Candlestick patterns are an ancient form of technical analysis developed in Japan around the 18th century. The invention is attributed to Japanese rice trader, Munehisa Homma, but it was not until the early 90s that the technique was popularized by Steve Nilson with his book, Japanese Candlestick Charting Techniques. This article aims to conduct an Event Study over all the S&P500 constituents to determine the real effectiveness of candlesticks as predictors.

In the first section, we explain the basics of candlesticks. Then, in the second section, we detail the procedure to conduct an event study. In the third section, we…

One fundamental assumption in standard finance models is the normality of stock returns. Modelling returns with a normal distribution simplifies many real-life issues, making it simple to implement in pricing and risk models. We can visually test this hypothesis by plotting out the histogram of the S&P500 historical returns.

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As we can see in the plot above, the normality assumption is quite naive. Usually, stocks and other traded assets returns show this kind of leptokurtic and skewed distributions.

The purpose of this article is to present a practical example of distribution fitting applied to stock returns to see if there…

In this article i present a simplified version of a Recurrent Neural Network model for stock price prediction. After an extensive research on Machine Learning and Neural Networks i wanted to present a guide to build, understand and use a model for predicting the price of a stock. Keep in mind that in this article i wont explain the basics of RNN and LSTM, i will go directly to the model explanation. The article is divided in three sections: 1-Data preprocessing, 2-Creating and training the model & 3-Evaluating the model.

1-Data Preprocessing

I first import a series of essential libraries.

import pandas…

Federico Glancszpigel

Finance student, Quant Finance track | Machine Learning enthusiast | Investment Banking researcher. Linkedin page:

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