Machine Learning & Mechanics of “Investment Matrix”: “Performance Optimisation & Risk Measurement of Bank Nifty”

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.