Blockchain, which is at the heart of bitcoin and other digital currencies, is a game-changing technology, particularly in the stock market and financial technology. These characteristics make blockchain adoption appealing for enhancing information security, privacy and reliability in a variety of scenarios. This article develops the trust with reputation-based management for the financial stock market. Here distributed ledger technology (DLT) has been integrated for implementing the ledger in which data is stored across a network of decentralized nodes. By this, proof-based ledger has been maintained with transparency, traceability and security. DLT here used is based on trust design in mitigating the malicious activities of the financial stock marketing technology. Then using Bayesian theory based on the fault tolerance (BDLT_FT) model has been used in improving the efficiency of DLT and financial technology. This acts as the protocol which reduces energy consumption and enhance network scalability and security in digital transactions like cryptocurrency The financial data related to blockchain cryptocurrency were collected between the time period 2000 and 2020. The dataset of the stock market is evaluated under the fault tolerance model with statistical analysis. The statistical evaluation of financial data with developed BDLT_FT expressed that there is no association between the fault tolerance and the classification error.

区块链是比特币和其他数字货币的核心，是一项改变游戏规则的技术，尤其是在股市和金融技术领域。这些特性使得区块链的采用对于在各种场景中增强信息安全、隐私和可靠性具有吸引力。本文针对金融股票市场开展基于信誉管理的信任。此处集成了分布式账本技术 (DLT) 以实现账本，其中数据存储在分散节点的网络中。通过这种方式，基于证明的分类账一直保持透明、可追溯和安全。这里使用的 DLT 是基于信任设计来减轻金融股票营销技术的恶意活动。然后将基于容错的贝叶斯理论（BDLT_FT）模型用于提高分布式账本技术和金融技术的效率。该协议可降低能源消耗并增强加密货币等数字交易中的网络可扩展性和安全性 与区块链加密货币相关的财务数据是在 2000 年至 2020 年期间收集的。股票市场的数据集在容错模型下进行评估用统计分析。用发达的BDLT_FT对金融数据的统计评价表明容错与分类错误之间没有关联。该协议可降低能源消耗并增强加密货币等数字交易中的网络可扩展性和安全性 与区块链加密货币相关的财务数据是在 2000 年至 2020 年期间收集的。股票市场的数据集在容错模型下进行评估用统计分析。用发达的BDLT_FT对金融数据的统计评价表明容错与分类错误之间没有关联。该协议可降低能源消耗并增强加密货币等数字交易中的网络可扩展性和安全性 与区块链加密货币相关的财务数据是在 2000 年至 2020 年期间收集的。股票市场的数据集在容错模型下进行评估用统计分析。用发达的BDLT_FT对金融数据的统计评价表明容错与分类错误之间没有关联。