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Forecasting the Performance of a Basket of Stocks

In order to beat a benchmark index or portfolio of a universe of stocks, it is necessary to identify a more appropriate weighting of the universe so that they will collectively perform better than the benchmark over time. In practice, this means allocating larger portions of the portfolio to high conviction stocks which are expected to perform well, and less or no capital to the stocks expected to underperform.

However, an index or universe is often constituted of dozens to hundreds of stocks which as a result will require months of thorough research and fundamental analysis in order to assess the attractiveness of each stock and whether it should feature in the portfolio or not.

This short white paper shows how machine learning can augment Analysts in order to help them prioritise their research.

Forecasting the top and worst performing stocks of a basket book

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