# The Function of Computers and Machine Learning in Contemporary Finance
In recent decades, the significance of computer science within finance and economics has transformed significantly. Nowadays, leading financial institutions heavily depend on computers to scrutinize extensive datasets and formulate strategies that enhance investment returns. Organizations like JP Morgan, Chase, and Barclays are pioneering the application of supercomputers to assess portfolio risks and forecast asset values. But how precisely do computers and advanced algorithms achieve these remarkable tasks? In this article, we will investigate two innovative technologies reshaping finance: sentiment analysis and machine learning.
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## Sentiment Analysis: Gauging Market Sentiment
The stock market, once swayed by the instincts of floor traders, has gradually shifted towards quantitative analysts — or “quants” — who employ complex mathematical algorithms to execute trades and predictions. This transition illustrates the wider movement in financial markets prioritizing data-driven decision-making over intuition.
A vital instrument in this evolution is **sentiment analysis**, a method enabling computers to ascertain the psychological state of the market from diverse media formats such as news articles and social media posts. Presently, sentiment analysis begins by utilizing lexicons, created internally or externally, to assist computers in interpreting financial news.
### Lexicons and The Dow Jones Lexicon (DJL)
For example, **Dow Jones** has created the **Dow Jones Lexicon (DJL)**, which serves as a specialized dictionary aimed at understanding financial data. The DJL compiles financial news, converts it into a format that machines can understand, and classifies sentiment as either **bullish** (positive) or **bearish** (negative). Players within the financial system can leverage this analysis to determine whether it is prudent to trade specific stocks.
Various financial organizations often develop custom lexicons tailored to their specific requirements, employing terminology and phrases that may mirror their distinctive strategies. Once armed with these dictionaries, computers can swiftly assess market sentiment. Analyzing keywords from headlines or initial paragraphs of articles yields especially insightful data, as these sections hold greater significance in shaping market trends.
For instance, computers reviewing an article containing terms like “dropped,” “down,” or “lost” will classify the stock as **bearish**, indicating it might be unfavorable to invest in that asset.
### Sentiment Analysis in Practice
Let’s consider a scenario. In a CNBC article published shortly after a new COVID-19 variant was identified in South Africa, several terms like “dropped,” “down,” and “lost” appeared. These terms would contribute to a decline in the sentiment score for the related stocks. A sentiment analysis tool could seamlessly incorporate these indicators into a broader algorithm that advises whether to buy or sell the assessed assets.
On the backend, quants create these sentiment analysis frameworks using programming languages like XML (Extensible Markup Language). XML code is employed to define data structures comprehensively. Here’s a sample:
“`xml
Status
Bearish
“`
This XML snippet might be modestly modified by market analysts, incorporating different keywords and formatting to align with the sentiment analysis.
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## Machine Learning: Predicting Market Trends
Another fundamental aspect of computer science’s impact on finance is **machine learning (ML)**. Machine learning is a branch of **artificial intelligence (AI)** that enables computers to learn autonomously through trial and error by detecting patterns in data. Within the finance sector, machine learning models are essential for anticipating future market shifts.
### Training the Machine Learning Model
Stock traders require precise data to effectively train machine learning models. Typically, an ML algorithm utilizes data points like opening prices, the day’s highest and lowest figures, trade volume, and closing prices to decipher market patterns. Below is a representation of historical Microsoft stock data that is frequently utilized for training an ML model:
| Date | Open | High | Low | Close | Adj Close | Volume |
|————|———-|———-|———-|———-|———–|———-|
| 1990-01-02 | 0.605903 | 0.616319 | 0.598090 | 0.616319 | 0.447268 | 53033600 |
| 1990-01-03 | 0.621528 | 0.626736 | 0.614583 | 0.619792 | 0.449788 | 113772800 |
| 1990-01-04 | 0.619792 | 0.638889 | 0.616319 | 0.638021 | 0.463017 | 125740800 |
| 1990-01-05 | 0.635417 | 0.638889 | 0.621528 | 0.622396 | 0.451