Quantpedia Premium – March 9th

New strategies

#974 – Trend-Following Strategy in Cryptocurrencies

Period of rebalancing: Daily
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Very complex strategy
Backtest period: 2018-2023
Indicative performance: 44.9%
Estimated volatility: 32.5%

Source paper:

Sadik, Sheikh: Cryptocurrencies: Stylized Facts, & Risk Based Momentum Investing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4666898
Abstract:
The motivation of this research is in two folds, to understand the distributional characteristics of cryptocurrencies by means of stylized facts, and also to assess the feasibility of risk based and trend following approaches to investing in this asset class. Cryptocurrencies are more of a recent phenomenon, unlike the traditional asset classes. This poses an explicit constraint on the availability of longer history and also reliability of investment performance. Acknowledging such constraint, I focus my analysis based on the few years of data that is available.

#975 – DUAL – Negative Correlation Among Stocks Characteristics Display Reversal in Stocks

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1963-2021
Indicative performance: 12.68%
Estimated volatility: 19.8%

Source paper:

Wang, Huaixin: Style Switching and Asset Pricing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4686997
Abstract:
This paper proposes a derivation of style demand (Barberis and Shleifer, 2003) and examines its implications for return autocorrelations. When investors exhibit characteristics-based trading and extrapolative belief, the asset demand switches between competing investment styles. Stocks sharing negative (positive) correlations within the characteristic space are categorized as dual (twin) styles and display cross-stock reversal (momentum). Trading strategies exploiting such predictability yield annualized returns of 12% from both reversals and momentum. The pricing effect of style switching remains robust after controlling for a wide range of cross-firm return predictability. Evidence from institutional trading supports the underlying mechanism. The framework also implies forecastable reversals and momentum in factor returns, which is confirmed in the data.

#976 – TWIN – Positive Correlation Among Stocks Characteristics Display Momentum in Stocks

Source paper:

#977 – Political Risk in Country Equity Indexes

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: ETFs
Complexity: Moderately complex strategy
Backtest period: 1992-2019
Indicative performance: 5.86%
Estimated volatility: 12.21%

Source paper:

Gala, Vito D. and Pagliardi, Giovanni and Shaliastovich, Ivan and Zenios, Stavros A.: Political risk everywhere
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4674860
Abstract:
Country risk premia include compensation for global political risk. Political risk premia drive international returns within and across asset classes, including equities, bonds, and currencies. A strong factor structure in politically sorted portfolios uncovers systematic variations in global political risk (P-factor). The P-factor commands a significant risk premium of 4.44% per annum with a Sharpe ratio of 0.70. Together with the global market portfolio, it explains up to three-quarters of cross-sectional variation in a large panel of asset returns. The P-factor is unspanned by the existing asset pricing factors, manifests in all asset classes, and is related to systematic variations in expected global growth and aggregate volatility.

#978 – Political Risk in Country Bond Indexes

Period of rebalancing: Yearly
Markets traded: bonds
Instruments used for trading: bonds, ETFs, futures
Complexity: Moderately complex strategy
Backtest period: 1992-2019
Indicative performance: 5.86%
Estimated volatility: 11.49%

Source paper:

#979 – Political Risk in Currencies

Period of rebalancing: Yearly
Markets traded: currencies
Instruments used for trading: CFDs, forwards, futures, swaps
Complexity: Moderately complex strategy
Backtest period: 1992-2019
Indicative performance: 4.5%
Estimated volatility: 6.52%

Source paper:

New research papers related to existing strategies:

#470 – Macroeconomic Announcement Beta Strategy
#471 – Macroeconomic Announcement Beta Reversal

Chen, Jingjing and Jiang, George: Investor Risk Appetite and High-Beta Stock Valuation Around Macroeconomic Announcements.
https://ssrn.com/abstract=4706283
Abstract:
We document a dramatic swing of high-beta stock returns around pre-scheduled macroeconomic announcements – from being negative on the day before, to positive on the day of, and negative again on the day after announcements. A feasible long-short strategy of betting against beta and betting on beta yields annualized 25.28% cumulative return over the three-day announcement window. Trading activities suggest that some (institutional) investors actively trade high-beta stocks to adjust risk exposure around the announcement. Options trading shows corroborating evidence that investors are averse to risk on days before and after announcements but willing to take risk on announcement days.

#826 – News Sentiment and Equity Returns – BERT ML Model

Kirtac, Kemal and Germano, Guido: Sentiment Trading with Large Language Models
https://ssrn.com/abstract=4706629
Abstract:
We investigate the efficacy of large language models (LLMs) in sentiment analysis of U.S. financial news and their potential in predicting stock market returns. We analyze a dataset comprising 965,375 news articles that span from January 1, 2010, to June 30, 2023; we focus on the performance of various LLMs, including BERT, OPT, FINBERT, and the traditional Loughran-McDonald dictionary model, which has been a dominant methodology in the fi- nance literature. The study documents a significant association between LLM scores and subsequent daily stock returns. Specifically, OPT, which is a GPT-3 based LLM, shows the highest accuracy in sentiment prediction with an accuracy of 74.4%, slightly ahead of BERT (72.5%) and FINBERT (72.2%). In contrast, the Loughran-McDonald dictionary model demonstrates considerably lower effectiveness with only 50.1% accuracy. Regression analyses highlight a robust positive impact of OPT model scores on next-day stock returns, with coefficients of 0.274 and 0.254 in different model specifications. BERT and FINBERT also exhibit predictive relevance, though to a lesser extent. Notably, we do not observe a significant relationship between the Loughran-McDonald dictionary model scores and stock returns, challenging the efficacy of this traditional method in the current financial context. In portfolio performance, the long-short OPT strategy excels with a Sharpe ratio of 3.05, compared to 2.11 for BERT and 2.07 for FINBERT long-short strategies. Strategies based on the Loughran-McDonald dictionary yield the lowest Sharpe ratio of 1.23. Our findings emphasize the superior performance of advanced LLMs, especially OPT, in financial market prediction and portfolio management, marking a significant shift in the landscape of financial analysis tools with implications to financial regulation and policy analysis.

#679 – Carbon Emmision Intensity in Stocks

Wang, Cong: Firms’ Carbon Emissions and Stock Returns
https://ssrn.com/abstract=4582276
Abstract:
In recent years, the surge in unanticipated climate change risk has led to green assets outperforming their brown counterparts, a trend that contradicts the theoretical expectation that brown assets, exposed to higher risk associated with climate change, should achieve higher return compensations. This paper presents empirical evidence from the U.S. stock market, utilizing both portfolio and individual stock analyses, to elucidate this discrepancy. Our findings reveal that, from 2002 to 2021, green portfolios, characterized by lower carbon emissions, consistently outperform brown portfolios. Similar patterns are observed at the firm level. We propose that unexpected concerns about climate change have shifted market preferences, leading to a differential demand shock for green and brown assets. This shift in preference is a key factor driving the superior performance of green assets over their brown counterparts.

#20 – Volatility Risk Premium Effect
#710 – Quantile Curves and the VRP

Lyle, Matthew R. and Riedl, Edward J. and Siano, Federico: Changes in Risk Factor Disclosures and the Variance Risk Premium
https://ssrn.com/abstract=4090024
Abstract:
This paper examines how changes in risk disclosures affect uncertainty about risk. We measure changes in risk disclosures using the addition and removal of individual risk factors to firms’ 10-K filings, identified via textual analysis of the risk factors section. Our market outcome is the variance risk premium (VRP), which captures the market’s pricing of uncertainty about firm risk. Following recent theoretical predictions, we predict and empirically document that newly disclosed signals of risk-factor exposure—reflected in added and removed individual risk factors—decrease the uncertainty surrounding firm risk, as proxied via the VRP. We further confirm that individual risk factors offer incremental insights compared to alternative textual risk measures. Collectively, our findings suggest that textually evaluating individual risk factors reveals information about the uncertainty regarding firm risk.

#426 – 1 Month Momentum in Bonds
#500 – Interest Rate Momentum in Global Yield Curves

Sihvonen, Markus: Yield Curve Momentum
https://ssrn.com/abstract=3965229
Abstract:
I analyze time series momentum along the Treasury term structure. Yield curve momentum is primarily due to changes in the level factor of yields. Because yield changes are partly induced by changes in the federal funds rate, yield curve momentum is related to post-FOMC announcement drift. The momentum factor is unspanned by the information in the term structure today and is hence inconsistent with standard term structure, macrofinance and behavioral models. I argue that the results are consistent with a model with unpriced longer term dependencies, which can be explained by a specific form of bounded rationality.

#536 – Machine Learning Stock Picking

Dessain, Jean: Machine Learning Models Predicting Returns: Why Most Popular Performance Metrics Are Misleading and Proposal for an Efficient Metric
https://ssrn.com/abstract=3927058
Abstract:
Numerous machine learning models have been developed to achieve the ‘real-life’ financial objective of optimising the risk/return profile of investment strategies. In the current article: (a) we present and classify the most popular performance metrics used in 190 articles analysed. We noticed that, in most articles, no attention is devoted to the criteria used to compare the algorithms. (b) We evaluate the ability of the metrics used in the literature to assess the efficiency of algorithms to improve investments results. We demonstrate that many of the most popular metrics, like mean squared error (MSE) or root mean squared error (RMSE), are inappropriate for this purpose while others, like accuracy or F1, are just weak. We explain why risk-adjusted return-based metrics are best-in-class, although they suffer from statistical limitations and do not allow easy comparison of algorithms across assets or over time. (c) We propose a new discriminant metric that measures the efficiency of AI models to optimize the risk-adjusted return, which is statistically more robust, and which can test the stability of models over time and across assets.

#329 – Portfolio Hedging Using VIX Options

Paoloni, Dominick and Hennessy, Patrick: Hedging vs. Diversification: Comparing the Cost of Hedging to the Cost of Diversification
https://ssrn.com/abstract=4114927
Abstract:
Given a low-interest-rate environment, the authors examine whether holding debt is still an optimal asset allocation in a portfolio framework compared with using a negative carry protective-put strategy. The basic premise is to examine utilizing direct equity hedges in a portfolio and compare the cost of these hedges with the potential cost of bonds as interest rates have set near multi-decade lows. The protective-put strategy is presented as an alternative to debt in a portfolio framework. This paper examines the performance of a portfolio that is more heavily weighted toward equity than a traditional portfolio and uses a small amount of the portfolio to directly hedge the equity exposure. The performance is compared with that of a 60-percent equity/40-percent bond portfolio. The paper also examines how adding a reliably negatively correlated asset to a portfolio can enhance risk-adjusted returns via a systematic rebalancing approach. The strong performance of the hedged equity approach, specifically since 2017, is attributable to the large presence of option sellers in the market. The authors find that the performance of the hedged equity portfolio is competitive with that of a traditional 60/40 portfolio.

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

How Much Bitcoin Should We Allocate To the Portfolio?

After years of waiting, the recent launch of spot Bitcoin ETFs marked a significant milestone in the cryptocurrency market, making Bitcoin even more accessible for investors. Spot ETFs provide a convenient and regulated way to gain exposure to Bitcoin without the need to hold the digital asset directly, potentially attracting a broader range of market participants. Many investors are waiting to see this change’s long-term impact on the cryptocurrency’s price while putting their faith in the potentially significant returns from Bitcoin within their investment portfolios. These events are taking place after two significant milestones in Bitcoin’s history – the introduction of BTC futures in 2017 and the launch of the BTC futures ETF (BITO) in 2021. While examining the whole history of Bitcoin may give the impression of a new super asset, we need to set realistic expectations. What have all these historical changes brought, and what lessons can we learn from similar occurrences involving other assets throughout history?

How to Better Estimate Long-Term Expected Returns

Despite the significant challenges in accurately forecasting market trends and economic conditions over such lengthy periods, this task remains critically important for investors, portfolio managers, and policy makers. They depend on these forecasts to develop effective investment strategies and policy frameworks.