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 BLUEROCK DIAMONDS PLC prediction models with Transductive Learning (ML) and Spearman Correlation1,2,3,4 and conclude that the LON:BRD stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:BRD stock.

Keywords: LON:BRD, BLUEROCK DIAMONDS 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 the best way to predict stock prices?
2. How do you pick a stock?
3. How do you decide buy or sell a stock?

## LON:BRD Target Price Prediction Modeling Methodology

Accurate stock market prediction is of great interest to investors; however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors' behavior. We consider BLUEROCK DIAMONDS PLC Stock Decision Process with Spearman Correlation where A is the set of discrete actions of LON:BRD 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(Spearman Correlation)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(Transductive Learning (ML)) X S(n):→ (n+6 month) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of LON:BRD 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:BRD Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:BRD BLUEROCK DIAMONDS PLC
Time series to forecast n: 14 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:BRD 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

BLUEROCK DIAMONDS PLC assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Spearman Correlation1,2,3,4 and conclude that the LON:BRD stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:BRD stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 4034
Market Risk5459
Technical Analysis8888
Fundamental Analysis7337
Risk Unsystematic4065

### Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 861 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. Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
3. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
4. Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.
5. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
6. L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
7. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
Frequently Asked QuestionsQ: What is the prediction methodology for LON:BRD stock?
A: LON:BRD stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Spearman Correlation
Q: Is LON:BRD stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:BRD Stock.
Q: Is BLUEROCK DIAMONDS PLC stock a good investment?
A: The consensus rating for BLUEROCK DIAMONDS PLC is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:BRD stock?
A: The consensus rating for LON:BRD is Hold.
Q: What is the prediction period for LON:BRD stock?
A: The prediction period for LON:BRD is (n+6 month)