Outliers: Not Always a Problem

1. Introduction

In the financial industry, outliers can be indicative of fraudulent activity. For example, a transaction that is significantly higher or lower than the average transaction amount could be a sign of fraud. By identifying outliers, we can investigate further to determine if fraud is occurring.

When analyzing data, outliers can be a cause for concern. They can skew results and make it difficult to draw accurate conclusions. However, it is important to remember that outliers are not always indicative of a problem. In fact, they can sometimes be a sign of good things.

One example of this is in the field of innovation. Outliers are often the ones who come up with new and groundbreaking ideas. They are the ones who are not afraid to think outside the box and challenge the status quo. Without outliers, the world would be a much less interesting place.

Another example is in the field of business. Outliers are often the ones who start successful companies. They are the ones who are not afraid to take risks and go after their dreams. Without outliers, the economy would be much less dynamic and prosperous.

Of course, outliers can also be a sign of a problem. For example, an outlier in a dataset of test scores could indicate that a student is struggling. In this case, it would be important to investigate the issue further to determine the cause of the problem.

However, it is important to remember that outliers are not always a problem. In fact, they can sometimes be a sign of good things. So, the next time you see an outlier, don't be too quick to judge. It could be the start of something great.

2. Using advanced statistical models and machine learning models to identify Anomalies.

There are several types of statistical models for the analysis of anomalies, which are commonly used for the analysis of Financial Fraud. Namely, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), Neural Networks, and specific Anomaly analysis models, such as Isolation Forest, and LOF.

This work implements the Local Outlier Factor (LOF) model, a technique used in anomaly detection within financial data.

1. LOF Model Development: The work begins by developing the LOF model (model_lof) using the provided financial data (data).
2. Obtaining Anomaly Scores: Anomaly scores are obtained from the LOF model and stored in the variable anomaly_scores. These scores represent the degree of anomaly for each data point in the dataset.
3. Visualizing Anomaly Scores: The work generates a plot to visualize the anomaly scores. This plot helps in understanding the distribution of anomalies within the Nvidia stock price dataset.
4. Identifying and Highlighting Anomalous Values: Anomalies are identified by selecting data points with anomaly scores greater than 1.6 (the standard deviation to the right of the distribution). These anomalous points are stored in the vector. Finally, these anomalous points on the plot by overlaying them with red points to visually emphasize their significance.

In financial terms, this code performs anomaly detection on Nvidia stock price data using the LOF model. It identifies data points that deviate significantly from the expected behavior, potentially indicating unusual or abnormal market conditions. The threshold of 1.6 standard deviations is used to define the level of anomaly, with points exceeding this threshold considered as significant outliers.

3. Analysis of Nvidia Stock Outliers in 2023 and 2024:

2023:

• June: A significant spike in Nvidia's stock price is observed, reaching a high of $437.9849 on June 20th. This increase coincides with the company's announcement of strong financial results for the first quarter of 2023, which exceeded analysts' expectations.
• September: Following the June peak, the stock price declines to a low of $419.0535 on September 26th. This decline can be attributed to a number of factors, including global economic uncertainty, the cryptocurrency market crash, and reduced demand for graphics cards.
• November: By the end of the year, the stock price recovers, reaching a high of $504.0220 on November 20th. This rebound can be attributed to positive expectations surrounding the launch of Nvidia's next-generation graphics cards.

2024:

• January: The stock price continues its upward trend, reaching a high of $522.5055 on January 8th.
• February: An exponential increase in the stock price is observed, reaching a high of $776.5936 on February 28th. This increase is unprecedented and it is unclear what is causing it. But we are sure that it is the combination of ChatRTX, and the presentation of its results and sales predictions. In addition to the growth expectations of AI.
• March: The stock price stabilizes, reaching a high of $950.0200 on March 25th.

Overall, Nvidia's stock performance in 2023 and 2024 has been highly volatile. Significant spikes and declines in the stock price have been observed, which can be attributed to a number of factors.

It is important to note that outliers are not always indicative of a problem. In Nvidia's case, the outliers may simply be the result of a successful company operating in a dynamic market.