Stock Prices have been modeled using a variety of techniques such as neural networks, simple regression based models and so on with limited accuracy. We attempt to use Random Walk method to model movements of stock prices with modifications to account for market sentiment. A simulator has been developed as part of the work to experiment with actual NASDAQ100 stock data and check how the actual stock values compare with the predictions. In cases of short and medium term prediction (1-3 months), the predicted prices are close to the actual values, while for longer term (1 year), the predictions begin to diverge. The Random Walk method has been compared with linear regression, average and last known value across four periods and has that the Random Walk method is no better that the conventional methods as at 95% confidence there is no significant difference between the conventional methods and Random Walk model.

Prediction of stock markets has been the research interest of many scientists around the world. Speculators who wish to make a “quick buck” as well as economists who wish to predict crashes, anyone in the financial industry has an interest in predicting what stock prices are likely to be. Clearly, there is no model which can accurately predict stock prices; else markets would be absolutely perfect! However, the problem is pertinent and any improvement in the accuracy of prediction improves the state of financial markets today. This forms the broad motivation of our study.