Financial Timesvswallstreet-online Nachrichten
Financial Times (37.5 % de acierto) vs wallstreet-online Nachrichten (36.8 % de acierto): cuántas veces el tono de los titulares de cada fuente coincidió con el movimiento posterior del precio.
Ninguna salió mejor frente al azar que la otra
Basado en 3149 y 1181 titulares comprobados
Posición #14 de — fuentes
Posición #13 de — fuentes
Tasa de acierto
Noticias comprobadas
Artículos Analizados
Relevancia Promedio
Análisis de sentimiento y sesgo
Lo alcistas o bajistas que suelen ser los titulares de cada fuente.
Distribución de sentimiento
Financial Times
wallstreet-online Nachrichten
Cobertura de contextos de mercado
| Acierto por mercado | Financial Times | wallstreet-online Nachrichten |
|---|---|---|
| AI infrastructure bonds | 1 (0%) | — |
| ASOS | — | 1 (0%) |
| AT&S | — | 1 (0%) |
| AT&T | — | 1 (0%) |
| AXA | — | 1 (0%) |
| Adobe | — | 1 (0%) |
| Airbus | — | 2 (0%) |
| Aixtron | — | 1 (0%) |
| Alibaba | — | 3 (1%) |
| Alibaba/Chinese Tech | — | 1 (0%) |
| Allianz | — | 2 (0%) |
| Alphabet | — | 1 (0%) |
| Amazon | — | 2 (0%) |
| American Express | — | 1 (0%) |
| Anthropic IPO | 1 (0%) | — |
| AppLovin | — | 1 (0%) |
| Apple | — | 3 (1%) |
| Argentine peso | 1 (0%) | — |
| Asian stocks | 85 (6%) | 3 (1%) |
| Australian stocks | 23 (2%) | 3 (1%) |
| Auto1 Group | — | 1 (0%) |
| Aviation/Transport | — | 1 (0%) |
| BMW | — | 1 (0%) |
| BNB | 3 (0%) | 1 (0%) |
| BYD | — | 3 (1%) |
| Bank | — | 1 (0%) |
| Bayer | — | 1 (0%) |
| BioNTech | — | 2 (0%) |
| Biogen | — | 1 (0%) |
| Biotech | — | 1 (0%) |
| Bitcoin | 18 (1%) | 21 (3%) |
| BlackRock | — | 1 (0%) |
| Brazilian | — | 1 (0%) |
| Brazilian Real | — | 2 (0%) |
| Brazilian markets | 1 (0%) | — |
| Bulgarian | — | 1 (0%) |
| Canadian markets | — | 1 (0%) |
| Cannabis | — | 2 (0%) |
| Carrefour | — | 1 (0%) |
| China 10-year government bonds | 1 (0%) | — |
| Chinese solar sector | — | 1 (0%) |
| Coca-Cola | — | 1 (0%) |
| Commodities | — | 1 (0%) |
| Copper | 2 (0%) | 2 (0%) |
| D-Wave | — | 1 (0%) |
| DAX | 10 (1%) | 54 (8%) |
| DHL Group | — | 1 (0%) |
| Daimler Truck | — | 1 (0%) |
| Dell Technologies | — | 1 (0%) |
| Deutsche Bank | — | 2 (0%) |
| Deutsche Telekom | — | 3 (1%) |
| EUR/USD | 4 (0%) | 4 (1%) |
| EssilorLuxottica | — | 1 (0%) |
| Estée Lauder | — | 1 (0%) |
| Ethereum | — | 1 (0%) |
| European natural gas | 1 (0%) | 1 (0%) |
| European stocks | 72 (5%) | 40 (6%) |
| Evotec | — | 1 (0%) |
| Fortinet | — | 1 (0%) |
| French bond | 1 (0%) | — |
| French government bonds | — | 2 (0%) |
| General Motors | — | 1 (0%) |
| Glencore | 1 (0%) | — |
| Gold | 44 (3%) | 28 (4%) |
| HP | — | 1 (0%) |
| Hapag-Lloyd | — | 1 (0%) |
| HelloFresh | — | 1 (0%) |
| Hensoldt AG | — | 1 (0%) |
| Honda | — | 1 (0%) |
| Honeywell Aerospace | — | 1 (0%) |
| Hungarian markets | — | 1 (0%) |
| IBM | — | 1 (0%) |
| IMAX | — | 1 (0%) |
| Iambic Therapeutics | — | 1 (0%) |
| Indian stocks | 72 (5%) | 3 (1%) |
| Indonesian | 1 (0%) | — |
| Indonesian stock | 1 (0%) | — |
| IonQ | — | 1 (0%) |
| Iranian Rial | — | 1 (0%) |
| Jakarta Stock Exchange | — | 1 (0%) |
| Japanese stocks | 52 (4%) | 2 (0%) |
| Kazakh tenge | 1 (0%) | — |
| Kraft Heinz | — | 1 (0%) |
| Kupfermarkt | — | 1 (0%) |
| Leonardo | — | 1 (0%) |
| Lithium | — | 1 (0%) |
| Lockheed Martin | — | 2 (0%) |
| Lufthansa stock, aviation sector | — | 1 (0%) |
| Mensch und Maschine | — | 1 (0%) |
| Meta Platforms | — | 2 (0%) |
| Micron | — | 1 (0%) |
| MiniMax | — | 1 (0%) |
| Moderna | — | 1 (0%) |
| NASDAQ | 12 (1%) | 16 (3%) |
| Nanexa | — | 1 (0%) |
| Nebius Group | — | 2 (0%) |
| NetApp | — | 1 (0%) |
| Netflix | — | 2 (0%) |
| NextDC | — | 1 (0%) |
| Nike | — | 2 (0%) |
| Nordex | — | 1 (0%) |
| Nordex AG | — | 1 (0%) |
| Novo Nordisk | — | 2 (0%) |
| Novo Nordisk, pharmaceutical | — | 1 (0%) |
| Nscale IPO | — | 1 (0%) |
| Oil | 411 (29%) | 96 (15%) |
| Oracle | — | 2 (0%) |
| Palantir | — | 1 (0%) |
| Palantir stock, analyst rating | — | 1 (0%) |
| Palfinger | — | 2 (0%) |
| Palo Alto Networks | — | 1 (0%) |
| PayPal | — | 2 (0%) |
| Procter & Gamble | — | 2 (0%) |
| Puma SE | — | 1 (0%) |
| RWE | — | 1 (0%) |
| Redcare Pharmacy | — | 1 (0%) |
| Renk AG | — | 2 (0%) |
| Rheinmetall | — | 1 (0%) |
| Rheinmetall / defense technology | — | 1 (0%) |
| Rivian stock, EV maker | — | 1 (0%) |
| SAP | — | 1 (0%) |
| SK Hynix | — | 1 (0%) |
| Saab AB | — | 1 (0%) |
| Salesforce | — | 1 (0%) |
| Salzgitter AG | — | 1 (0%) |
| Samsung Electronics | — | 2 (0%) |
| Sandisk | — | 1 (0%) |
| Sartorius | — | 1 (0%) |
| Shopify-related e-commerce software | — | 1 (0%) |
| Siemens | — | 1 (0%) |
| Siemens AG | — | 1 (0%) |
| Siemens Energy | — | 3 (1%) |
| Silver | 3 (0%) | 11 (2%) |
| SoftBank | — | 1 (0%) |
| South Korean | 1 (0%) | 1 (0%) |
| South Korean solar | — | 1 (0%) |
| Space sector | — | 1 (0%) |
| SpaceX | — | 1 (0%) |
| Swiss Franc | — | 1 (0%) |
| Take-Two Interactive | — | 2 (0%) |
| Target | — | 1 (0%) |
| Technip Energies | — | 1 (0%) |
| Tesla | — | 3 (1%) |
| Thyssenkrupp | — | 1 (0%) |
| Turkish | 1 (0%) | — |
| UK markets | 79 (6%) | 3 (1%) |
| US Real Estate | — | 1 (0%) |
| US Soybean | — | 1 (0%) |
| US banking sector | 1 (0%) | — |
| US short-term interest rates | 1 (0%) | — |
| USA | 221 (16%) | 57 (9%) |
| USD/JPY | 20 (1%) | 1 (0%) |
| Uber Technologies, ride-hailing | — | 1 (0%) |
| Union Pacific | — | 1 (0%) |
| Uranium | — | 1 (0%) |
| Verbio AG | — | 1 (0%) |
| Verizon | — | 1 (0%) |
| Vertiv | — | 1 (0%) |
| Vestas Wind Systems | — | 1 (0%) |
| Vincorion | — | 1 (0%) |
| Vonovia | — | 2 (0%) |
| XRP | — | 5 (1%) |
| Xiaomi | — | 1 (0%) |
| Xpeng | — | 1 (0%) |
| Zinc | — | 2 (0%) |
| critical minerals | 1 (0%) | — |
| cryptocurrencies (Crypto) | 17 (1%) | 7 (1%) |
| general | — | 1 (0%) |
| global food | 1 (0%) | — |
| global markets | 147 (10%) | 15 (2%) |
| high-yield credit markets | 1 (0%) | — |
| junk-rated corporate bonds | 1 (0%) | — |
| online retail / e-commerce | — | 1 (0%) |
| private | 1 (0%) | — |
| private markets | 1 (0%) | — |
| restaurant | — | 1 (0%) |
| technology stocks (Tech) | 101 (7%) | 98 (15%) |
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Listas de seguimiento y el panorama de noticias, cuando quieras
Cuántas veces acertaron las noticias
La tasa de acierto de cada fuente frente al azar
Preguntas Frecuentes
¿Qué es más fiable: Financial Times o wallstreet-online Nachrichten?▼
¿Cómo se calculan estas comparaciones?▼
¿Qué mercados cubren en común Financial Times y wallstreet-online Nachrichten?▼
Nuestra Metodología
Una IA clasifica el tono de cada artículo como alcista, bajista o lateral para el mercado que trata. Desde el primer precio al que un lector podía operar tras la publicación, comprobamos el movimiento a los 1, 3 y 7 días. La tasa de acierto de una fuente se compara con el azar para las mismas valoraciones y resultados dentro de cada mercado, de modo que cubrir mercados tranquilos o agitados no aporta nada. Cada diferencia lleva un intervalo del 95 %; las comprobaciones cercanas en el tiempo se remuestrean juntas en bloques de 7 o 14 días, porque comparten movimientos del mercado. Un veredicto requiere al menos 300 comprobaciones y debe mantenerse tras corregir por el número de fuentes probadas (Holm). Responde a «¿se movió el mercado así?»: una tasa de acierto, no una rentabilidad. AIamond no hace previsiones propias.