Stocks of different companies are generally categorized into various industrial sectors based on the domain they operate. Individual stock prices are much more difficult to predict, and an investment bears a significant risk of unanticipated failure while thematic investment allows diversification within a sector and hence avoids the risk of failure of a single stock. In thematic investment, a user identifies one or more industrial sectors based on thematic preference and then distributes the corpus among various stocks from those sectors. If sectors are chosen wisely then it is possible to have a positive return even with the sudden downfall of some stock. To identify the sectors that will yield a positive return in the medium term, we have selected ten technical indicators (TI), and designed backpropagation neural networks (BPNN) with scaled conjugate gradient learning. We have evaluated the BPNN models with 12 sectoral index price history from NSE and achieved a comparable accuracy of 81.05% in prediction. In the next step, we have predicted sectoral index movement for one month to six months in the future, and based on the predicted gains we ranked the sectors. Based on the given investment horizon, top-k(=4) sectors are recommended to an investor. We validated our strategy on NSE sectoral data and achieved a recommendation accuracy of 87.5%.