New Strategies:
#970 – Systematic Innovation Factor in Stocks
Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 2013-2023
Indicative performance: 18.24%
Estimated volatility: 26.12%
Source paper:
Vojtko & Pauchlyová: How to Build a Systematic Innovation Factor in Stocks
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4716642
Abstract:
The aim of this article is multifold. It aims to answer the research question: does a portfolio consisting of top innovators outperform the S&P 500 index? To address this question, a strategy of investing long in top innovators according to their ranking is developed, and its performance is compared to that of the broad-based index. Based on the common belief that higher innovativeness carries higher risk, it aims to evaluate the volatility associated with innovative stocks. Additionally, it aims to analyze the impact of sector factors on the portfolio’s performance. Finally, it conducts a comparative analysis between the portfolio’s performance and that of the ARK Innovation ETF (ARKK), which specifically focuses on investing in companies relevant to the theme of disruptive innovation.
#971 – International Market Timing With Moving Average Distance
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: ETFs
Complexity: Simple strategy
Backtest period: 1990-2020
Indicative performance: 20.26%
Estimated volatility: 20.14%
Source paper:
Abudy, Menachem (Meni) and Kaplanski, Guy and Mugerman, Yevgeny: Market Timing with Moving Average Distance: International Evidence
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4652949
Abstract:
We explore the ability of the distance between short- and long-run moving averages, called MAD, to predict future returns of international market-wide indices. MAD portfolios yield abnormal profits after transaction costs, which do not reverse in the long run. This suggests that anchoring to long-run moving averages is a global phenomenon that applies also to market-wide indices. The annualized MAD portfolios’ alpha values are double-digit with Sharpe ratios significantly higher than those of the global benchmarks. Similar results for developed economies and developed markets indicate that international diversification is still effective and offers significant economic benefits even among developed countries.
#972 – Google Trends Predict When to Bet Against Beta
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2004-2019
Indicative performance: 7.85%
Estimated volatility: 6.25%
Source paper:
Piccoli, Pedro, When to Bet Against Beta? Ask Google
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4713273
Abstract:
In this paper, I document that investor attention negatively predicts betting against beta returns. Using Google Search Volumes toward US market indices as my proxy to attention, I find that this relation holds after controlling for competitive factors and different search terminologies and in most of the other G7 countries. The results also indicate that investor attention presents a unique capacity to explain future BAB performance that is not shared by other famous variables, such as liquidity constraints, sentiment, lottery demand or volatility. On aggregate, the findings suggest that individual investors play a relevant role on BAB performance.
#973 – Network Momentum across Asset Classes
Period of rebalancing: Monthly
Markets traded: bonds, commodities, currencies, equities
Instruments used for trading: CFDs, futures
Complexity: Very complex strategy
Backtest period: 2000-2022
Indicative performance: 7%
Estimated volatility: 5.2%
Source paper:
Zohren, S., Dong, X., Roberts, S., Pu, X. S.: Network Momentum across Asset Classes
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4540651
Abstract:
We investigate the concept of network momentum, a novel trading signal derived from momentum spillover across assets. Initially observed within the confines of pairwise economic and fundamental ties, such as the stock-bond connection of the same company and stocks linked through supply-demand chains, momentum spillover implies a propagation of momentum risk premium from one asset to another. The similarity of momentum risk premium, exemplified by co-movement patterns, has been spotted across multiple asset classes including commodities, equities, bonds and currencies. However, studying the network effect of momentum spillover across these classes has been challenging due to a lack of readily available common characteristics or economic ties beyond the company level. In this paper, we explore the interconnections of momentum features across a diverse range of 64 continuous future contracts spanning these four classes. We utilise a linear and interpretable graph learning model with minimal assumptions to reveal the intricacies of the momentum spillover network. By leveraging the learned networks, we construct a network momentum strategy that exhibits a Sharpe ratio of 1.5 and an annual return of 22%, after volatility scaling, from 2000 to 2022. This paper pioneers the examination of momentum spillover across multiple asset classes using only pricing data, presents a multi-asset investment strategy based on network momentum, and underscores the effectiveness of this strategy through robust empirical analysis.
New research papers related to existing strategies:
#83 – Pre-Holiday Effect
Albertson, Nikolas L. and Tierney, Heather L.R. and Cline, Jeffrey W. and Boylan, Daniel Boylan: Abnormal Returns and Stock Performance Prior and after Federal Holidays
https://ssrn.com/abstract=4703461
Abstract:
Using the De Bondt and Thaler’s abnormal returns model with data from the New York Stock Exchange from 2014 to 2018 and the Standard and Poor’s 500 Index, this paper finds that statistically significant positive abnormal returns are possible with respect to certain U.S. federal holidays, which are Martin Luther King Day, President’s Day, Christmas Day, and Memorial Day. The top two-thirds of the New York Stock Exchange based on market capitalization also provides an opportunity for earning positive abnormal returns. Further partitioning into the day before and day after a federal holiday yields positive abnormal returns with the best performance being provided by the day before a federal holiday subsample.
#5 – FX Carry Trade
Institute for Monetary and Financial Research, Hong Kong: FX Arbitrage and Market Liquidity: Statistical Significance and Economic Value
https://ssrn.com/abstract=4131220
Abstract:
This working paper was written by Wai-Ming Fong (The Chinese University of Hong Kong), Giorgio Valente (University of Leicester) and Joseph K. W. Fung (Hong Kong Baptist University).
This paper studies covered interest parity arbitrage violations in foreign exchange markets and their relationship with market liquidity using a novel and unique dataset of tick-by-tick firm quotes for all financial instruments involved in the arbitrage strategy. The statistical analysis reveals that arbitrage opportunities are larger in size and slower to dissipate when market liquidity is poorer. Furthermore, their economic value is sizable but arbitrage profits only accrue to traders who are able to obtain low trading costs. These findings are consistent with a competitive equilibrium with real frictions when some traders have a comparative advantage in arbitrage trading.
#345 – Betting Against Correlation Effect
Pasetti, Tommaso and Montagna, Dennis Marco: The Low-Risk Effect, from Betting Against Beta to Betting Against Correlation
https://ssrn.com/abstract=3995496
Abstract:
The aim of this work is to analyze the so-called “Low Risk Effect” and the evolution of the risk-reward relationship in time. Perhaps one of the milestones of the whole modern finance has been the investigation and the debate about the positive relationship between risk and reward in asset allocation, but are we sure that this theoretical paradigm is able to provide also empirical evidence? Starting form the “father” of Modern Portfolio Theory, passing through Sharpe’s CAPM and Black 1972, we analyzed in deep the so-called “Low Risk Effect” and the debate between leverage constraints and behavioural theories. We concluded our journey decomposing Betting Against Beta (Asness, Frazzini and Pedersen) and analysing their last contribution: Betting Against Correlation (BAC), a factor that goes long low correlation stocks and shorts high correlation ones. Starting from the BAC methodology framework, we decided to create some modifications in order to test the goodness of the model in terms of performance against the reference index. Finally we tried to implement a profitable strategy for S&P500 over the time interval 2003-2021, evidencing the phases of negative correlated stocks and arriving to define strategy’s sector composition. To conclude our work we performed a sectorial analysis in which we investigated the composition of Long/Short portfolios for our best strategy Correlation Weighted qBAC, trying to evidence the main drivers for strategy’s performance and critical issues in the last few years. To go further we built a “walking correlation analysis” that resulted useful to observe the dynamic evolution of stocks correlation in time both against the market and within the sector.
#28 – Value and Momentum Factors across Asset Classes
Holcblat, Benjamin and Lioui, Abraham and Weber, Michael: Anomaly or Possible Risk Factor? Simple-To-Use Tests
https://ssrn.com/abstract=4064159
Abstract:
Asset pricing theory predicts high expected returns are a compensation for risk. However, high expected returns might also represent anomalies due to frictions or behavioral biases. We propose two complementary tests to assess whether risk can explain differences in expected returns, provide general-equilibrium foundations, and study their properties in simulations. The tests account for any risk disliked by risk-averse individuals, including high-order moments and tail risks. The tests do not rely on the validity of a factor model or other parametric statistical models. Empirically, we find risk cannot explain a large majority of differences in expected returns of characteristic-sorted portfolios.
And several interesting free blog posts that have been published during the last 2 weeks:
The Distribution of Stock Market Concentration in the U.S. Over the History
More and more, a few mega-cap companies dominate the US stock market performance. Financial journals come up with different names for those stocks every few years. They are now called the “Magnificent Seven”, but we all remember FAANG, right? Naturally, several questions arise – Is the current status quo, when the stock market capitalization is highly concentrated among the few extremely large companies, an exception or rule over history? And what’s the impact of this concentration on the performance of the one particular factor – the Size premium? We present the research paper written by Emery and Koëter that tries to answer those questions.
Gauging Existing Technical Fundamental Features through Mutual Information
Investing truly is an intense intellectual undertaking. For a Portfolio Manager (PM) to execute an investment, they must first convince themselves, then others, that the rationale behind the investment is sound. The variables they utilize in developing their rationale are of the upmost importance; These variables inevitably serve as a foundation in the evaluation of a given Asset, and therefore possess the power to influence a PM’s level of confidence in the investment. If a variable is weak, it can lead to a poor diagnosis of the asset in question, which can lead to unfavorable results on a given investment. If a variable is strong, then it will indeed provide insight into asset and therefore help paint a clear picture into the future of the asset. To be on the right side of this sword, it is imperative that portfolio managers correctly implement quantitative reasoning if not within their decision-making process, then definitely around it. This article introduces the theory of mutual information as a tool for asset managers to gauge the predictive efficiency of their selected variables.
Robustness Testing of Country and Asset ETF Momentum Strategies
The efficacy of ETF momentum strategies, while robust until around 2010, began to show signs of waning in subsequent years. This observation raises questions about the sustainability and adaptability of these strategies in varying market cycles. Central to this research is exploring how various factors/parameters—such as the ranking period, the selection quantity of assets, and the liquidity of ETFs—impact the performance of ETF momentum strategies. The aim is to uncover whether these strategies can deliver sustainable alpha in the complex and ever-evolving market landscape of the 2020s.
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
#559 – Jump Risk in Commodities
#965 – Exploration of Long Short ETF Momentum Strategies
#970 – Systematic Innovation Factor in Stocks
