The two tests were adopted by ChatGPT. This suggests that technology may have taken a significant step forward in transforming language content, including news, Twitter scripts and speech records, into trading signals.

This transformation process is not new in Wall Street and the quantitative analysts have long used language models that support chat machines to guide tactics. However, recent studies have shown that the technology developed by OpenAI has reached a completely new level in terms of nuance and contextualization.

Slavi Marinov, the head of the quantification of the hedge fund Man AHL machine learning, stated: “This is one of the few cases in which the subject matter is turned into reality.” Over the years, the company has been using natural language processing techniques to deal with text content, including financial statements and reddit notes.

The first paper was entitled “ChatGPT’s ability to misinterpret UPU”. In this paper, two U.S.C. fellows found that the ChatGPT was closest to human beings in judging whether the Central Bank’s declaration was a gang or a horse. Research by Anne Lundgaard Hansen and Sophia Kazinnik, who were full of the Federation’s reserves, showed that ChatGPT had defeated model BERT from Google and classification based on terminology.

ChatGPT was even able to explain its classification of statements to the APF policy in a manner similar to that of its own analysts. The UNAF analysts’ interpretation was used as a benchmark for this study.

An example was given in the May 2013 statement by the United States reserves: “In general, the labour market situation has improved in recent months, but the unemployment rate remains high.” The manual intelligently explained that the sentence was sent out because it indicated that the economy had not recovered completely. This is similar to the findings of the analysts, Blasen, who are described in his paper as “a 24-year-old man who is very wise and cursory”.

The second paper is entitled “ChatGPT’s ability to predict the movement of equity? Predictability of returns and broad language models. Alexandro Lopes-Lira (Alejandro Lopez-Lira) and Tanghua (Yuehua Tang) of the University of Florida made ChatGPT a financial expert to read the title of the press. They use news after the end of 2021, and the training data for ChatGPT do not cover this period.

The study found that the judgement given by ChatGPT was statistically linked to the subsequent movement of the corresponding stock. This indicates that the ChatGPT is able to correctly interpret the meaning of the press title.

One example of the paper allowed ChatGPT to judge whether the news, “Rimini Street was fined US$ 63 million in proceedings with a bone”, was beneficial or unfavourable for the bone. ChatGPT considers it to be advantageous, since this penalty “may enhance investor confidence in the ability to protect intellectual property rights in bone paper and increase industry demand for bone products and services”,

For most of the senior quantifiable analysts, the use of natural language processing techniques to measure the popularity of a stock in Twitter or an analysis of the company’s latest news has become common practice. However, the progress shown by the ChatGPT seems to open the entire new information world and help the wider financial professionals to make more accessible the technologies.

It is not surprising that, for Malinov, the current manual intelligent reading capacity is almost the same as human beings, but that ChatGPT is likely to accelerate the technology application process.

When Man AHL first constructed the model, it only quantified the need for the hedge fund to mark each sentence as positive or negative for an asset and provide a model for articulating language for manual wisdom. The company then turned the entire process into a game, ranking participants and calculating their recognition of each sentence, so that all workers can participate.

These two new papers show that ChatGPT can even carry out similar tasks without specialized training. A study by the United States Consortium indicates that such so-called untrained learning has outpaced previous technologies and that technology will become better when fine-tuned according to specific examples.

Malinov was also a co-founder of a natural language processing company. He said: “ Previously, you must follow the data. Now you can achieve the data mark by designing the correct tips for ChatGPT.”

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Apple pointed out that it may not be convenient for the vehicle operator to get off and connect the vehicle to the charging station. In addition, when it is necessary to manually connect the vehicle to the electric vehicle charging station for charging, the human operator must be present. The system and method described in the patent allows the electric vehicle to be automatically connected to the charging station without manual connection by the operator.

As an example, a charging station may include a charging plug installed using one or more passive translation tables that change position when in contact with a structure on the vehicle. The vehicle side structure, including the charging socket, can drive the vehicle to align with the charging plug through manual control or autonomous control.

Figure 1a of Apple’s patent is a schematic diagram of the charging station and the vehicle in the disconnected position; As shown in Figure 1b, the charging station is connected with the vehicle. Fig. 10 is a block diagram of a charging station assembly and a vehicle charging system assembly.