Financial Timesvswallstreet-online Nachrichten
Financial Times (37.5 % Trefferquote) vs. wallstreet-online Nachrichten (36.8 % Trefferquote): wie oft der Schlagzeilen-Ton jeder Quelle zur späteren Kursbewegung passte.
Keine schnitt gegen den Zufall besser ab als die andere
Basierend auf 3149 bzw. 1181 geprüften Schlagzeilen
Trefferquote
Geprüfte Meldungen
Analysierte Artikel
Durchschn. Relevanz
Stimmungs- & Tendenzanalyse
Wie bullisch oder bärisch die Schlagzeilen jeder Quelle tendenziell sind.
Stimmungsverteilung
Financial Times
wallstreet-online Nachrichten
Marktkontext-Abdeckung
| Trefferquote nach Markt | 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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Wie oft die Nachrichten richtig lagen
Die Trefferquote jeder Quelle im Vergleich zum Zufall
Häufig gestellte Fragen
Was ist zuverlässiger: Financial Times oder wallstreet-online Nachrichten?▼
Wie werden diese Vergleiche berechnet?▼
Welche Märkte decken Financial Times und wallstreet-online Nachrichten gemeinsam ab?▼
Unsere Methodik
Eine KI ordnet den Tenor jedes Artikels für den behandelten Markt als steigend, fallend oder seitwärts ein. Ab dem ersten Kurs, zu dem ein Leser nach der Veröffentlichung hätte handeln können, prüfen wir die Bewegung nach 1, 3 und 7 Tagen. Die Trefferquote einer Quelle wird mit dem Zufall für dieselben Einschätzungen und Ergebnisse innerhalb jedes Marktes verglichen; wer über ruhige oder bewegte Märkte schreibt, gewinnt dadurch nichts. Jeder Abstand hat ein 95-%-Intervall; zeitlich nahe Prüfungen werden in Blöcken von 7 oder 14 Tagen gemeinsam neu gezogen, weil sie Marktbewegungen teilen. Ein Urteil braucht mindestens 300 Prüfungen und muss auch nach der Korrektur für die Zahl der getesteten Quellen (Holm) bestehen. Das beantwortet „Bewegte sich der Markt so?“: eine Trefferquote, keine Rendite. AIamond selbst macht keine Prognosen.