Evidence map›Paper›PMID 36518995›Full record

ArticleFrontiers in computational neuroscience2022

Mathematical processing of trading strategy based on long short-term memory neural network model.

Han-Yang Wang, An-Qi Li, Chao-Chen Tie, Chao-Jun Wang, Yun-Hua Xu

Abstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Han-Yang WangSchool of Economics, Anhui University of Finance and Economics, Bengbu, China.
An-Qi LiSchool of Finance, Anhui University of Finance and Economics, Bengbu, China.
Chao-Chen TieSchool of Finance, Anhui University of Finance and Economics, Bengbu, China.
Chao-Jun WangSchool of Finance, Anhui University of Finance and Economics, Bengbu, China.
Yun-Hua XuSchool of Business Administration, Anhui University of Finance and Economics, Bengbu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

At present, gold and bitcoin have become mainstream assets in market transactions. Due to the volatility of gold and bitcoin prices, we can buy and sell assets like gold and bitcoin the same way we buy and sell stocks. The research goal of this article is to develop an optimal trading strategy that maximizes our post-trade returns. By studying the relationship between the two, on the one hand, it supplements and enriches the theoretical research on the rate of return of gold and Bitcoin, on the other hand, it provides a certain reference for investors to construct investment strategies. The research on the cointegration relationship between them has important practical significance. At the same time, it has important practical significance for the research on the cointegration relationship between bitcoin and gold.

Indexed as

Bitcoingoldlong short-term memory network modelmathematical processingtrading strategy

Identifiers

PMID36518995
PMCPMC9742274

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.