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Steve Thawornwong
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Motivation: Identify changes in trends at an early stage. 7 ... Lockheed Martin (LMT), Caterpillar (CAT), and Delta Air Lines (DAL) Short-term prediction ... – PowerPoint PPT presentation
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Title: Steve Thawornwong
1
Engineering Management Graduate Seminar
Using Technical Analysis Indicators as Inputs to
Neural Networks for Predicting Stock Movements
- By
- Steve Thawornwong
- Dr. David Enke, Dissertation Supervisor
- September 24, 2001
2
Introduction
- Stock trend prediction
- Monetary rewards
- Difficult to perform
- Many factors are involved
- Complex relationships
- Technical Analysis Past Perspective
- Has been criticized
- Built on weak foundations
- Does history repeat itself?
3
Introduction
- Technical Analysis Current Perspective
- Viable analytical option
- Markets may not be fully efficient
- A mixture of human, political, and economical
behaviors - Use data of past prices and volumes
- Rely on strong empirical regularities
- Not always evident, masked by noise, and vary
from security to security - Difficult for producing consistent results
4
Introduction
- Artificial neural networks
- Non-linearity
- No priori assumptions required
- Useful in security investment
5
Research Investigation
- Used neural networks to enhance the effectiveness
of technical analysis - Objective Can neural networks be used to better
uncover the regularities? - Five popular technical indicators were selected
- Three neural networks were examined
- Three major stocks were selected
- Trading strategies were developed
- Predictability and profitability were evaluated
6
Literature Review
- Two forecasting approaches
- Technical analysis
- Fundamental analysis
- Technical analysis
- Price and volume movements
- Charts are a primary tool
- Some patterns are expected to repeat
- Motivation Identify changes in trends at an
early stage
7
Literature Review
- Fundamental analysis
- Economic forces of supply and demand
- Relevant information
- Determine intrinsic value
- Samples of information
- Economic variables
- Industry specific
- Company specific
8
Literature Review
- Stock prediction with neural networks (NNs)
- Various studies used fundamental factors
- Historical time-series of prices were used
- Some technical indicators were included in a few
studies - The abilities of technical indicators when using
NNs were rarely addressed - At best, they relied on a few indicators or a
single NN - Technical indicators were not effectively tested
9
Why neural networks?
- Strong regularities of technical analysis were
captured using linear relationships - No linear evidence for human, political, and
economical behaviors - Conflicting signals often occur
- Investors may get confused
- Diverse rationales from various technical
analysts - Depended on experience, criteria, observation,
etc. - NNs can help manage unclear regularities and
contradicted signals
10
Why neural networks?
- Preprocessing steps were required to improve NN
performance - Applicable to certain aspects of technical
indicators - NN abilities could be further improved if
original stock prices and volumes are
preprocessed gt technical indicators - Using technical indicators would allow NNs to
concentrate on details for accurate predictions
11
Stock Selection
- Three major stocks
- Lockheed Martin (LMT), Caterpillar (CAT), and
Delta Air Lines (DAL) - Short-term prediction
- 08/22/1996 to 12/29/1999 (846 trading days)
- Adjusted for splits and dividend distributions
- Data were divided into two periods
- First period 08/22/1996 06/30/1999 (720 days)
- Second period 07/01/1999 12/29/1999 (126 days)
- First period for training and validating the NNs
- Second period for out-of-sample evaluation
12
Indicator Selection
- Five technical indicators
- MA, RSI, MFI, SO, and MACD
- Commonly used criteria were considered
- Moving Average (MA)
- Average value of a stock price
13
Indicator Selection
- Relative Strength Index (RSI)
- Compares price gains to price losses
14
Indicator Selection
- Money Flow Index (MFI)
- Accounts for volumes
15
Indicator Selection
- Stochastic Oscillator (SO)
- Indicate overbought or oversold
- Displayed by K and D (3-day MV of K)
16
Indicator Selection
- Moving Average Convergence/Divergence (MACD)
- A difference between an ? 0.075 and an ? 0.15
exponential moving average (EMA)
17
Data Preprocessing
- Technical trading criteria are based on
particular patterns and recent movements - Eight variables exist from five technical
indicators - Closing Price (CP), RSI, MFI, MA, K, D, MACD,
and Signal Line (SL) - These variables were preprocessed to replicate
the movement patterns for neural networks - (1) CPt CPt-1
- (2) MAt CPt, (3) Kt Dt, (4) MACDt SLt
- No preprocessing for (5) RSI and (6) MFI
18
Data Preprocessing
- Three-day lags to account for recent movement
- CPt CPt-1, CPt-1 CPt-2, CPt-2 CPt-3, RSIt,
RSIt-1, RSIt-2, MFIt, MFIt-1, MFIt-2, MAt CPt,
MAt-1 CPt-1, MAt-2 CPt-2, Kt Dt, Kt-1
Dt-1, Kt-2 Dt-2, MACDt SLt, MACDt-1
SLt-1, and MACDt-2 SLt-2 - Eighteen inputs were used to predict the
direction of the next day stock price (Ct1) - Time lags were used throughout the experiment
- Similar to the real world
19
Trading Practices
- Profited from an increase (long position) and a
decline (short position) in stock prices - Charged 1 round-trip transaction cost
- Close a long position and create a short position
- Cover a short position and open a long position
- Transaction cost v.s. excessive trading
- Developed a trading strategy in connection with a
predicted signals - Assisted in making trading decisions and allowed
for systematic trading
20
Trading Strategies
- Make a daily decision whether maintaining a
current position or making a trade - Money becomes illiquid in the position for that
day - Trading Criteria
- If Ct1 1, then maintain the current long
position, or cover a short position and open a
long position (pay the 1 transaction cost and
receive the profit/loss of the covered short
position) - Else (if Ct1 1), then maintain the current
short position, or close a long position and
create a short position (pay the 1 transaction
cost and receive the profit/loss of the closed
long position)
21
Trading Strategies
- Similar trading strategies and practices were
used for technical indicators - A short position when a sell signal is generated,
and vice versa - Current position maintained when no signal
- No trading when a signal suggested a similar
position
22
Profitability Measure
- Accumulation of gains/losses for the whole
trading period - Day 0 a buy signal gt Day 1 a long position at
x1 - Day 1 a short signal gt Day 2 a short position
at x2 - Total transaction cost 0.005(x1) 0.01(x2)
- Total investment x1 0.005(x1) 0.01(x2)
- Gain/Loss x2 x1
- Rate or return x2 x1 0.005(x1)
0.01(x2) /x1
0.005(x1) 0.01(x2) - No reinvestment of profits
23
Neural Network Modeling
- Three neural networks were implemented
- Scaled input values into range 1 and 1
- Feed-forward neural network (FNN)
- Single layer architecture, sigmoid tangent
function, and resilient learning algorithm - Early stopping for generalization
- First period (720 days) 576 days (80) for
training and 144 days (20) for validating - The hidden neurons and learning rate were
determined during network training
24
Neural Network Modeling
25
Neural Network Modeling
- Probabilistic Neural Network (PNN)
- A two-layer network based on the estimation of
probability density function - First layer computes distances of input vectors
- Second layer classifies probabilities into a
class - Neither validation nor early stopping is required
- 720 days (first period) for network modeling
- A smoothing parameter equal to 1.00
26
Neural Network Modeling
27
Neural Network Modeling
- Learning Vector Quantization Network (LVQ)
- A two-layer (competitive and linear) network
- Competitive layer indicates subclasses of input
vectors - Linear layer combines subclasses into a class
- LVQ2 algorithm was used
- 0.5 upward and downward class percentages
- Similar methodologies as those used in the FNN
28
Neural Network Modeling
29
Empirical Results
- Evaluated using the untouched out-of-sample data
(Second period) - Predictability Results
- SIGN Proportion of time that the signals of
stock price changes are correctly predicted - Buy-and-Hold Always investing in each stock
- PortNN Outputs of three NN models were combined
- e.g. 1 of FNN, 1 of PNN, and 1 of LVQ gt an
upward direction
30
Empirical Results
31
Empirical Results
- Profitability Results
- of Trades Total number of transactions
- Total R Total rate of return on investment
- Port R Equally weighted portfolio of the three
securities - Port R wRLMT wRCAT wRDAL
32
Empirical Results
33
Conclusions
- Used neural networks to improve the predictive
ability of technical indicators - Three stocks were tested to support the
robustness - Different technical indicators did not work well
for all securities - Neural networks help manage contradicting signals
from technical indicators - Neural networks help improve the predictability
and profitability of the technical indicators
34
Engineering Management Graduate Seminar
Using Technical Analysis Indicators as Inputs to
Neural Networks for Predicting Stock Movements
- By
- Steve Thawornwong
- Dr. David Enke, Dissertation Supervisor
- September 24, 2001
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