The stock market is very volatile and hard to predict accurately due to the uncertainties affecting stock prices. However, investors and stock traders can only benefit from such models by making informed decisions about buying, holding, or investing in stocks. Also, financial institutions can use such models to manage risk and optimize their customers' investment portfolios. In this paper, we use the Long Short-Term Memory (LSTM-RNN) Recurrent Neural Networks (RNN) to predict the daily closing price of the Amazon Inc. stock (ticker symbol: AMZN). We study the influence of various hyperparameters in the model to see what factors the predictive power of the model. The root mean squared error (RMSE) on the training was 2.51 with a mean absolute percentage error (MAPE) of 1.84%.

The world of the stock market is an intricately complex financial ecosystem that demands years of dedicated study to comprehend fully. It relies on risk mitigation practices and fundamental theoretical techniques to engage in speculation regarding stock and cryptocurrency fluctuations. However, this realm is progressively becoming more inclusive, with accessibility expanding beyond traditional educational barriers. Technological advancements, coupled with the ease of entry into this domain and the information-disseminating power of social networks, contribute to a rising number of individuals participating in this financial movement. What makes this evolution disruptive is that the same tools facilitating accessibility also exert influence on the way market trends unfold. This paper delves into the escalating impact of social media within the financial sphere, emphasizing the heightened accessibility to information and market involvement facilitated by platforms like Twitter and Reddit. It sheds light on how social media plays a pivotal role in market manipulation, as evidenced by phenomena such as the r/wallstreetbets subreddit, where meme-based strategies were employed to inflate the prices of stocks like GameStop. The study explores the utilization of social media by influential figures, exemplified by Elon Musk, who leverage their platforms to sway market movements. Additionally, this paper addresses instances of misinformation, such as the confusion surrounding Virgin Galactic's shares following a SpaceX failure and the introduction of "AGUA" in the Mexican stock market, leading to widespread misunderstandings. The paper extends its examination to the effects of social media on cryptocurrencies, highlighting how comments from public figures can significantly impact the prices of Bitcoin and Dogecoin. Overall, it underscores the imperative need for adaptation to these changes in the digital financial paradigm.

Financial markets have become more and more complex, so has been the number of data sources. Stock price prediction has hence become a tough but important task. The time dependencies in stock price movements tend to escape from traditional models. In this work, a hybrid ARIMA-LSTM model is suggested to enhance accuracy of stock price forecasts. Based on time series indicators like adjusted closing prices of S&P 500 stocks over a decade (2010–2019), the ARIMA-LSTM model combines influences of both autoregressive time series forecasting with the substantial sequence learning property of LSTM. Data preprocessing in all aspects including missing values interpolation, outlier’s detection and data scaling – Min-Max guarantees data quality. The model is trained on 90/10 training/testing split and met with main performance metrics: MaE, MSE & RMSE. As indicated in the results, the proposed ARIMA-LSTM model gives a MAE value and MSE value of 0.248 and 0.101 respectively and RMSE of 0.319, a measure high accuracy on stock price prediction. Coupled comparative analysis with other Artificial Neural Networks (ANN) and BP Neural Networks (BPNN) are examples of machine learning reference models, further illustrates the suitability and superiority of ARIMA-LSTM approach as compared to the underlying models with the least MAE and strong predictive capability. This work demonstrates the efficiency of integrating the classical time series models with deep learning methods for financial forecasting.