A Random Walk Down Wall Street

By Karolin Baecker. 2016

422 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market (2017, methodology): “Due to the uncertainty, complexity and non-linearity of the financial market data traditional statistical methods were not successful enough to do analyses and predictions based on financial market data [3].”
Evaluating and understanding text-based stock price prediction models (2014, background): “In support of the Random Walk Theory, Malkiel (2005) argues that professional investment managers have not been able to consistently outperform their index benchmarks.”
AIS Electronic (unknown): “A different perspective on prediction comes from Random Walk Theory (Malkiel, 1973).”
Temporal Relational Ranking for Stock Prediction (2018, background): “the Efficient Markets Hypothesis) among financial economists [28, 31].”
Stock market's price movement prediction with LSTM neural networks (2017, background): “There is also the Random-walk hypothesis [2] which claims that a stock price changes independently of its history, in other words, tomorrow’s price will only depend on tomorrow information regardless of today’s price.”