Name
Abstract | ||
|---|---|---|
Financial markets are comprised of many instruments, are complex. and are constantly changing. However, it is interesting to consider what, if any, commonalities persist in markets over long time horizons. In particular. one can study financial markets under nominal and stressed market conditions, and attempt to discern which market parameters remain invariant and which market parameters change. Herein, we study financial markets from the perspective of low-dimensional manifolds that capture the inherent characteristics of the high-dimensional data that represent the market performance. Using Russell 3000 constituents, we estimate intrinsic dimension of the US equity market over 30 years (1986-2016) and analyze those times where the change in dimension is abnormal. In particular, our focus in on novel applications of nonlinear techniques such as Isomap and autoencoders, as opposed to linear technique such as principal component analysis (PCA). Such ideas have many applications, including portfolio diversification and market crash analysis. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BigData47090.2019.9006343 | 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) |
Keywords | Field | DocType |
Russell 3000, principal component analysis, multidimensional scaling, Isomap, autoencoder | Econometrics,Diversification (finance),Autoencoder,Multidimensional scaling,Computer science,Intrinsic dimension,Equity (finance),Artificial intelligence,Financial market,Principal component analysis,Machine learning,Isomap | Conference |
ISSN | Citations | PageRank |
2639-1589 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nitish Bahadur | 1 | 0 | 0.34 |
| Randy Paffenroth | 2 | 99 | 14.17 |
| Gajamannage Kelum | 3 | 2 | 1.73 |