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Earlier sources: Markets
Markets connect capital, goods, services, and labor. This region follows economic developments, public markets, venture capital, fintech, and commerce. Source attention does not constitute financial advice or forecast investment returns.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗247 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- arXiv · Published · First archived
Loss Choice or Model Choice? The Role of Forecast Level in Cryptocurrency Volatility Forecasting ↗
Volatility forecasts play a central role in financial risk management because their overall level and day-to-day movements affect downstream decisions. Most studies compare forecasting models while keeping the training loss fixed. Yet losses emphasise different errors and can target different properties of future volatility, so raw comparisons may combine persistent forecast-level differences with differences in daily forecast movements. This leaves unresolved whether the importance of loss choice comes mainly from the forecast level it targets or from differences that remain after level adjus…
- GitHub · Repository created · First archived
yinxiuqu/alphalens-cna ↗
中国 A 股因子研究工具链 —— alphalens 的替代品：六道防线 / 推断层 / 数据体检 / A 股成交规则
- arXiv · Published · First archived
Target alignment, dilution and forecast selection when cross-sectional forecasts share a common target ↗
Forecasters often score the same units per date against one standardized realized outcome. We show that every standardized forecast splits exactly into a component aligned with this common target and a component uncorrelated with it. Three consequences follow: forecast-error correlation largely mirrors forecast correlation and is therefore a poor measure of diversity; an equally weighted combination beats a no-information forecast only when average alignment is large relative to the combination's dispersion; and the gain from adding a forecaster separates into genuine improvement and mere dilu…
- arXiv · Published · First archived
Modeling interest rate swap volatility with GARCH processes ↗
We examine the conditional volatility dynamics of the USD 1Yx10Y forward swap rate using GARCH(1,1), GJR-GARCH(1,1), and a two-regime Markov-switching GARCH (MSGARCH) model. The analysis uses daily data from 2007 to 2023 and incorporates market-implied measures (ATM swaption volatility and the SRVIX in- dex) together with a broad set of diagnostic tests. Standard GARCH and GJR- GARCH models show stable short-run parameters, but the intercept ω varies markedly across rolling windows, causing instability in the implied long-run vari- ance. This pattern, confirmed by the Nyblom test, motivates ad…
- arXiv · Published · First archived
Hierarchical Multi-Task Learning with Liquidity-Aware Signals for Stock Forecasting ↗
Stock price forecasting is a long-standing challenge in computational finance, driven by the inherent randomness of markets and complex temporal patterns. While recent deep-learning models have raised forecasting accuracy by jointly modeling inter-stock and temporal price dynamics, they conflate inter-stock relationships with intra-stock temporal dependencies and focus solely on the univariate objective of price movement. To address these limitations, we propose LiMT, a Hierarchical Multi-Task Learning framework that integrates liquidity-aware signals for stock price forecasting. LiMT employs…
- OpenAlex · Published · First archived
Building sustainable influencer partnerships within the creator economy ↗
Influencer marketing has become an integral part of retail branding strategies. As the creator economy continues to expand, influencer collaborations are now viewed as strategic partnerships rather than promotional activities. Despite this transition, sustaining authentic and long-term brand-influencer partnerships remains a significant yet underexplored challenge. Combining Social Exchange Theory (SET) and Self-Determination Theory (SDT) into a unified motivational-exchange framework, this research focuses on how brand practices operate simultaneously as relational exchange resources and as p…
- GitHub · Repository created · First archived
ismaelsoilet/jev-harness ↗
Zero-dependency System One decision harness: 5 semantic gates saving frontier AI agent tokens on trivial errors & doom loops. Python + TypeScript + Rust. MCP-compatible.
- arXiv · Published · First archived
Financial Language Models as Applied Artificial Intelligence Systems for News-Based Trading under Market Frictions ↗
Financial language models can transform unstructured firm-specific news into structured decision signals, but financial AI research lacks an integrated deployment framework for evaluating whether those signals remain useful in financial decision systems. Computer science research has developed strong methods for time-series forecasting, text classification, multimodal stock prediction, graph-based market modeling, and machine-learning operations, yet these streams do not provide a domain-specific protocol that jointly tests financial language-model outputs under event-time observability, proba…
- arXiv · Published · First archived
OrderFusion+: Probabilistic Buy--Sell Price Trajectory Forecasting in Intraday Electricity Markets ↗
Intraday electricity markets enable participants to adjust energy positions close to delivery, with price forecasts necessary to support trading and the scheduling of flexible electricity resources as well as increasingly responsive consumers. Forecasting model specifications have progressed from using macro-features, such as renewable generation and load, to the micro-features of continuous orderbooks. A recent advanced deep learning model, OrderFusion, explicitly models micro-level buy-sell orderbook interactions. However, despite its superior comparative forecasting performance, it consider…
- arXiv · Published · First archived
HARN: Hierarchical Associative Resonance Network for Event-Driven Multi-Timeframe Forecasting ↗
Financial time series evolve across multiple temporal resolutions, challenging forecasting systems to incorporate newly available information without repeatedly recomputing unchanged representations. We introduce HARN, a Hierarchical Associative Resonance Network for event-driven multi-timeframe forecasting. HARN maintains persistent representations across temporal levels and updates each level only when its corresponding completed bar becomes available. The architecture combines causal multi-scale temporal encoding, gated associative memory, cross-level resonance, and hierarchical evidence ag…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Markets
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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