Machine Learning And Ai In Boston Forex Trading: Profitable Applications

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Machine Learning And Ai In Boston Forex Trading: Profitable Applications

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By Theyazn H. H. Aldhyani Theyazn H. H. Aldhyani Scilit Preprints.org Google Scholar 1, 2, * and Ali Alzahrani Ali Alzahrani Scilit Preprints.org Google Scholar 1, 3

Saudi Investment Bank Chair of Investment Awareness Studies, Deanship of Scientific Research, Vice President of Graduate Studies and Scientific Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Psa: You May Be Trading Against Powerful Ai (coming From A Machine Learning Researcher)

Received: 15 September 2022 / Revised: 27 September 2022 / Accepted: 27 September 2022 / Published: 30 September 2022

The creation of reliable stock market models allows investors to make better informed decisions. The trading model may reduce the risk associated with investment and enable traders to choose companies that offer the highest returns. However, due to the high degree of correlation between stock prices, the analysis of the stock market is made more difficult by cluster processing methods. Stock market forecasting has entered a highly technologically advanced era with the advent of technological marvels such as global digitization. For this reason, artificial intelligence models have become more important due to the continuous increase in market capitalization. The novelty of the proposed research is the development of a dynamic time series model based on deep dependence for forecasting future stock market prices. The main objective of this study was to develop an intelligent framework capable of predicting the direction in which stock market prices will move based on the financial time series as input. Among cutting-edge technologies, artificial intelligence has become the backbone of many different models that predict the direction of markets. In particular, deep learning techniques have been successful in predicting market behavior. In this article, we propose a framework based on short-term memory (LSTM) and a combination of convolutional neural network (CNN-LSTM) and LSTM to predict the closing prices of Tesla, Inc. and Apple, Inc. These predictions were made using data collected over the past two years. The mean square error (MSE), root mean square error (RMSE), normalized root mean square error (NRMSE), and Pearson’s correlation measures (R) were used to calculate the findings of deep learning stock prediction models. Among the two deep learning models, the CNN-LSTM model scored slightly better (Tesla: R-squared = 98.37%; Apple: R-squared = 99.48%). The CNN-LSTM model has shown superior performance compared to single LSTM deep learning and existing stock market price forecasting systems.

A high standard of living can be achieved when countries prioritize the growth and development of their economies in order to maintain adequate levels of public spending. In today’s fast-paced economy, large enterprises are emerging to take advantage of untapped opportunities and adapt to the ever-changing international market [1, 2]. The stock market is a marketplace where various assets can be bought and sold by investors participating in public, private, and mixed ownership stock markets [3]. Shares of publicly traded companies are traded on the open market, while shares of private companies are traded on the private exchange. Mixed-ownership stock exchanges are invested in businesses whose common shares can be traded on the public stock market only in limited circumstances. Mixed ownership stock exchanges are established in countries such as the United Kingdom (London Stock Exchange) and the United States (New York Stock Exchange) [4, 5, 6, 7, 8, 9].

Investors have been trying to predict the volatility of stock prices since the inception of the stock market. At that time, however, the amount of data accessible to them was limited, and the methods of processing this data were specific. Since then, the amount of data made available to investors has increased dramatically, and new methods of processing this data have also been made available. Despite all the recent advances in technology and advanced trading algorithms, it is still a very difficult challenge for most academics and investors to accurately predict the movement of stock prices. Traditional models, which have been used for decades and are often based on fundamental analysis, technical analysis, and statistical methods (such as regression in [10]), often cannot fully reflect the complexity of the problem at hand. .

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The stock market serves as the backbone of the entire economy, and the main objectives of any investment in the stock market are to achieve high returns and minimize losses [4]. Therefore, countries should strive to strengthen their stock markets, because doing so is associated with economic growth [11]. Since the stock market is a potential source of quick returns on investment, making profitable stock market predictions is an effective way to become financially independent. Stock market forecasting is not linear, which makes it very difficult to predict the stock prices of a particular firm in a particular market [12]. As a result, researchers and investors need to identify methods that may lead to more accurate results and greater earnings [13]. Long-standing machine learning models, such as autoregressive integrated moving average (ARIMA), are inferior to traditional machine learning models [14]. Additionally, research shows that deep learning models such as short-term memory (LSTM) perform better than machine learning models such as support vector regression (SVR) [15]. Artificial neural networks (ANNs) have been shown to be a superior deep learning model to support vector machines [16].

Advances in machine learning and deep learning have created new opportunities to build models to predict stock price movements based on time series data characterized by high cardinality, such as large object (LOB) data. These models are used to predict how stock prices will change in the future. As a direct result, this particular field has received ever-increasing attention from researchers over the past few years. The performance of the recommended models in terms of prediction is generally reported to be very high. Authors of some modern machine learning and deep learning (e.g., [17, 18]) claim that these models have more than 80% accuracy. From a more pragmatic perspective, these results look too good to be replicated when trading stocks in the real world.

It is important to remember, however, that the stock market is a trading platform that is ultimately governed by the forces of supply and demand. In this paper, deep learning models are developed to anticipate indicators of momentum, energy, and volatility for the purpose of assisting investors in making decisions that can provide accuracy and safety against rapid volatility in countermeasures. To find the most accurate strategy for predicting future price fluctuations, we conducted extensive research.

Forecasting exchange rates and stock markets has been the subject of a large number of studies [19, 20, 21, 22, 23] in recent years. In [24], a generative adversarial network architecture is described, and LSTM is recommended to be used as a generator. As a structural classification, the multi-layer perceptron (MLP) was proposed. Several metrics were used to compare GAN, LSTM, ANN, and SVR. Across the board, the generative neural network (GAN) model has been shown to be the most effective. The use of big data can make it possible to innovate faster and more efficiently. Examples of financial innovations that have contributed to the development of the financial sector and the expansion of the economy include exchange-traded funds, leveraged funds, and equity funds [25, 26, 27, 28, 29, 30].

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Because of this issue, there is a need for intelligent systems that can retrieve real-time price information, which can improve the ability of investors to increase their profits [28]. Decision support, modeling technology, and automation

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