What Can We Extract From the Financial Influencers’ Advice?

Social media are often the main and primary choice of information in almost every area of our lives, and they also influence the financial decisions of retail traders and investors. A lot of people give opinions anywhere on the Internet; some are respected, others are disrespected, some are more well-known, and others obscure. But the power of those people, financial influencers, as a group, is substantial as they create the market sentiment. But what’s the real value of their advice? Can we extract useful information from their opinions?

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Are Alternative Social Data Predictors Useful for Effective Allocation to Country ETFs?

The part of the attention of our own research from the last few months was a little skewed on the side of countries’ indices and their corresponding ETFs representing them, and we finally conclude our “trilogy” of investigation on the efficiency of these markets. Firstly, we analyzed price-based valuation measures, and then, in November, we investigated the impact of military expenditures on the performance of international stock markets. We will wrap up this mini-series by analyzing a few additional alternative datasets containing variables we thought might be of interest in meaningfully describing each country’s societal standing – the climate change awareness index, the happiness score, the corruption perception index, and the income inequality score.

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Military Expenditures and Performance of the Stock Markets

“Si vis pacem, para bellum”, is an old Roman proverb translated to English as “If you want peace, prepare for war”, and it is the main idea behind the military policy of a lot of modern national states. In the current globally interconnected world, waging a real “hot war” has very often really negative trade and business repercussions (as the Russian Federation realized in 2022). Still, even though wars among developed nations are luckily not as popular as they used to be, modern states heavily invest in their own defense. Nobody wants to be caught military unprepared in case of a local or global geopolitical crisis. A strong military should bring a safe environment to do business, and trade should flourish uninterrupted. But are all those national military expenditures financially rewarded? Do stock markets of countries with a strong military outperform their peers? That’s the question we have decided to answer in the following analysis.

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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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Which Alternative Risk Premia Strategies Works as Diversifiers?

In the ever-evolving world of finance, the quest for stable returns and risk mitigation remains paramount. Traditional asset classes, such as stocks and bonds, have long been the cornerstone of investment portfolios, but their inherent volatilities and susceptibilities to market fluctuations necessitate a more diversified approach. Enter the domain of alternative risk premia (ARP) – strategies designed to capture returns from diverse sources of risk, often orthogonal to traditional market risks. Our exploration in this blog post delves deep into this subject, shedding light on which ARP strategies can truly serve as robust diversifiers in the complex financial tapestry.

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What’s the Key Factor Behind the Variation in Anomaly Returns?

In a game of poker, it is usually said that when you do not know who the patsy is, you’re the patsy. The world of finance is not different. It is good to know who your counterparties are and which investors/traders drive the return of anomalies you focus on. We discussed that a few months ago in a short blog article called “Which Investors Drive Factor Returns?“. Different sets of investors and their approaches drive different anomalies, and we have one more paper that helps uncover the motivation of investors and traders for trading and their impact on anomaly returns.

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Analysis of Price-Based Quantitative Strategies for Country Valuation

The motivation for this study comes from the idea of simplifying the concept of relative valuation among the countries. There exist several ideas for relative value approaches that compare the “visible price” (or market capitalization) of the stock market to some unseen “intrinsic value” of the market. The ideas of what we can use to measure the unseen “intrinsic value” of each individual country/market are numerous – it may be a number derived from GDP (like in a Buffet Indicator), total earnings of listed companies in the selected country (Shiller’s CAPE ratio), or ratios derived from yields, demographic, etc., etc. We asked ourselves – can we create a relative valuation model and use just the price data?

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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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