Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. We evaluate ICU Medical prediction models with Transfer Learning (ML) and Lasso Regression1,2,3,4 and conclude that the ICUI stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold ICUI stock.

Keywords: ICUI, ICU Medical, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Market Outlook
2. Nash Equilibria
3. Is Target price a good indicator?

## ICUI Target Price Prediction Modeling Methodology

Nowadays, people show more and more enthusiasm for applying machine learning methods to finance domain. Many scholars and investors are trying to discover the mystery behind the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) to test which one performs the best in predicting the stock trend. We consider ICU Medical Stock Decision Process with Lasso Regression where A is the set of discrete actions of ICUI stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.1,2,3,4

F(Lasso Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Transfer Learning (ML)) X S(n):→ (n+4 weeks) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of ICUI stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

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?

## ICUI Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: ICUI ICU Medical
Time series to forecast n: 20 Sep 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold ICUI 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%

## Conclusions

ICU Medical assigned short-term B2 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Lasso Regression1,2,3,4 and conclude that the ICUI stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold ICUI stock.

### Financial State Forecast for ICUI Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2Ba1
Operational Risk 3176
Market Risk4381
Technical Analysis5682
Fundamental Analysis5382
Risk Unsystematic7939

### Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 851 signals.

## References

1. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
2. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
3. J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
4. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
5. Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
6. Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
7. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
Frequently Asked QuestionsQ: What is the prediction methodology for ICUI stock?
A: ICUI stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Lasso Regression
Q: Is ICUI stock a buy or sell?
A: The dominant strategy among neural network is to Hold ICUI Stock.
Q: Is ICU Medical stock a good investment?
A: The consensus rating for ICU Medical is Hold and assigned short-term B2 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of ICUI stock?
A: The consensus rating for ICUI is Hold.
Q: What is the prediction period for ICUI stock?
A: The prediction period for ICUI is (n+4 weeks)