AUC Score :
Short-Term Revised1 :
Dominant Strategy :
Time series to forecast n:
ML Model Testing : Modular Neural Network (CNN Layer)
Hypothesis Testing : Linear Regression
Surveillance : Major exchange and OTC
1The accuracy of the model is being monitored on a regular basis.(15-minute period)
2Time series is updated based on short-term trends.
Key Points
This exclusive content is only available to premium users.About IVA
This exclusive content is only available to premium users.
IVA American Depository Shares Stock Price Forecasting Model
To forecast Inventiva S.A. American Depository Shares (IVA) stock performance, we propose a machine learning model leveraging a comprehensive dataset. This model integrates various financial indicators, macroeconomic factors, and market sentiment data. The dataset will encompass historical IVA stock price data, fundamental financial ratios (e.g., revenue, earnings per share, debt-to-equity ratio), industry benchmarks, and relevant economic indicators (e.g., GDP growth, inflation rates, interest rates). Crucially, news sentiment analysis and social media data will be incorporated to capture market sentiment fluctuations. This multifaceted approach will allow the model to capture both the fundamental strengths and weaknesses of the company, as well as external factors influencing market perception. Feature engineering will be pivotal in creating informative variables from the raw data, preparing it for optimal model performance. Preprocessing techniques such as handling missing values and standardizing features are integral to ensure data quality and model robustness.
The model architecture will employ a Recurrent Neural Network (RNN) specifically tailored for time series data. RNNs excel at capturing sequential dependencies within financial markets. The RNN's architecture will include LSTM (Long Short-Term Memory) units to manage long-term dependencies in the data. The model will be trained using a substantial portion of the historical dataset, separated into training, validation, and testing sets. Hyperparameter tuning will be implemented using techniques such as grid search or Bayesian optimization to optimize model performance and generalization capability. Metrics including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) will be used to assess the model's accuracy and precision in predicting stock price movements. Regularization techniques like dropout will be considered to prevent overfitting. Furthermore, the model will be continuously monitored and re-trained with updated data to maintain its predictive accuracy.
Post-training, the model will generate predicted stock price trends over a specified forecast horizon. Risk assessment will be an integral part of the analysis, with the model providing not only the predicted price but also the associated uncertainty levels. The model output will be visualized in a user-friendly format, providing clear interpretations of the projected price trajectory for IVA stock. This report will explicitly address potential limitations of the model and the inherent uncertainties in forecasting future stock prices. Model explainability will also be addressed through techniques such as feature importance analysis to aid in understanding the factors influencing the predictions. Regular performance evaluations against actual stock price data will ensure the ongoing reliability and relevance of the model. Continuous improvement of the model through data updates and refined algorithms will be critical for sustained predictive power.
ML Model Testing
n:Time series to forecast
p:Price signals of IVA stock
j:Nash equilibria (Neural Network)
k:Dominated move of IVA stock holders
a:Best response for IVA target price
For further technical information as per how our model work we invite you to visit the article below:
How do KappaSignal algorithms actually work?
IVA Stock Forecast (Buy or Sell) Strategic Interaction Table
Strategic Interaction Table Legend:
X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)
Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)
Z axis (Grey to Black): *Technical Analysis%
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook | B3 | Ba3 |
Income Statement | B1 | Baa2 |
Balance Sheet | C | B2 |
Leverage Ratios | Caa2 | Ba3 |
Cash Flow | Caa2 | B2 |
Rates of Return and Profitability | B3 | Ba3 |
*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?
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