Machine Learning in Stock Price Trend Forecasting

BY

PRADEEP KUMAR REDDY MUSKU

• Stock prices are dynamic and depends on known and unknown factors

• Stock Prediction Methodologies• Fundamental

• Technical

• Efficient Market Hypothesis(EMH)• Strong

• Semi Strong

• Weak

Implementation

• Data Collection

• Model Selection• Next-Day model

• Long-Term model

• Feature Selection

Data Collection

• Training data is collected from Bloomberg database

• 3M stock was picked and it contains 1471 data points (1/9/2008 to 11/8/2013)

• There are 16 features that can be used for this learning theory. Some of them are

• PE ratio

• 50-day moving average

• Current Enterprise value

Model Selection

• Learning Theories used:• Logistic Regression

• Gaussian Discriminant analysis

• Quadratic Discriminant analysis

• Support Vector Machine(SVM)

• Accuracy = The number of days that the model correctly classified the testing data total no of training days

• Next – Day Model

Model Logistic Regression

GDA QDA SVM

Accuracy 44.5% 46.4% 58.2% 55.2%

• Long – Term Model

Predicting a stock sign of difference between tomorrow’s stock price and that of certain days ego

Feature Selection

Trading Strategy

• Used 990 of the 1470 data points to fit the model.

• Made the investment decision based on the model.

Comparison

• This model has outrun the performance of the stock, with an annualized return of 19.3% vs 12.5%