Title: Indian Stock Market Prediction using Augmented Financial Intelligence ML Show Chinese title

Title: 使用增强金融智能机器学习的印度股市预测

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 stock market deals with much more uncertainty. Recognising the limited knowledge and exposure to stocks among certain investors, this paper proposes price prediction models using Machine Learning algorithms. In this work, five Machine learning models are built using Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU and the last one is built using LSTM and GRU algorithms. Later these models are assessed using MAE scores to find which model is predicting with the highest accuracy. In addition to this, this paper also suggests the use of human intelligence to closely predict the shift in price patterns in the stock market The main goal is to identify Superforecasters and track their predictions to anticipate unpredictable shifts or changes in stock prices. By leveraging the combined power of Machine Learning and the Human Intelligence, predictive accuracy can be significantly increased. Show Chinese abstract

Abstract: 本文提出了使用机器学习算法并结合超级预测者预测的价格预测模型，旨在提升投资决策的质量。 构建了五种机器学习模型，包括双向LSTM、ARIMA、CNN与LSTM的组合、GRU以及使用LSTM和GRU算法构建的模型。 这些模型通过平均绝对误差（Mean Absolute Error）来评估其预测准确性。 此外，本文建议通过识别超级预测者并跟踪他们的预测，来应对股票价格中难以预料的变化或波动。 当结合机器学习和自然语言处理技术时，这些用户的预测可以进一步提高股票价格预测的准确性。 预测任何商品的价格都是一项艰巨的任务，但在股票市场中预测股票价格则涉及更多的不确定性。 认识到部分投资者对股票了解有限，本文提出了使用机器学习算法构建的价格预测模型。 在此工作中，构建了五个机器学习模型，分别使用双向LSTM、ARIMA、CNN与LSTM的组合、GRU，最后一个模型使用LSTM和GRU算法。 随后，这些模型通过平均绝对误差（MAE）得分进行评估，以确定哪个模型具有最高的预测准确性。 除此之外，本文还建议利用人类智能来精确预测股市价格模式的变化。 主要目标是识别超级预测者并跟踪他们的预测，以应对股票价格中不可预测的变化或波动。 通过结合机器学习和人类智能的力量，可以显著提高预测准确性。