The concept of “Don’t work for money; let money work for you,” popularized by Robert T. Kiyosaki in “Rich Dad Poor Dad,” is an attractive yet complex idea to implement.
There are many opportunities for wealth growth in today’s world, with the stock market being a key player. However, making financial decisions amidst market instability can be a daunting task. Stock values can be greatly influenced by public opinion, which can be shaped by various media outlets, regardless of their actual relation to the companies in question.
The rapid advancement in technology has led to a surge in data generation, necessitating real-time management of large datasets. This has led to the pressing need for data processing systems capable of handling large volumes of real-time data. Twitter, a rich source of real-time information, has become a crucial tool for understanding public attitudes towards companies. By gathering, processing, and analyzing real-time Twitter data, we can make informed predictions about stock prices.
Streaming data, which continuously flows from various sources like websites, mobile apps, and social media, offers valuable insights for analyzing and predicting user behavior. Using historical data, we can continually improve classification models to increase their accuracy. This involves using tools like Flink Streaming to process large datasets and data ingestion tools like Twitter API and Apache Flume for analysis.
Financial data analysis, especially stock prediction, often depends on the efficient market hypothesis, which suggests that changes in stock prices mainly reflect the public’s sentiment towards a company. Positive feedback can greatly enhance a company’s market reputation, and negative feedback can do the opposite, as seen in Tesla’s stock increase following positive product reviews. Numerous studies have shown the usefulness of such data in various areas, such as analyzing the spread of epidemics, with the fundamental idea being the detection of customer or user sentiment to inform predictions.
The methodology used involves Lambda architecture, a resilient, open-source model designed for processing big data with low latency and high throughput. This process uses the Twitter API as the data source, ingests events through an events channel, and uses the Flink real-time streaming pipeline to transform and store the data in Kafka topics. Flink ML, a library that provides machine learning APIs, helps build ML pipelines for training and inference tasks. Recurrent Neural Network (RNN) is often used for model training on historical data, with sentiments classified based on predefined ranges as show in Table 1.
| Condition | Prediction |
| -∞<S<0:0 | beyond range |
| 0:0 <S<1:0 | negative |
| 0:1<S<2:0 | neutral |
| 0:2<S<3:0 | positive |
| 0:3<S<∞ | beyond range |
Table 1: Sentiments Classification
A multitude of experimental validations and research affirm the practice of utilizing the real-time stock prices of premier firms as a benchmark for performance evaluation. The forecasted stock values for the test dataset are juxtaposed with the real values. This comparison serves as a testament to the accuracy of the prediction model. Figure 2 illustrates the relationship between the real and forecasted stock prices. A high degree of correlation signifies a robust and reliable model. This methodology is a cornerstone in financial analytics, playing a crucial role in investment strategy and risk mitigation.
Prabhu Patel, a real-time data streaming expert, says that real-time data streaming is transforming the business landscape. It’s addressing numerous critical issues and automating processes. This technology empowers machines to make decisions in real-time, eliminating the need for human intervention. The application of data streaming in financial institutions is set to redefine future operations, making outcomes more predictable.
The prediction of future stock prices based on public sentiment is a fascinating research area closely related to mood analysis. As more and more personal opinions are shared online, research suggests that automated sentiment analysis of Twitter feeds can provide valuable insights for predicting individual stock price movements.
Prabhu Patel
Prabhu Patel is an expert in data streaming with 17 years of experience. He is the founder of the American Association of Data Streaming Professionals (aadsp.org) in the USA and holds esteemed positions as a Fellow member of IETE and a Senior member of IEEE. Patel earned his master’s degree in information systems from Minot State University in North Dakota (USA). You can contact him at prabhu.patel@aadsp.org
