Simulation and Assessment of Bitcoin Prediction Using Machine Learning Methodology

The market for digital currencies is rapidly growing, attracting traders, investors, and businesspeople on a worldwide scale that hasn't been witnessed in this century. By providing comparison studies and insights from the price data of crypto currency marketplaces, it will help in recording the behaviour and habits of such a lucratively demanding and rapidly expanding business. The bitcoin market is reaching one of its peak levels ever in 2021. The emergence of new exchanges has made cryptocurrencies more approachable to the general public, hence boosting their attractiveness. This has increased the number of users and interest in cryptocurrencies, along with a number of reliable crypto ventures started by some of the founders. Virtual currencies are growing more and more well-liked, and businesses like Tesla, Dell, and Microsoft are now embracing them. Decentralized digital currencies are becoming more and more popular, thus it's more crucial than ever to properly inform the public about the new currencies as they proliferate so that people are aware of what they possess and how their money is being invested. Analysis shows that soft computing and machine learning techniques can anticipate more accurately than any other technique now available to researchers. Finally, it is claimed that ANN, SVMs, and other similar machine learning techniques are useful for predicting global stock market fluctuations.

Predicting Credit Ratings using Deep Learning Models – An Analysis of the Indian IT Industry

Due to the complexity of transactions and the availability of Big Data, many banks and financial institutions are reviewing their business models. Various tasks get involved in determining the credit worthiness like working with spreadsheets, manually gathering data from customers and corporations, etc. In this research paper, we aim to automate and analyze the credit ratings of the Information and technology industry in India. Various Deep-Learning models are incorporated to predict the credit rankings from highest to lowest separately for each company to find the best fit model. Factors like Share Capital, Depreciation & Amortisation, Intangible Assets, Operating Margin, inventory valuation, etc., are the parameters that contribute to the credit rating predictions. The data collected for the study spans between the years FY-2015 to FY-2020. As per the research been carried out with efficiencies of different Deep Learning models been tested and compared, MLP gained the highest efficiency for predicting the same. This research contributes to identifying how we can predict the ratings for several IT companies in India based on their Financial risk, Business risk, Industrial risk, and Macroeconomic environment using various neural network models for better accuracy. Also it helps us understand the significance of Artificial Neural Networks in credit rating predictions using unstructured and real time Financial data consisting the influence of COVID-19 in Indian IT industry

DeepLOB: Deep Convolutional Neural Networks for Limit Order Books

We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to capture the spatial structure of the limit order books as well as LSTM modules to capture longer time dependencies. The proposed network outperforms all existing state-of-the-art algorithms on the benchmark LOB dataset [1]. In a more realistic setting, we test our model by using one year market quotes from the London Stock Exchange and the model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments. Importantly, our model translates well to instruments which were not part of the training set, indicating the model's ability to extract universal features. In order to better understand these features and to go beyond a "black box" model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features which translate well to other instruments is an important property of our model which has many other applications.Comment: 12 pages, 9 figure

Simulation and Assessment of Stock Market Forecasting Using Machine Learning Methodology

This paper explores the application of neural network-based machine learning methodologies for stock market forecasting, an area of significant interest due to its potential to yield high returns. The study employs deep learning models, particularly Long Short-Term Memory (LSTM) networks, recognized for their ability to process time series data and capture temporal dependencies that are crucial in understanding stock market behaviors. The methodology involves collecting extensive historical stock price data, including open, close, high, low prices, and volume traded. This data is preprocessed to normalize the values and convert them into a format suitable for LSTM networks. The neural network architecture is designed with multiple layers, including dropout layers to prevent overfitting, and is trained on a substantial dataset to predict future stock prices based on past patterns. The performance of the LSTM model is evaluated using metrics such as root mean squared error (RMSE) and mean absolute error (MAE), comparing its predictive accuracy with traditional statistical methods and simpler machine learning models. The results indicate that LSTM networks can significantly improve the accuracy of stock market forecasts, demonstrating the model's efficacy in capturing complex stock price movements and providing a reliable tool for investors and financial analysts. The study not only confirms the viability of using sophisticated machine learning techniques in financial markets but also opens avenues for further research into neural network optimizations for enhanced predictive performance

ANALISIS JARINGAN SARAF TIRUAN BACKPROPGATION UNTUK PENINGKATAN AKURASI PREDIKSI HASIL PERTANDINGAN SEPAKBOLA

arigan Saraf Tiruan dengan metode backpropagation merupakan salah satu algoritma yang optimal untuk menyelesaikan seperti prediksi hasil pertandingan sepakbola. Proses menentukan hasil diawali dengan menguji dataset dengan arsitektur neural network, menentukan parameter input, nilai weight hingga nilai hidden layer dan output layer. Kemudian optimasi terhadap hasil pengujian pertama pada dataset training dioptimasi dengan sigmoid function sehingga mencapai konvergensi. Pengujian akan terus berulang dengan menggunakan neural network backpropagation sampai iterasi maksimal dan hasil mendekati nilai target awal. Selanjutnya dari output yang diperoleh akan dicari nilai error rata-rata