In this paper, we further explore the application of the model in portfolio construction. Specifically, we apply the model to a portfolio of equity ETFs, an asset class that is more volatile than currency assets. What is different about equity ETFs is that they tend to have high correlations among themselves, that is to say, ETF on consumer product and retail equities tend to be highly correlated with ETF on industrial equities. This characteristic of ETFs poses a challenge to the non-correlation assumption in factor estimation we made in the original model. In order to address this problem, we allow the model to include correlation terms in our factor covariance matrix. This approach helps absorb more data variances in factor estimation, which in turn decreases variances in return predictions. We compare the new model with the original LTF model on a set of 10 equity ETFs and find that the new model outperforms the original one, thus validating our modification. This new model is highly applicable to all kinds of portfolio analysis as long as strong correlation is observed.

In section 2, we briefly explain the original LTF model proposed by Nakajima and West, followed by an introduction of our new model – Latent Threshold Factor Correlation Model (LTDFCM). We evaluate the correlation behavior of a portfolio of 10 equity ETFs in section 3, and find our new model does a better job explaining data noises, approbating our assumption. In section 4, we further our analysis using the same portfolio and make daily return predictions over a period of 3 months. We construct our portfolio using maximum return portfolio algorism, i.e. maximizing portfolio return based on a pre-determined portfolio risk tolerance. Empirical results show that LTDFCM outperform the overall market and the original LTF model. We then give an extended future research direction in section 5 for interested scholars.