Cluster-Then-Predict and Predictive Algorithms (Logistic Regression)
DOI:
https://doi.org/10.26438/ijcse/v6i2.249252Keywords:
Predictive Algorithms, Regression, Classification, Clustering, Logistic Regression, Stock Returns, Cluster-Then-PredictAbstract
Stock market is playing a vital role as investments option and investors make short-term investments as well as long-term investments. But here the main question arises “Where to invest?” and “when to invest?” even if an investor is aware about where to invest, it is still unpredictable whether or not stocks will have good future returns over time. To eliminate this dilemma predictive algorithms were introduced that will help investors in making investments by predicting which stocks will have positive expected returns. However, predicting stock returns with predictive algorithms alone is not enough. Clustering algorithms are widely used to cluster the stocks that have related returns over time. Using Cluster-Then-Predict approach we are going to prove that it provides more accurate results than the original predictive (Logistic Regression) model.
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