This paper investigates the structural dynamics of stock market volatility through the Financial Chaos Index, a tensor- and eigenvalue-based measure designed to capture realized volatility via mutual fluctuations among asset prices. Motivated by empirical evidence of regime-dependent volatility behavior and perceptual time dilation during financial crises, we develop a regime-switching framework based on the Modified Lognormal Power-Law distribution. Analysis of the FCIX from January 1990 to December 2023 identifies three distinct market regimes, low-chaos, intermediate-chaos, and high-chaos, each characterized by differing levels of systemic stress, statistical dispersion and persistence characteristics. Building upon the segmented regime structure, we further examine the informational forces that shape forward-looking market expectations. Using sentiment-based predictors derived from the Equity Market Volatility tracker, we employ an elastic net regression model to forecast implied volatility, as proxied by the VIX index. Our findings indicate that shifts in macroeconomic, financial, policy, and geopolitical uncertainty exhibit strong predictive power for volatility dynamics across regimes. Together, these results offer a unified empirical perspective on how systemic uncertainty governs both the realized evolution of financial markets and the anticipatory behavior embedded in implied volatility measures.

Simulated environments are increasingly used by trading firms and investment banks to evaluate trading strategies before approaching real markets. Backtesting, a widely used approach, consists of simulating experimental strategies while replaying historical market scenarios. Unfortunately, this approach does not capture the market response to the experimental agents' actions. In contrast, multi-agent simulation presents a natural bottom-up approach to emulating agent interaction in financial markets. It allows to set up pools of traders with diverse strategies to mimic the financial market trader population, and test the performance of new experimental strategies. Since individual agent-level historical data is typically proprietary and not available for public use, it is difficult to calibrate multiple market agents to obtain the realism required for testing trading strategies. To addresses this challenge we propose a synthetic market generator based on Conditional Generative Adversarial Networks (CGANs) trained on real aggregate-level historical data. A CGAN-based "world" agent can generate meaningful orders in response to an experimental agent. We integrate our synthetic market generator into ABIDES, an open source simulator of financial markets. By means of extensive simulations we show that our proposal outperforms previous work in terms of stylized facts reflecting market responsiveness and realism.

Machine learning can assess the effectiveness of mathematical tools used to predict the movements of financial markets, according to new Cornell research based on the largest dataset ever used in this area. The researchers' model could also predict future market movements, an extraordinarily difficult task because of markets' massive amounts of information and high volatility. "What we were trying to do is bring the power of machine learning techniques to not only evaluate how well our current methods and models work, but also to help us extend these in a way that we never could do without machine learning," said Maureen O'Hara, the Robert W. Purcell Professor of Management at the SC Johnson College of Business. O'Hara is co-author of "Microstructure in the Machine Age," published July 7 in The Review of Financial Studies. "Trying to estimate these sorts of things using standard techniques gets very tricky, because the databases are so big. The beauty of machine learning is that it's a different way to analyze the data," O'Hara said.

Machine learning can assess the effectiveness of mathematical tools used to predict the movements of financial markets, according to new Cornell research based on the largest dataset ever used in this area. The researchers' model could also predict future market movements, an extraordinarily difficult task because of markets' massive amounts of information and high volatility. "What we were trying to do is bring the power of machine learning techniques to not only evaluate how well our current methods and models work, but also to help us extend these in a way that we never could do without machine learning," said Maureen O'Hara, the Robert W. Purcell Professor of Management at the SC Johnson College of Business. O'Hara is co-author of "Microstructure in the Machine Age," published July 7 in The Review of Financial Studies. Other Cornell co-authors are: David Easley, the Henry Scarborough Professor of Social Science in the College of Arts and Sciences and professor of information science in Computing and Information Science; and Marcos Lopez de Prado, professor of practice in Operations Research and Information Engineering in the College of Engineering and chief information officer of True Positive Technologies.

With the increasing popularity of electric vehicles, distributed energy generation and storage facilities in smart grid systems, an efficient Demand-Side Management (DSM) is urgent for energy savings and peak loads reduction. Traditional DSM works focusing on optimizing the energy activities for a single household can not scale up to large-scale home energy management problems. Multi-agent Deep Reinforcement Learning (MA-DRL) shows a potential way to solve the problem of scalability, where modern homes interact together to reduce energy consumers consumption while striking a balance between energy cost and peak loads reduction. However, it is difficult to solve such an environment with the non-stationarity, and existing MA-DRL approaches cannot effectively give incentives for expected group behavior. In this paper, we propose a collective MA-DRL algorithm with continuous action space to provide fine-grained control on a large scale microgrid. To mitigate the non-stationarity of the microgrid environment, a novel predictive model is proposed to measure the collective market behavior. Besides, a collective behavior entropy is introduced to reduce the high peak loads incurred by the collective behaviors of all householders in the smart grid. Empirical results show that our approach significantly outperforms the state-of-the-art methods regarding power cost reduction and daily peak loads optimization.

INTRODUCTION My idea is related to the application of machine learning, based on rudimentary pattern analysis and matching, onto the field of forensic analysis of the financial markets. I hope to raise money to support the creation of a body of work in software and machine learning that (1) performs forensic analysis of financial markets via the application of my concepts, theories, and findings on what I refer to market consensus (2) while at the same time, create the opportunity to rebuild my credentials to allow me to return to work. INTERNATIONAL ATTENTION AND INTEREST This work has WORLDWIDE public interest as well as international value. Specifically, in the forensic mode, anomalies can be detected and understood whereas in the predictive mode, they can be used to help understand market behavior and evaluate hedges. The work is not limited to forensic analysis but also predictive modeling but at this time, I prefer to focus not its predictive power.

Modern portfolio theory (MPT) is a hypothesis about investment theory that Harry Markowitz published in 1952. Since that time, Markowitz's theory has been one of the most influential forces in finance for both academics and practitioners. Markowitz asserted that risk-averse investors could construct portfolios of assets that maximize return for a given level of risk. The application of MPT allows investors to create an optimal portfolio of assets for any particular level of risk. Depending on the individual's risk tolerance, they should invest in the return-maximizing set of assets.

One of the most difficult problems with creating predictive models in financial markets is to find a system that will have a high accuracy in different market conditions. The identification of the transition between different market regimes will create several contradictory problems, when using machine learning. The machine learning classifier will almost for sure start to memorize the connections between features and targets instead of finding the general relations between the two if the data is not split into two set, one for training and one for testing. This is a standard procedure and called out of sample testing. If the test time period with out of sample testing is not sufficient long enough, the window in time may not cover all different market regimes.