Stock Price Forecasting and Hypothesis Testing Using Neural Networks

08/28/2019
by   Kerda Varaku, et al.
15

In this work we use Recurrent Neural Networks and Multilayer Perceptrons to predict NYSE, NASDAQ and AMEX stock prices from historical data. We experiment with different architectures and compare data normalization techniques. Then, we leverage those findings to question the efficient-market hypothesis through a formal statistical test.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/18/2023

Stock Price Prediction using Dynamic Neural Networks

This paper will analyze and implement a time series dynamic neural netwo...
research
03/28/2021

Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models

Designing robust frameworks for precise prediction of future prices of s...
research
10/20/2016

Reasoning with Memory Augmented Neural Networks for Language Comprehension

Hypothesis testing is an important cognitive process that supports human...
research
07/05/2023

The Predictability of Stock Price: Empirical Study onTick Data in Chinese Stock Market

Whether or not stocks are predictable has been a topic of concern for de...
research
08/25/2022

Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting

This paper presents a novel way to apply mathematical finance and machin...
research
11/21/2018

Multivariate Forecasting of Crude Oil Spot Prices using Neural Networks

Crude oil is a major component in most advanced economies of the world. ...
research
07/11/2018

Statistical estimation of superhedging prices

We consider statistical estimation of superhedging prices using historic...

Please sign up or login with your details

Forgot password? Click here to reset