BOKF Options & Futures Prediction

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 evaluate BOK FINANCIAL CORP prediction models with Transductive Learning (ML) and Sign Test^1,2,3,4 and conclude that the BOKF 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 Buy BOKF stock.

Keywords: BOKF, BOK FINANCIAL CORP, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Prediction Modeling
2. How useful are statistical predictions?
3. Can we predict stock market using machine learning?

BOKF Target Price Prediction Modeling Methodology

Recently, a lot of interesting work has been done in the area of applying Machine Learning Algorithms for analyzing price patterns and predicting stock prices and index changes. Most stock traders nowadays depend on Intelligent Trading Systems which help them in predicting prices based on various situations and conditions, thereby helping them in making instantaneous investment decisions. We consider BOK FINANCIAL CORP Stock Decision Process with Sign Test where A is the set of discrete actions of BOKF 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(Sign Test)^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(Transductive Learning (ML)) X S(n):→ (n+3 month) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of BOKF 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?

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

Sample Set: Neural Network
Stock/Index: BOKF BOK FINANCIAL CORP
Time series to forecast n: 10 Sep 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy BOKF 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

BOK FINANCIAL CORP assigned short-term Baa2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Sign Test^1,2,3,4 and conclude that the BOKF 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 Buy BOKF stock.

Financial State Forecast for BOKF Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Baa2	Ba3
Operational Risk	62	81
Market Risk	83	34
Technical Analysis	70	62
Fundamental Analysis	75	55
Risk Unsystematic	85	87

Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 769 signals.

References

1. M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016
2. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
3. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
4. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
5. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
6. Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
7. Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]

Frequently Asked Questions

Q: What is the prediction methodology for BOKF stock?
A: BOKF stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Sign Test
Q: Is BOKF stock a buy or sell?
A: The dominant strategy among neural network is to Buy BOKF Stock.
Q: Is BOK FINANCIAL CORP stock a good investment?
A: The consensus rating for BOK FINANCIAL CORP is Buy and assigned short-term Baa2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of BOKF stock?
A: The consensus rating for BOKF is Buy.
Q: What is the prediction period for BOKF stock?
A: The prediction period for BOKF is (n+3 month)

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