Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators. We evaluate MarketAxess prediction models with Modular Neural Network (Market Direction Analysis) and Beta1,2,3,4 and conclude that the MKTX 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 MKTX stock.

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

## Key Points

1. Short/Long Term Stocks
2. Nash Equilibria
3. What is the best way to predict stock prices?

## MKTX Target Price Prediction Modeling Methodology

Stock market or Share market is one of the most complicated and sophisticated way to do business. Small ownerships, brokerage corporations, banking sector, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithm to predict the future stock price for exchange by using open source libraries and preexisting algorithms to help make this unpredictable format of business a little more predictable. We consider MarketAxess Stock Decision Process with Beta where A is the set of discrete actions of MKTX 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(Beta)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(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+8 weeks) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of MKTX 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?

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

Sample Set: Neural Network
Stock/Index: MKTX MarketAxess
Time series to forecast n: 21 Oct 2022 for (n+8 weeks)

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

MarketAxess assigned short-term Baa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Beta1,2,3,4 and conclude that the MKTX 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 MKTX stock.

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

Rating Short-Term Long-Term Senior
Outlook*Baa2B1
Operational Risk 8947
Market Risk7339
Technical Analysis8259
Fundamental Analysis8458
Risk Unsystematic8688

### Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 878 signals.

## References

1. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
2. Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
3. Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
4. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
5. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
6. Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
7. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
Frequently Asked QuestionsQ: What is the prediction methodology for MKTX stock?
A: MKTX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Beta
Q: Is MKTX stock a buy or sell?
A: The dominant strategy among neural network is to Hold MKTX Stock.
Q: Is MarketAxess stock a good investment?
A: The consensus rating for MarketAxess is Hold and assigned short-term Baa2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of MKTX stock?
A: The consensus rating for MKTX is Hold.
Q: What is the prediction period for MKTX stock?
A: The prediction period for MKTX is (n+8 weeks)