Global Collective Dynamics of Financial Market Efficiency Using Attention Entropy with Hierarchical Clustering
Abstract
1. Introduction
2. Literature Review
3. Methods
3.1. Attention Entropy
3.2. Hierarchical Clustering
3.3. Stochastic Block Model (SBM)
4. Experiments and Data
4.1. Experiments
4.2. Data
5. Results
5.1. Long-Term Efficiency with Clustering
5.2. Short-Term Efficiency with Sliding Window
5.3. Short-Term Efficiency with SBM Network
6. Discussion and Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Mean | Stdev | Skewness | Kurtosis | ARCH (10) | ARCH (20) | Augmented Dickey–Fuller | |
---|---|---|---|---|---|---|---|
S&P 500 | 0.07 | 1.55 | 0.16 | 3.53 | 230.1 *** | 250.07 *** | −29.91 *** |
Dow Jones Industrial Average | 0.03 | 1.22 | −0.49 | 16.05 | 1262.23 *** | 1311.84 *** | −15.14 *** |
NASDAQ Composite | 0.05 | 1.38 | −0.50 | 9.24 | 998.5 *** | 1045.3 *** | −14.49 *** |
NYSE Composite Index | 0.02 | 1.32 | −0.68 | 13.41 | 1225.34 *** | 1295.24 *** | −14.79 *** |
NYSE American Composite Index | 0.01 | 1.31 | −1.00 | 16.49 | 984.88 *** | 1161.64 *** | −15.2 *** |
Russell 2000 | 0.03 | 1.62 | −0.66 | 8.60 | 1146.88 *** | 1214.77 *** | −14.73 *** |
Vix | 0.00 | 7.70 | 1.05 | 6.14 | 192.18 *** | 195.78 *** | −25.58 *** |
FTSE 100 | 0.00 | 1.20 | −0.41 | 10.03 | 788.14 *** | 834.46 *** | −23.79 *** |
DAX PERFORMANCE-INDEX | 0.02 | 1.39 | −0.23 | 8.39 | 583.26 *** | 674.27 *** | −22.63 *** |
CAC 40 | 0.01 | 1.42 | −0.28 | 8.25 | 628.1 *** | 688.32 *** | −23.59 *** |
ESTX 50 PR.EUR | 0.00 | 1.43 | −0.32 | 7.99 | 589.85 *** | 613.74 *** | −29.08 *** |
EURONEXT 100 | 0.01 | 1.30 | −0.38 | 9.10 | 692.21 *** | 759.31 *** | −23.48 *** |
BEL 20 | −0.00 | 1.30 | −0.65 | 10.77 | 584.09 *** | 614.49 *** | −12.24 *** |
Nikkei 225 | 0.01 | 1.47 | −0.45 | 8.38 | 1054.16 *** | 1098.86 *** | −63.39 *** |
HANG SENG INDEX | 0.00 | 1.48 | −0.03 | 8.96 | 997.94 *** | 1106.29 *** | −10.72 *** |
SSE Composite Index | 0.00 | 1.53 | −0.61 | 5.44 | 408.67 *** | 472.35 *** | −12.97 *** |
Shenzhen Component | 0.02 | 1.78 | −0.55 | 3.45 | 364.1 *** | 426.68 *** | −14.66 *** |
S&P/ASX 200 | 0.01 | 1.12 | −0.69 | 7.80 | 1129.81 *** | 1175.54 *** | −14.4 *** |
ALL ORDINARIES | 0.01 | 1.07 | −0.66 | 9.66 | 768.79 *** | 817.83 *** | −37.55 *** |
S&P BSE SENSEX | 0.04 | 1.37 | −0.21 | 13.61 | 543.65 *** | 561.24 *** | −11.96 *** |
Jakarta Composite Index | 0.03 | 1.27 | −0.58 | 9.61 | 508.49 *** | 614.77 *** | −18.47 *** |
S&P/NZX 50 INDEX GROSS | 0.03 | 0.74 | −0.65 | 8.21 | 886.56 *** | 947.68 *** | −21.0 *** |
KOSPI Composite Index | 0.02 | 1.23 | −0.55 | 10.44 | 966.23 *** | 990.46 *** | −12.24 *** |
TSEC weighted index | 0.02 | 1.14 | −0.42 | 5.11 | 488.16 *** | 541.14 *** | −14.08 *** |
S&P/TSX Composite index | 0.01 | 1.15 | −1.08 | 20.75 | 1126.95 *** | 1262.88 *** | −11.45 *** |
IBOVESPA | 0.02 | 1.74 | −0.44 | 10.17 | 1174.02 *** | 1267.65 *** | −26.78 *** |
IPC MEXICO | 0.02 | 1.18 | −0.02 | 7.29 | 724.97 *** | 843.78 *** | −27.88 *** |
MERVAL | 0.10 | 2.32 | −2.67 | 50.22 | 37.67 *** | 58.42 *** | −61.87 *** |
TA-125 | 0.02 | 1.09 | −1.17 | 12.07 | 466.91 *** | 527.72 *** | −18.56 *** |
Average | 0.02 | 1.58 | −0.52 | 11.01 | - | - | - |
Cluster | Market | Country |
---|---|---|
1 | FTSE 100 | United Kingdom |
DAX PERFORMANCE-INDEX | Germany | |
2 | BEL 20 | Belgium |
3 | CAC 40 | France |
EURONEXT 100 | France | |
4 | IPC MEXICO | Mexico |
5 | S&P 500 | United States |
6 | SSE Composite Index | China |
Shenzhen Component | China | |
7 | Dow Jones Industrial Average | United States |
NYSE Composite Index | United States | |
8 | NASDAQ Composite | United States |
Russell 2000 | United States | |
9 | S&P/ASX 200 | Australia |
ALL ORDINARIES | Australia | |
10 | Nikkei 225 | Japan |
MERVAL | Argentina | |
11 | TA-125 | Israel |
12 | TSEC weighted index | Taiwan |
IBOVESPA | Brazil | |
13 | Jakarta Composite Index | Indonesia |
KOSPI Composite Index | Republic of Korea | |
14 | HANG SENG INDEX | Hong Kong |
S&P/TSX Composite index | Canada | |
15 | S&P BSE SENSEX | India |
NYSE American Composite Index | United States | |
Vix | United States |
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Cho, P.; Kim, K. Global Collective Dynamics of Financial Market Efficiency Using Attention Entropy with Hierarchical Clustering. Fractal Fract. 2022, 6, 562. https://doi.org/10.3390/fractalfract6100562
Cho P, Kim K. Global Collective Dynamics of Financial Market Efficiency Using Attention Entropy with Hierarchical Clustering. Fractal and Fractional. 2022; 6(10):562. https://doi.org/10.3390/fractalfract6100562
Chicago/Turabian StyleCho, Poongjin, and Kyungwon Kim. 2022. "Global Collective Dynamics of Financial Market Efficiency Using Attention Entropy with Hierarchical Clustering" Fractal and Fractional 6, no. 10: 562. https://doi.org/10.3390/fractalfract6100562
APA StyleCho, P., & Kim, K. (2022). Global Collective Dynamics of Financial Market Efficiency Using Attention Entropy with Hierarchical Clustering. Fractal and Fractional, 6(10), 562. https://doi.org/10.3390/fractalfract6100562