Quantpedia Premium Update – July 25th

New Strategies:

#1026 – Combining Systematic Trend-Following and Global Macro Strategies

Period of rebalancing: Monthly
Markets traded: bonds, commodities, currencies, equities
Instruments used for trading: CFDs, ETFs, futures
Complexity: Very complex strategy
Backtest period: 1962-2023
Indicative performance: 17.87%
Estimated volatility: 10.28%

Source paper:

Vyas, Aidan, Evaluating the Performance of Systematic Trend-Following and Global Macro Strategies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4745633
Abstract:
This study conducts a detailed exploration of two prominent investment strategies: systematic trend-following and systematic global macro. Utilizing a comprehensive dataset encompassing diverse countries, asset classes, and economic indicators, we evaluate the performance of these strategies both independently and in combination. A key innovation in our systematic trend-following approach involves incorporating the magnitude of price movements, alongside their direction, significantly enhancing strategy performance. In the realm of global macro strategies, we delve into the intricate relationship between macroeconomic variables and market dynamics, uncovering insights into asset price determinants. When these strategies are integrated, they not only complement each other’s strengths but also demonstrate a remarkable synergy. This amalgamation yields a diversified portfolio approach with minimal correlation to the equity market, thereby offering a potent tool for risk mit- igation and portfolio diversification. Overall, our findings illuminate the nuanced intricacies of these strategies and their potential to redefine portfolio management paradigms.

#1027 – Hedge Funds Exploiting Climate Concerns

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: funds
Complexity: Moderately complex strategy
Backtest period: 2012-2022
Indicative performance: 7.11%
Estimated volatility: 8%

Source paper:

Aragon, George O. and Jiang, Yuxiang and Joenvaara, Juha and Tiu, Cristian Ioan: Are Hedge Funds Exploiting Climate Concerns?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4801745
Abstract:
We measure a hedge fund’s exposure to climate concerns using its return covariation with the returns on a green-minus-brown stock portfolio (GMB beta). Hedge funds in the top GMB beta decile outperform those in the bottom decile by 8% per year on a risk-adjusted basis. We provide evidence that the managers of these funds are more skilled in exploiting the market’s climate concerns. Hedge funds’ aggregate portfolio of green stocks outperforms the market portfolio of green stocks, and their demand for put options on green stocks reliably anticipates lower stock returns. We also find that hedge funds tend to hold stocks with lower future carbon emissions, and investors reward high-GMB beta funds with greater flows.

#1028 – Sentiment Analysis of Bloomberg Markets Wrap Using ChatGPT

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: CFDs, ETFs, futures
Complexity: Very complex strategy
Backtest period: 2010-2023
Indicative performance: 1.2%
Estimated volatility: 1.4%

Source paper:

Lefort, Baptiste and Benhamou, Eric and Ohana, Jean-Jacques and Saltiel, David and Guez, Beatrice and Jacquot, Thomas, Sentiment Analysis of Bloomberg Markets Wrap Using ChatGPT: Application to the NASDAQ
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4780150
Abstract:
In this paper, we use a comprehensive dataset of daily Bloomberg Financial Market Summaries spanning from 2010 to 2023, published by Yahoo Finance, CNews and multiple large financial medias, to determine how global news headlines may affect stock market movements. To make this analysis more effective, we employed ChatGPT. First, from the vast pool of daily financial updates, we identify the top global news headlines that could potentially have a significant influence on stock prices. Second, for each headline, we question ChatGPT to answer whether the news might lead to a rise, fall in stock prices or is indecisive. This two-stage method proves more effective than posing a direct question to the entire text. By gathering ChatGPT’s predictions day by day, we formed an overall market sentiment score. We transform this score into a practical investment strategy in the NASDAQ index, demonstrating the significance of minimizing noise in sentiment scores by initially accumulating and then detrending them. This approach showcases that ChatGPT’s analysis of news headlines can provide valuable insights into future stock market behaviors and be a valuable tool to develop intuitive NLP-driven investment strategies leveraging news predictive power.

#1029 – 52-Week High Bond Alpha Strategy

Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: bonds
Complexity: Very complex strategy
Backtest period: 2003-2022
Indicative performance: 5.97%
Estimated volatility: 4.58%

Source paper:

Keshavarz; Javad; Sirmans; Stace: The 52-Week High, Downside Risk, and Corporate Bond Returns
https://ssrn.com/abstract=4826999
Abstract:
We show that the 52-week high stock anomaly predicts corporate bond returns, even beyond spillover effects of traditional stock momentum and earnings announcement drift. By anchoring on the 52-week high, the price-to-high (PTH) ratio provides the stock market’s strongest perspective on negative productivity shocks and potential downside risk of the firm. Empirically, PTH is inversely related to credit spreads and forecasts negative earnings surprises and ratings downgrades. A long-short bond strategy based on the 52-week high earns a monthly alpha of 48 bps after controlling for bond risk factors and is robust across bond types and markets.

#1030 – Crypto Skewness Strategy

Period of rebalancing: Monthly
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Moderately complex strategy
Backtest period: 2018-2022
Indicative performance: 35.41%
Estimated volatility: 28.23%

Source paper:

Tekulová; Paula: Skewness/Lottery Trading Strategy in Cryptocurrencies
https://ssrn.com/abstract=4158525
Abstract:
The research examines the skewness of cryptocurrencies. The aim is to investigate the performance of portfolios based on skewness. We constructed two models. The first model, called the monthly model, calculates for each day the skewness based on the last 30 days. The obtained results show that the performance is negative for every chosen portfolio. After adjusting the model, we observed a significant performance improvement. For our second model, called the yearly model, we calculated each day the skewness based on the last 360 days. The yearly strategy works very well, and we have a positive performance even in the crisis period.

New research papers related to existing strategies:

#31 – Market Seasonality Effect in World Equity Indexes

Kamstra, Mark J. and Kramer, Lisa A. and Levi, Maurice D. and Wermers, Russ: Seasonal Asset Allocation: Evidence from Mutual Fund Flows
https://www.cambridge.org/core/journals/journal-of-financial-and-quantitative-analysis/article/seasonal-asset-allocation-evidence-from-mutual-fund-flows/06BB18462CC138CD003DBB358BA922A9
Abstract:
We analyze the flow of money between mutual fund categories, finding strong evidence of seasonality in investor risk aversion. Aggregate investor flow data reveal an investor preference for safe mutual funds in autumn and risky funds in spring. During September alone, outflows from equity funds average $13 billion, controlling for previously documented flow determinants (e.g., capital-gains overhang). This movement of large amounts of money between fund categories is correlated with seasonality in investor risk aversion, consistent with investors preferring safer (riskier) investments in autumn (spring). We find consistent evidence in Canada and also in Australia, where seasons are offset by 6 months.

#67 – Industry Momentum – Riding Industry Bubbles

Zarattini, Carlo and Antonacci, Gary: A Century of Profitable Industry Trends
https://ssrn.com/abstract=4857230
Abstract:
This paper evaluates the profitability of an industry-based long-only trend-following portfolio. Utilizing 48 industry portfolios from 1926 to 2024, our analysis explores the model’s profitability over a century, highlighting its adaptability and effectiveness across diverse market epochs. We assess the overall profitability of the model and examine the distribution of long-term returns and associated risks. Our analysis includes the impact of individual industry contributions on overall portfolio performance, focusing on the frequency and average profitability of trades at both the portfolio and industry levels. The Timing Industry strategy achieves an average annual return of 18.2% with an annual volatility of 12.6%, resulting in a Sharpe Ratio of 1.39, compared to the US equity market’s 9.7% return, 17.1% volatility, and 0.63 Sharpe Ratio. The model’s outperformance is underscored by an annualized alpha of 10.9%, with the timing strategy reducing drawdown by almost 60% compared to a passive long exposure. Further investigations reveal the active strategy’s ability to fully participate during market upswings while significantly limiting exposure during downturns. In the final section, we introduce 31 sector ETFs provided by State Street Global Advisors and backtest the same trading methodology over the last 20 years. The ETFs successfully replicate the model’s exposure and returns. We also assess the impact of commissions and slippage, demonstrating that the active timing strategy remains largely profitable even with high trading costs.This paper evaluates the profitability of an industry-based long-only trend-following portfolio. Utilizing 48 industry portfolios from 1926 to 2024, our analysis explores the model’s profitability over a century, highlighting its adaptability and effectiveness across diverse market epochs. We assess the overall profitability of the model and examine the distribution of long-term returns and associated risks. Our analysis includes the impact of individual industry contributions on overall portfolio performance, focusing on the frequency and average profitability of trades at both the portfolio and industry levels. The Timing Industry strategy achieves an average annual return of 18.2% with an annual volatility of 12.6%, resulting in a Sharpe Ratio of 1.39, compared to the US equity market’s 9.7% return, 17.1% volatility, and 0.63 Sharpe Ratio. The model’s outperformance is underscored by an annualized alpha of 10.9%, with the timing strategy reducing drawdown by almost 60% compared to a passive long exposure. Further investigations reveal the active strategy’s ability to fully participate during market upswings while significantly limiting exposure during downturns. In the final section, we introduce 31 sector ETFs provided by State Street Global Advisors and backtest the same trading methodology over the last 20 years. The ETFs successfully replicate the model’s exposure and returns. We also assess the impact of commissions and slippage, demonstrating that the active timing strategy remains largely profitable even with high trading costs.

#132 – Dynamic Commodity Timing Strategy
#432 – Investment-Momentum Strategy
#715 – Investment Effect in China
#840 – Investment Factor in Indian Stocks

Institute for Monetary and Financial Research, Hong Kong: Limits-to-Arbitrage, Investment Frictions, and the Investment Effect: New Evidence
https://ssrn.com/abstract=4131868
Abstract:
This working paper was written by F.Y. Eric C. Lam (Hong Kong Institute for Monetary Research), Ya Li (The Open University of Hong Kong), Wikrom Prombutr (California State University) and K.C. John Wei (Hong Kong Polytechnic University).

This study comprehensively reexamines the debate over behavioral and rational explanations for the investment effect in an updated sample. We closely follow the previous literature and provide several differences. All our tests include five prominent measures of corporate investment and corporate profitability either as a standard control or as a structural variable in q-theory and recent investment-based asset pricing models. We test simple composite indices of limits-to-arbitrage or investment frictions. The competing explanations are compared by controlling the frictions indices against each other in regressions and by analyzing the effect of orthogonalized frictions indices. Both classical and Bayesian inferences show that limits-to-arbitrage tend to be supported by more evidence than investment frictions for all investment measures. Investment frictions are clearly important for investment-to-assets. Various robustness checks regarding model specifications and index definitions are performed. The relative importance of the two hypotheses depends on the variables used in constructing the indices. When idiosyncratic volatility and cash flow volatility are used in measuring investment frictions, the inference is more favorable for the rational explanation.

#1030 – Crypto Skewness Strategy

Chen, Yan and Liu, Yakun: Skewness Risk and the Cross-Section of Cryptocurrency Returns
https://ssrn.com/abstract=4869652
Abstract:
This paper investigates whether investors can earn higher profits by holding cryptocurrencies with lower asymmetry risk. Firstly, employing a non-parametric method of bootstrap resampling for testing, we found that the larger the market capitalization of cryptocurrencies, the more left-skewed their performance, and the smaller the market capitalization, the more right-skewed their performance, consistent with the findings of Jiang et al. (2020) in the stock market. Secondly, both portfolio-level analyses and cross-sectional regressions at the cryptocurrency level suggest a negative cross-sectional relationship between asymmetry risk and future returns in the cryptocurrency market. Additionally, we find that the prediction of skewness in the cryptocurrency market originates from idiosyncratic risk rather than systematic risk, which is inconsistent with the phenomenon found by Langlois (2020) in the stock market, where systematic skewness risk outperforms idiosyncratic risk. Finally, in addition to the risk-return tradeoff theory, the limits-to-arbitrage theory also offers some explanatory power for these results. Collectively, our findings highlight the significant role of asymmetry risk in determining cryptocurrency prices.

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

Designing Robust Trend-Following System

It is not easy to build a robust trend-following strategy that will withstand different difficult market conditions and bring consistent results. The author of today’s work was not frightened by this task and delivered a full framework on how to design a robust trend-following strategy step by step.