Quantpedia Premium Update – September 27th

New Strategies

#1049 – Intermediate Uncertainty Crypto Strategy

Period of rebalancing: Weekly
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Moderately complex strategy
Backtest period: 2018-2023
Indicative performance: 94.19%
Estimated volatility: 38.98%

Source paper:

Han, SeungOh: Nonlinear relationship between cryptocurrency returns and price sensitivity to market uncertainty
https://ssrn.com/abstract=4881385
Abstract:
This paper examines the relationship between cryptocurrency returns and price sensitivity to unexpected changes in market uncertainty, as measured by U.S. stock market volatility, from June 2018 to February 2023. Cryptocurrencies with intermediate uncertainty risk earn a risk-adjusted weekly return of 5.73% higher than those with low and high uncertainty risk, after controlling for market, size, reversal, and liquidity factors, demonstrating the non-linearity between cryptocurrency returns and VIX betas. Overpaying for lottery-like cryptocurrencies lowers expected returns, further explaining this nonlinear relationship. The relationship remains robust using (1) two-pass cross-sectional regression, (2) various quantile portfolios, and (3) alternative risk factors.

#1050 – FX Option Volume Ratio Predicts EUR USD Exchange Rate

Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: CFDs, forwards, futures, swaps
Complexity: Moderately complex strategy
Backtest period: 2000-2016
Indicative performance: 4.11%
Estimated volatility: 11.1%

Source paper:

Bao, Kun and Chen, Denghui and Gu, Chen and Papakroni, Erlina and Stan, Raluca and Wang, Muhan: The Informational Role of Forex Option Volume
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4906975
Abstract:
This paper investigates the effect of foreign exchange (FX) option trading volume on the underlying EUR/USD futures market. Our in-sample and out-of-sample tests show that the FX put-call volume ratio can predict future exchange rate changes. Greater put-call volume ratios predict a depreciation of the Euro relative to the US dollar. We use a predictive regression forecast model based on the put-call ratio to propose a trading strategy that performs better than the simple strategy of buying and holding Euros, or than the strategy of trading based on the prevailing mean forecast method. Overall, trading volume in the FX option market seems to facilitate information flow into the underlying FX futures market.

#1051 – Insights from the Geopolitical Sentiment Index made with Google Trends

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: ETFs
Complexity: Simple strategy
Backtest period: 2008-2023
Indicative performance: 3.6%
Estimated volatility: 9.34%

Source paper:

Desai, Shaun and Vojtko, Radovan and Cisár, Dominik: Insights from the Geopolitical Sentiment Index made with Google Trends
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4948780
Abstract:
This paper examines the relationship between geopolitical sentiment and equity markets using the Geopolitical Sentiment Index (GSI), constructed from Google Trends data. Initially, we explored the impact of geopolitical stress on defence-focused ETFs but found no significant effect. Shifting focus, we investigated how geopolitical risk influences the performance differential between small-cap (IWM) and large-cap (SPY) stocks. A reversal trading strategy based on monthly GSI changes was employed, with the 12-month GSI percentage change proving most effective, achieving a Sharpe ratio of 0.38. The GSI shows potential for guiding investment decisions in volatile geopolitical environments.

#1052 – Dynamic Hedging with Commodities

Period of rebalancing: Monthly
Markets traded: bonds, commodities, equities
Instruments used for trading: CFDs, ETFs, funds, futures
Complexity: Simple strategy
Backtest period: 1988-2024
Indicative performance: 9.44%
Estimated volatility: 9.36%

Source paper:

Gupta, Rahul and Somani, Ajay: Rethinking the 60/40 Portfolio: Dynamic Hedging with Commodities
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4909458
Abstract:
Historically, the negative correlation between stocks and bonds has been a cornerstone of the 60/40 strategy, offering a hedge against market volatility and economic downturns. However, recent shifts in market dynamics, particularly the increasing correlation between these asset classes during periods of high inflation, have challenged the effectiveness of this traditional allocation. This paper examines the evolving relationship between stocks and bonds, the impact of inflation on asset correlations, and the potential benefits of incorporating commodities as a dynamic hedge to enhance portfolio performance in the current economic environment.

#1053 – How to Improve Commodity Momentum Using Intra-Market Correlation

Period of rebalancing: Monthly
Markets traded: commodities
Instruments used for trading: CFDs, ETFs, funds, futures
Complexity: Moderately complex strategy
Backtest period: 2008-2024
Indicative performance: 13.97%
Estimated volatility: 16.54%

Source paper:

Vojtko, Radovan; Pauchlyová, Margaréta: How to Improve Commodity Momentum Using Intra-Market Correlation
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4964417
Abstract:
Momentum strategies have seen diminishing returns across various asset classes in recent years. This paper proposes an innovative approach to improve momentum performance in commodity markets using an intra-market correlation filter. We use the relationship between short-term and long-term correlations as a predictor for when to apply momentum or reversal trading depending on market conditions. Our findings demonstrate that when the short-term correlation exceeds the long-term correlation, a momentum strategy—going long on top-performing ETFs and short on underperformers—yields optimal results. Conversely, when the short-term correlation is lower, a reversal strategy is more effective. This combined approach significantly enhances returns, nearly doubling those of standalone momentum or reversal strategies, while maintaining manageable levels of risk.

New research papers related to existing strategies:

#679 – Carbon Emmision Intensity in Stocks

Hambel, Christoph and van der Sanden, Floor: Reevaluating the Carbon Premium: Evidence of Green Outperformance
https://ssrn.com/abstract=4923252
Abstract:
The carbon premium refers to the excess returns of brown firms over their green counterparts. Our findings provide robust evidence supporting a negative carbon premium in the US based on a sample with more than 3,500 publicly listed firms from 2007 to 2023, indicating that green firms tend to outperform brown firms. The key findings carry over to the global sample with more than 10,000 firms across 90 countries. We show how this conclusion is contingent upon several critical factors, including the treatment of unscaled emissions, the inclusion of vendor-estimated emissions, temporal considerations regarding emissions and accounting data, and the empirical framework employed. We demonstrate that those findings are primarily driven by vendor-estimated emissions, and the carbon premium becomes non-significant if we restrict the sample to firms that report their emissions.

#264 – Dividend Risk Premium Strategy

Piccotti, Louis R.: Time Varying Dividend Risk Premia
https://ssrn.com/abstract=4897959
Abstract:
The dividend risk premium (DRP) is examined at the portfolio and firm levels. At the portfolio level, the DRP is procyclical and trend-stationary with a half-life of 3.72 months. The Global Financial Crisis represents a structural break in dividend strip pricing and in DRP half-lives. Following the GFC, strip return alpha becomes significantly positive and DRP half-lives become significantly longer. At the firm level, stocks robustly display mean-reversion in DRPs. Investor sentiment and interest rates significantly explain DRP levels and magnitudes. The relationship between DRPs and real rates suggests that the DRP-implied neutral rate for the economy (r-star) is 1.471%.

#604 – Reversal on Straddles
#699 – Stock and Bond Returns Predict Currency Returns

Phylaktis, Kate and Yamani, Ehab Abdel-Tawab: Foreign Currency Forecasting: What Can Stock and Bond Markets Tell Us?
https://ssrn.com/abstract=4930469
Abstract:
This paper provides the first comprehensive investigation on the informational role of financial markets in the profitable predictability of exchange rates in an out-of-sample (OOS) context. Within a comparative analysis framework across developed and emerging countries, we examine if international stock and bond returns can be exploited as a predictor for future exchange rate changes (statistical test), and if an economically profitable trading strategy can be executed (economic test). Our central finding is that currency traders can beat emerging markets conditional on correctly predicting the direction of the OOS forecasted currency returns induced by emerging stock and, to a lesser extent, bond market returns. By contrast, this profitability is not visible in developed countries data. This asymmetric evidence, on the power of stock and bond returns in the profitable predictability of currency returns across developed and emerging countries, provides several important insights to researchers and practitioners alike.

#77 – Betting Against Beta Factor in Stocks
#386 – Enhanced Betting Against Beta Strategy in Equities

Herculano, Miguel C.: Betting Against (Bad) Beta
https://arxiv.org/abs/2409.00416
Abstract:
Frazzini and Pedersen (2014) Betting Against Beta (BAB) factor is based on the idea that high beta assets trade at a premium and low beta assets trade at a discount due to investor funding constraints. However, as argued by Campbell and Vuolteenaho (2004), beta comes in “good” and “bad” varieties. While gaining exposure to low-beta, BAB factors fail to recognize that such a portfolio may tilt towards bad-beta. We propose a Betting Against Bad Beta factor, built by double-sorting on beta and bad-beta and find that it improves the overall performance of BAB strategies though its success relies on proper transaction cost mitigation.

#286 – Spread (Basis) Momentum within Currencies

Fan, Minyou and Han, Xing and Li, Ang and Liu, Jiadong: Understanding the Performance of Currency Basis-Momentum
https://ssrn.com/abstract=4925173
Abstract:
We conduct an in-depth analysis of basis momentum (BM) in currency markets and investigate its relationship with the prevalent market anomalies. We find that BM strategies generate significant excess returns and high Sharpe ratios with various formation periods. The abnormal returns of the BM strategies are not fully explained by the closely related carry and momentum factors. We further decompose the BM signal into four components and find that the carry-and momentum-related signals jointly contribute the most to the return of the BM strategy. By adding the BM factor to the existing asset pricing models, however, the goodness-of-fit of panel regressions witnesses only marginal improvement.

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

Mahmood, Atif and Mehmood, Asad and Terzani, Simone and De Luca, Francesco and Djajadikerta, Hadrian Geri: ESG Performance and Firm Value: Evidence from Eu-Listed Firms
https://ssrn.com/abstract=4930587
Abstract:
We investigate how environmental, social, and governance (ESG) performance affects firm value. We consider listed firms in EU countries and extract panel data from the Bloomberg database from 2012 to 2021. Our final sample comprises 976 firms from 26 EU countries with 11 industry sectors. We apply the ordinary least squares technique to analyse the data. The results show that ESG performance positively and significantly influences firm value. It represents that ESG issues are relevant to stakeholders’ concerns, and addressing such issues increases firm value. This study provides important implications for practitioners and stakeholders.

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

Revisiting Trend-following and Mean-reversion Strategies in Bitcoin

Over the past few years, significant shifts in the financial landscape have reshaped the dynamics of global markets, including the cryptocurrency sector. Events such as the ongoing war in Ukraine, rising inflation rates, the soft landing scenario in the US economy, and the recent Bitcoin halving have all profoundly impacted market sentiment and price movements. Given these developments, we decided to revisit and reassess trading strategies, specifically Trend-following and Mean-reversion in Bitcoin published in 2022, which utilized data from November 2015 to February 2022. This new study explores how these strategies would have performed from November 2015 to August 2024, taking recent changes into account. The study also examines market changes between February 2022 and August 2024, highlighting developments since previous research. Additionally, it evaluates the influence of seasonality on Bitcoin’s price action, similar to our previous article – The Seasonality of Bitcoin. By analyzing these factors, we aim to provide deeper insights into the evolving behavior of the world’s leading cryptocurrency and guide investors through the complexities of today’s market environment.

How to Improve Commodity Momentum Using Intra-Market Correlation

Momentum is one of the most researched market anomalies, well-known and widely accepted in both public and academic sectors. Its concept is straightforward: buy an asset when its price rises and sell it when it falls. The goal is to take advantage of these trends to achieve better returns than a simple buy-and-hold strategy. Unfortunately, over the last decades, we have been observers of the diminishing returns of the momentum strategies in all asset classes. In this article, we will present an intra-market correlation filter that can help significantly improve commodity momentum performance and return this strategy once again into the spotlight.

What Drives Crypto Asset Prices?

Cryptocurrencies are no longer just a whim of computer nerds, they are part of the mainstream finance and often accepted part of fixed allocation for an overall diversified portfolio. We will not try to predict, whether they are here to stay in the future or will be subject to failure. This is a topic that has been touched on infinitely. Our interest caught up a purely practical paper by Austin Adams, Markus Ibert, and Gordon Liao, in which the authors apply classic macro-finance principles to identify the impact of monetary policy and risk sentiment in conventional markets on crypto asset prices. So let’s explore their results …

ETF Re-balancing and Hedge Fund Front-Running Trades

Uninformed long-term investors provide an easy target for short-term traders, and they often unscrupulously take advantage of them. But ETF investors with long investment time horizons can mitigate some of the front-running costs if they take transactional costs into account to calculate whether it is economically optimal to participate in these “market games” (exchange and broker fees + classical opportunity costs of actively participating in strategy execution). Today, we will turn our attention to the paper “ETF Rebalancing, Hedge Fund Trades, and Capital Market” from Wang, Yao, and Yelekenova to better understand complex relationship between ETFs (their investors) and hedge funds.