This document presents research on using machine learning and deep learning models to predict stock prices. The researcher collected stock price data for two companies, Tata Steel and Hero Motocorp, at 5-minute intervals over two years. Eight classification and eight regression models were tested on this data to predict opening stock prices. The results showed that deep learning models like LSTM outperformed other regression models, while ANN performed best among classification models on average. Identifying individual errors in the best-performing LSTM model is identified as a potential next step.

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Stock Price Prediction Using Machine Learning and Deep Learning
Frameworks
Presentation · December 2018
DOI: 10.13140/RG.2.2.35704.49923
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2. Stock Price Prediction Using
Machine Learning and Deep
Learning Frameworks
Jaydip Sen
NSHM College of Management & Technology,
Kolkata, INDIA
6th International Conference on Business Analytics and Intelligence
December 20 - 22, 2018, Bangalore, INDIA

3. Objective of the Work
• The primary objective of the work is to develop a
robust framework for predicting stock price
movement based on stock price data at 5 minutes
interval of time from the National Stock Exchange
(NSE).
• Our contention is that such a granular approach can
model the inherent dynamics and can be fine-tuned
for immediate forecasting of stock price movement.

4. Outline
• Related Work
• Methodology
• Results `
Regression
Classification
• Observations on Results
• Conclusion

5. Related Work
• The literature trying to prove or disprove the efficient
market hypothesis can be classified into three strands.
• The first strand consist of studies using simple
regression techniques on cross sectional data.
• The second strand has used time series models and
techniques to forecast stock returns following
econometric techniques like ARIMA, Granger Causality,
ARDL, etc.
• The third strand includes work using machine learning
and deep learning tools for prediction of stock returns .

6. Methodology (1)
• We use the Metastock tool for collecting data on stock
price movement of two stocks – Tata Steel and Hero
Motocorp at 5 minutes interval of time over two year
period: January 2013 – December 2014.
• The raw data consisted of the following variables: (i)
date, (ii) time, (iii) open, (iv) high, (v) low, (vi) close,
and (vii) volume.
• We also collected the NIFTY index values
corresponding to each time slot to use it as a market
sentiment variable.
• The total time interval in a day is broken into three
slots and aggregate values of the above variables
are computed for each slot for each day.
 Morning slot: 9:00 AM – 11:30 AM
 Afternoon slot: 11:35 AM – 1:30 PM
 Evening slot: 1:35 PM – till the closure

7. Methodology (2)
• We derive the following eleven variables for building the predictive
models.
 month
 day_month
 day_week
 time
 open_perc
 sensex_perc
 low_diff
 high_diff
 close_diff
 vol_diff
 range_diff
• open_perc is taken as the response variable and it is predicted using
the values of the other variables as predictors. For regression based
techniques, we predict the value of the open_perc in the next slot
based on predictor values in the previous slots and for classification
techniques we instead of the value, we attempt to predict the
movement (positive or negative) of the open_perc in the next slot.

8. Methodology (3)
• We applied eight classification techniques
and eight regression techniques for
predicting the stock price values or stock
price movements for two stocks Tata Steel
and Hero Moto Corp.
• Three cases are considered:
 Case I: Model built using 2013 data and tested
on the same data
 Case II: Model built using 2014 data and
tested on the same data
Case III: Model built on 2013 data and tested
on 2014 data.

9. Results – Classification (1)
Logistic Regression

10. Results – Classification (2)
KNN Classification

11. Results – Classification (3)
Decision Tree Classification

12. Results – Classification (4)
Bagging Classification

13. Results – Classification (5)
Boosting Classification

14. Results – Classification (6)
Random Forest Classification

15. ANN model with three hidden nodes for Hero Motocorp Model Case III
Results – Classification (7)

16. Results – Classification (7)
ANN Classification

17. Results – Classification (8)
SVM Classification

18. Summary Results – Classification

19. Results – Regression (1)
Multivariate Regression

20. Decision Tree model for Tata Steel for Case II
Results – Regression (2)

21. Results – Regression (2)
Decision Tree Regression

22. Results – Regression (3)
Bagging Regression

23. Results – Regression (4)
Boosting Regression

24. Results – Regression (5)
Random Forest Regression

25. Results – Regression (6)
ANN Regression

26. Results – Regression (7)
SVM Regression

27. LSTM Multivariate Regression
• LSTM is a variant of Recurrent Neural Network
(RNN) – neural networks with feedback loops.
• In such networks output at the current time slot
depends on the current inputs as well as previous
state of the network.
• LSTM overcomes the problem of vanishing and
exploding gradients by utilizing three gates – forget
gates, input gates and output gates.
• Forget gates - control what information to throw
away from the memory at an instant of time
• Input gates – controls the new information that is
added to the cell state form the current input
• Output gates – conditionally decide what to output
from the memory cells.

28. LSTM Multivariate Regression
• LSTM is a variant of Recurrent Neural Network
(RNN) – neural networks with feedback loops.
• In such networks output at the current time slot
depends on the current inputs as well as previous
state of the network.
• LSTM overcomes the problem of vanishing and
exploding gradients by utilizing three gates – forget
gates, input gates and output gates.
• Forget gates - control what information to throw
away from the memory at an instant of time
• Input gates – controls the new information that is
added to the cell state form the current input
• Output gates – conditionally decide what to output
from the memory cells.

29. LSTM Forget Gates
The forget gate takes two
inputs h_t-1 and x_t. h_t-1 is
the hidden state from the
previous cell (i.e., the output
from the previous cell and
x_t is the input at that
particular time step)The
inputs are multiplied by
weight matrices and a bias
is added and the resultant
input is passed through a
sigmoid function. The inputs
for which sigmoid produces
“0” are forgotten.

30. LSTM Input Gates
The input gates work on three-step
process.
1, Regulating what values need to
be added to the cell state. This is
done by passing h_t-1 and x_t
through a sigmoid function.
2. Creating a vector containing all
possible values that can be added to
the cell state – done using the tanh
function. The function produces
output from -1 to +1.
3. Multiplying the value of the
sigmoid output to the tanh output
and then adding this useful
information to the cell state via an
addition operation

31. LSTM Output Gates
These gates work in three steps:
1. Creating a vector after
applying tanh function to the cell
state
2. Passing h_t-1 and x_t through
a sigmoid function so that
outputted values may be
regulated
3. Multiplying the results of 1
and 2 and sending it as the
output and also the hidden state
of the next cell

32. LSTM Implementation Details
• Python Language
• Tensorflow deep learning framework of
Goggle
• Loss function: MAE
• Optimizer: ADAM
• Optimum epoch: 60
• Batch size: 100
• Sequential() function in the Tensorflow
module used for building the LSTM network

33. Results – Regression (8)
LSTM Regression

34. Results – Regression (8)
Loss Convergence in LSTM Model (Tata Steel Case III)
(x-axis: no of epochs, y-axis: percentage of loss)

35. Summary Results – Regression

36. Observations on Classification
• Among the classification techniques, Boosting expectedly
performed the best for Case I and II since these two cases
actually measured the performance of the model on the
training data.
• For Case III, Bagging and Logistic Regression are found to
have performed the best on classification accuracy for Tata
Steel and Hero Motocorp stock data respectively.
• ANN is found to have performed best on two of the metrics
for both the stocks – Sensitivity and NPV for Tata Steel
stock data, Specificity and PPV for the Hero Motocorp
stock data.
• Since the Hero Motocorp stock data is very imbalanced for
both the years 2013 and 2014, the techniques that yielded
the highest value of Sensitivity for the data – Random
Forest – has also performed very well in classification

37. Observations on Regression
• For the regression techniques, the deep learning technique
– LSTM – outperformed all machine learning techniques
on all metrics for both the stocks by quite a large margin.
• For the Tata Steel stock, Random Forest performed the
best among the machine learning techniques under Case I
and Case II
• For the Hero Motocorp stock, Decision Tree produced the
best results under Case I and Case II.
• Multivariate Regression produced the lowest RMSE/ Mean
value for both the stocks under Case III.
• ANN and SVM produced the highest correlation values for
the Tata Steel stock and the Hero Motocorp stock
respectively.

38. Conclusion
• We have presented a framework of predictive models for stock
price movement prediction in a short-term time interval using
machine learning and deep learning approaches.
• We built eight regression and eight classification models and
tested those models on two different stock data for year time
interval – Jan 2013 to Dec 2014.
• Extensive results have been presented on performance of these
models.
• It has been observed that while among the classification
techniques ANN has, on the average, produced the best results,
LSTM outperformed, by a large margin, all other regression
models.
• Identifying the individual error values in regression by LSTM and
studying those errors constitute a future course of work.

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42. Thank You!
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