Financial markets are inherently non-stationary: structural breaks and macroeconomic regime shifts often cause forecasting models to fail when deployed out of distribution (OOD). Conventional multimodal approaches that simply fuse numerical indicators and textual sentiment rarely adapt to such shifts. We introduce macro-contextual retrieval, a retrieval-augmented forecasting framework that grounds each prediction in historically analogous macroeconomic regimes. The method jointly embeds macro indicators (e.g., CPI, unemployment, yield spread, GDP growth) and financial news sentiment in a shared similarity space, enabling causal retrieval of precedent periods during inference without retraining. Trained on seventeen years of S&P 500 data (2007-2023) and evaluated OOD on AAPL (2024) and XOM (2024), the framework consistently narrows the CV to OOD performance gap. Macro-conditioned retrieval achieves the only positive out-of-sample trading outcomes (AAPL: PF=1.18, Sharpe=0.95; XOM: PF=1.16, Sharpe=0.61), while static numeric, text-only, and naive multimodal baselines collapse under regime shifts. Beyond metric gains, retrieved neighbors form interpretable evidence chains that correspond to recognizable macro contexts, such as inflationary or yield-curve inversion phases, supporting causal interpretability and transparency. By operationalizing the principle that "financial history may not repeat, but it often rhymes," this work demonstrates that macro-aware retrieval yields robust, explainable forecasts under distributional change. All datasets, models, and source code are publicly available.

Transaction costs and regime shifts are major reasons why paper portfolios fail in live trading. We introduce FR-LUX (Friction-aware, Regime-conditioned Learning under eXecution costs), a reinforcement learning framework that learns after-cost trading policies and remains robust across volatility-liquidity regimes. FR-LUX integrates three ingredients: (i) a microstructure-consistent execution model combining proportional and impact costs, directly embedded in the reward; (ii) a trade-space trust region that constrains changes in inventory flow rather than logits, yielding stable low-turnover updates; and (iii) explicit regime conditioning so the policy specializes to LL/LH/HL/HH states without fragmenting the data. On a 4 x 5 grid of regimes and cost levels with multiple random seeds, FR-LUX achieves the top average Sharpe ratio with narrow bootstrap confidence intervals, maintains a flatter cost-performance slope than strong baselines, and attains superior risk-return efficiency for a given turnover budget. Pairwise scenario-level improvements are strictly positive and remain statistically significant after multiple-testing corrections. We provide formal guarantees on optimality under convex frictions, monotonic improvement under a KL trust region, long-run turnover bounds and induced inaction bands due to proportional costs, positive value advantage for regime-conditioned policies, and robustness to cost misspecification. The methodology is implementable: costs are calibrated from standard liquidity proxies, scenario-level inference avoids pseudo-replication, and all figures and tables are reproducible from released artifacts.

Abstract--W e formulate automated market maker (AMM) rebalancing as a binary detection problem and study a hybrid quantum-classical self-attention block, Quantum Adaptive Self-Attention (QASA). QASA constructs quantum queries/keys/values via varia-tional quantum circuits (VQCs) and applies standard softmax attention over Pauli-Z expectation vectors, yielding a drop-in attention module for financial time-series decision making. Using daily data for BTCUSDC over Jan-2024-Jan-2025 with a 70/15/15 time-series split, we compare QASA against classical ensembles, a transformer, and pure quantum baselines under Return, Sharpe, and Max Drawdown. I. Introduction Decentralized finance (DeFi) has grown rapidly, with automated market makers (AMMs) becoming core primitives for on-chain liquidity provisioning [1]. AMMs implement constant-function designs (CFMMs) whose pricing and value properties have been formalized in recent theory, clarifying when pool prices are informative and how payoff replication arises [2], [3].

The primary objective of this research is to build a Momentum Transformer that is expected to outperform benchmark time-series momentum and mean-reversion trading strategies. We extend the ideas introduced in the paper Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture to equities as the original paper primarily only builds upon futures and equity indices. Unlike conventional Long Short-Term Memory (LSTM) models, which operate sequentially and are optimized for processing local patterns, an attention mechanism equips our architecture with direct access to all prior time steps in the training window. This hybrid design, combining attention with an LSTM, enables the model to capture long-term dependencies, enhance performance in scenarios accounting for transaction costs, and seamlessly adapt to evolving market conditions, such as those witnessed during the Covid Pandemic. We average 4.14% returns which is similar to the original papers results. Our Sharpe is lower at an average of 1.12 due to much higher volatility which may be due to stocks being inherently more volatile than futures and indices.

Whether it's a horrible manager at work or a particularly unlikeable ex-partner, everyone knows at least one person they'd describe as an a**hole. Now, scientists from the Franklin College of Arts and Sciences have revealed the key characteristics of a**holes – and say middle-aged men are most likely to have them. The core traits include manipulation, aggression, and entitlement, as well as irresponsibility and anger. The'Big Five' personality traits are: Openness - People who are generally open have a higher degree of intellectual curiosity and creativity. They are also more unpredictable and likely to be involved in risky behaviour such as drug taking.

"Artificial intelligence" is a popular marketing buzzword that's made its way to the event industry as event tech providers add and promote more AI features. What is artificial intelligence, and when should you care? Artificial Intelligence (AI) and machine learning technology have come a long way in recent years, and they have a wide range of applications. However, there are some common misconceptions about what AI actually is, having often become synonymous with automation or, more generally, thoughtful computer programming. In the event industry in particular, more and more event tech offerings now market AI features.

Deep reinforcement learning (DRL) has reached super human levels in complex tasks like game solving (Go and autonomous driving). However, it remains an open question whether DRL can reach human level in applications to financial problems and in particular in detecting pattern crisis and consequently dis-investing. In this paper, we present an innovative DRL framework consisting in two sub-networks fed respectively with portfolio strategies past performances and standard deviations as well as additional contextual features. The second sub network plays an important role as it captures dependencies with common financial indicators features like risk aversion, economic surprise index and correlations between assets that allows taking into account context based information. We compare different network architectures either using layers of convolutions to reduce network's complexity or LSTM block to capture time dependency and whether previous allocations is important in the modeling. We also use adversarial training to make the final model more robust. Results on test set show this approach substantially over-performs traditional portfolio optimization methods like Markowitz and is able to detect and anticipate crisis like the current Covid one.

In a 2018 Accenture Digital Health report, 75 percent of respondents said technology played an essential role in managing their health. When it comes to artificial intelligence (AI) powered digital health and wearable devices, 72 percent said they're willing to share their wearable data with their health insurance plan. The report also found that when AI and robotics consumer interest surpassed the choices available today for virtual care. At the Consumer Electronics Show (CES) 2020 in Las Vegas, January 7-10, 2020, AI-powered digital health devices will be prevalent. For people with hearing loss or who are visually impaired, machine learning in digital health devices can open new possibilities to hear conversations more clearly or see the world around them.

In a 2018 Accenture Digital Health report, 75 percent of respondents said technology played an essential role in managing their health. When it comes to artificial intelligence (AI) powered digital health and wearable devices, 72 percent said they're willing to share their wearable data with their health insurance plan. The report also found that when AI and robotics consumer interest surpassed the choices available today for virtual care. At the Consumer Electronics Show (CES) 2020 in Las Vegas, January 7-10, 2020, AI-powered digital health devices will be prevalent. For people with hearing loss or who are visually impaired, machine learning in digital health devices can open new possibilities to hear conversations more clearly or see the world around them.

Good news for road warriors: Keeping track of travel expenses is getting a whole lot easier. From mobile apps with voice recognition to online travel-booking sites, new technology is allowing employees to record and create expense reports on the fly as they travel, so they don't have to spend as much time after the fact tallying up their costs. The innovations are reducing one of the biggest headaches business travelers face: keeping track of receipts, submitting expense reports on time and facing questions about the legitimacy of certain charges. "I am so glad the days of having to carry around and protect receipts are over," says Michael Jacobs, chief procurement officer for office-supply company Staples Inc., which uses expense-processing technology from Coupa Software. In addition to streamlining expense reporting, the tools are helping organizations save money by making it easier for employees to comply with company travel policies.