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My Personal Research & Development Activities
Research in Stock Price Prediction Using Financial Mathematics, Econophysics, and Machine Learning
My research in stock price prediction is driven by a curiosity for quantitative finance, creative problem solving, and self-learning. My work focuses on developing mathematical models to describe the complex dynamics of stock price movements and applying these models to build ensemble econometric models for accurate price forecasting. This research spans multiple disciplines, including financial mathematics, econophysics, and machine learning, to understand and predict market behaviour.Mathematical Modelling of Stock Price Movements
The nature of stock price movements is a key area of study within both finance and statistical physics (also known as econophysics). Current models explore the random processes, volatility dynamics, fractals, and non-linear behaviour that characterize financial markets. Commonly, stock prices are modeled using stochastic processes such as Geometric Brownian Motion (GBM), with additional techniques like GARCH (Generalized Autoregressive Conditional Heteroskedasticity) for volatility modelling, power-law distributions for extreme events, and agent-based models for simulating market dynamics.My research aims to synthesize these existing models, integrating insights from various theories to create a unified framework for predicting future stock prices. This involves testing the scientific validity of these models by comparing them against empirical market data, refining and adjusting them to improve their accuracy.Building Econometric Models for Stock Price Prediction
A core aspect of my work involves developing ensemble econometric models that combine multiple forecasting techniques. These models use extracted features, signals, and categories from mathematical representations of stock price movements. In the field of quantitative finance and algorithmic trading, such features are critical for making informed predictions, identifying market trends, and automating decision-making processes.To enhance prediction accuracy, I incorporate neural networks, particularly deep learning models, which excel in handling large datasets and capturing complex, non-linear relationships that traditional financial models may miss. These advanced machine learning techniques are increasingly popular in financial prediction and are widely applied in the realm of robo-advisors and investech (investment technology).Ongoing Research and Scientific Evaluation
The validity of financial models is often debated, as their effectiveness depends on several factors, including assumptions, time frames, and market conditions. My research also involves the scientific evaluation of financial models, continuously testing and refining these models to ensure they remain relevant and accurate under varying market scenarios.Conclusion
Therefore, in conclusion, my research and development activities are driven primarily by my curiosity, research based goal oriented self-learning and need for creative problem solving. It involves research into techniques for mathematically describing the nature of stock price movements (and by extension / generalization of such techniques to describe similar random / seAdditional Resources:
Last Updated: 30-05-2025 IST
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Public Key: 5F0A7C59DA493504 on https://pgp.mit.edu/
Licences: This work is licensed under Attribution-NonCommercial-NoDerivatives 4.0 International and is owned by Ankit Gupta
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