The normaly distributed daily returns in stock trading

TL;DR

This paper proposes a quantitative stock trading strategy based on normality tests, correlation analysis, and standard scores, aiming to improve stock selection despite the inherent unpredictability of stock movements.

Contribution

It introduces a novel approach combining normality testing and correlation analysis for optimal stock selection in trading strategies.

Findings

The method achieves a win/loss ratio greater than 51%.

Normality tests can effectively inform stock trading decisions.

Correlation analysis enhances multi-stock trading analysis.

Abstract

In this report, we talked about a new quantitative strategy for choosing the optimal(s) stock(s) to trade. The basic notions are generally very known by the financial community. The key here is to understand 1) the standard score applied to a sample and 2) the correlation factor applied to different time series in real life. These notions are the core of our research. We are going to begin with the introduction section. In this part, we talked about variance, covariance, correlation factor, daily returns in stock trading and the Shapiro-Wilk test to test the normality of a time serie. Next to that, I talked about the core of my method (what do you do if you want to pick the optimal(s) stock(s) to trade). At the end of this report, I talked about a new idea if you want to analyze more than one stock at the time. All my work goes with a primary reflexion : forecasting a stock direction is a random walk and nobody can be 100 % sure where a stock is going. All we can do, is to pretend to have a technic with a win/loss ratio greater than 51 %.