Quantitative sentiment analysis

[JeffBrines]

Small issue to flag here; in the training data I see the author has determined this to be neutral -

_Example 4:

News: “Estee Lauder Q2 adj. EPS $2.11; FactSet consensus $1.90.”

The statement about Estee Lauder and FactSet consensus is neutral, as it merely states the facts without indicating a positive or negative sentiment. Here, FinGPT accurately identifies the neutrality of the statement._

Quantitatively, this is an EPS beat by $0.21 which may be enough for the stock to reactive positively to the news. I'd suggest we count this as "positive sentiment" not "neutral sentiment". I know the LLM is going to struggle with numerical analysis but this is what we need to really drive the AI to the promised land.

We are entering very grey territory once the reported EPS is within a certain range of consensus. Understanding the standard deviation within consensus estimates would really help determine if something is within the boundary of "neutral" "positive" or "negative" but this is likely a too granular at this point.

[zhumingpassional]

thanks for your suggestions.

we will check it again.

[lihongteng]

感谢您的建议。

我们将再次检查。

作为经常和成熟能够稳定盈利交易者打交道的从业人员，程序是能够从市场成交数据上看出市场的情绪。因为市场的消息是滞后，且大多数是不真实的，所以直接用成交数据来训练模型才是出路。就像是扑克游戏一样，只根据对手每个回合下注比例和行为，就足够判断对方情绪和牌力了。

As a practitioner who regularly deals with mature, stable and profitable traders, the program is able to read market sentiment from market turnover data. Because market information is lagging and mostly untrue, training models directly on trading data is the way to go. Just like a poker game, it is enough to judge the opponent's mood and card power based on the proportion of bets and behavior of the opponent each turn.

我对国内交易市场的渠道是熟悉的，或许我们能够沟通出好的思路。

I am familiar with the channels of the domestic trading market, maybe we can communicate a good idea.