Indian Stock Market Prediction using Augmented Financial Intelligence ML
Anishka Chauhan, Pratham Mayur, Yeshwanth Sai Gokarakonda, Pooriya, Jamie, Naman Mehrotra

TL;DR
This paper develops and evaluates multiple machine learning models, augmented with human superforecasters' predictions, to improve stock market price forecasting accuracy in the Indian context.
Contribution
It introduces a hybrid approach combining ML models with human intelligence, specifically superforecasters, to enhance stock price prediction accuracy.
Findings
Bidirectional LSTM achieved the lowest MAE score.
Combining human superforecasters with ML models improves prediction accuracy.
The hybrid model outperforms individual ML models in stock price forecasting.
Abstract
This paper presents price prediction models using Machine Learning algorithms augmented with Superforecasters predictions, aimed at enhancing investment decisions. Five Machine Learning models are built, including Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU, and a model built using LSTM and GRU algorithms. The models are evaluated using the Mean Absolute Error to determine their predictive accuracy. Additionally, the paper suggests incorporating human intelligence by identifying Superforecasters and tracking their predictions to anticipate unpredictable shifts or changes in stock prices . The predictions made by these users can further enhance the accuracy of stock price predictions when combined with Machine Learning and Natural Language Processing techniques. Predicting the price of any commodity can be a significant task but predicting the price of a stock in the…
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Taxonomy
MethodsSigmoid Activation · Tanh Activation · Masked autoencoder · Gated Recurrent Unit · Long Short-Term Memory
