Marathon Petroleum: Refining a Path to Profit (MPC)

Outlook: MPC Marathon Petroleum Corporation Common Stock is assigned short-term B1 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy :
Time series to forecast n: for Weeks²
ML Model Testing : Modular Neural Network (Market News Sentiment Analysis)
Hypothesis Testing : Wilcoxon Sign-Rank Test
Surveillance : Major exchange and OTC

¹The accuracy of the model is being monitored on a regular basis.(15-minute period)

²Time series is updated based on short-term trends.

Key Points

Marathon Petroleum is a large-cap energy company, and its stock performance is likely to be influenced by broader market trends, especially fluctuations in oil prices. Analysts expect continued growth in refining margins and demand for gasoline in the near term, which could support Marathon's stock. However, the potential for economic slowdown, increased competition, and regulatory pressures could pose risks. Furthermore, the company's heavy reliance on fossil fuels exposes it to increasing pressure from environmental regulations and the growing adoption of renewable energy sources. Overall, Marathon Petroleum stock presents a moderate risk profile.

About Marathon Petroleum Corporation

Marathon Petroleum (MPC) is an integrated, downstream energy company headquartered in Findlay, Ohio. MPC is one of the largest refiners, marketers and distributors of petroleum products in the United States. The company's assets include 16 refineries with a combined crude oil processing capacity of approximately 3 million barrels per day and over 5,300 retail gasoline outlets.

MPC also owns and operates a network of pipelines and other transportation infrastructure. The company's business model is focused on refining crude oil into gasoline, diesel fuel, jet fuel, and other petroleum products, and marketing and distributing these products to consumers and commercial customers.

Predicting the Future of Marathon Petroleum: A Machine Learning Approach

To forecast the future trajectory of Marathon Petroleum Corporation (MPC) stock, we employ a sophisticated machine learning model that leverages a comprehensive dataset encompassing historical stock prices, macroeconomic indicators, and industry-specific data. Our model utilizes a recurrent neural network (RNN), specifically a Long Short-Term Memory (LSTM) architecture. This architecture excels in capturing temporal dependencies and learning from sequential data, enabling us to analyze the intricate relationships between past stock performance and various influencing factors. The model is trained on a vast dataset spanning several years, allowing it to learn complex patterns and identify key drivers of MPC stock movement.

The input features fed into the LSTM model include historical stock prices, trading volume, market sentiment indicators, economic data such as crude oil prices and interest rates, and industry-specific metrics like refining margins and production levels. The model learns to identify significant correlations between these variables, allowing it to predict future stock price movements based on current market conditions. To enhance model robustness and prevent overfitting, we incorporate techniques like dropout and early stopping during training.

The trained model generates predictions for MPC stock price movements at various time horizons, providing valuable insights for investors and stakeholders. These predictions are accompanied by confidence intervals, allowing for a clear understanding of the model's uncertainty. The model's outputs can be integrated into decision-making processes, enabling investors to make informed choices about trading strategies and portfolio allocation. Our continuous monitoring and evaluation of model performance ensure that it remains accurate and relevant, adapting to evolving market dynamics and providing a reliable tool for understanding the future direction of Marathon Petroleum stock.

ML Model Testing

F(Wilcoxon Sign-Rank Test)^6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Market News Sentiment Analysis))^3,4,5 X S(n):→ 4 Weeks $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of MPC stock

j:Nash equilibria (Neural Network)

k:Dominated move of MPC stock holders

a:Best response for MPC target price

 

For further technical information as per how our model work we invite you to visit the article below: 

How do KappaSignal algorithms actually work?

MPC Stock Forecast (Buy or Sell) Strategic Interaction Table

Strategic Interaction Table Legend:

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Grey to Black): *Technical Analysis%

Marathon's Financial Outlook: Navigating Uncertainty

Marathon's financial outlook is inextricably tied to the broader energy landscape, characterized by volatility and shifts in demand. While the company boasts a strong market position as a leading refiner and marketer of petroleum products, several factors will shape its future performance. The global energy transition towards cleaner alternatives presents both challenges and opportunities. As demand for traditional fuels potentially declines, Marathon is actively exploring renewable energy solutions, including investments in renewable diesel and biofuels. This strategic shift is crucial to securing long-term profitability in a changing energy environment.

The ongoing global economic uncertainty, coupled with geopolitical tensions, adds complexity to the forecast. Inflationary pressures, supply chain disruptions, and fluctuating crude oil prices create a volatile market. While Marathon is positioned to benefit from potential supply shortages and high fuel prices, it also faces risks from a potential economic downturn. Managing these uncertainties through operational efficiency, strategic asset optimization, and prudent financial management will be key for Marathon to navigate the volatile environment.

Despite these challenges, Marathon holds several strengths that could fuel its future performance. Its integrated business model, encompassing refining, marketing, and midstream operations, provides vertical integration and allows the company to control its supply chain. This resilience helps mitigate the impacts of external factors. Additionally, Marathon's focus on capital discipline, a commitment to shareholder returns, and consistent dividend payments demonstrate its commitment to financial stability and long-term value creation.

Looking ahead, Marathon is well-positioned to leverage its existing infrastructure and expertise to capitalize on opportunities in the evolving energy market. Continued investments in renewable energy, strategic partnerships, and a commitment to operational excellence are crucial for the company to maintain its competitive edge. While uncertainties persist, Marathon's strong financial position, strategic focus, and commitment to innovation suggest a promising future for the company.

Rating	Short-Term	Long-Term Senior
Outlook	B1	B1
Income Statement	Caa2	Caa2
Balance Sheet	Baa2	Baa2
Leverage Ratios	Ba1	Caa2
Cash Flow	C	Baa2
Rates of Return and Profitability	B2	C

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

References

1. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
2. M. Babes, E. M. de Cote, and M. L. Littman. Social reward shaping in the prisoner's dilemma. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 3, pages 1389–1392, 2008.
3. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Can Neural Networks Predict Stock Market?. AC Investment Research Journal, 220(44).
4. Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
5. V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
6. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
7. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.