Emotional Response Modeling in Financial
Markets: Boston Stock Exchange Data Analysis
by
Patrick Michael McCaney
Submitted to the Department of Electrical Engineering and Computer
Science
in partial fulfillment of the requirements for the degree of
Master of Engineering in Computer Science and Engineering
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
May 2004 [J-e
co4z
© Patrick Michael McCaney, MMIV. All rights reserved.
The author hereby grants to MIT permission to reproduce and
distribute publicly paper and electronic copies of this thesis document
MASSACHUS
in whole or in part.
S
E
JUL 20 2004
Author
LIBRARIES
......
..........
..
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Departmef o Electrical Engineering and Computer Science
1k May 20, 2004
Certified by...
...............
Andrew W. Lo
Harris & Harris Group Professor
Supervisor
7-/Thesis
Certified by...
...
. .
. . . ...
Dmitry Repin
-- Postdoctoral Associate
'bAdsfuevisor
Accepted by......
.
.....
..
A~thur C. Smith
Chairman, Department Committee on Graduate Students
BARKER
2
Emotional Response Modeling in Financial Markets: Boston
Stock Exchange Data Analysis
by
Patrick Michael McCaney
Submitted to the Department of Electrical Engineering and Computer Science
on May 20, 2004, in partial fulfillment of the
requirements for the degree of
Master of Engineering in Computer Science and Engineering
Abstract
In this thesis, physiological data is analyzed in the context of financial risk processing, specifically investigating the effects of financial trading decisions and situations
on the physiological responses of professional market makers. The data for this analysis comes from an experiment performed on market makers at the Boston Stock
Exchange. This analysis involved significant preprocessing of large financial and physiological data sets. Short-term and long term analysis of financial and performance
based event markers of the data are performed and the results interpreted. There
are two main conclusions. First, negative performance events are found to be the
the main driver of physiological responses; positive performance events have minimal
deviations from baseline physiological signals. Second, a long term analysis of events
yield more substantial physiological changes than a short term analysis.
Thesis Supervisor: Andrew W. Lo
Title: Harris & Harris Group Professor
Thesis Supervisor: Dmitry Repin
Title: Postdoctoral Associate
3
4
Acknowledgments
I would first like to thank my parents, Frank and Ellen McCaney, for providing me
with such strong support in all my endeavors (especially academic), for always being
there when I needed them, and for being amazing role models.
I would also like to thank Carol Bell, Barbara Koch, Kay Sollimo, Donald Estel,
and Bill Coleman for their encouragement and dedication to my growth as a well
rounded individual.
I would especially like to thank Dmitry Repin and Andrew Lo for including me
in the research being conducted at the Laboratory for Financial Engineering and for
their supervision and continuous refinement of this research.
5
6
Contents
1
2
Introduction
15
1.1
Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15
1.2
Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16
1.3
Outline.
17
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Data
19
2.1
Physiology Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19
2.1.1
Drift Analysis and Correction . . . . . . . . . . . . . . . . . .
20
2.1.2
Downsampling
. . . . . . . . . . . . . . . . . . . . . . . . . .
22
2.1.3
Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . .
23
2.1.4
Artifact Removal . . . . . . . . . . . . . . . . . . . . . . . . .
26
2.2
Financial Data
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26
2.2.1
Original Data . . . . . . . . . . . . . . . . . . . . . . . . . . .
27
2.2.2
Data Extraction . . . . . . . . . . . . . . . . . . . . . . . . . .
27
2.2.3
Data Cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . .
28
2.2.4
Data Output
28
. . . . . . . . . . . . . . . . . . . . . . . . . . .
3 Event Marker Classification
31
3.1
Financial Event Markers . . . . . . . . . . . . . . . . . . . . . . . . .
32
3.2
Performance Event Markers
33
. . . . . . . . . . . . . . . . . . . . . . .
4 Methodology
4.1
37
Noise Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
37
4.2
Short Term Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .
38
4.3
Long Term Analysis
. . . . . . . . . . . . . . . . . . . . . . . . . . .
40
43
5 Results
5.1
5.2
6
Financial Event Markers . . . . . . . . . . . . . . . . . . . . . . . . .
43
5.1.1
Big vs. Small Trades . . . . . . . . . . . . . . . . . . . . . . .
43
5.1.2
Wide vs. Narrow Bid/Ask Trades . . . . . . . . . . . . . . . .
44
5.1.3
Long vs. Short Trades . . . . . . . . . . . . . . . . . . . . . .
45
. . . . . . . . . . . . . . . . . . . . . . .
46
5.2.1
Good vs. Bad Trades . . . . . . . . . . . . . . . . . . . . . . .
46
5.2.2
Positive vs. Negative Good/Bad Trade Ratio
. . . . . . . . .
46
5.2.3
Positive vs. Negative P&L . . . . . . . . . . . . . . . . . . . .
47
Performance Event Markers
51
Discussion
6.1
6.2
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
51
6.1.1
Short Time Scale . . . . . . . . . . . . . . . . . . . . . . . . .
51
6.1.2
Long Time Scale . . . . . . . . . . . . . . . . . . . . . . . . .
52
Event Marker Analysis . . . . . . . . . . . . . . . . . . . . . . . . . .
53
6.2.1
Financial Event Markers . . . . . . . . . . . . . . . . . . . . .
53
6.2.2
Performance Event Markers . . . . . . . . . . . . . . . . . . .
54
Temporal Analysis
55
7 Conclusion
7.1
Future Work.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
56
List of Figures
2-1
This figure illustrates the placement of the physiological sensors on the
m arket m akers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2-2
20
This figure shows typical responses for each physiological variable. The
x-axis for each graph represents time
(
of a second for the first six
signals and I of a second for the remaining two signals). The y-axis
represents intensity of response. . . . . . . . . . . . . . . . . . . . . .
2-3
This figure shows a histogram of the distribution of drift for the 19
drift calculations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2-4
21
22
The downsampling process is shown above. Part A shows downsampling when drift is not accounted for. Part B shows downsampling
when drift is accounted for. The lowpass filter illustrated in Part B
between the interpolator and decimator has a gain of 375 and a cutoff
of
'25 for the 32 Hz data sources and a cutoff of
96'45
for the 256 Hz
.
24
2-5
This figure shows the feature extraction of skin conductance response.
25
2-6
This figure shows the feature extraction of heart rate from an EKG
...........
data sources. ..............
...
. . ..
(shown on the left side) and from a BVP (show on the right side). . .
2-7
25
This figure shows a sample record output from the logging program on
the trader's machine. In this case, the trader has an automatic execution of a buy order for 400 shares of WAG, the Walgreen Company, at
$29.95 a share at 10:40:21. . . . . . . . . . . . . . . . . . . . . . . . .
9
27
4-1
This figure shows an example of a short term analysis. The red line at
11 seconds indicates the event marker, occurring at the time of a long
or short trade. The average physiological response for tension is shown
over the twenty-one second interval. The dashed black lines around
the baseline response represent the IQR mean (i.e. the boundary for a
significant response).
4-2
. . . . . . . . . . . . . . . . . . . . . . . . . . .
39
This figure shows an example of a long term time analysis. The red lines
at 100 and 400 seconds indicate the domain of the event of interest, the
range of a time period with a positive or a negative P&L. The average
physiological response for tension is shown over the five hundred second
interval. The dashed black lines around the baseline response represent
the IQR mean (i.e. the boundary for a significant response. . . . . . .
5-1
41
This figure shows the results of significant physiological responses, skin
conductance response and heart rate variability, in the Big vs. Small
Trade event marker.
5-2
. . . . . . . . . . . . . . . . . . . . . . . . . . .
43
This figure shows the results of significant physiological responses, skin
conductance response and heart rate variability, in the Wide vs. Narrow Bid/Ask Spread Trade event marker. . . . . . . . . . . . . . . . .
5-3
44
This figure shows the results of significant physiological responses, tension, forehead temperature, heart rate (as extracted by BVP), and
heart rate variability, in the Long vs. Short Trades event marker.
5-4
. .
This figure shows the results of the significant physiological response,
heart rate variability, in the Good vs. Bad Trades event marker. . . .
5-5
45
46
This figure shows the results of significant physiological responses,
tension, forehead temperature, heart rate (as extracted by BVP and
EKG), and heart rate variability, in the Positive vs. Negative Good/Bad
Trade Ratio event marker. . . . . . . . . . . . . . . . . . . . . . . . .
10
49
5-6
This figure shows the results of significant physiological responses,
tension, forehead temperature, heart rate (as extracted by BVP and
EKG), and heart rate variability, in the Positive P&L vs. Negative
P&L event m arker. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11
50
12
List of Tables
6.1
This table shows the alignment of event markers in the temporal analysis, as well as a summary of the significant responses for each subject
for each event marker. The abbreviations in the table are the following:
T=Tension, S=SCR, H=Forehead Temperature, F=Finger Temperature, HB=Heart Rate from BVP, HE=Heart Rate from EKG, and
V=Heart Rate Variability. . . . . . . . . . . . . . . . . . . . . . . . .
6.2
52
This table shows the distribution of event markers in the event marker
analysis, as well as a summary of the significant responses for each
subject for each event marker. The abbreviations in the table are the
following: T=Tension, S=SCR, H=Forehead Temperature, F=Finger
Temperature, HB=Heart Rate from BVP, HE=Heart Rate from EKG,
and V=Heart Rate Variability . . . . . . . . . . . . . . . . . . . . . .
13
53
14
Chapter 1
Introduction
The principles of modern finance theory are strongly based on the fundamentals
of rational decision making, that "expectations [of prices], since they are informed
predictions of future events, are essentially the same as the predictions of the relevant
economic theory" [8], and on the efficient market hypothesis, which asserts that stock
prices reflect all available information.
However, recent research into the irrational behavior of financial markets (most
recently into the rise and fall of the Internet bubble in the late 1990s) had lead to
the establishment of a different approach: behavioral finance. Proponents of this approach argue that emotional responses influence the risks and financial decisions that
people make. In a recent paper, Elster traces the effect emotions have on economic
decisions [3].
1.1
Problem Statement
This research examines the relationship between physiological variables and financial
risk processing.
In particular, the goal is to determine if significant physiological
responses to different market events exist and if any trends exist in those responses.
In particular, six market makers on the Boston Stock Exchange (BSE) had physiological signals measured while performing their daily functions in the market. These
signals were then analyzed according to several financial and performance event mark15
ers.
1.2
Background
Current research into the link between physiology and trading performance is underway at the Laboratory for Financial Engineering at MIT by Professor Andrew Lo and
Dr. Dmitry Repin. One area of this research consists of measuring the physiological
signals and the financial environment of professional market makers on the Boston
Stock Exchange. In a recent paper, Lo and Repin concluded that emotional responses
are a significant factor in the real-time signal processing of financial risks [4]. They
suggest further studies that attempt to relate changes in physiological variables to
financial decision-making processes.
In order to examine emotional responses of traders, current research has focused
on physiological manifestations of the autonomic nervous system (ANS). A detailed
explanation of the pathways of the central nervous system and their relation to physiological variables is presented by Barchas[1]. A convenient property of ANS responses
is that they occur on the order of several seconds, and therefore are temporally close
to the changes in the financial environment that a trader observes [4]. This property permits the alignment of financial and physiological data sets so the two can be
studied in conjunction.
Loewenstein discuses the role visceral factors, "a wide range of negative emotions
(e.g., anger, fear), drive states (e.g., hunger, thirst, sexual desire), and feeling states
(e.g.
pain) that grab people's attention and motivate them to engage in specific
behaviors" [5] play in economic decision making. Additionally, McGoun and Skubic
argue that "there is clearly an emotional component in our formation of expected
wealth" [7].
Therefore, significant physiological changes resulting from emotional
responses to changes in wealth should be exhibited in the analysis of the Boston
Stock Exchange data.
16
1.3
Outline
The first chapter of this thesis covers the introduction of the research, motivation for
the research, definition of the problem, and provides some background information.
The second chapter describes the financial and physiology data sources used in
the analysis, and the preprocessing necessary to prepare them for analysis.
The third chapter explains the construction of analysis process, utilizing event
markers to study physiological responses around different events and over different
periods of time. The six event markers are:
* Large versus Small Trades
" Trades with Large Bid/Ask Spreads versus Trades with Small Bid/Ask Spreads
" Long versus Short Trades
" Good versus Bad Trades
" Positive versus Negative Good to Bad Trade Ratio
" Positive versus Negative P&L
The fourth chapter explains the methodology of how the analysis was performed
and covers how noise estimation of the physiology data was used to create a definition
of statistically significant results.
The fifth chapter reviews the results obtained from the analysis of the data for
the six event markers.
The sixth chapter discusses the results from Chapter 5 and interprets their significance.
The seventh chapter concludes the thesis with a summary of the salient findings
of the research and provides guidance to future research on this topic.
17
18
Chapter 2
Data
The data for this research was collected in an experiment performed by Dr. Dmitry
Repin and Professor Andrew Lo at the Boston Stock Exchange. Physiological and
financial data were collected from eight market makers during their first ninety minutes of trading. However, the data for two of the market makers is incomplete so only
the siz market makers with a complete data set are used for analysis. The financial
data set was recorded by a logging program running on the market maker's terminal.
The physiology data set was recorded by eight sensors placed on the market maker's
body. This information serves as the basis for the analysis in the rest of the paper.
2.1
Physiology Data
The specific physiological data in this project are the temperature on the forehead
and index finger, skin conductance response (SCR), blood volume pulse (BVP), and
electromyographical (EMG) data on the underside of the wrist on the Flexor Digitorum and on the Trapezius muscle group on the back of the shoulder. These signals
were all recorded at 32 Hertz using ProComp+ data acquisition hardware. Two other
signals, recorded at 256 Hertz using the same acquisition hardware, are an electrocardiogram (EKG) and an electroencephalogram (EEG). Figure 2-1 shows the placement
of these sensors.
The forehead and finger temperature measurements were recorded by thermistors.
19
Frontal Lobe
Temperature
EEG
Trapezi-us EMG
EKG
Forearm EMG
BVP
Figer
Temperature
Figure 2-1: This figure illustrates the placement of the physiological sensors on the
market makers.
The skin conductance response measured the electrical skin conductance from the
palmar sites, where change in the intensity of the response is associated with sweat
gland activity. Blood volume pulse is measured using photoplethysmography, the
process of applying a light source to and sensor to the thumb and measuring the
light that is reflected by the tissue. Electromyography measurements are obtained
by placing electrodes on the skin to record the muscle's activity that leaks to the
surface. Electrocardiogram measurements are recorded by placing electrodes on and
around the chest to record the electrical activity of the heart. Electroencephalogram
measurements are recorded from a bipolar prefrontal setup (Fpl-Fp2) referenced to
the ear. Figure 2-2 shows the placement of these sensors [2].
2.1.1
Drift Analysis and Correction
Currently the physiology data lacks a strong synchronization mechanism. When
the physiology recording begins, a clock on the data acquisition hardware begins
measuring time. Approximately every forty minutes, the time on the data acquisition
20
-100
50,
LU
'U-0
0
400
200
0
600
500
1000
1500
2000
2500
92
4.5
E
4
3.5
U91.5
0
1000
2000
3000
-
90.5
4000
5000
96
30
K
E
0
- 95.5
95
25
20'~
0
500
(a
500
1000
1500
0
1000
w 0 0
w
-500
2000
10000
4000
6000
15000
8000
10000
0
-10w-
0
1000
2000
0
3000
500
1000
1500
2000
Figure 2-2: This figure shows typical responses for each physiological variable. The
x-axis for each graph represents time (- of a second for the first six signals and ' of
a second for the remaining two signals). The y-axis represents intensity of response.
hardware is recorded along with the time on a clock.
Drift = (Timemarker 2 - Timemarker: 3 o) - (Timeclock 2 - Timeclock9 3: o)
(2.1)
If the recorder is operating correctly, then the difference between measurements should
be the same for the software connected to the data acquisition hardware and for the
clock. There is a discrepancy between them and its calculation is shown in Equation
2-1. Therefore, there is a consistent drift in the recording device. The average drift
is 0.28 seconds per minute of elapsed time. Figure 2-3 shows the distribution of
measured drift times.
In order to account for the drift in the recording device, it is necessary to add a
shift in the downsampling procedure explained below.
21
Drift "stogram
~~1~
7r
6
84
z
2
1
0.2
0.22
0.24
0.34
0.3
0.32
0.28
0.28
Drift (seconds of drift per elapsed minute)
0.36
0.38
0.4
Figure 2-3: This figure shows a histogram of the distribution of drift for the 19 drift
calculations.
2.1.2
Downsampling
The raw data files are significantly large. A typical financial data set of ninety minutes
worth of information is about twenty megabytes and the corresponding physiology
data set is about thirty-five megabytes. Numerical packages such as MATLAB have a
difficulty dealing with such a large quantity of information with minimal computation
time. Therefore, it is necessary to downsample the data.
Downsampling typically occurs by reducing the sampling rate by an integer factor. If no drift were present in the recorded data, then it would straightforward to
compress the data. The 32 Hz data (finger and forehead temperatures, SCR, BVP,
and shoulder and forearm EMG) would be downsampled by a factor of 32; the 256
Hz data (EEG and EKG) would be downsampled by a factor 256. Figure 2-4a shows
the downsampling process in this case.
22
However, the presence of a drift in the data acquisition hardware requires a noninteger downsampling factor. Because the drift is .28 seconds per minute the effective
downsampling rate is
12056
for the 32 Hz data and
for 256 Hz data.
32 + 32 *
Downsampling Factor 32 Hz
Downsampling Factor 256 Hz
'6448
-
=
28
60
32
12056
375
256 + 256 * 2
60
256
96448
375
In order to downsample by a noninteger factor it is necessary to combine decimation (downsampling) and interpolation (upsampling). For the 32 Hz data, it is
necessary to first upsample the original data by 375. In order to ensure that the
Nyquist rate (the minimum rate that data can be sampled such that no aliasing of
data occurs) is fulfilled, the signal is passed through a low pass filter with a gain of
375 and a cutoff of 120'.
Finally the signal is downsampled by 12056. The output
is a one second downsampled version of the original data with the drift accounted
for. Similarly for the 256 Hz data, the original signal is upsampled by 375, then
passed through a low pass filter with a gain of 375 and a cutoff of
'4,
and finally
downsampled by 96448 [9]. Figure 2-4b shows the downsampling process in this case.
2.1.3
Feature Extraction
In total, we extract seven physiological features (tension, SCR, forehead temperature,
finger temperature, heart rate variability, and heart rate from BVP and EKG) from six
channels (EMG on shoulder, SCR, finger temperature, forehead temperature, BVP,
EKG). However, only two are of the seven physiological features are directly extracted
from an original data channel (finger and forehead temperature). The remaining five
physiological features are extracted from the remaining six data sources: tension, skin
23
A.
1135
$12056
ro.
Figure 2-4: The downsampling process is shown above. Part A shows downsampling
when drift is not accounted for. Part B shows downsampling when drift is accounted
for. The lowpass filter illustrated in Part B between the interpolator and decimator
has a gain of 375 and a cutoff of 12o's for the 32 Hz data sources and a cutoff of 96448
for the 256 Hz data sources.
conductance response, heart rate variability, and heart rate (as extracted from EKG
and BVP).
The tension feature extraction is simply the original tension signal with a cutoff
where all portions of the signal that are less than half the standard deviation of the
tension signal are set to zero. The remaining portions of the tension signal stay the
same.
The skin conductance response (SCR) is extracted from the voltage measurements
that measure changes in conductivity on the palmar sites. A typical SCR extraction is
shown in Figure 2-5, where the shaded region marks to a skin conductance response.
The SCR for the non-shaded regions is set to zero. The level of the SCR is a graded
scale where the skin conductance response value calculated as the ratio of the relative
maximum of a peak to the minimum before the peak.
Heart rate extraction is achieved from two separate sources: an electrocardiogram
and blood volume pulse. The sharp peaks shown on the left side of Figure 2-6 are
24
M
Galvanic Skin Response
8.8
8.75 k
8.7
8.65 8.6-
8.558.5
8.45
8.4
4
.uno
4.075
ine
4.095
X 110
Figure 2-5: This figure shows the feature extraction of skin conductance response.
-204
l
p
20
1
204 3M
.26 3Y1
2.20TIM
2
2?
2f 3,0
22
20
.1
lt
22
21
212
21
2 214
404
lf0
4.010
Figure 2-6: This figure shows the feature extraction of heart rate from an EKG (shown
on the left side) and from a BVP (show on the right side).
known as the R complex of the typical QRS response of the EKG. By measuring the
distance between the R complexes, known as the R to R interval, the heart rate is
extracted. Similarly, the peaks on the right side of Figure 2-6 correspond to the peaks
of the reflected light (as measured by the photoplethysmography) during each heart
beat.
The final feature extraction is heart rate variability, derived from the heart rate
25
extracted from EKG. Heart rate from EKG is used for heart rate variability calculation because heart rate from BVP provides a less consistent and reliable measurement
of heart rate. Heart rate variability is calculated using the instaneous heart rates extracted from EKG that, basically, represented heart rate deceleration or acceleration,
and is shown in the following equation:
HR Variabilityt
2.1.4
=
HRt
-
HRt-1 + HRt- 2 + HRt- 3 + HRt- 4 + HRt-5
5
(2.2)
Artifact Removal
During the physiology recording sessions, abnormal measurements may appear due
to movement by the subject. These artifacts contaminate the data and provide an
inaccurate view of the trader's physiology. In order to minimize the effect artifacts
have on the analysis of the data, the EMG on the forearm is used to detect artifacts.
An artifact exists if the intensity of the EMG on the forearm exceeds the overall
mean of the intensity of this EMG. The artifact location information obtained from
the EMG on the forearm is used to remove artifacts from the heart rate (extracted
from BVP and EKG), heart rate variability, and tension. Specifically, in locations
where an artifact exists, the data at that point is removed and replaced with a NaN.
2.2
Financial Data
The financial data recorded for this research project contained the bid/ask spreads
of stock prices, the amount (i.e. position size) of a stock held or owed (i.e. short),
records of individual trades conducted by the trader, and realized, unrealized, and
total profit and loss across all stocks during the trading session. All of the data is
time-stamped.
26
Original Data
2.2.1
The original financial data set is obtained from a logging program on the market
maker's machine. As the market maker interacts with the financial trading software,
the logging program records his/her actions. Although the output of the logger has
less data than the researcher expected in the ideal case as seen in Figure 2-7, there
are several fields containing useful information. These include the timestamp; the
type of window, source, destination, command; and the field data information.
RecLen=1791Time=08:40:21ISource=COMMHANDIDest=TRADEILength=911
Op=105IOpDesc=BLSTRATUSINPUT ICMD=361CMDdesc= 36 - WWrtiel
Window=13Wdesc=13 - Bottom alertIFLD=0FLDdata=WAG B 400 29.95 GTX
Figure 2-7: This figure shows a sample record output from the logging program on
the trader's machine. In this case, the trader has an automatic execution of a buy
order for 400 shares of WAG, the Walgreen Company, at $29.95 a share at 10:40:21.
2.2.2
Data Extraction
Extraction of the pertinent information described above is achieved using regular
expression parsing. The window, command, source, and destination flags are used to
identify the type of information in the field data tags. In particular, the information
is sorted into several information categories: manual trade execution, manual change
of an automatic trade order, automatic trade execution, profit and loss information,
updates of bid/ask spread for a particular stock price, and position updates for a
particular stock. The parsing program performs three main tasks:
1. Identify type of information.
2. Extract relevant information.
3. Write the timestamp, type of information, and relevant information to a tabdelimited output file.
27
2.2.3
Data Cleaning
The data must be cleaned, by removing the redundant entries, before extraction from
the logging program is complete. The program caches the relevant information and
flushes it to the log file only when the buffer is full. To complicate the problem,
the data is time stamped not when a particular event occurs, but rather when it is
flushed out from the buffer and into the logfile. Therefore, sometimes it appears that
a trader put on more than one trade at a given time or that a stock has multiple
bid/ask prices. Fortunately, the buffer will flush its buffer every two seconds if it is
not filled. In order to remove conflicting information from entries with the same time
stamp, it is necessary to clean the data output.
The logging program uses a first-in first-out buffer so any entry that appears
later in the log is a more recent version of the particular information. Therefore,
with records of the same time stamp, the record at the bottom is the most recent
information. This rule is used to settle ties between the same information category
and time stamp; the more recent version (the record further down the log) is kept
and the older information is removed. It is important to note that this procedure is
only used when the same type of information category has two or more records with
the same timestamp. The output of this cleaning program is a tab delimited text file.
2.2.4
Data Output
The final stage of preprocessing of the financial data, before it can be used in the
analysis, is to scrub all letters and replace them with numerical characters.
The
output of this stage is a flat text file consisting of the following: stock ID, time, bid
price, ask price, position size, unrealized profit and loss, realized profit and loss, and
total profit and loss. The two types of information that contain letters are the stock
name and the information category. Stock symbols are easily scrubbed; the unique
stock names are listed in alphabetical order and then replaced with numbers starting
at 1 and incrementing until each unique stock symbol has a one to one relationship
with a number. The information category description is used to determine how the
28
information contained in a particular record is stored. Because a flat text file is used
for output, all the information fields are present for each record, and as a result some
fields do not have complete entries. For example, a profit and loss update will have
a NaN (not a number) inserted for the stock, bid price, ask price, and position size
categories.
29
30
Chapter 3
Event Marker Classification
In order to examine the data explained in Chapter 2, it is necessary to define meaningful financial events. Unlike a typical experiment where it is possible to observe the
response to a change in a single variable, this is a field study focused on the physiological responses of market makers during their daily activity. Stated in another way,
the conditions under which each trader operated are not controlled. As a result, it
is only possible to measure the physiological responses and then determine possible
causes a posteriori. In order to determine the causes of these responses, different event
markers are defined. They are related to financial decisions or situations encountered
by the traders and related to performance based indicators. Identifying these event
markers allows the study of the physiological responses to different events in a market
maker's daily activities, even without the ability to control for when a specific event
occurs.
These event markers are classified into two broad groups: financial event markers
and performance based markers. The financial event markers are:
" Large versus Small Trades
" Trades with Large Bid/Ask Spreads versus Trades with Small Bid/Ask Spreads
" Long versus Short Trades
The performance based metrics are:
31
* Good versus Bad Trades
" Positive versus Negative Good to Bad Trade Ratio
" Positive versus Negative P&L
3.1
Financial Event Markers
The first financial event market distinguishes small trades versus large trades. This
event marker is constructed by ordering all trades by their size (in dollar amounts);
whether the stock is bought or sold is not a factor for this event marker. The top x%
of this ordering were classified as large trades and the bottom x% were classified as
small trades, where x% represents the amount of the ordering to use as components of
the analysis. The remaining trades were used to represent the baseline. This measure
is aimed to distinguish between taking a small bet on a stock or a large bet on a
stock.
Another financial event marker distinguishes trades with wide bid/ask spreads
versus trades with narrow bid/ask spreads. A market for a particular stock is actually
defined by two prices, the bid price (what you receive for selling the stock) and
the ask price (what it costs you to buy the stock) in the current market. A wide
bid/ask spread is defined as a spread in a particular stock that is two standard
deviations greater than the mean bid/ask spread of that stock. The trades with
narrow bid/spreads are obtained by ordering all bid/ask spreads from smallest to
largest and then taking the top x from that list, where x is the number of trades with
wide bid/ask spreads. The remaining trades are used to represent the baseline. This
event marker aims to distinguish between events where a trader makes a riskier move
(by trading a stock with a wide bid/ask spread, which means that the market cannot
readily agree on it price) than a safer one (by trading a stock with a narrow bid/ask
spread, which means the market can more easily agree on the stock's current value,
but perhaps providing less opportunity for making profits).
The last financial event marker distinguishes long versus short positions. A long
32
position is when a trader owns stock; this implies he/she believes it will increase in
value. A short position is when a trader sells a stock that he borrowed from another
trader or institution; this implies he/she believes it will decrease in value. However,
for this event marker, the definitions of long and short is slightly modified. Typically,
long and short are used to define a type of position; however, here they are used to
define a type of trade. A long trade is used to define increasing a trader's position,
accomplished by the following three scenarios: increasing an existing long position by
buying more stock, decreasing an existing short position by buying stock, or buying
stock that has not been previously owned. A short trade is used to define decreasing
a trader's position, accomplished by the following means: decreasing an existing
long position by selling stock, increasing an existing short position by selling more
stock, or selling stock that has not been previously held. The remaining trades are
used to represent the baseline. This event marker aims to examine the physiological
differences between buying a stock versus selling a stock short.
3.2
Performance Event Markers
The first performance event marker distinguishes between good trades and bad trades.
Although there are many ways to classify a trade as good or bad, for this study, a
trade is considered good if it has a positive effect on profit and loss in the short run
(30 seconds) and bad if it has a negative effect. In order to determine the effect an
individual trade has on profit and loss, it is necessary to back out the P&L for the
specific stock since the complete P&L information extracted from the financial logfiles
only contains aggregate P&L. To extrapolate the P&L for an individual stock, the
bid and ask prices of stock thirty seconds after the trade are used. For a stock that
is bought, the ask prices are used; for a stock that is sold, the bid prices are used.
Profit & LOssBuy
=
Shares Bought * (Ask Pricet+
30
Profit & LossSell
=
Shares Sold * (Bid Pricet+ 3 0
-
33
- Ask Pricet)
Bid Pricet)
The extrapolated P&L information is split into two groups: P&L greater than zero
(i.e. good trades) and P&L less than zero (i.e. bad trades). This measure examines
how a trader's physiology reacts to a single good or bad trade.
Another performance event marker distinguishes a positive good trade to bad
ratio from a negative good trade to bad ratio. In order to establish a ratio, a five
minute interval is used to count the number of bad and good trades. The term ratio
is slightly misleading because, in fact, it represents the number of good trades minus
the number of bad trades. A difference is used instead of a ratio because there exist
five minute intervals with no bad trades at all, which would have placed a zero in
the denominator and made the ratio infinite. Additionally, looking at the difference
allowed us to ignore sample size issues, where only a few trades have been made. For
example, if four trades are made over five minutes and three are good and one is bad,
then the good/bad ratio is a positive three. Now if fifteen trades are made and nine
are good and six are bad, then the good bad trade ratio is a positive one and half.
However, in both cases, the difference between the number of good and bad trades is
three, but the ratio decreases in half as the sample size of trades increases by almost
four fold. By strictly looking at the difference of good to bad trades, it is possible
to disregard some sample size issues. The good to bad trade ratios greater than zero
are placed into the positive good to bad trade ratio group, and the good to bad trade
ratios less than zero are placed into the negative good to bad trade ratio group. The
remaining trades are used to represent the baseline. This measure examines how a
trader's physiology reacts to a period of time characterized by predominantly good
or bad trading.
The final performance event marker distinguishes between a positive profit and loss
over a five minute interval and a negative profit and loss over the same interval. Even
though a five minute is used, each trade's individual profit and loss is determined
after thirty seconds and not over the course of the five minute interval. Thus, we
wish to study how a trader reacts to period of a number of good trades or bad trades,
34
not necessarily how the trader's positions changes over five minutes. For example, a
trader may only make a single trade (e.g. a small loss) over a five minute interval, but
his/her other positions increase in value because their stock prices increase. Therefore,
the total P&L over the five minute interval is positive even though the only direct
interaction the trader had with the market is negative. If the value of the positions
are determined over the entire range instead of just thirty seconds after a particular
trade, then this event marker can be misleading.
35
36
Chapter 4
Methodology
This chapter covers the analysis procedure of the event markers listed and explained
in Chapter 3. Specifically, the construction of the short term time analysis and the
long term time analysis is explained. The noise estimation process, which serves as
the basis for establishing the boundaries of significant results, is also explained.
4.1
Noise Estimation
In order to set boundaries on what is considered to be a significant change in a
physiological variable, it is necessary to define a typical deviation from the baseline.
In this case, an interquartile range (IQR) is used. The IQR computes the difference
between the 75th and 25th percentiles in each physiological variable. The IQR is a
robust estimate of the dispersion of the data because changes in the extreme ranges
of the data (i.e. the upper and lower 25%) have no effect on the IQR. Additionally, if
outliers exist in the physiological data, then the IQR is a better representation than
the standard deviation as estimate of the spread of the data. Even though during
preprocessing, we attempt to remove artifacts in each data set with the EMG on the
forearm, not all artifacts are removed. Therefore, for the physiology data that contain
outliers, IQR is a good estimate of the spread of the data [10].
In order to obtain a good estimate of the IQR for each physiological variable, a
Monte Carlo simulation is performed. Specifically, thirty random data sets consisting
37
of thirty consecutive seconds worth of data are drawn for each variable for each trader.
The IQR for each random data set is calculated, and an average IQR for each drawing
is calculated. This random drawing is performed one thousand times, and finally the
average IQR across the one thousand drawings is calculated.
The average IQR for each physiological variable for each trader is written to a tab
delimited text file for use in the analysis below. The IQR is used as the boundary for
a significant change in physiology; if a physiological variable exceeds the IQR, then
the change in the variable is deemed significant.
4.2
Short Term Analysis
The short term analysis consists of examining event markers over a short period
of time: for ten seconds before and after the occurrence of the event.
The short
term analysis is used for all three financial event markers (big versus small trades,
long versus short trades, and wide versus narrow bid/ask spread trades) and the
good versus bad trade performance event marker. Each trader is analyzed separately
because the baseline physiological response differs from individual to individual.
Each short term time analysis consists of three distinct parts for each physiological
variable:
1. The average baseline response
2. The response of the first component of the event marker
3. The response of the second component of the event marker
The response of the first component of the event marker is simply just one side of the
analysis being performed; the response of the second component of the event marker
is the opposing side of the analysis. For example, the biggest trades are the first
component of the event marker and the smallest trades are the second component of
the big versus small trade event marker.
The analysis uses the event marker in conjunction with the financial data set to
determine the time(s) that the particular event occurs. This data set of times is used
38
to isolate the physiology into twenty-one second blocks (ten seconds before and after
the event marker).
However, the event markers simply divide the financial time series into two different components. If the analysis is performed with just the two components, there
is no baseline to compare to. Therefore, it is necessary to only take a percentage of
each of the components. As a result, the remaining data blocks are grouped into the
baseline signal. For example, for the good trades versus bad trades event marker,
the financial time series is split into two components: good and bad trades. In order
to take the most relevant trades and to create a baseline signal, only top 50% of the
best and worst trades(in terms of P&L) are used for the two components of the event
marker. Finally, for each of the three responses listed above, the results are averaged
and plotted. An example of the short term analysis is shown in Figure 4-1.
Tension Data
2.5
2-
-- Long Positions (14
2
---
events)
Short Positions (22 everts)
Baseline (363 events)
...
1.5 -
101
20
25
Time (sec)
Figure 4-1: This figure shows an example of a short term analysis. The red line at
11 seconds indicates the event marker, occurring at the time of a long or short trade.
The average physiological response for tension is shown over the twenty-one second
interval. The dashed black lines around the baseline response represent the IQR mean
(i.e. the boundary for a significant response).
The trends for each physiological variable for each event marker are analyzed visu39
ally. The response is considered to be statistically significant when the physiological
variable exceeds the IQR, as shown by the dashed black lines. The significant trends
are compared across all traders to see if a persistent trend emerges across some or all
of the traders.
4.3
Long Term Analysis
The long term analysis consists of looking at event markers defined over a longer
period of time, for five minutes, as well as 100 seconds before and after that interval.
The longer term analysis is used for the remaining two performance based indicators:
positive good trade to bad ratio versus a negative good trade to bad ratio, and positive
profit and loss over a five minute interval versus a negative profit and loss over the
same interval. Similar to the short term analysis, each trader is analyzed separately
and the responses of both components of the event marker, as well as the baseline
signal, are generated in the same manner. An example of the long term time analysis
is shown in Figure 4-2.
As similar to the short term time analysis, the trends for each physiological variable for each event marker are analyzed visually; a response is considered significant
if the physiological variable exceeds the IQR. The significant trends are compared
across all traders to see if a persistent trend emerges across some or all of the traders.
40
HR from BVP Data
-
Positive P/IL over 5 mins (12 events)
Negative P/IL over 5 mins (4 events)
-- Basenne (9 events)
-
100
95-
90~
85
80
L
''r 0I
100
200
300
400
500
6W0
lime (sec)
Figure 4-2: This figure shows an example of a long term time analysis. The red lines
at 100 and 400 seconds indicate the domain of the event of interest, the range of a
time period with a positive or a negative P&L. The average physiological response for
tension is shown over the five hundred second interval. The dashed black lines around
the baseline response represent the IQR mean (i.e. the boundary for a significant
response.
41
42
Chapter 5
Results
Financial Event Markers
5.1
Big vs. Small Trades
5.1.1
The first financial event marker, the top 10%of big trades compared to the top 10%of
small trades, showed significant results in skin conductance response and heart rate
variability. For SCR, there is usually small spike several seconds before a large trade.
SCR from smaller trades are generally more consistent with the baseline. Three of
HR Vaawy Dat
SCRt Daft
12-
-TOPBiget 10%OfTrdes
es10%ofTrades
TOP $=MOO
Top Samu5.10% ofTrades
30
I
251--
0,4-
0.2-
-0
0
2
-
510
..... - ..........
.........
Is
20
25
Tirm (see)
0
5101520
25
Tk" ($00)
Figure 5-1: This figure shows the results of significant physiological responses, skin
conductance response and heart rate variability, in the Big vs. Small Trade event
marker.
43
the six subjects exhibit this trend. HR variability shows greater HR deceleration
for both big trades and low trades, but big trades usually have an even greater HR
deceleration than small trades. Three of the six subjects exhibit this trend. Figure
5-1 shows the graphical interpretation of these results.
5.1.2
Wide vs. Narrow Bid/Ask Trades
The next financial event marker is a trade with a wide bid/ask spread (greater than
a two standard deviations away from the mean bid/ask spread) versus a trade with
a narrow bid/ask spread. Only skin conductance response and heart rate variability
show significant responses. Large bid/ask spread skin conductance responses usually
have a jump at or slightly before the trade and persist for several seconds. Three of the
six subjects exhibit significant responses of this trend. Both large and small bid/ask
spread trade responses show HR deceleration. Four of the six subjects exhibit significant levels of the above phenomena. Figure 5-2 shows the graphical interpretation
of these results.
SCR
0,8-
Data
MR
VarwAbly
2Ci
-Lar"s
- Snallevt
-Beseliet
07
Dae
rd-Ak weed Trades
BidA SWead Trades
10
0.10
0.3
00
5
o
e(s)5
20
-25
L
0
5
10
1520
TW See)
Figure 5-2: This figure shows the results of significant physiological responses, skin
conductance response and heart rate variability, in the Wide vs. Narrow Bid/Ask
Spread Trade event marker.
44
5.1.3
Long vs. Short Trades
For the next financial event marker, the top 50%of long trades versus the top 50%of
short trades, four physiological signals showed significant results: tension, forehead
temperature, heart rate (calculated from BVP), and heart rate variability. Tension
is higher for both long and short trades, but short trade responses are generally
have the highest tension. Three of the six subjects exhibit this trend. For both
long and short trades, there is a depression in forehead temperature. Both long and
short trade responses have a higher heart rate (as calculated from BVP), but short
trade responses have a slightly higher heart rate than long trades. While only two
of six subjects exhibit this trend on a significant level, five of six subjects exhibit
Forehead Twarae Data
Taftio" Data
-$tatPosm"Oaw
-hosa.*1e
50.5
55.5
S
8.S
87.5
O's
5
20
0Is-
s
25
to
2S
ft Vabo"~t Dat
HR from SVP Data
40
35
-L..
S hor
20
...
.....
25
74L
.20
~704
-15
10
Lr Pos*on
0
510
is
20
25
0
5
15
10
2
25
ram t,.d)
Tlm (fre)
Figure 5-3: This figure shows the results of significant physiological responses, tension,
forehead temperature, heart rate (as extracted by BVP), and heart rate variability,
in the Long vs. Short Trades event marker.
45
this trend in general. Finally, long and short trade responses show decreased heart
rate variability (e.g. increased heart rate deceleration).
Three of the six subjects
exhibit significant levels of the above phenomena. Figure 5-3 shows the graphical
interpretation of these results.
5.2
5.2.1
Performance Event Markers
Good vs. Bad Trades
The first performance based event marker is 50% of good trades versus 50% of bad
trades. Only heart rate variability was significant for this event marker; both good and
bad trades show HR deceleration relative to the baseline. Three of six subjects exhibit
significant responses of this trend. Figure 5-4 shows the graphical interpretation of
these results.
20;
-
GodTrades
(103eetmre
.sdBad
Trade
Sae* (138 .e0s(
1
is
0
20
a108"
20
Figure 5-4: This figure shows the results of the significant physiological response,
heart rate variability, in the Good vs. Bad Trades event marker.
5.2.2
Positive vs. Negative Good/Bad Trade Ratio
The next performance event marker is positive good/bad trade ratios versus negative good/bad trade ratios over a five minute interval. The positive good/bad trade
ratios are listed from the greatest difference of good to bad trades to the smallest
difference (but still positive) of good to bad trades; the negative good/bad trade
46
ratios are ordered similarly. The top
2
of each ordering are used for the positive
and negative good/bad trade components.
Five physiological signals show signifi-
cant results: tension, forehead temperature, heart rate (calculated from BVP and
EKG), and heart rate variability. For tension, a good/bad trade ratio over a period
of five minutes shows a much higher response for negative good/bad trade ratios than
for positive good/bad trade ratios, which are similar to the baseline. Four of the
six subjects exhibit this trend. The next physiological signal, forehead temperature,
shows that positive good/bad trade ratios have a higher forehead temperature than
negative good/bad trade ratios. Additionally, during the five minute period, positive
good/bad trade ratios tend to exhibit a decrease in temperature, whereas negative
good/bad trade ratios tend to exhibit an increase in temperature. Five of the six
subjects exhibit this trend. Heart rate is usually higher than the baseline for negative
good/bad trade ratios while positive good/bad trade ratios are either slightly higher
than the baseline or right around it. Three of the six subjects exhibit this trend,
when BVP is used to calculate heart rate. Four of the six subjects exhibit this trend,
when EKG is used to calculate heart rate. Both positive good/bad trade ratios and
negative good/bad trade ratios show HR deceleration. Three of the six subjects exhibit significant responses of this trend. Figure 5-5 shows the graphical interpretation
of these results.
5.2.3
Positive vs. Negative P&L
The final performance event marker is 80% of positive profit and loss intervals versus
80% of negative profit and loss intervals. Five physiological signals showed significant
results: tension, forehead temperature, heart rate (calculated from BVP and EKG),
and heart rate variability. For tension, a negative P&L over a period of 5 minutes
shows a much higher response than for a positive P&L, which is similar to the baseline.
Four of six subjects exhibit this trend. For forehead temperature, a positive P&L over
5 minutes has a higher forehead temperature than the baseline (although sometimes
it is only slightly higher) and a negative P&L over the same interval has a much lower
forehead temperature. Additionally, the forehead temperature usually rises over the
47
5 minute interval for negative P&L intervals. All six subjects exhibit this trend. For
heart rate, negative P&L intervals have a much higher heart rate than positive P&L
intervals, which have heart rates near the baseline. Three of six subjects exhibit this
trend, when heart rate is calculated from BVP. Four of six subjects exhibit this trend,
when heart rate is calculated from EKG. For heart rate variability, both positive P&L
intervals and negative P&L intervals show increased heart rate deceleration. Three of
six subjects exhibit significant responses of this trend. Figure 5-6 shows the graphical
interpretation of these results.
48
Tension Data
Fordilead Thimparatire
Data
Goodiad Trode Ruioover Susn,
-Negative
2
95.1
9521-
1.5
I
I
94.0
05
0
-~~~
-
-0.31
........
200 ..
100
04.8-
PC"GO
G6ooledTrai Iatieoovwim
oeiv
s
NegatitGoodiad Trade Ratio over 6 ru
Baeine
a
--
---
-----------
0
500
400
300
TWOn (saC)
0
Tie
HR **m
HR tier BVP Data
(.sa0
EKG
Data
92
76
00
74
as
72
88
70
~84
S
S
82
6
84
-
Positve Goo&VSd Trsa Rat* ow 5ve
**
Po-tiv GOdOadTrad
sito ocri
Negative Goo&dad Trade Ratioovei S emdo
Saoeiua
Negadve GoodBad Trade Ratio OVer 5 vins
Basaie*
21--
76t
-
-
200 --
300
400
600
-
200
100
0
Thne ("0c
HR Vaiafity
3W05400S50
rows (86eC)
Data
40-
tso
5
Pootive Goodlfld Trade Retia over Siius
Goodtad Trade Rati ovartinn
--
-Negative
-l0~ --
0
100
-~
-
200
300
ine tec(
400
5003
800
Figure 5-5: This figure shows the results of significant physiological responses, tension,
forehead temperature, heart rate (as extracted by BVP and EKG), and heart rate
variability, in the Positive vs. Negative Good/Bad Trade Ratio event marker.
49
Tetuton Data
92.5
28[
-
Forahead
100
200
---- -Temperatue Data
02
915
01
80.8
II
'e
go.
55
0.5
/
89
88.5
0
-Postive
-Necativ
0
100
200
PItoe
i
R11 ove 6 neon
300
as
-Negative
400
871
S00
H4R tr
MR fmra BVP Data
~
.
o. e .
.
.oitv
- NeativeP11.over 6
mme
Basoe
_
__
eons.
100-
P&t am 5 tOUR
400
3W0
I.
SW0
EKG Da
..
98
08-
6
I,
as
86.-
-NgativPA.
too0
200
300
500
400
00
70
100
0
300
200
400
I
over 6 os
SW0
tVastatitay Dota
SD
P
rSen
...
......
if
20
10
0
100
2006
300
Tim. (se)
400
500
600-
Figure 5-6: This figure shows the results of significant physiological responses, tension,
forehead temperature, heart rate (as extracted by BVP and EKG), and heart rate
variability, in the Positive P&L vs. Negative P&L event marker.
50
S00
Chapter 6
Discussion
In order to interpret the results obtained in Chapter 5, the results are sliced into two
main categories: temporal analysis and event marker analysis.
6.1
Temporal Analysis
The temporal analysis consists of comparing a short term stimulus to a long term
stimulus. Table 6.1 shows the grouping of event markers into short and long term
analyses, as well as a summary of significant responses for each subject for each event
marker. It is important to note that this table shows all significant responses, not just
the significant responses that fit the trend for a particular event marker. Therefore,
the number of subjects displaying a significant response in this table may not equal
the number of subjects displaying a particular trend for an event marker, as discussed
in Chapter 5.
6.1.1
Short Time Scale
When the results are viewed as short term versus long term event markers, the short
term effects are generally indistinguishable from each other.
Typically only skin
conductance response and heart rate variability have significant trends relative to the
baseline. Specifically, the skin conductance response tends to be higher for riskier
51
Subject
Large
vs.
Small
Trades
001
-
002
003
004
005
006
S,V
SV
S,V
TV
T,S,V
Short Term
Long
Wide
vs.
vs.
Short
Narrow
Bid/Ask
Trades
Spreads
S
S
SH,V
T,F,HB,V
T,H,V
TV
T,S,H,HB
S,V
T,S,V
S,V
V
TS
Good
vs.
Bad
Trades
S
SV
T,SHE,V
V
T,V
TS
Long Term
Positive
Positive
vs.
vs.
Negative
Negative
P&L
Good/Bad
Ratio
T,S,H
TH,HE
T,H,V
T,H,F,HB,V
S,H,V
T,H,HBHR
T,S,H,F,HB,HE
T,H,V
TFHB,HE,V
T,H,V
T,H,HBHR
T,S,H,HB,HE
Table 6.1: This table shows the alignment of event markers in the temporal analysis,
as well as a summary of the significant responses for each subject for each event
marker. The abbreviations in the table are the following: T=Tension, S=SCR,
H=Forehead Temperature, F=Finger Temperature, HB=Heart Rate from BVP,
HE=Heart Rate from EKG, and V=Heart Rate Variability.
events (e.g. big trades and short trades), and heart rate variability decreases for both
components of the event marker (i.e. the non-baseline responses).
Additionally, this particular sample of professional market makers may be trained
to disregard a particular event.
Over the course of their career, they may become
adjusted to short term swings in the market and their physiology does not respond
to these changes on a significant level.
6.1.2
Long Time Scale
In general, the long term analysis reveals more noticeable and significant changes
in physiology.
In this particular study, tension, forehead temperature, heart rate
(calculated from BVP and EKG), and heart rate variability show significant trends.
Tension increases for negative events (e.g.
interval of negative P&L), but remains
around the baseline for positive events. Forehead temperature is lower for negative
events but increases steadily over the time interval. Heart rate is higher for negative
events, but is similar to the baseline for positive events. Finally, heart rate variability
is lower (i.e.
larger heart rate deceleration) for both positive and negative events
relative to the baseline.
52
Therefore, for the sample of market makers on the Boston Stock Exchange, it is
reasonable to conclude that it takes more than a single event to produce a change
in their physiology. Longer periods of time are required to enter zones where events
begin to affect physiological changes and potentially affect financial risk processing.
6.2
Event Marker Analysis
The second slicing of results, the event marker analysis, distinguishes between financial event markers and performance based event markers. Table 6.2 shows the
grouping of event markers into financial and performance based events. As with the
temporal analysis, this table shows all significant physiological responses, not just the
significant responses that fit the trend for a particular event marker.
Subject
001
002
003
004
Financial Events
Large
Long
Wide
vs.
vs.
vs.
Small
Short
Narrow
Trades
Trades
Bid/Ask
Spreads
S
S
S,V
SHV
SV
S,V
SV
T,FHB,V
T,H,V
Performance Events
Good
Positive
Positive
vs.
vs.
vs.
Bad
Negative
Negative
Trades
Good/Bad
P&L
Ratio
S
T,S,H
T,H,HE
S,V
T,H,V
T,H,V
T,S,V
S,V
T,S,HEV
V
T,H,F,HBV
S,H,V
T,F,HB,HE,V
T,H,V
005
T,V
T,V
V
T,V
T,H,HBHR
TH,HB,HR
006
T,S,V
T,S,HHB
TS
TS
T,S,H,F,HB,HE
T,S,H,HB,HE
Table 6.2: This table shows the distribution of event markers in the event marker
analysis, as well as a summary of the significant responses for each subject for
each event marker. The abbreviations in the table are the following: T=Tension,
S=SCR, H=Forehead Temperature, F=Finger Temperature, HB=Heart Rate from
BVP, HE=Heart Rate from EKG, and V=Heart Rate Variability.
6.2.1
Financial Event Markers
For this sample of traders, there are only minimal changes in physiology for financial
event markers. In particular, only heart rate variability and SCR are consistently
related to financial event markers. Heart rate variability generally is lower (i.e. higher
53
heart rate deceleration) for both positive and negative events relative to the baseline.
The skin conductance response tends to increase for riskier events (e.g. big trades,
short trades, and trades with wide bid/ask spreads).
Forehead temperature is a
significant physiological variable for the long versus short trade event marker, but it
is not significant for any subject in the other two financial event markers.
The drop in heart rate variability is the result of an increase in heart rate deceleration, which can be attributed to a presence of an emotional stimuli [6]. In general,
financial event markers exhibit minimal changes in physiology relative to performance
event markers.
6.2.2
Performance Event Markers
In general, the performance event marker analysis shows a strong effect on the physiological variables. The different performance based metrics yield very similar responses
for tension and heart rate variability, and to a lesser extent, skin conductance response
and heart rate. Tension typically increases for bad trades, periods of time with more
bad trades than good trades, and negative P&L. Heart rate variability typically decreases (i.e. increased heart rate deceleration) for both positive and negative events
relative to the baseline. Skin conductance increases for negative events (bad trades
and a negative good/bad trade ratio). Heart rate usually increases for periods of
negative good/bad trade ratios and negative P&L.
Overall, only negative performance has a substantial effect on physiology. Positive performance events typically have a physiological response similar to the baseline.
This result suggests that traders respond physiologically (potentially because of emotional responses) to poor trading, but not to good trading. This result may be caused
by the reward mechanisms of trading, where good performance results in an increase
in wealth, but a poor performance results in a loss of a job and potentially a career.
Therefore, the more extreme consequence of job loss relative to the reward of increased wealth may cause physiological responses to be more substantial for negative
performance.
54
Chapter 7
Conclusion
This thesis provides behavioral finance with quantitative support that long term performance has a significant effect on traders' physiology. More specifically, negative
performance events are the main driver of physiological responses; positive performance events have minimal deviations from baseline physiological responses. Additionally, a long term analysis of events yield more substantial physiological changes
than a short term analysis; this result suggests that traders are seasoned to not be
affected by a single trade. Furthermore, this may imply that professional financial
traders operate in "the zone", which can either be positive or negative.
These conclusions have several important ramifications. First, it may be possible
to monitor traders physiology in order to whether they are currently in a positive
or negative performance trading zone. Second, the strong physiological responses associated with trading performance events imply the presence of emotional responses
that may interfere with economically rational decision making. Finally, given that
seasoned traders are minimally affected by financial events, it may be possible to decrease the physiological (and emotional) response to performance events by changing
the reward structure for traders. This change could help traders adjust emotionally
to dealing with losses over a longer time horizon.
55
7.1
Future Work
This thesis should help setup guidelines for future studies and experiments. Measurements of physiology should focus on heart rate variability, skin conductance response,
tension , and forehead temperature. Heart rate appears promising as a physiological
variable; however, improved mechanisms for detecting artifacts and improving the
accuracy of current heart rate extraction algorithms should be researched.
Addi-
tionally, sample sizes and length of experimentation should be increased to improve
the confidence level of the significance of the study's findings. A more accurate synchronization mechanism for recording the physiology is necessary to eliminate drift
estimation and correction, and should lead to more accurate results, as well as less
computational work.
This thesis also suggests new directions for related research. In particular, longer
term studies could measure the effect that physiological responses have on future
performance. Another vein of research is investigating how different reward structures
affect physiological responses. Finally, other factors contributing to a trader's decision
making should be established. These factors include, but should not be limited to,
years of experience in the financial markets, risk and psychological profiles, amount
of trading capital, and age.
56
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