Can neural networks predict stock market? (TKR Stock Forecast) (Forecast)

The nature of stock market movement has always been ambiguous for investors because of various influential factors. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms. We evaluate Timken prediction models with Active Learning (ML) and ElasticNet Regression^1,2,3,4 and conclude that the TKR stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold TKR stock.

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

Key Points

1. Stock Forecast Based On a Predictive Algorithm
2. Short/Long Term Stocks
3. Can stock prices be predicted?

TKR Target Price Prediction Modeling Methodology

Stock market is a promising financial investment that can generate great wealth. However, the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement. Researchers have used various methods in computer science and economics in their quests to gain a piece of this volatile information and make great fortune out of the stock market investment. This paper investigates various techniques for the stock market prediction using artificial neural network (ANN). We consider Timken Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of TKR 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(ElasticNet Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Active Learning (ML)) X S(n):→ (n+8 weeks) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of TKR 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?

TKR Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: TKR Timken
Time series to forecast n: 11 Oct 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold TKR 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

Timken assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with ElasticNet Regression^1,2,3,4 and conclude that the TKR stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold TKR stock.

Financial State Forecast for TKR Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Ba3
Operational Risk	32	53
Market Risk	57	77
Technical Analysis	49	56
Fundamental Analysis	37	84
Risk Unsystematic	62	51

Prediction Confidence Score

Trust metric by Neural Network: 76 out of 100 with 759 signals.

References

1. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
2. J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
3. Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
4. H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
5. Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
6. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
7. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.

Frequently Asked Questions

Q: What is the prediction methodology for TKR stock?
A: TKR stock prediction methodology: We evaluate the prediction models Active Learning (ML) and ElasticNet Regression
Q: Is TKR stock a buy or sell?
A: The dominant strategy among neural network is to Hold TKR Stock.
Q: Is Timken stock a good investment?
A: The consensus rating for Timken is Hold and assigned short-term B3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of TKR stock?
A: The consensus rating for TKR is Hold.
Q: What is the prediction period for TKR stock?
A: The prediction period for TKR is (n+8 weeks)

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