Marco,
FWIW. Gemini is not shy about giving stock market advice (while ChatGPT is). BTW, I like Claude 3 a lot too. Gemini has real-time access to the web that it seems to use in its analysis.
I started playing with Gemini when it first became available. Gemini seems free to give financial advice while ChatGPT would have none of it today for me. One possible advantage s that Gemini claims to be constantly scraping the web for sentiment analysis. As it states here:
"The sentiment analysis provided was based on a combination of factors:
1. Real-time monitoring: I continuously track social media platforms like Twitter, Stock Twits, and Reddit for mentions of Tesla. This allows me to capture the most recent trends and discussions.
2. Sentiment analysis algorithms: I employ advanced natural language processing (NLP) techniques to analyze the text content of posts. These algorithms can identify positive, negative, or neutral sentiment based on word choice,context, and other linguistic cues."
Question: : Please asses everything you know about TESLA including its financial filings. Give me a prediction as to how it will do relative to the SP500 along with a probability of going in that direction."
Answer. "Outperformance: There is a high probability (70-80%) that Tesla will continue to outperform the S&P 500 over the next several years, driven by its strong market position, innovative products, and growth potential.
** Underperformance: However, there is also a lower probability (20-30%) of underperformance if Tesla faces significant challenges in meeting its production targets, managing costs, or navigating regulatory risks."*
Addendum for ML nerds (like me). Gemini's answer about ML methods that it possibly hallucinates it is using for sentiment analysis: As per Gemini:
"Sentiment Analysis Methods: A Deeper Dive
Yes, I do utilize boosting techniques as part of my sentiment analysis methods. Boosting is a machine learning ensemble method that combines multiple weak learners (classifiers) to create a strong learner. This approach has proven effective in improving the accuracy of sentiment analysis models.
Key Methods Employed:
- Natural Language Processing (NLP) Techniques:
- Tokenization: Breaking down text into individual words or phrases.
- Stemming and Lemmatization: Reducing words to their root form to capture semantic meaning.
- Part-of-Speech Tagging: Identifying the grammatical role of words in sentences.
- Dependency Parsing: Analyzing the syntactic structure of sentences to understand relationships between words.
- Feature Engineering:
- N-grams: Sequences of words of varying lengths (e.g., unigrams, bigrams, trigrams) to capture contextual information.
- TF-IDF (Term Frequency-Inverse Document Frequency): A weighting scheme that assigns higher scores to words that appear frequently in a document but are less common across the corpus.
- Word Embeddings: Representing words as dense vectors in a continuous space, capturing semantic similarities.
- Machine Learning Algorithms:
- Support Vector Machines (SVMs): A supervised learning algorithm that finds the optimal hyperplane to separate data points.
- Naive Bayes: A probabilistic classifier based on Bayes' theorem, assuming independence between features.
- Random Forest: An ensemble method that combines multiple decision trees to improve accuracy.
- Gradient Boosting Machines (GBMs): A boosting algorithm that iteratively trains weak learners and combines their predictions.
- Deep Learning Models:
- Recurrent Neural Networks (RNNs): Models that can process sequential data, such as text, by maintaining a hidden state that captures information from previous inputs.
- Long Short-Term Memory (LSTM) Networks: A type of RNN that addresses the vanishing gradient problem, allowing it to learn long-term dependencies in sequences.
- Transformer Models: Architectures that have become popular for natural language tasks, leveraging self-attention mechanisms to capture relationships between words.
Boosting is particularly effective in sentiment analysis because it can help address class imbalance and improve the accuracy of models, especially when dealing with complex language patterns and nuances.
By combining these methods and techniques, I can effectively analyze text data and extract meaningful sentiment information."
Jim
