**Outlook:**Bit Digital Inc. Ordinary Shares assigned short-term B2 & long-term B1 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 13 Dec 2022**for (n+16 weeks)

**Methodology :**Statistical Inference (ML)

## Abstract

In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. (Devadoss, A.V. and Ligori, T.A.A., 2013. Stock prediction using artificial neural networks. International Journal of Data Mining Techniques and Applications, 2(1), pp.283-291.)** We evaluate Bit Digital Inc. Ordinary Shares prediction models with Statistical Inference (ML) and Logistic Regression ^{1,2,3,4} and conclude that the BTBT stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy**

## Key Points

- What are buy sell or hold recommendations?
- Operational Risk
- What is prediction in deep learning?

## BTBT Target Price Prediction Modeling Methodology

We consider Bit Digital Inc. Ordinary Shares Decision Process with Statistical Inference (ML) where A is the set of discrete actions of BTBT 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(Logistic Regression)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Statistical Inference (ML)) X S(n):→ (n+16 weeks) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of BTBT stock

j:Nash equilibria (Neural Network)

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?

## BTBT Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**BTBT Bit Digital Inc. Ordinary Shares

**Time series to forecast n: 13 Dec 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy**

**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 (Grey to Black): *Technical Analysis%**

## Adjusted IFRS* Prediction Methods for Bit Digital Inc. Ordinary Shares

- The underlying pool must contain one or more instruments that have contractual cash flows that are solely payments of principal and interest on the principal amount outstanding
- In cases such as those described in the preceding paragraph, to designate, at initial recognition, the financial assets and financial liabilities not otherwise so measured as at fair value through profit or loss may eliminate or significantly reduce the measurement or recognition inconsistency and produce more relevant information. For practical purposes, the entity need not enter into all of the assets and liabilities giving rise to the measurement or recognition inconsistency at exactly the same time. A reasonable delay is permitted provided that each transaction is designated as at fair value through profit or loss at its initial recognition and, at that time, any remaining transactions are expected to occur.
- When rebalancing a hedging relationship, an entity shall update its analysis of the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its (remaining) term (see paragraph B6.4.2). The documentation of the hedging relationship shall be updated accordingly.
- An entity's risk management is the main source of information to perform the assessment of whether a hedging relationship meets the hedge effectiveness requirements. This means that the management information (or analysis) used for decision-making purposes can be used as a basis for assessing whether a hedging relationship meets the hedge effectiveness requirements.

*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

Bit Digital Inc. Ordinary Shares assigned short-term B2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Statistical Inference (ML) with Logistic Regression ^{1,2,3,4} and conclude that the BTBT stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy**

### Financial State Forecast for BTBT Bit Digital Inc. Ordinary Shares Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B2 | B1 |

Operational Risk | 51 | 74 |

Market Risk | 43 | 77 |

Technical Analysis | 45 | 58 |

Fundamental Analysis | 74 | 42 |

Risk Unsystematic | 71 | 36 |

### Prediction Confidence Score

## References

- Mikolov T, Yih W, Zweig G. 2013c. Linguistic regularities in continuous space word representations. In Pro- ceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–51. New York: Assoc. Comput. Linguist.
- 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.
- R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
- Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
- Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
- S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
- Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35

## Frequently Asked Questions

Q: What is the prediction methodology for BTBT stock?A: BTBT stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Logistic Regression

Q: Is BTBT stock a buy or sell?

A: The dominant strategy among neural network is to Buy BTBT Stock.

Q: Is Bit Digital Inc. Ordinary Shares stock a good investment?

A: The consensus rating for Bit Digital Inc. Ordinary Shares is Buy and assigned short-term B2 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of BTBT stock?

A: The consensus rating for BTBT is Buy.

Q: What is the prediction period for BTBT stock?

A: The prediction period for BTBT is (n+16 weeks)

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