
Estimated volatility: 4.1%

Source paper:

Tzotchev, Dobromir, Market-neutral Carry Strategies: Harvesting Carry Without Market Risk
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4726362
Abstract:
Carry strategies have become one of the main pillars of risk-premia investing. As pointed out by Kolanovic (2013) in ‘Systematic Strategies across Asset Classes’, an inherent feature of standard carry strategies is their long market/short volatility exposure. Carry trades are typically unwound during periods of falling risk appetite and liquidity elimination which results in pronounced negative skewness. In the current publication we propose a portfolio construction methodology that eliminates the market exposure from the carry portfolio. We show that there is an explicit link between market-neutral carry portfolios and alpha portfolios and we apply a uniform statistical approach across asset classes to neutralize the market factors exposure. Market-neutral carry portfolios across currencies, rates, credit, commodities and equity indices are constructed by explicitly taking into account the asset class specific features and tying the expected P&L to the carry premium. Significant diversification benefits arise from combining the individual asset-class carry strategies into a cross-asset carry portfolio. The market-neutral cross-asset carry portfolio exhibits positive skewness at various return horizons. There are also strong diversification benefits between the market-neutral carry strategy and the trend-following strategy.

New research papers related to existing strategies:

#714 – Illiquidity Factor in China

Zhang, Tong and Cheng, Hang and Shi, Yongdong: Unlocking the True Price Impact: Intraday Liquidity and Expected Return in China’s Stock Market
https://ssrn.com/abstract=4675832
Abstract:
The rise of automated trading systems has made stock trading more accessible and convenient, reducing the link between traditional illiquidity measures and stock returns. However, empirical data in China’s stock market shows conflicting results. We find a significantly positive correlation between intraday illiquidity and future returns in China’s stock market. We offer that the pricing ability of this intraday illiquidity originates from the correlation between trading activity and intraday return. This finding provides compelling out-of-sample evidence for the debate regarding the pricing of the Amihud (2002) measure in the U.S. market. Additionally, we create an intraday-return illiquidity factor that outperforms Liu, Stambaugh, and Yuan (2019) sentiment factors in China’s stock market.

#575 – Momentum and Low Risk Effects in India
#620 – Long Term Time-series Momentum in India
#949 – The 52-Week High Effect in India

Raju, Rajan: Timing the Tide: The Impact of Rebalancing Periods in Momentum Investing in Indian Equities
https://ssrn.com/abstract=4687044
Abstract:
The frequency of rebalancing is a critical aspect of momentum portfolios with substantial implications for returns and risk. This paper, the final instalment of our trilogy of papers, examines portfolios derived from universe sizes of 200, 500, and 750 stocks, with holdings of 15, 30, and 50, across four weighting schemes over varying rebalancing intervals of 1, 2, 3, and 6 months on returns, risks, factor exposures, and other metrics. The critical finding of the study is that shorter rebalancing periods capture the effect of academic momentum more effectively. This insight is significant for its implications on strategy optimisation, particularly in the vibrant and evolving context of Indian equity markets. The paper contributes to academic and practical aspects of momentum investing, addressing a literature gap, and offering guidance for investment managers and DIY investors navigating momentum strategies.

#766 – Sentiment Factor in the Cross-Section of Commodity Futures

Chakraborty, Sunandan and Jagabathula, Srikanth and Subramanian, Lakshminarayanan and Venkataraman, Ashwin: News Event-driven Forecasting of Commodity Prices
https://ssrn.com/abstract=4716473
Abstract:
Problem definition. Commodity prices have exhibited significant volatility in recent times, which poses an exogenous risk factor for commodity-processing and commodity-trading firms. Accurate commodity price forecasts can help firms leverage data-driven procurement policies that incorporate the underlying price volatility for financial and operational hedging decisions. However, historical prices alone are insufficient to obtain reasonable forecasts because of the extreme volatility.

Methodology/results. Building on the hypothesis that commodity prices are driven by real-world events, we propose a method that automatically extracts events from news articles and combines them with price data using a neural network-based predictive model to forecast prices. In addition to achieving a high prediction accuracy that outperforms several benchmarks (by up to 13%), our proposed model is also interpretable, which allows us to identify meaningful events driving the price fluctuations. We found that the events frequently associated with major fluctuations in the price include “natural” (floods, cold waves, poor rainfall, etc.), hike (hikes in fuel prices, fares, highway tolls, etc.), policy (govt. actions curbing/promoting imports and/or exports, etc.), and elections, all of which are known drivers of price change.We used a corpus containing about 1.6 million news articles of a major Indian newspaper spanning 15 years and daily prices of four crops (onion, potato, rice and wheat) in India to perform this study. Our proposed approach is flexible and can be used to predict other time series data such as disease incidence levels or macroeconomic indicators that are also influenced by real-world events.

Managerial implications. Firms can leverage price forecasts from our system to design inventory and procurement policies in the face of uncertain commodity prices. Commodity merchants can also use the forecasts to design optimal storage policies for physical trading of commodities when prices are volatile. Our findings can also significantly impact policymakers, who can leverage the information of impending price changes and associated events to mitigate the negative effects of price shocks.

#525 – Double-Sorting all Possible Strategies

Cheng, Tingting and Zhao, Junyi and Zhao, Albert Bo and Jiang, Shan: Is Machine Learning a Necessity? A Regression-Based Approach for Stock Return Prediction
https://ssrn.com/abstract=4690875
Abstract:
We propose a simple, linear-regression-based method for prediction of the time series of stock returns. The method achieves out-of-sample performances comparable to machine learning methods while having ignorable computational costs. The key component of the method is to integrate a straightforward cross-market factor screening into the iterated combination method proposed by Lin, Wu, and Zhou (2018). Our empirical results on the U.S. stock market show that the method outperforms many state-of-the-art machine learning methods in certain periods. The method also exhibits greater utility gain and investment profits in most periods after considering transaction costs.

#460 – ESG Level Factor Investing Strategy
#461 – ESG Factor Momentum Strategy

Alves, Rómulo and Krueger, Philipp and van Dijk, Mathijs A.: Drawing Up the Bill: Are ESG Ratings Related to Stock Returns Around the World?
https://ssrn.com/abstract=4674146
Abstract:
We aim to provide the most comprehensive analysis to date of the relation between ESG ratings and stock returns, using 16,000+ stocks in 48 countries and seven different ESG rating providers. We find very little evidence that ESG ratings are related to global stock returns over 2001-2020. This finding obtains across different regions, time periods, ESG (sub)ratings, ESG momentum, ESG downgrades and upgrades, and best-in-class strategies. We further find little empirical support for prominent hypotheses from the literature on the role of ESG uncertainty and of country-level ESG social norms, ESG disclosure standards, and ESG regulations in shaping the relation between ESG and global stock returns. Overall, our results suggest that ESG investing did not systematically affect investment performance during the past two decades.

And several interesting free blog posts that have been published during the last 2 weeks:

Cryptocurrency Market Dynamics Around Bitcoin Futures Expiration Events

In the rapidly evolving landscape of cryptocurrency markets, understanding the underlying dynamics that drive price movements and investor sentiment can be a matter of survival. However, there are myriad facets of trading reality, and the only thing that we can do is to slowly understand them one after another, one step at a time. This article picks one corner of the cryptocurrency market and sheds a little light on it. We have already written a few times about the importance of the introduction of Bitcoin futures and their impact on the Bitcoin price. Therefore, in this article, we will specifically examine Bitcoin’s behavior around the critical events when Bitcoin futures expire.

Music Sentiment and Stock Returns around the World

There was a time in history when researchers believed that we, as a human species, act ultimately reasonably and rationally (for example, when dealing with financial matters). What arrived with the advent of Animal spirits (Keynes) and later Behavioral Finance pioneers such as Kahneman and Tversky was the realization that it is different from that. We often do not do what is in our best interest; quite the contrary. These emotions are hardly reconcilable with normal reasoning but result in market anomalies.

Researchers love to find causes and reasons and link behavioral anomalies to stock market performance. A lot of anomalies are related to various sentiment measures, derived from alternative data sources and today, we present an interesting new possible relationship – investors’ mood and sentiment proxied by music sentiment!