Machine Learning & Mechanics of “Investment Matrix”: “Performance Optimisation & Risk Measurement of Bank Nifty”
Nitin Kulshrestha1, Vinay Kumar Srivastava2

1Nitin Kulshrestha, PhD Scholar Alabbar School of Management, Raffles University, Neemrana (Raj.), India.
2Dr. Vinay Kumar Srivastava, Founder Honorary Secretary of Indian Society for Management Development and Research (ISMDR) and Managing Editor of Arash.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3298-3302 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8557038620/2020©BEIESP | DOI: 10.35940/ijrte.F8557.038620

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Abstract: Purpose: The zeal and reason to write this research paper are to evaluate the performance & risk measurement of Bank Nifty based on Machine learning, Technical Analysis & Monte Carlo Simulation. Design /Methodologies/Approach: To achieve our desired results for this study, we use moving average (auto-optimization method) as a technical analysis return optimization tool & Monte Carlo Simulation as a risk analysis tool, & at the end harmonize both of the results, & compare with buy hold strategy. We use Bank Nifty end of day historical closing data of past five years i.e.1 Jan 2015 – 31 Dec 2019, For this study using Amibroker software. Originality & Value: This research paper is beneficial for anyone who wants understand Bank Nifty on the ground of technical analysis & risk measurement technique (MCs), & also to synergies the strength of two studies. Research Limitations: In appropriate input can lead to creating wrong simulation result, there are no. of unknown factors that simulation cannot truly understand or account during the process. Practical implication: Understanding stock market results is essential to make further decisions related to risk & reward ratio. The results imply that Moving average give outstanding returns on Bank Nifty in medium to long run, & Monte Carlo Simulation having functional judgemental abilities on probabilities basis towards risk & returns. Furthermore, by apply both the technique for risk analysis, simultaneously give outstanding risk & return optimization of Bank Nifty.
Keywords: Technical Analysis, Moving Average, Indicators, National Stock Exchange, Expert system, Bank Nifty
Scope of the Article: Machine Learning.