Impact of Dataset Selection on the Performance of Trading Strategies

It would be great if the investment factors and trading strategies worked all around the world without change and under all circumstances. But, unfortunately, it doesn’t work like that. Some of the strategies are market-specific, as shown in this short analysis. The Chinese market has its own specifics, mainly higher representation of retail investors and lower efficiency. And it’s not alone; countless strategies work just in cryptocurrencies, selected futures, or some other derivatives markets. So, what’s the takeaway? Simple, it’s really important to understand that each anomaly is linked to the underlying dataset and market structure, and we need to account for it in our backtesting process.

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A Simple Approach to Market-Timing Strategy Replication

In previous articles, we discussed the ideas behind portfolio replication with market factors. However, overall robustness of the results suffers significantly if the model portfolio or trading strategy we attempt to synthetize is driven by a market-timing model. We do not know the rules driving the underlying strategy we could apply ourselves beforehand. Furthermore, there is no simple mechanism of market-timing rule detection we could potentially utilize in our regression model. Hypothetically, we could include a variety of market-timing strategies into the factor universe. But since there are countless market-timing methods, covering everything is simply unrealistic. Particularly in context of historic factor universe construction. In an attempt to capture the effects of underlying timing rules, we came up with a simple approach to address this problem to a somewhat satisfactory extent.

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How to Replicate Any Portfolio

Would you like to see the performance of your portfolio 100 years back in history? Do you want to analyze the risk of your strategy under 100 years of real historical scenarios? All of these, and much more, will be soon (in a few days) available for Quantpedia Pro subscribers. How? We will explain today how we can model a 100-year history of your portfolio.

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Introducing Quantpedia Answers

Approximately 18 months ago, when we started Quantpedia Pro service, we promised to systematically expand its analytical capabilities by adding new tools and reports to it. We kept this promise and enlarged Quantpedia Pro to over 30 reports with hundreds of tables and charts. Factor regression analysis, risk scenarios, seasonality analysis, alternative weighting schemes, risk parity, CPPI, volatility targeting, correlation analysis, Markowitz portfolio optimization, clustering, market phases analysis, ETF replication etc. offer insight into the matters of portfolio construction or risk management. But our disciplined tempo also means that some users can become lost in the number of tools Quantpedia Pro offers. Therefore, we would like to introduce to you our new Quantpedia Answers section, which contains practical examples of how to use the growing capabilities of Quantpedia Pro reporting.

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How to Improve Post-Earnings Announcement Drift with NLP Analysis

Post–earnings-announcement drift (abbr. PEAD) is a well-researched phenomenon that describes the tendency for a stock’s cumulative abnormal returns to drift in the direction of an earnings surprise for some time (several weeks or even several months) following an earnings announcement. There have been many explanations for the existence of this phenomenon. One of the most widely accepted explanations for the effect is that investors under-react to the earnings announcements. Although we already addressed such an effect in some of our previous articles and strategies, we now present a handy method of improving the PEAD by using linguistic analysis of earnings call transcripts.

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Multi Strategy Management for Your Portfolio

If you follow Quantpedia’s blogs, you probably know that Quantpedia PRO already contains multiple risk management and portfolio construction tools for your quantitative investment strategies. The newest Quantpedia PRO tool (available in a few days) will analyze something completely different, though – how to manage multi-strategy portfolios. The newest Quantpedia PRO tool (available in a few days) will analyze something completely different, though – how to manage multi-strategy portfolios. You can easily apply these multi-strategy overlays to various types of underlying – ETFs, systematic strategies, multi-asset portfolios, or multi-strategy portfolios. This article again serves as a primer for the new report’s methodology.

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