Understanding the business cycle is crucial for building economic stability, guiding business planning, and informing investment decisions. The business cycle refers to the recurring pattern of expansion and contraction in economic activity over time. Economic analysis is inherently complex, incorporating a myriad of factors (such as macroeconomic indicators, political decisions). This complexity makes it challenging to fully account for all variables when determining the current state of the economy and predicting its future trajectory in the upcoming months. The objective of this study is to investigate the capacity of machine learning models in automatically analyzing the state of the economic, with the goal of forecasting business phases (expansion, slowdown, recession and recovery) in the United States and the EuroZone. We compared three different machine learning approaches to classify the phases of the business cycle, and among them, the Multinomial Logistic Regression (MLR) achieved the best results. Specifically, MLR got the best results by achieving the accuracy of 65.25% (Top1) and 84.74% (Top2) for the EuroZone and 75% (Top1) and 92.14% (Top2) for the United States. These results demonstrate the potential of machine learning techniques to predict business cycles accurately, which can aid in making informed decisions in the fields of economics and finance.

Bayesian learning, a fundamental concept in statistical inference and decision-making, has gained significant traction across various fields due to its ability to integrate prior knowledge with new information. As a robust methodology, Bayesian learning has been widely acknowledged for its adaptability and precision in handling uncertainty and updating beliefs (Gelman et al., 1995). This manuscript expands upon the Bayesian learning framework (Baley and Veldkamp, 2023) through uniquely addressing the action and state-dependent signal variance in the agents' information set. At the core of this framework is the concept that the precision of the signal received by an agent is contingent upon both the agent's action and the actual state, for example, based on their congruence or tracking error (Daly, 2018; du Sart and van Vuuren, 2021; Orlik and Veldkamp, 2014; Rompotis, 2011; Stone et al., 2013; Yang and Huang, 2022).

It is an issue of great importance for policy and investment decision makers (Schwert, 1989; Fama, 1990; Corradi et al., 2013; Chauvet et al., 2013). Empirical studies have been used to examine whether stock market volatility, which behaves differently in expansion and recession periods, can be predicted by macroeconomic variables (Schwert, 1989; Hamilton and Lin, 1996). Research has also established a link between stock market volatility and macroeconomic fundamentals (Engle and Rangel, 2008; Diebold and Yilmaz, 2008; Corradi et al., 2013; Choudhry et al., 2016). However, despite recent successes in developing machine learning (ML) models for predicting financial prices of different assets (see e.g., Gu et al. (2020); Heaton et al. (2017); Gu et al. (2021); Bianchi et al. (2021)), there is little literature discussing the impact of business cycles and market volatility on stock price forecasting with ML models. This paper fills this gap and explores the data-shifting effects of market volatility resulted from recessions on ML models. Specifically, we focus on answering the following three research questions in this work: 1. Do ML models perform differently during the recession compared to non-recession? 2. Does including recession data in the in-sample (training) period improve ML performance?

The global financial crisis and Covid recession have renewed discussion concerning trend-cycle discovery in macroeconomic data, and boosting has recently upgraded the popular HP filter to a modern machine learning device suited to data-rich and rapid computational environments. This paper sheds light on its versatility in trend-cycle determination, explaining in a simple manner both HP filter smoothing and the consistency delivered by boosting for general trend detection. Applied to a universe of time series in FRED databases, boosting outperforms other methods in timely capturing downturns at crises and recoveries that follow. With its wide applicability the boosted HP filter is a useful automated machine learning addition to the macroeconometric toolkit.

This manuscript proposes to extend the information set of time-series regression trees with latent stationary factors extracted via state-space methods. First, it allows to handle predictors that exhibit measurement error, non-stationary trends, seasonality and/or irregularities such as missing observations. Second, it gives a transparent way for using domain-specific theory to inform time-series regression trees. As a byproduct, this technique sets the foundations for structuring powerful ensembles. Their real-world applicability is studied under the lenses of empirical macro-finance. Keywords: Ensemble learning, Factor models, State-space models, Time series, Unobserved components.Introduction In time series, the simplicity of regression trees (Morgan and Sonquist, 1963; Breiman et al., 1984; Quinlan, 1986) comes at a cost: irregularities, complicated periodic patterns and non-stationary trends cannot be explicitly modelled, and this is unfortunate given that many real-world examples are subject to them. Following, in spirit, Harvey et al. (1998), this paper proposes to pre-process problematic predictors using state-space representations general enough to deal with all these complexities at once. This operation can be thought as an automated feature engineering process that extracts stationary patterns hidden across multiple predictors, while handling problematic data characteristics. Besides, when the state-space representation is compatible with domain-specific theory, this becomes a transparent way for extracting signals with structural interpretation. The resulting stationary common components, referred hereinbelow as stationary dynamic factors, are then employed as regular predictors for standard time-series regression trees. This manuscript calls them factor-augmented regression trees to stress their dependence on latent components. I thank Matteo Barigozzi and Kostas Kalogeropoulos for their valuable suggestions and supervision; Serena Lariccia and Qiwei Yao for their helpful comments on a preliminary draft of this article.

The idea of AI (Artificial Intelligence) has existed over the last 60 years, often oscillating between periods of high market expectations and stretched dull periods with no concrete developments. However, in current times, AI is no longer just a buzzword and has started making inroads in our daily lives. From search engine recommendations to targeted advertisements, from virtual assistant apps to chatbots, AI is sneaking into our lives. It is going to stay and most probably will make waves in our worlds in the coming times. Naturally, AI has started entering the real estate industry as well.

The rapid growth and market power of superstars causes complex challenges for society and governments, leading to increased antitrust enforcement, fines, and increased regulation. The reactions by markets and regulators is the primary reason why superstars in one business cycle are knocked out of the top decile by the next business cycle, which was confirmed by McKinsey & Company's research on superstar firms. Given these headwinds, it will become increasingly difficult for superstar firms to maintain a similar growth rate moving forward, providing an opportunity for competitors. In addition, tens of billions of dollars of investment in AI research and development over the last decade has resulted in sharply lower cost of adoption with much higher performance levels, opening the option for most organizations to adopt advanced AI systems.

The Hodrick-Prescott (HP) filter is one of the most widely used econometric methods in applied macroeconomic research. The technique is nonparametric and seeks to decompose a time series into a trend and a cyclical component unaided by economic theory or prior trend specification. Like all nonparametric methods, the HP filter depends critically on a tuning parameter that controls the degree of smoothing. Yet in contrast to modern nonparametric methods and applied work with these procedures, empirical practice with the HP filter almost universally relies on standard settings for the tuning parameter that have been suggested largely by experimentation with macroeconomic data and heuristic reasoning about the form of economic cycles and trends. As recent research has shown, standard settings may not be adequate in removing trends, particularly stochastic trends, in economic data. This paper proposes an easy-to-implement practical procedure of iterating the HP smoother that is intended to make the filter a smarter smoothing device for trend estimation and trend elimination. We call this iterated HP technique the boosted HP filter in view of its connection to L2-boosting in machine learning. The paper develops limit theory to show that the boosted HP filter asymptotically recovers trend mechanisms that involve unit root processes, deterministic polynomial drifts, and polynomial drifts with structural breaks -- the most common trends that appear in macroeconomic data and current modeling methodology. A stopping criterion is used to automate the iterative HP algorithm, making it a data-determined method that is ready for modern data-rich environments in economic research. The methodology is illustrated using three real data examples that highlight the differences between simple HP filtering, the data-determined boosted filter, and an alternative autoregressive approach.

Members of The Oracles share their systems to streamline your business to run like a Swiss watch. It's now possible to convert your business metrics into data points and then turn those data points over to an artificial intelligence engine that optimizes many things like price and digital marketing spend. Getting familiar with artificial intelligence and machine learning is crucial. Get your company on the Google Cloud or Microsoft Azure. Google Cloud has built-in tools for automation and you'll automatically get the latest advancements as Google regularly updates it.

It seems to me that the hype about AI makes really difficult for experienced investors to understand where the real value and innovation are. I would like then to humbly try to bring some clarity to what is happening on the investment side of the artificial intelligence industry. We have seen as in the past the development of AI has been stopped by the absence of funding, and thus studying the current investment market is crucial to identify where AI is going. First of all, it should be clear that investing in AI is extremely cumbersome: the level of technical complexity goes out of the pure commercial scope, and not all the venture capitalists are able to fully comprehend the functional details of machine learning. This is why the figures of the "Advisors" and "Scientist-in-Residence" are becoming extremely important nowadays.