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Quantpedia Composite Seasonality in MesoSim

June 13, 2024

In one of our older posts titled ‘Case Study: Quantpedia’s Composite Seasonal / Calendar Strategy,’ we offer insights into seasonal trading strategies such as the Turn of the Month, FOMC Meeting Effect, and Option-Expiration Week Effect. These strategies, freely available in our database, are not only examined one by one, but are also combined and explored as a cohesive composite strategy. In partnership with Deltaray, using MesoSim — an options strategy simulator known for its unique flexibility and performance — we decided to explore and quantify how our Seasonal Strategy performs when applied to options trading. Our motivation is to investigate whether this strategy can be improved in terms of risk and return. We aim to systematically harvest the VRP (volatility risk premium) timing the entries using calendar strategy to avoid historically negative trading days.

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Active vs. Passive Life Cycle Savings Strategies

June 3, 2024

The main goal of our new article is to explore the efficacy of passive versus active management strategies in the context of savings for long-term financial goals. By analyzing the performance of nine distinct asset classes, including Double Leveraged ETFs and an implementation of the Pragmatic Asset Allocation (PAA) strategy, over an almost-century-long horizon, we simulate and compare the outcomes of three passive and three active strategies. This comparative analysis focuses on their influence on key investment characteristics, including Final Portfolio Size, Maximum Drawdown, and Maximum Loss, to determine their potential in enhancing long-term investment results.

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How to Build a Systematic Innovation Factor in Stocks

February 2, 2024

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.

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Are Commodities a Good Investment? It Depends on the Country

September 22, 2023

In recent years, the diversification potential of commodities has come under scrutiny. While the majority of studies examining the role of commodities in a portfolio typically focus on U.S. investors or those dealing primarily with U.S. dollar-denominated assets, Dequiedt et al. (2023) offer a unique perspective by considering the viewpoint of domestic investors in a sample of 38 developed and emerging countries. The study explores the relationship between diversification benefits of commodities for local investors and country’s level of commodity risk exposure. Findings reveal that incorporating commodities tends to enhance the Sharpe ratio of the optimal domestic asset portfolios in most countries with low commodity dependence but doesn’t benefit highly commodity-dependent ones.

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Which Investors Drive Factor Returns?

June 20, 2023

If different investors share a common goal, why are there differences in strategy choices and portfolio characteristics across investor classes? Elsaify (2022) attempts to provide an answer. In his study, he documents heterogeneity in investors’ processing abilities, which is the key factor influencing investor’s strategy choice and finds that such heterogeneity stems from factor timing ability.

According to the results, hedge funds seem to have the highest attention capacity, the most precise information and excel at factor timing. On the other hand, long-term investors (insurance companies and pension funds), brokers, and short-sellers exhibit low attention capacity because of their timing inability. They spend relatively more attention on the fundamental, their portfolios have the least dispersion and variance and their impact on factor returns is limited.

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