Abstract
Over the past decade, extensive research on stock market prediction using machine learning models has been conducted. In this framework, different approaches for data standardisation methods have been used for financial time series analysis and to assess the impact of data standardisation on the final prediction outcome. The paper uses the feature-level optimal rolling-window batch data standardisation method to improve the machine learning model's predictive power significantly. Along with the standardisation method, the paper explores the performance of the automated feature interactions learner (Deep Cross Networks) effect on a plethora of technical indicators aiming at predicting the movements of the NIFTY50 index in India, as these predicted changes are reflected in options contracts. Feature-level optimal rolling window data standardisation can identify the optimal window of time such that the correlation between features and the response variable is maximized, with most features correlating at 0.7. In the experiment, 48% of important technical indicators negatively correlated with the response variable. The Deep Cross Network regression model combined with the optimal rolling window batch data standardisation method outperformed all other model configurations at weekly and monthly data frequency. It achieved a directional hit rate of 69.52% (weekly) and 79.17% (monthly) and root mean square error of 2.82 (weekly) and 5.01 (monthly), generating a profit 5.5 times (weekly) and 2.85 times (monthly) greater than the benchmark buy-and-hold strategy providing opposing evidence to the sub-martingale model.
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Source: Own calculations based on the correlation analysis between features and response variable and N = 1170 weeks of NIFTY50 index data from NSE, India, for March 1999 to September 2021 (Jariwala 2020)
Source: Own calculations based on the non-window correlation analysis and feature-level optimal rolling window standardisation algorithm. N = 1170 weeks of NIFTY50 index data from NSE, India, for March 1999 to September 2021 (Jariwala 2020)
Source: Deep Cross Network architecture used in modelling and predicting the NIFTY50 Index in India from novel machine learning approach for predicting NIFTY50 index in India
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Notes
An Indian stock index with 50 of the largest Indian companies was launched in 1996 and accounts for 13 sectors of the Indian economy (Wikipedia 2021).
The model is retrained periodically using all the data available until that point in time (Mehtab et al., 2021).
The predicted response variable can also be used to build a one-week maturity NIFTY50 futures strategy.
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Nagula, P.K., Alexakis, C. A Novel Machine Learning Approach for Predicting the NIFTY50 Index in India. Int Adv Econ Res 28, 155–170 (2022). https://doi.org/10.1007/s11294-022-09861-8
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DOI: https://doi.org/10.1007/s11294-022-09861-8
Keywords
- Efficient Market
- Machine Learning
- Technical Indicators Interactions
- Deep Cross Networks
- Rolling Window Data Standardisation
- NIFTY50