Life is not always rosy; many tragedies and unexpected events hurt individuals and society. While some are hardly avoidable, such as natural disasters, some others as wars, are generally only functions of hate and greed. In the case of predictable events, risk measures can be employed, but unexpected outbreaks of aggression can hardly be hedged across the spectrum of different financial assets. We had previously touched on a similar topic and looked at some historical geopolitical shocks and price reactions around that time. Now, we would like to do a short review of an interesting 140-page paper by Dat Mai and Kuntara Pukthuanthong (2022), which, while not providing actionable strategy, provides insightful retrospection and takes war topic modeling to the higher level, covering developing narratives and influence factors extensively.

We have brought attention to the importance of evaluating factors models in different market regimes, and now, we will take a closer look at the size factor. Size [SMB (small minus big)] factor is a popular investment choice for asset investigation by many portfolio managers worldwide. The Size earned prominence in Fama and French’s three and five-factor models, and enjoy the continued discussion about its place in today’s portfolio construction. But it’s crucially important for investors seeking to capture the Size premium to realize that it is dependent on the monetary policy being pursued by the Federal Reserve, as the monetary easing seems to induce a Size premium.

What happens to the stock prices when inflation expectations decrease or increase? The authors Manav Chaudhary and Benjamin Marrow, in their paper Inflation Expectations and Stock Returns, explore this topic and find that when inflation expectation is high, stock prices also rise in their value. The evidence they present suggests that stocks have been a hedge against expected inflation for the last couple of decades and that this effect is present across stocks from all industries.

Quantpedia’s mission is simple – we want to analyze and process academic research related to quant/algo trading and simplify it into a more user-friendly form to help everyone who looks for new trading strategy ideas. It also means that we are a highly focused quant-research company, not an asset manager, and we do not manage any clients’ funds or managed accounts. But sometimes, our readers contact us with a request to help them to translate strategy backtests performed in Quantconnect into paper trading or real-trading environment. The following article is a short case study that contains a few useful tips on how to do it.

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.

The recent surge in global inflation sent shock waves across financial markets and affected the complicated relationship between stocks and bonds. Today, we would like to present you with a review of two interesting papers, which provide both a deep and easy-to-understand examination of the correlation structure of those two main asset classes. The first paper reviews specifics in various parts of the world, and the second one summarizes known information about the macroeconomic drivers of the US stock-bond correlation.