Recurrent Neural Networks (RNNs) is a sub type of neural networks that use feedback connections. Several types of RNN models are used in predicting financial time series. This study was conducted to develop models to predict daily stock prices based on Recurrent Neural Network (RNN) Approach and to measure the accuracy of the models developed and identify the shortcomings of the models if present. We evaluate DOWNING RENEWABLES & INFRASTRUCTURE TRUST PLC prediction models with Modular Neural Network (Financial Sentiment Analysis) and Statistical Hypothesis Testing1,2,3,4 and conclude that the LON:DORE 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 Sell LON:DORE stock.

Keywords: LON:DORE, DOWNING RENEWABLES & INFRASTRUCTURE TRUST PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. How do you know when a stock will go up or down?
2. Dominated Move
3. Trust metric by Neural Network

## LON:DORE Target Price Prediction Modeling Methodology

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 consider DOWNING RENEWABLES & INFRASTRUCTURE TRUST PLC Stock Decision Process with Statistical Hypothesis Testing where A is the set of discrete actions of LON:DORE 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(Statistical Hypothesis Testing)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 (Financial Sentiment Analysis)) X S(n):→ (n+3 month) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of LON:DORE 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:DORE Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: LON:DORE DOWNING RENEWABLES & INFRASTRUCTURE TRUST PLC
Time series to forecast n: 22 Sep 2022 for (n+3 month)

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

DOWNING RENEWABLES & INFRASTRUCTURE TRUST PLC assigned short-term Caa2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Statistical Hypothesis Testing1,2,3,4 and conclude that the LON:DORE 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 Sell LON:DORE stock.

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

Rating Short-Term Long-Term Senior
Outlook*Caa2Ba3
Operational Risk 4847
Market Risk3283
Technical Analysis4146
Fundamental Analysis3643
Risk Unsystematic3289

### Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 558 signals.

## References

1. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
2. 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.
3. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
4. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
5. J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989
6. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
7. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
Frequently Asked QuestionsQ: What is the prediction methodology for LON:DORE stock?
A: LON:DORE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Statistical Hypothesis Testing
Q: Is LON:DORE stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:DORE Stock.
Q: Is DOWNING RENEWABLES & INFRASTRUCTURE TRUST PLC stock a good investment?
A: The consensus rating for DOWNING RENEWABLES & INFRASTRUCTURE TRUST PLC is Sell and assigned short-term Caa2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:DORE stock?
A: The consensus rating for LON:DORE is Sell.
Q: What is the prediction period for LON:DORE stock?
A: The prediction period for LON:DORE is (n+3 month)