{"id":214688,"date":"2024-11-05T12:57:17","date_gmt":"2024-11-05T17:57:17","guid":{"rendered":"https:\/\/ibkrcampus.com\/campus\/?p=214688"},"modified":"2024-11-05T12:57:03","modified_gmt":"2024-11-05T17:57:03","slug":"trading-using-llm-generative-ai-sentiment-analysis-in-finance-part-ii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/trading-using-llm-generative-ai-sentiment-analysis-in-finance-part-ii\/","title":{"rendered":"Trading using LLM: Generative AI & Sentiment Analysis in Finance \u2013 Part II"},"content":{"rendered":"\n

Learn about LLM and Generative AI with Part I<\/a>.<\/em><\/p>\n\n\n\n

Sentiment analysis of FOMC transcripts<\/h2>\n\n\n\n

FOMC transcripts refer to the financial records of the Federal Open Market Committee<\/strong> meetings. FOMC transcripts provide key insights into monetary policy, economic assessments, and future outlooks, shaping U.S. monetary policy and hence, the market sentiment and trading strategies.<\/p>\n\n\n\n

The analysis begins with data collection from the Federal Reserve\u2019s official website. The transcripts are then preprocessed to remove irrelevant sections and focus on content that reflects market sentiment. FinBERT is used to assign sentiment scores, helping traders gauge whether the sentiment is positive or negative.<\/p>\n\n\n\n

The following table represents sentiment scores of FOMC transcripts at a minute frequency. Each row corresponds to a specific minute during the transcript. For example, the meeting text from 19:30 to 19:31 is stored in the \u2018text\u2019 column and the sentiment score of this text, which is 0.395, is stored in the column \u2018sentiment_score\u2019.<\/p>\n\n\n\n

This analysis helps quantify how the sentiment changes over time during the FOMC meeting.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Figure: Table with FOMC transcripts text at minute frequency and its sentiment score<\/p>\n\n\n\n

Next, we will discuss the trading strategy based on sentiment analysis.<\/p>\n\n\n\n

Trading strategy based on sentiment analysis<\/h3>\n\n\n\n

The strategy revolves around analysing rolling sentiment scores and establishing specific thresholds for trading decisions.<\/p>\n\n\n\n

Generating Trade Signals: <\/strong>The first step involves calculating the rolling mean of sentiment scores, which reflects the average sentiment over the minute-wide data collected throughout the FED meeting. By averaging these scores, traders can gauge the prevailing market sentiment and make informed trading decisions based on the trends observed.<\/p>\n\n\n\n

You can find the rolling sentiment score in the \u2018rolling_sentiment_score\u2019 column in the following table. It should be noted that the sentiment score values are rounded off to two decimals.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

<\/p>\n\n\n\n

Figure: Table with FOMC transcripts text with their sentiment score and rolling sentiment score.<\/p>\n\n\n\n

For example, the rolling sentiment score at 19:30:00 (0.14) is an average of sentiment scores so far, which is an average of 0.4 and -0.12.<\/p>\n\n\n\n

Similarly, the rolling sentiment score at 19:32:00 (0.08) is an average of 3 sentiment scores 0.4, -0.12, -0.05.<\/p>\n\n\n\n

Setting Thresholds:<\/strong> In this strategy, a sentiment score greater than 0 indicates positive sentiment, while a score below 0 suggests negative sentiment. In this example, a threshold of 0.1 will be used.<\/p>\n\n\n\n

Entry and Exit Rules:<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Figure: Entry rules of long and short position<\/p>\n\n\n\n

Long Position:<\/strong> Enter when the rolling sentiment score is greater than 0.1. Exit the position either when the rolling sentiment falls below -0.1 or at the last minute of the FOMC meeting.<\/p>\n\n\n\n

Short Position: <\/strong>Open a short position when the rolling sentiment score is less than -0.1. Exit when the rolling sentiment exceeds 0.1 or at the last minute of the FOMC meeting.<\/p>\n\n\n\n

Let us now check out the real-world application of using some news or information and performing sentiment analysis on the same.<\/p>\n\n\n\n

Stay tuned for the next installment to learn about\u00a0 real-world applications<\/em>.<\/p>\n\n\n\n

Originally posted on\u00a0QuantInsti<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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