Market timing

Lunch Effect in the U.S. Stock Market Indices

21.August 2024

In the complex world of financial markets, subtle patterns often reveal themselves through careful observation and analysis. Among these is the intriguing phenomenon we can call the “Lunch Effect,” a pattern observed in U.S. stock indexes where market performance tends to exhibit a distinct positive shift immediately after the lunch break, following a typically negative or flat performance earlier in the trading day right before the lunch. This lunchtime revival is not an isolated occurrence; it shares a curious connection with the “Overnight Effect,” a well-documented tendency for the U.S. stock market to experience the bulk of its appreciation during non-trading hours, with relatively little movement during the trading day itself. Together, these effects underscore the intricate dynamics of market behavior, where timing and investor psychology play crucial roles in shaping intraday and overnight market performance. Understanding these patterns can offer valuable insights into the rhythm of the markets and the underlying factors that drive short-term price movements.

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Payout-Adjusted CAPE

19.August 2024

Professor Robert Shiller’s CAPE (cyclically adjusted price-to-earnings) ratio is well-known among the investment community. His methodology for assessing a valuation of the U.S. equity market is undoubtedly the most cited and discussed. Therefore, it’s not surprising that there exists quite a lot of papers that try to refine and expand the CAPE’s methodology. One such last attempt is the work of James White and Victor Haghani, whose research paper revolves around the use of a modified version of the Cyclically-Adjusted Price Earnings (CAPE) ratio, termed P-CAPE. Their methodology aims to improve the estimation of long-term expected real returns of the stock market by incorporating the dividend payout ratio into the traditional CAPE metric.

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Combining Discretionary and Algorithmic Trading

25.July 2024

The area we want to explore today is an interesting intersection between quantitative and more technical approaches to trading that employ intuition and experience in strictly data-driven decision-making (completely omitting any fundamental analysis!). Can just years of screen time and trading experience improve the metrics and profitability of trading systems through discretionary trading actions and decisions?

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What’s the Size of the Risk Premia (from the Analysts’ Perspective)

22.May 2024

The topic of today’s short blog post concerns a subject that’s connected to everybody participating in financial markets worldwide: different subjective return expectations. It is reasonable to have some expected returns you can count on if you are putting your money at risk. But how do they differ between different market professionals? And are return expectations influenced by recessions? We will look closely at financial analysts and their views on risk premia. The main point from the authors of the analyzed paper stresses the idea that analysts are counter-cyclical.

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Can Google Trends Sentiment Be Useful as a Predictor for Cryptocurrency Returns?

17.April 2024

In the fast-paced world of cryptocurrencies, understanding market sentiment can provide a crucial edge. As investors and traders seek to anticipate the volatile movements of Bitcoin, innovative approaches are continuously explored. One such method involves leveraging Google Trends data to gauge public interest and sentiment towards Bitcoin. This approach assumes that search volume on Google not only reflects current interest but can also serve as a predictive tool for future price movements. This blog post delves into the intricacies of using Google Trends as a sentiment predictor, exploring its potential to forecast Bitcoin prices and discussing the broader implications of sentiment analysis in the financial market.

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Impact of Business Cycles on Machine Learning Predictions

15.April 2024

As an old investing adage goes, “Everybody’s a genius in a bull market.” It is easy to fall victim to the Dunning-Kruger effect, where attribution bias makes us mistake our luck for abilities. When the business cycles change, there are great problems with precise stock price predictability. And this is not the only problem for humans, who are baffled by many mental heuristics. Machine learning algorithms experience similar problems, too. What is happening, and why is it so? A new paper by Wang, Fu, and Fan gives an explanation and proposes some remedies …

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