Analysis on S&P 500 Sectors: Quant Stats, Optimal Weights, and Opportunities

(Credit: Bloomberg/Getty Images) Quant gurus has expressed their anaylis over the S&P 500 sectors. Quant stats are highlighted with its apects on optimal weights and opportunities. They stressed out that though they are experts of the fields, it is good to understand a well-diversified portfolio. They said that quant models are cannot replace human decision-making. Analysis on S&P 500 Sectors: Quant Stats, Optimal Weights, and Opportunites
October 15
6:00 AM 2016

Yesterday, Hickey Walters, highlighted the S&P 500 stocks with the highest percentage of buy and sell ratings.

 Marko Kolanovic, JPM Quant, discharged his latest report today, we expect him to read his latest disposition on the position of quant funds, on the relative imbalance of risk parity suggesting that the market is poised for an inflection point, either lower or higher.

 Kolanovic, in just over two weeks whose unprecedented ability to predict short-term market moves is starting to seem a little strange, alarmed that the next "significant risk for the S&P500" was the overcrowding of the "macro momentum bubble. Particularly, he said that there is an emerging negative feedback loop that is becoming a significant risk for the S&P 500.

On the other hand, in the report of SPDR S&P SPY, which deals with some basic quantitative stats and engineered features on the S&P 500 sectors. It takes a look at return stats, risk-reward tradeoffs, alternatives measures of risk over return which includes details on the sectoral downturns and tail events. Lastly, the sectors' co-momentums with the S&P 500 itself are analyzed. It also feature some risk/return co-relationships that are not so popular.

The second portion of the series is going to build more portfolios comprised of different weights of the S&P 500 sectors. Each one of it is optimized for a specific goal. It always place great emphasis on broad diversification (i.e. all sectors will be included in the portfolios). The portfolios will be highlighted to optimize for maximum return, minimum variance or minimum expected tail loss. More importantly, combinations of these as long-only and long/short portfolios. Moreover at the core, it will construct several portfolios based on entirely new risk-return metrics and quantitative measures.

These portfolios will feature weightings of the S&P 500 sectors that can logically outperform the S&P 500 over the longer-term at a lower downside risk. Finally, the upcoming article will also be filled with interpretation as to why certain sectors are basically assumed to outperform as parts of well-diversified but absolutely, alternatively-weighted portfolios. Instinctively, the portfolios will go beyond overweighting past champions and underweighting or shorting past under-performers.

Furthermore Jim Pyke, a quant guru said that in an uncertain market, it is good to have a well-defined and diversified portfolio across different geographies, sectors and asset classes. But, intuition is not always the best judge of finding diversification favorable chances. A more analytical way would be to look at correlation coefficients between the potential investments and the broader market.

Wherefore, investors need to understand the limits of quant models, and this involves some geeky math. Since at the end of the quant will never replace human decision making.



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