# Stock Price Forecasting and Hypothesis Testing Using Neural Networks

**Authors:** Kerda Varaku  
**arXiv:** [1908.11212](https://arxiv.org/abs/1908.11212)  
**Date:** 2019-08-28  
**Summary page:** https://www.summarizepaper.com/en/paper/1908.11212v1/

## Key points

- Study by Kerda Varaku and colleagues on stock price forecasting using neural networks
- Utilization of Recurrent Neural Networks and Multilayer Perceptrons for predicting stock prices from NYSE, NASDAQ, and AMEX
- Evaluation of different neural network architectures and data normalization techniques to enhance prediction accuracy
- Challenge to the efficient-market hypothesis through formal statistical testing
- Emphasis on practical applications of neural networks in stock price forecasting and implications for economic theories

## Summary

In their study "Stock Price Forecasting and Hypothesis Testing Using Neural Networks," authors Kerda Varaku and colleagues explore the use of advanced machine learning techniques in financial forecasting. Specifically, they employ Recurrent Neural Networks and Multilayer Perceptrons to predict stock prices from three major exchanges - NYSE, NASDAQ, and AMEX - based on historical data. Through a series of experiments, the researchers evaluate various neural network architectures and data normalization techniques to improve prediction accuracy. Additionally, the study challenges the efficient-market hypothesis through a formal statistical test, shedding light on potential limitations or biases in financial markets. This critical analysis adds depth to the research by highlighting practical applications of neural networks in stock price forecasting and their implications for economic theories. Overall, this study contributes valuable insights to both finance and artificial intelligence by showcasing how cutting-edge technologies can analyze and potentially disrupt traditional financial paradigms. , , , , .