Why Supermicro Computer Stock Is Falling Due to Nvidia & SoundHound AI News? Embrace Financial Literacy & Overcome ‘Brown Girl Syndrome’!!
You take one day off, and it already feels like you’re running late on your commitments. It’s the Brown Girl Syndrome kicking in. No matter how much you do, it always feels like it’s not enough. Obvio, your parents never appreciated any efforts when you were a child, which built an inner voice saying, ‘Hey, do more… No, that’s not enough. You should be doing more than that.’ Until the ultimate praise isn’t received.
Such a cringe, right? You get traumatized and left to deal with and heal on your own.
I’ve read and observed so much about human psychology to understand how my brain is wired and why!
But guess what? I believe in being unapologetically me!
I work towards myself to better myself, and I do enjoy that.
Little wins!
On that note of self-healing and self-making journey, I want to understand AI and its impact on our finances!
Are you an AI Stocks investor? Trust me this is going to be the easiest yet risky strategy of all time. We just follow the lead of AI & invest in companies that are AI-aligned. Investors in the AI sector are experiencing a slight pullback in some of the market’s most explosive stocks today, notably impacting Supermicro Computer (SMCI).
As of Wednesday’s trading, Supermicro Computer stock (SMCI) witnessed a decline of 2.9% as of 3 p.m. ET, according to data from S&P Global Market Intelligence.
Although the company’s shares had dropped as much as 4.6% earlier in the day, there’s no specific business-related news driving this downturn.
However, the valuation of Supermicro Computer is being influenced by recent developments involving other prominent AI companies, particularly Nvidia and SoundHound AI.
Nvidia’s Impact on Supermicro Computer Stock:
Nvidia, a leading player in the AI space, recently released its fourth-quarter results, surpassing market expectations and driving substantial gains in its stock as well as other top AI companies. However, following these significant gains, investors seem to be showing some caution towards Nvidia’s stock.
This cautious sentiment was further fueled by recent Securities and Exchange Commission (SEC) filings revealing significant share sales by two members of Nvidia’s board of directors, Harvey Jones and Mark Perry.
While these sales may not directly reflect Nvidia’s long-term performance outlook, they contribute to investor uncertainty regarding future catalysts for the stock.
SoundHound AI’s Influence on Supermicro Computer Stock:
SoundHound AI has emerged as another high-profile AI stock, experiencing explosive growth with its share price surging 247% in the last month alone. However, recent analyst coverage from Northland suggested a cautious approach, leading to a downgrade of the stock’s rating from “outperform” to “market perform.” This downgrade, coupled with a 6.3% decline in SoundHound AI’s stock price, has had a mild spillover effect on Supermicro Computer stock.
In conclusion, the fluctuations in Supermicro Computer’s stock price today can be attributed to broader market trends and developments affecting prominent AI companies like Nvidia and SoundHound AI. Investors should approach investment decisions with a balanced perspective, considering both short-term market movements and long-term growth potential.
let’s see a more detailed example that uses a machine learning technique, such as a predictive model for stock prices. We’ll use historical stock data to train a model and then make predictions for future stock prices. We’ll implement this using a simple linear regression model for demonstration purposes.
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
# Load historical stock data
historical_data = pd.read_csv('historical_stock_data.csv')
# Preprocess data
historical_data['Date'] = pd.to_datetime(historical_data['Date'])
historical_data.set_index('Date', inplace=True)
# Feature engineering
historical_data['Price_Diff'] = historical_data['Close'].diff() # Price difference
historical_data['Price_Pct_Change'] = historical_data['Close'].pct_change() # Price percentage change
historical_data.dropna(inplace=True)
# Define features and target variable
X = historical_data[['Price_Diff', 'Price_Pct_Change']]
y = historical_data['Close']
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train linear regression model
model = LinearRegression()
model.fit(X_train, y_train)
# Make predictions
predictions = model.predict(X_test)
# Evaluate model
mse = mean_squared_error(y_test, predictions)
print(f'Mean Squared Error: {mse}')
# Plotting predictions vs. actual values
plt.figure(figsize=(12, 8))
plt.plot(y_test.index, y_test.values, label='Actual Close Price', color='blue')
plt.plot(y_test.index, predictions, label='Predicted Close Price', color='red')
plt.title('Predicted vs. Actual Close Prices')
plt.xlabel('Date')
plt.ylabel('Close Price')
plt.legend()
plt.grid(True)
plt.show()In this example:
- We load historical stock data from a CSV file.
- We preprocess the data by converting the ‘Date’ column to DateTime format and setting it as the index.
- We engineer features such as price difference and price percentage change.
- We split the data into training and testing sets.
- We train a linear regression model on the training data.
- We make predictions using the trained model on the testing data.
- We evaluate the model using Mean Squared Error (MSE).
- Finally, we plot the predicted vs. actual close prices to visualize the performance of the model.
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