ac investment research

Eastman Chemical Company Stock Forecast & Analysis


Abstract

When evaluating strong or exceptional liquidity, we include all capital expenses forecast in the next 24 months, including discretionary growth capital spending. We evaluate the prediction models (Colpitts Oscillator with Chi-Square)1,2,3 and conclude that the EMN stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold EMN stock.


Keywords: EMN, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis.

Introduction

We consider the full spectrum of human trading interaction (varying from data based analysis to market signals, from trend actions to speculative ones and many more) and adapt them to the machine learning model with support of engineers to mimic and future-reflect everyday trading experiences. To do that we focus on an approach known as Decision making using Game Theory. We apply principles from Game Theory to model the relationships between rating actions, news, market signals and decision making. 

 

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

How do AC Investment Research machine learning (predictive) algorithms actually work?

EMN Stock Forecast (Buy or Sell) for (n+3 month)

Stock/Index: EMN Eastman Chemical Company
Time series to forecast n: 06 Aug 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold EMN stock.

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 (Yellow to Green): *Technical Analysis%


*As part of stock rating surveillance, Neural network continuously analyze real-time and historical data. If network see events taking place that impact our view on an issuer's relative performance, we adjust our ratings accordingly to communicate our views so the market has the correct perception of how we view relative stock performance.

What Are the Top Stocks to Invest in Right Now?

Forecast Model for EMN

  • We aim to implement a reasonable definition of ACE and TAC, but certain conditions or reporting differences may require additional adjustments to the equity of the reported joint shareholders. For example, the adjustments may apply when we evaluate that some transactions are artificially inflated, such as regulation of capital instruments indirectly financed by mutual cross -holding or regulation of capital instruments that are indirectly financed through a relevant party. Like a holding company or sister company.
  • The analysis also deals with some factors specific to the beings we believe that they can distinguish an individual obligation from their peers. These may include diversification of the products and services of the obligation for a financial institution and risk concentrations. Obliging factors may also include operational activity, general competitive location, strategy, governance, financial policies, risk management practices and risk tolerance.
  • Since it supports our developing market assumed study, there is a high correlation between institutional assumed rates and dominant crises and macroeconomic volatility.
  • The quantitative factors that we evaluate for state institutions are different from the factors we evaluate for commercial organizations; It usually includes additional items for both economic factors and budget and financial performance and dominant obligations. The economic side of the analysis typically includes demographic properties, reserve and growth expectations. The budget and financial party usually include budget reserves, external liquidity and structural budget performance. For sovereign obligations, additional quantitative factors that may be related to our analysis according to our opinion include financial policy flexibility, monetary policy flexibility, international investment position and potential support for the financial sector.
  • Basic financial indicators for commercial organizations generally include profitability, leverage, cash flow adequacy, liquidity and financial flexibility. Other critical factors for financial institutions and insurance companies may include asset quality, loss reserves, asset responsibility management and capital adequacy. Unbalanced elements such as securities, derivative exposure, leases and retirement obligations may also be part of quantitative analysis. Cash flow analysis and liquidity assume the increasing importance for companies with speculative grade rating ('BB+' and low).
  • We do not make adjustments for the impact of foreign exchange translation gains or losses for the impact of the foreign exchange translation gains or losses included in the other comprehensive income in accordance with the US general accounting principles (GAAP). These gains or losses are reflected to ACE and TAC.
  • The quantitative side of the analysis focuses primarily on the financial analysis and may include the evaluation of accounting principles and applications of the obligation.

Conclusions

EMN assigned short-term B3 & long-term B3 forecasted stock rating. We evaluate the prediction models (Colpitts Oscillator with Chi-Square)1,2,3 and conclude that the EMN stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold EMN stock.

Financial State Forecast for Eastman Chemical Company

Rating Short-Term Long-Term Senior
Outlook*B3B3
Operational Risk 8150
Market Risk3834
Technical Analysis6033
Fundamental Analysis3432
Risk Unsystematic4285

Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 648 signals.

References

  1. . Yue and C. Guestrin. Linear submodular bandits and their application to diversified retrieval. In Advances in Neural Information Processing Systems 24, pages 2483–2491, 2011.
  2. Simonyan, Karen and Zisserman, Andrew. Very deep con- volutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
  3. Dulac-Arnold, Gabriel, Denoyer, Ludovic, Preux, Philippe, and Gallinari, Patrick. Fast reinforcement learning with large action sets using error-correcting output codes for mdp factorization. In Machine Learning and Knowledge Discovery in Databases, pp. 180–194. Springer, 2012.
AC Investment Research

In our experiment, we focus on an approach known as Decision making using game theory. We apply principles from game theory to model the relationships between rating actions, news, market signals and decision making.

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