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?

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.

“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.

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.

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…

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?