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 BLACKROCK SUSTAINABLE AMERICAN INCOME TRUST PLC prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Beta1,2,3,4 and conclude that the LON:BRSA 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 LON:BRSA stock.

Keywords: LON:BRSA, BLACKROCK SUSTAINABLE AMERICAN INCOME TRUST PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Operational Risk
2. What is statistical models in machine learning?
3. Market Risk

## LON:BRSA Target Price Prediction Modeling Methodology

Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend. We consider BLACKROCK SUSTAINABLE AMERICAN INCOME TRUST PLC Stock Decision Process with Beta where A is the set of discrete actions of LON:BRSA 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 (News Feed Sentiment 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 LON:BRSA 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:BRSA Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: LON:BRSA BLACKROCK SUSTAINABLE AMERICAN INCOME TRUST PLC
Time series to forecast n: 13 Nov 2022 for (n+8 weeks)

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

## Adjusted IFRS* Prediction Methods for BLACKROCK SUSTAINABLE AMERICAN INCOME TRUST PLC

1. However, depending on the nature of the financial instruments and the credit risk information available for particular groups of financial instruments, an entity may not be able to identify significant changes in credit risk for individual financial instruments before the financial instrument becomes past due. This may be the case for financial instruments such as retail loans for which there is little or no updated credit risk information that is routinely obtained and monitored on an individual instrument until a customer breaches the contractual terms. If changes in the credit risk for individual financial instruments are not captured before they become past due, a loss allowance based only on credit information at an individual financial instrument level would not faithfully represent the changes in credit risk since initial recognition.
2. Financial assets that are held within a business model whose objective is to hold assets in order to collect contractual cash flows are managed to realise cash flows by collecting contractual payments over the life of the instrument. That is, the entity manages the assets held within the portfolio to collect those particular contractual cash flows (instead of managing the overall return on the portfolio by both holding and selling assets). In determining whether cash flows are going to be realised by collecting the financial assets' contractual cash flows, it is necessary to consider the frequency, value and timing of sales in prior periods, the reasons for those sales and expectations about future sales activity. However sales in themselves do not determine the business model and therefore cannot be considered in isolation. Instead, information about past sales and expectations about future sales provide evidence related to how the entity's stated objective for managing the financial assets is achieved and, specifically, how cash flows are realised. An entity must consider information about past sales within the context of the reasons for those sales and the conditions that existed at that time as compared to current conditions.
3. For lifetime expected credit losses, an entity shall estimate the risk of a default occurring on the financial instrument during its expected life. 12-month expected credit losses are a portion of the lifetime expected credit losses and represent the lifetime cash shortfalls that will result if a default occurs in the 12 months after the reporting date (or a shorter period if the expected life of a financial instrument is less than 12 months), weighted by the probability of that default occurring. Thus, 12-month expected credit losses are neither the lifetime expected credit losses that an entity will incur on financial instruments that it predicts will default in the next 12 months nor the cash shortfalls that are predicted over the next 12 months.
4. IFRS 15, issued in May 2014, amended paragraphs 3.1.1, 4.2.1, 5.1.1, 5.2.1, 5.7.6, B3.2.13, B5.7.1, C5 and C42 and deleted paragraph C16 and its related heading. Paragraphs 5.1.3 and 5.7.1A, and a definition to Appendix A, were added. An entity shall apply those amendments when it applies IFRS 15.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

BLACKROCK SUSTAINABLE AMERICAN INCOME TRUST PLC assigned short-term Ba3 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Beta1,2,3,4 and conclude that the LON:BRSA 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 LON:BRSA stock.

### Financial State Forecast for LON:BRSA BLACKROCK SUSTAINABLE AMERICAN INCOME TRUST PLC Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3B1
Operational Risk 7641
Market Risk4579
Technical Analysis8337
Fundamental Analysis6484
Risk Unsystematic4957

### Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 585 signals.

## References

1. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
2. N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
3. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
4. Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
5. Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
6. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
7. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:BRSA stock?
A: LON:BRSA stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Beta
Q: Is LON:BRSA stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:BRSA Stock.
Q: Is BLACKROCK SUSTAINABLE AMERICAN INCOME TRUST PLC stock a good investment?
A: The consensus rating for BLACKROCK SUSTAINABLE AMERICAN INCOME TRUST PLC is Hold and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:BRSA stock?
A: The consensus rating for LON:BRSA is Hold.
Q: What is the prediction period for LON:BRSA stock?
A: The prediction period for LON:BRSA is (n+8 weeks)