Portfolio Optimization in DSE Using Financial Indicators, LSTM & PyportfolioOpt
Portfolio Optimization in DSE Using Financial Indicators, LSTM & PyportfolioOpt
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DOI：
10.34104/ijmms.021.074084
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Hasan M Sami;Lana Fardous;Debangshu Saha Ruhit
中科院分区：
文献类型：
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作者：
Hasan M Sami;Lana Fardous;Debangshu Saha Ruhit
由于其对非线性预测方法的预测能力，LSTM（长短期记忆）将时间序列预测的方法改变了几倍。界定国际市场金融决策的技术标识符和各种金融基准的程序兼容性也影响到孟加拉国市场。MACD和RSI等问题作为技术研究者，EPS和PE比率的财务比率方面在DSE的资产选择中发挥着重要作用。给定与预期功能模型一致的充分训练，RNN有可能以类似的方式进行思考，并且可能的结果在本文中展示。由于门控结构，即通过梯度递减和梯度爆炸保留重要信息，丢弃无关信息，LSTM在基于人类行为的非线性预测方面取得了重大进展。在本研究中，我们比较了两种不同的投资组合，这两种投资组合将依赖于LSTM在预测最大潜在产出方面的未来预测能力，这是使用投资组合优化原则来证明的。
Due to its suitable power to anticipate using Non-Linear forecasting methodologies, LSTM (Long Short-Term Memory) has changed the approach to time series prediction several folds. Process compatibilities of technical identifiers and various financial benchmarks that are defining financial decision-making in international markets are affecting Bangladesh Market as well. Issues like MACD and RSI as a technical investigator and financial ratio aspects of EPS and PE Ratio play an important role in the selection of assets in DSE. Given adequate training in line with intended functionality models, RNN has the potential to think through in a similar manner and the probable results are exhibited in this paper. Because of the Gated Structure, which refers to retaining important information and discarding irrelevant information through diminishing gradient and exploding gradient, LSTM has achieved significant advances in nonlinear forecasting that is based on human behavior. In this study, we compared two alternative portfolios that will be dependent on LSTM's future forecasting capabilities in terms of projecting the greatest potential output, which is demonstrated using Portfolio Optimization principles.