An Object-Oriented Bayesian Framework for the Detection of Market Drivers
<p>Behavior of the RSS and BIC varying the number of breakpoints from 0 to 3.</p> "> Figure 2
<p>Structural breaks in the S&P 500 time series.</p> "> Figure 3
<p>A representation of the insurance network with Object–Oriented Bayesian Networks. Source: (<a href="#B14-risks-07-00008" class="html-bibr">Langseth and Nielsen 2003</a>).</p> "> Figure 4
<p>OOBN on the S&P 500 time series during the first time-slice.</p> "> Figure 5
<p>Unveiling the complexity in the S&P 500 drivers. From top to bottom and from left to right, the BN defining the ties among growth (<b>a</b>); momentum/technical analysis (<b>b</b>); sentiment (<b>c</b>); and value indicators (<b>d</b>).</p> "> Figure 6
<p>Tornado plots for the nodes in the Growth class. From top to bottom and from left to right, the plot shows the sensitivity of each variable (node) to changes in the other within the class.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data
2.2. Preliminary Data Analysis
2.3. Methodology
- -
- the graph structure , where is the set of vertexes, and is the set of directed edges;
- -
- a finite probability space , where is the probability space, is a -algebra on and a measure on , such that: ; , and , if ;
- -
- a set of random variables defined on , one for each node of the graph whose conditional probability distributions express the strengths of dependency relations between the random variable and its parent connection on the graph:
3. Simulation and Results
3.1. Experiment Design
- Set .
- Select .
- (OOBN–L) Build the OOBN using the Chow–Liu procedure combined to the NPC algorithm.
- (OOBN–L) Derive the CPT for each node with the EM procedure and the related probability for the S&P 500 of going up (1), down (2) or side-ward (0) and extract the highest.
- (OOBN-L) If the highest probability for the S&P 500 is associated to the up state, then put the signal and buy; if highest probability for the S&P 500 is associated to the down state, then set and sell; otherwise, set and maintain the position.
- (OOBN-T) Select and compute the time-series of log-returns:
- (OOBN-T) For each price level in :
- (a)
- Evaluate:
- (b)
- Compute the sign of (9):
- (c)
- Compute:
- (OOBN-T) With the time series: check the goodness of the trading signals with the bundle of performance measures provided in Table 8.
- Set and go to Step 2.
3.2. Discussion
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A. The Expectation–Maximization Algorithm
- Choose an initial estimate of .
- (E-step) Compute the auxiliary Q–function based on .
- (M-step) Compute to maximize the auxiliary Q–function.
- Set and repeat from Step 2 until convergence.
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1 | |
2 | Chicago Board of Exchange. |
3 | Moving Average Convergence–Divergence. |
4 | |
5 | |
6 |
Name | Network ID | Investment Areas | |||
---|---|---|---|---|---|
Growth | Value | Sentiment | Momentum and TA | ||
Gold | GOLD | x | |||
Unemployment rate | UNEMP | x | |||
DXY index | DXY | x | |||
Gross Domestic Product | GDP_G | x | |||
Wheat | WHEAT | x | |||
Crude oil | OIL | x | |||
Copper | COPPER | x | |||
Price to Cash Flow | PCF | x | |||
Sales Growth | SALESG | x | |||
EBITDA growth | EBITDAG | x | |||
Enterprise Value to EBITDA | EVEBITDA | x | |||
Price to Book Value | PBV | x | |||
Price to Sales | PS | x | |||
Earnings price per Share | EPS | x | |||
EPS growth | EPSG | x | |||
Dividend Yield | DVDYLD | x | |||
Profit margin | PROFIT | x | |||
Profit per Sale | PS | x | |||
Return on Equity | ROE | x | |||
Ebitda margin | EBITDAMRG | x | |||
Implied Volatility in 52 weeks | VOLA52W | x | |||
VIX | VIX | x | |||
Relative Strength Index | RSI | x | |||
Rate of change | ROC | x | |||
20 and 50 Moving Average Cross | MA2050 | x | |||
Moving Average Convergence | MACD | x | |||
Divergence |
RSS | 118,462,323 | 30,094,169 | 26,358,449 | 23,555,096 |
BIC | 13,040 | 11,834 | 11,730 | 11,643 |
Estimate | Std. Error | t-Value | ||
---|---|---|---|---|
Intercept | 7.0269 | 0.0089 | 790.648 | *** |
segment2 | 0.0917 | 0.0112 | 8.177 | *** |
segment3 | 0.5204 | 0.0146 | 35.741 | *** |
segment4 | 0.7667 | 0.0147 | 52.211 | *** |
Time-Slice | ID | First | End | Length | Mean | Median | Min | Max | Std | 1st Q | 3rd Q |
---|---|---|---|---|---|---|---|---|---|---|---|
2000–2004 | TS1 | 03/01/00 | 02/02/04 | 215 | 1145.0989 | 1125.1700 | 800.5800 | 1527.4599 | 201.2463 | 988.8201 | 1316.8900 |
2004–2009 | TS2 | 09/02/04 | 16/03/09 | 267 | 1251.6363 | 1261.4899 | 683.3800 | 1561.8000 | 176.3480 | 1161.5150 | 1387.0599 |
2009–2013 | TS3 | 23/03/09 | 16/12/13 | 248 | 1295.5064 | 1287.0899 | 815.9400 | 1818.3199 | 228.7867 | 1116.9075 | 1414.6950 |
2013–2018 | TS4 | 23/12/13 | 19/03/18 | 222 | 2165.6753 | 2092.2600 | 1782.589 | 2872.8701 | 249.4976 | 1990.3999 | 2347.5125 |
1/11 | 1/11 | 2/11 | 4/11 | |
2/11 | 1/11 | 1/11 | 4/11 | |
1/11 | 1/11 | 1/11 | 3/11 | |
4/11 | 3/11 | 4/11 | 1 |
1/4 | 1/3 | 2/4 | |
2/4 | 1/3 | 1/4 | |
1/4 | 1/3 | 1/4 | |
Sum | 1 | 1 | 1 |
ID | First | End | Length (in Weeks) |
---|---|---|---|
03/01/00 | 01/09/03 | 215 | |
08/09/03 | 02/02/04 | 21 | |
09/02/04 | 01/09/08 | 240 | |
08/09/08 | 16/03/09 | 27 | |
23/03/09 | 17/06/13 | 223 | |
24/06/13 | 16/12/13 | 25 | |
23/12/13 | 09/10/17 | 200 | |
16/10/17 | 19/03/18 | 22 |
Performance Measure | Abbreviation | Formula |
---|---|---|
% Correct Directional Change | %CDC | |
Annualized return | AR | |
Annualized volatility | AV | |
Sharpe Ratio | SR | |
Number of Up periods | NUP | |
Number of Down periods | ND | |
Average gain in up periods | AG | |
Average loss in down periods | AL | |
Average gain/loss ratio | AGL |
DXY | 1 | 1 | 2 | 1 |
(46.15) | (42.48) | (38.76) | (40.73) | |
PE | 2 | 1 | 1 | 1 |
(51.39) | (38.14) | (35.83) | (37.06) | |
RSI | 1 | 1 | 2 | 1 |
(39.68) | (48.10) | (41.15) | (41.76) | |
PCRatio | 1 | 2 | 1 | 2 |
(41.35) | (39.66) | (43.36) | (40.59) | |
Overall | 1 | 2 | 1 | 2 |
(38.12) | (42.93) | (43.09) | (43.49) |
Performance Measure | ||||
---|---|---|---|---|
%CDC | 0.8421 | 0.4 | 0.7391 | 0.7 |
AR | 0.2148 | 0.4409 | 0.1912 | 0.2139 |
AV | 0.0283 | 0.1113 | 0.0280 | 0.0385 |
SR | 7.5949 | 3.9609 | 6.8179 | 5.558 |
NUP | 16 | 10 | 17 | 14 |
ND | 4 | 16 | 7 | 7 |
AG | 0.0052 | 0.022 | 0.0052 | 0.0062 |
AL | 0.0082 | 0.0205 | 0.0049 | 0.0082 |
AGL | 0.6321 | 1.0749 | 1.0562 | 0.7505 |
B&H | Näive | OOBN-b | |
---|---|---|---|
1.0507 | 1.0507 | 1.0858 | |
0.7989 | 1.2208 | 1.2418 | |
1.0548 | 1.0548 | 1.0919 | |
1.0283 | 0.9707 | 1.0897 |
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De Giuli, M.E.; Greppi, A.; Resta, M. An Object-Oriented Bayesian Framework for the Detection of Market Drivers. Risks 2019, 7, 8. https://doi.org/10.3390/risks7010008
De Giuli ME, Greppi A, Resta M. An Object-Oriented Bayesian Framework for the Detection of Market Drivers. Risks. 2019; 7(1):8. https://doi.org/10.3390/risks7010008
Chicago/Turabian StyleDe Giuli, Maria Elena, Alessandro Greppi, and Marina Resta. 2019. "An Object-Oriented Bayesian Framework for the Detection of Market Drivers" Risks 7, no. 1: 8. https://doi.org/10.3390/risks7010008