Insights from the Geopolitical Sentiment Index made with Google Trends

Throughout history, geopolitical stress and tension has been ever-present. From ancient civilizations to today’s world, global dynamics have been largely shaped by wars, terrorism, and trade disputes. Financial markets, as always, have keenly observed and been significantly influenced as a result.

Our article delves into understanding this relation between geopolitical stress and financial markets, particularly the equity market. To briefly explain our approach, we seek to quantify geopolitical stress through an observable Geopolitical Stress Index (GSI). Using this index, we can explore the relation between geopolitical sentiment, good and bad, and instruments available on financial market. Lastly, we seek to see if geopolitical sentiment is something that can be used to impact trading decisions and develop profitable trading strategies.

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Outperforming Equal Weighting

Equal-weighted benchmark portfolios are sometimes overshadowed by the more popular market capitalization benchmarks but are still popular and often used in practice. One of the advantages of equal-weighted portfolios is that academic research shows that in the long term, they tend to outperform their market-cap-weighted peers, mainly due to positive loadings on well-known factor premiums like size and value. So, if equal weighting outperforms market-cap weighting (in the long term), what options do we have if we want to outperform equal weighting? A recent paper by Cirulli and Walker comes to our aid with an interesting proposal …

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Less is More? Reducing Biases and Overfitting in Machine Learning Return Predictions

Machine learning models have been successfully employed to cross-sectionally predict stock returns using lagged stock characteristics as inputs. The analyzed paper challenges the conventional wisdom that more training data leads to superior machine learning models for stock return predictions. Instead, the research demonstrates that training market capitalization group-specific machine learning models can yield superior results for stock-level return predictions and long-short portfolios. The paper showcases the impact of model regularization and highlights the importance of careful model design choices.

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Decreasing Returns of Machine Learning Strategies

Traditional asset pricing literature has yielded numerous anomaly variables for predicting stock returns, but real-world outcomes often disappoint. Many of these predictors work best in small-cap stocks, and their profitability tends to decline over time, particularly in the United States. As market efficiency improves, exploiting these anomalies becomes harder. The fusion of machine learning with finance research offers promise. Machine learning can handle extensive data, identify reliable predictors, and model complex relationships. The question is whether these promises can deliver more accurate stock return predictions…

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Is It Good to Be Bad? – The Quest for Understanding Sin vs. ESG Investing

What are our expectations from the ESG theme on the portfolio management level? The question is whether ESG investing also offers some kind of “alternative alpha”, or outperformance against the traditional benchmarks. There are managers and academics who are enthusiastic and hope for the outperformance of the good ESG stocks. However, the academic research community is really split. Some academic papers show positive alpha for “Saints” (good ESG stocks); others show significantly positive alpha for “Sinners” (bad ESG stocks). So, how it’s in reality? Is it “Good to be Bad”? Or the other way around?

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Performance of Factor Strategies in India

India is a big emerging market, actually the second biggest after China. We primarily look at developed markets, mostly the U.S. and Europe, and from Emerging Markets, China at most, and we are aware that we neglect this prospective country. We would like to correct this notion and give attention to a country that is (along with China) being cited as a new potential rising superpower and already looking to take the lead of Emerging Markets (EM) countries. Today, we would like to review the paper that analyzes the performance of main equity factors (with an emphasis on the Quality factor) and is a good starting point to understand the specifics of factor investing strategies in India.

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Evaluating Factor Models in China

Today, we will evaluate some specifics that are akin to the now second-largest market in the world – China. The abundance of “shell companies” creates a problem when researchers try to uncover sources of alpha in the Chinese market. We present recent research by Zhiyong Li and Xiao Rao (2022) that proposes a new alternative filter, which excludes the stocks with a high estimated shell probability when constructing equity factor models.

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Are There Intraday and Overnight Seasonality Effects in China?

At the moment, there is a lot of attention surrounding overnight anomalies in various types of financial markets. While such effects have been well documented in research, especially in US equities and derivatives, there are other asset classes that are not as well addressed. A recent (2022) paper from Jiang, Luo, and Ye contributed appealing evidence in favor of validating these phenomena in the Chinese market. We highlight the finding that the market MKT factor beta premium is earned exclusively overnight and tend to reverse intraday (and in smaller potency also value HML and profitability RMW), which is the same finding as for the US equities. In contrast, the size SMB factor exhibit significantly opposite pattern: positive intraday premium and negative overnight premium (and the same for investment CMA factor).

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Takeover Factor Explains the Size Effect

The size effect assumes a negative relationship between average stock returns and firm size. In other words, it states that low capitalization stocks outperform stocks with large capitalization. Although generally accepted, the size effect keeps being challenged. Researchers have been asking how important the firm size characteristic actually is, and whether it is possible to replace the traditional size factor of Fama and French asset pricing model (1993) with more accurate factor. Recently, one potential challenger has emerged – so-called takeover factor, employed by Easterwood et al. (2022). In their study, they work on the assumption that small firms are often targets of takeovers, which gives us a different perspective on merger and acquisition news in regards to size effect. Their results show that M&A component of average returns explains the size premium entirely.

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Nuclear Threats and Factor Performance – Takeaway for Russia-Ukraine Conflict

The Russian invasion of Ukraine and its repercussions continue to occupy front pages all around the world. While using nuclear forces in war is probably a red line for all of the mature world, there is still possible to use nuclear weapons for blackmailing. What will be the impact of such an event on financial markets? It’s not easy to determine, but we tried to identify multiple events in the past which were also slightly unexpected and carried an indication of nuclear threat and then analyzed their impact on financial markets.

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