Stocks are possibly the most popular financial instrument invented for building wealth and are the centerpiece of any investment portfolio. The advances in trading technology has opened up the markets so that nowadays nearly anybody can own stocks. From last few decades, there seen explosive increase in the average person's interest for stock market. In a financially explosive market, as the stock market, it is important to have a very accurate prediction of a future trend. Because of the financial crisis and recording profits, it is compulsory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires progressive algorithms of machine learning with help of Artificial Intelligence (AI). We evaluate STANDARD CHARTERED PLC prediction models with Ensemble Learning (ML) and Linear Regression1,2,3,4 and conclude that the LON:STAN stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:STAN stock.

Keywords: LON:STAN, STANDARD CHARTERED PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. What is Markov decision process in reinforcement learning?
2. Investment Risk
3. What is a prediction confidence?

## LON:STAN Target Price Prediction Modeling Methodology

This study aims to predict the direction of stock prices by integrating time-varying effective transfer entropy (ETE) and various machine learning algorithms. At first, we explore that the ETE based on 3 and 6 months moving windows can be regarded as the market explanatory variable by analyzing the association between the financial crises and Granger-causal relationships among the stocks. We consider STANDARD CHARTERED PLC Stock Decision Process with Linear Regression where A is the set of discrete actions of LON:STAN 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(Linear 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(Ensemble Learning (ML)) X S(n):→ (n+1 year) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of LON:STAN 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?

## LON:STAN Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:STAN STANDARD CHARTERED PLC
Time series to forecast n: 23 Sep 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:STAN 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

STANDARD CHARTERED PLC assigned short-term Ba1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Linear Regression1,2,3,4 and conclude that the LON:STAN stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:STAN stock.

### Financial State Forecast for LON:STAN Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba3
Operational Risk 4342
Market Risk9077
Technical Analysis7580
Fundamental Analysis8389
Risk Unsystematic6744

### Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 637 signals.

## References

1. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
2. Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
3. Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]
4. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
5. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
6. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
7. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
Frequently Asked QuestionsQ: What is the prediction methodology for LON:STAN stock?
A: LON:STAN stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Linear Regression
Q: Is LON:STAN stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:STAN Stock.
Q: Is STANDARD CHARTERED PLC stock a good investment?
A: The consensus rating for STANDARD CHARTERED PLC is Hold and assigned short-term Ba1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:STAN stock?
A: The consensus rating for LON:STAN is Hold.
Q: What is the prediction period for LON:STAN stock?
A: The prediction period for LON:STAN is (n+1 year)