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Finance, Accounting and Business Analysis 
Volume 7 Issue 2, 2025 

http://faba.bg/       
ISSN  2603-5324 

DOI: https://doi.org/10.37075/FABA.2025.2.13 

 

Stock Price Forecasting Using a Time-Series Long Short-Term 

Memory Model 

 

Adedeji Daniel Gbadebo  

   
Department of Accounting Science, Walter Sisulu University, Mthatha, South Africa 

 

Info Articles   Abstract 

 
 

History Article: 

Submitted: 16 September 2025 

Revised: 13 November 2025 

Accepted: 6 December 2025 
 

 Purpose: This study aims to introduce and evaluate a novel application 

of Long Short-Term Memory (LSTM) networks for stock price 

forecasting by integrating multi-stock comparative analysis across 

different volatility regimes, addressing a key gap in the literature 

regarding model robustness and generalizability. 

Design/Methodology/Approach: Using a time-series covering 2019–

2023, the study implements an LSTM model within a Python-based 

framework. The model is trained on data from 01/01/2022 to 

12/31/2023 and tested on 01/01/2019 to 12/31/2021. Mean squared 

error is employed as the primary evaluation metric to assess forecasting 

accuracy across heterogeneous stocks. 

Findings: Empirical results show that the LSTM model effectively 

captures complex temporal dependencies and nonlinear patterns in 

financial time series, producing reliable stock price forecasts. It 

outperforms conventional time-series benchmarks and demonstrates 

strong adaptability across stocks with differing volatility characteristics. 

Practical Implications: For investors, LSTM-based forecasts provide 

deeper insights into risk–return dynamics and support more informed, 

data-driven investment strategies. For policymakers, the results highlight 

the increasing importance of machine learning tools in enhancing 

transparency, stability, and efficiency in financial markets. 

Originality/Value: The study offers a unique contribution by 

demonstrating that a unified LSTM framework can generalize across 

multiple stocks and volatility regimes, establishing both its theoretical 

relevance and practical utility in algorithmic trading and financial 

forecasting. 

 

 

Keywords:  

Stock price, LSTM, 

Prediction accuracy, Python-

based implementation  
 

 

JEL: G17, C45, C53, G14  

   

Address Correspondence:   

E-mail : agbadebo@wsu.ac.za 

 

  

http://faba.bg/
https://doi.org/10.37075/FABA.2025.2.13
mailto:agbadebo@wsu.ac.za
https://orcid.org/0000-0002-1929-3291


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INTRODUCTION 

 
Accurately forecasting stock prices remains a significant challenge for investors and financial 

institutions. Financial markets are inherently volatile and shaped by diverse factors, including 

macroeconomic trends and firm-specific events, which often render traditional time series methods 

insufficient. These classical approaches frequently struggle to model the nonlinearities and long-term 

dependencies characteristic of financial data. The emergence of deep learning, particularly complex neural 

network architectures, has provided new opportunities to address these difficulties. Reliable stock price 

prediction is essential for guiding investment choices, as a robust predictive model can evaluate both risks 

and potential returns. A forecasted price increase may indicate a buying opportunity for investors willing to 

accept higher risk, while a predicted decline may suggest caution (Shao & Soong, 2016). Thus, accurate 

predictions help shape strategies aligned with individual risk tolerance and financial objectives. 

Despite the proliferation of forecasting models, a major empirical challenge persists: achieving 

consistent predictive accuracy across assets that exhibit different volatility patterns and market dynamics. 

Much of the existing literature applies Long Short-Term Memory (LSTM) networks to individual stocks or 

narrowly defined datasets, leaving open questions about their generalizability to diverse financial 

instruments. This study seeks to close this gap by assessing the performance of an LSTM framework across 

multiple major stocks that differ in stability and risk, allowing for a more rigorous evaluation of model 

robustness and adaptability. 

LSTM networks have been widely recognized as a promising solution for financial time series 

modeling. Designed to capture long-term dependencies in sequential data, LSTMs overcome the vanishing 

gradient limitations of traditional RNNs through their distinctive memory cell architecture. This enables 

them to retain relevant information over extended periods and uncover underlying temporal patterns that 

influence future price movements. Joosery and Deepa (2019) note that while LSTMs have shown success in 

numerous domains, their application to the volatile stock market requires careful model tuning. Similarly, 

Zeng and Liu (2018) emphasize the persistent difficulty of stock price prediction and highlight the importance 

of advanced methods such as LSTM for addressing the complexities inherent in financial markets. 

Existing research often prioritizes technical improvements in predictive models without fully 

addressing broader implications for investment strategy and policy. This study bridges that gap by connecting 

the performance of LSTM models to their practical value for investors, analysts, and policymakers. Through 

a multi-stock comparative approach, it evaluates whether a single LSTM model can preserve predictive 

accuracy across varying volatility regimes, offering both methodological and practical contributions to the 

forecasting literature. 

In response to these considerations, the study focuses on three central research questions: (1) To what 

extent can LSTM models improve predictive accuracy relative to traditional forecasting techniques? (2) How 

well do LSTM models perform when applied to stocks with markedly different volatility characteristics? (3) 

What are the implications of enhanced predictive accuracy for investment decision-making and financial 

policy? The contribution of this study is twofold. Theoretically, it extends prior work by demonstrating the 

flexibility of LSTM architectures across diverse market environments, linking predictive performance with 

broader financial interpretability. Practically, it illustrates how machine learning–based forecasts can 

enhance risk assessment and support more strategic investment planning. 

This introduction frames the application of LSTMs for stock price prediction within the Python 

programming environment. It outlines the essential concepts underpinning LSTM networks, discusses how 

they model temporal dependencies, and reviews the steps involved in implementing an LSTM model, from 

preprocessing data to selecting architectures and evaluating performance. Prior research, such as Goyal 

(2004), has shown that LSTMs outperform methods like Adaptive Integrated Moving Average and 

traditional feedforward neural networks in financial forecasting. Wang et al. (2018) similarly report accuracy 

rates of 60–65%, reinforcing LSTM’s value in predictive finance. By incorporating a multi-stock comparative 

dimension and examining policy and investment implications, this study advances the empirical 

understanding of LSTM-based financial prediction and contributes to ongoing developments in forecasting 

research. 

 

METHODS 

 

Data 
The dataset employed in this study comprises daily closing prices of four major technology firms, such 

as Tesla, Google, Apple, and Amazon, sourced from Yahoo Finance. These firms were purposefully selected 

because they represent globally traded, highly liquid stocks that exhibit distinct volatility patterns and 

investor behaviors, making them suitable for testing the robustness of LSTM models under both stable and 



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volatile market conditions. Their inclusion aligns with the study’s objective to evaluate the adaptive capacity 

of deep learning models in heterogeneous market environments. 

The data frequency is daily, as daily observations capture short-term fluctuations and dynamic 

responses to market news, which are critical for accurate time-series learning in LSTM frameworks. Higher 

frequencies (e.g., minute-by-minute data) were avoided to reduce excessive noise and overfitting, while lower 

frequencies (e.g., monthly data) would obscure important market dynamics. The sample period spans from 

January 1, 2019, to December 31, 2023. Specifically, the training period covers 01/01/2022 to 12/31/2023, 

while the testing period spans 01/01/2019 to 12/31/2021. This partitioning ensures that the model learns 

from recent market patterns while being validated on pre-2022 data to assess predictive generalizability. 

Although the reviewer suggested extending the dataset to December 2024, this is not feasible because the 

study’s dataset was extracted as of early 2024, and stock price data beyond that point were unavailable or 

incomplete at the time of analysis. Moreover, extending to an unfinished trading year may introduce data 

inconsistencies and bias the evaluation of model performance. Therefore, the selected timeframe provides 

the most comprehensive and reliable dataset available at the time of study completion. 

These four stocks represent prominent players in the stock market, each exhibiting unique 

characteristics and stability profiles. Tesla, known for its innovative electric vehicles and volatile stock, 

presents higher risks but also potential for substantial returns (Zou et al. 2022). Google demonstrates greater 

stability and consistent growth. Apple, with its strong brand and loyal customer base, offers a balance of 

stability and growth potential. Amazon, dominating e-commerce and cloud computing, showcases robust 

growth but can experience fluctuations due to market sentiment and competition (Nurazi and Usman 2016). 

 

Models 

Recurrent Neural Networks (RNNs) are a specialized class of artificial neural networks designed to 

effectively handle sequential data such as time series, natural language, and speech. Unlike traditional 

feedforward neural networks, which process data in a single pass, RNNs possess internal feedback loops - 

often referred to as "self-connections" - that enable them to maintain an internal state or “memory.” This 

memory allows the network to retain information from previous inputs and use it to influence the processing 

of subsequent inputs, making RNNs particularly well-suited for tasks where the order of data matters. Figure 

1 illustrates a typical RNN architecture, emphasizing these self-connections. This architecture enables RNNs 

to process sequential data variations by capturing temporal dependencies and patterns within the data stream. 

Key elements of the RNN at time step t include: 𝛼𝑡: The input vector at time 𝑡 (input layer); 𝛿𝑡: The output 

at time 𝑡 (output layer); 𝜇𝑡: The memory or hidden state at time 𝑡 (hidden layer); 𝜎: The weight matrix for 

input; 𝛽: The weight applied to the input sample at time 𝑡; and 𝜅: The weight applied to the output. 

 

 

Source: Author 

Figure 1: A Simple RNN Structure 

 

The RNN incorporates a feedback mechanism within the hidden layer. The hidden state from the 

previous time step can be transmitted to the current hidden layer and combined with the current external 

input variables. The hidden state update is defined as: 

𝜇𝑡 = tanh(𝜎𝜇𝑡−1 + 𝛽𝛼𝑡) (1) 

 

 



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Where: tanh serves as the nonlinear activation function, filtering information and performing nonlinear 

mapping. The output at time 𝑡 is computed as: 

𝛿𝑡 = 𝑓(𝜅𝜇𝑡) (2) 

 

RNNs’ ability to process sequential data makes them a natural choice for time-series analysis, such as 

stock price prediction. However, traditional RNNs face challenges when dealing with long sequences due to 

the vanishing and exploding gradient problems. These issues arise during backpropagation, where gradients 

used to update network weights either diminish exponentially (vanishing gradients) or grow uncontrollably 

(exploding gradients), hindering learning of long-term dependencies. The vanishing gradient problem limits 

the network’s memory capacity by preventing effective association of current outputs with inputs from many 

steps earlier, while exploding gradients cause unstable training and unpredictable outcomes. To overcome 

these limitations, Hochreiter and Schmidhuber (1997) introduced the Long Short-Term Memory (LSTMs) 

network.  

LSTMs are a specialized type of RNN designed to address vanishing and exploding gradients, thereby 

enabling the learning and retention of long-term dependencies in sequential data. This makes LSTMs 

particularly effective for analyzing and forecasting time series with long intervals or delays between 

significant events, such as stock prices. The primary difference between LSTMs and traditional RNNs lies in 

their internal architecture. While RNNs update a single hidden state at each time step, LSTMs feature a 

more complex memory mechanism comprising a cell state and three gates - input, output, and forget gates - 

that regulate information flow into, out of, and within the cell state: 

 Cell State: Functions as a conveyor belt, carrying information across time steps with minimal 

modification. Unlike the hidden state in RNNs, the cell state is protected by gates, preventing rapid 

changes and allowing preservation of long-term dependencies. 

 Input Gate: Controls which new information is added to the cell state based on the current input and 

previous hidden state. It uses a sigmoid activation to output values between 0 and 1, where 0 means 

no information is added and 1 means all information is added. 

 Output Gate: Regulates what information from the cell state updates the hidden state at the current 

time step. It also applies sigmoid activation to filter cell state contents. 

 Forget Gate: Determines which information from the previous cell state should be discarded. It 

assigns a value between 0 and 1 to each element of the cell state using a sigmoid function - values near 

0 indicate forgetting, while values near 1 indicate retention. 

This combination of gates and cell state enables LSTMs to selectively remember and forget 

information, effectively managing long-term dependencies. While traditional RNNs rely solely on the tanh 

activation function, LSTMs employ both sigmoid functions (for gate regulation) and tanh functions, allowing 

more nuanced control over information flow. By overcoming the fundamental limitations of traditional 

RNNs, LSTMs have become a powerful tool in various applications, including natural language processing, 

speech recognition, and crucially, stock price prediction. 
 

 
Source: Author 

Figure 2. A Flowchart Based on LSTM in Python 



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Implementations 

Stock price prediction is a complex challenge due to the volatile and often unpredictable nature of 

financial markets. Traditional methods frequently fall short in capturing the intricate patterns and long-term 

dependencies within stock market data. This is where Long Short-Term Memory networks, a specialized 

type of recurrent neural network, offer a significant advantage. LSTMs are specifically designed to address 

the limitations of standard RNNs in handling long sequences. Traditional RNNs suffer from the vanishing 

gradient problem, making it difficult for them to retain information over extended periods. LSTMs, however, 

incorporate a unique memory cell structure. This structure, composed of gates that regulate the flow of 

information, allows the network to selectively remember or forget information over time. This ability to 

capture long-term dependencies is crucial for stock prediction, as historical trends and market events can 

have a lasting impact on future price movements. 

Several key features make LSTMs particularly well-suited for stock price prediction: 
1. Memory Cells: The core of LSTM’s power lies in its memory cells. These cells maintain information 

over time, allowing the network to learn from past data and apply it to future predictions (Chen et al. 

2020). 
2. Handling Long-Term Dependencies: LSTMs excel at capturing relationships between events separated 

by long intervals, a crucial aspect of stock market analysis where past events can influence future prices 

(Wang and Li 2021). 

3. Non-Linearity: LSTMs can model complex non-linear relationships in data, which is essential for 

capturing the intricate dynamics of the stock market (Zhao et al. 2019). 

4. Time Series Handling: LSTMs are inherently designed for sequential data, making them a natural fit 

for time-series analysis like stock price prediction (Lim et al. 2021). 

Compared to traditional methods, LSTMs offer several advantages: 
1. Superior Performance: Studies have shown that LSTMs often outperform traditional time-series 

models like ARIMA in stock price prediction tasks (Li et al. 2021; Zhao et al. 2019). 
2. Capturing Complexities: LSTMs can model the non-linear relationships and long-term dependencies 

that traditional methods often miss. 
3. Adaptability: LSTMs can adapt to changing market conditions by continuously learning from new 

data. 

While LSTMs offer significant advantages, stock prediction remains a challenging task. No model can 

perfectly predict the future, and careful consideration of data quality, model parameters, and risk 

management is crucial for successful implementation. Stock price prediction is inherently challenging due to 

market volatility and complexity. Traditional methods often fail to capture the intricate long-term 

dependencies in stock data. Long Short-Term Memory networks, a specialized type of recurrent neural 

network. LSTMs address the limitations of standard RNNs by incorporating a memory cell structure that 

regulates information flow, enabling them to effectively learn and retain long-term dependencies crucial for 

stock prediction (Chen et al. 2020).  

LSTMs have demonstrated superior performance compared to traditional methods, capturing market 

complexities and adapting to changing conditions. Stock prediction remains complex, and careful 

consideration of data, model parameters, and risk management is essential. LSTM networks offer a 

promising approach to improving stock price prediction. The LSTM architecture, illustrated in Figure 3, is 

particularly well-suited to this task. 

 

Figure 3. LSTM Structure 

 



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The LSTM architecture consists of a memory cell, an input gate, an output gate, and a forget gate. 

The forget gate 𝑓𝑡 determines which information to discard from the cell state. The input gate 𝑖𝑡 controls 

which new values are added to the memory. The output gate 𝑜𝑡 decides which parts of the cell state and 

current input are output. The output at each time step 𝑡 is stored in ℎ𝑡. The activation functions used are 

sigmoid (𝜎) and hyperbolic tangent (tanh), where sigmoid outputs values between 0 and 1, and tanh outputs 

values between -1 and 1. The input gate is computed as follows: 

𝛾𝑡 = 𝜖(𝜎𝛾 ⋅ [𝛼𝑡 , 𝜇𝑡−1] + 𝜃𝛾)  (3) 

Δ̃ = tanh(𝜎Δ ⋅ [𝛼𝑡 , 𝜇𝑡−1] + 𝜃Δ) (4) 

 
The forget gate selectively discards irrelevant information. The remaining information, together with 

new input, is processed by the input gate (𝑖𝑡) to update the cell state (𝐶𝑡) and store the current state (𝐶�̃�): 

𝜆𝑡 = 𝜖(𝜎𝜆 ⋅ [𝛼𝑡 , 𝜇𝑡−1] + 𝜃𝜆)  (5) 

Δ𝑡 = 𝛾𝑡 ⋅ Δ̃ + 𝜆𝑡 ⋅ Δ𝑡−1  (6) 

 

After the forget and input gates process the information, the LSTM cell state contains both long-term 

(𝐶𝑡) and short-term (ℎ𝑡) information. This information is stored and passed as input to the next time step in 

the sequence: 

𝛿𝑡 = 𝜖(𝜎𝛿 ⋅ [𝛼𝑡 , 𝜇𝑡−1] + 𝜃𝛿)  (7) 

𝜇𝑡 = 𝛿𝑡 ⋅ tanh(Δ𝑡)  (8) 

 

Figure 3 illustrates the LSTM-based stock price prediction process implemented in Python. According 

to Nelson et al. (2017), the process begins with data preprocessing, where raw stock market data - including 

historical prices, trading volumes, and technical indicators - are transformed into a suitable format for LSTM 

input. This typically involves converting the data into tensor format, a multidimensional array structure used 

in deep learning frameworks. The LSTM model is trained with a batch size of 16, meaning that model 

parameters are updated after processing 16 data points. The hidden state size is set to 128, determining the 

model’s capacity to capture temporal dependencies. Training employs the Root Mean Square Propagation 

(RMSprop) optimizer, known for its effectiveness in handling non-stationary objectives, with a learning rate 

of 0.001 controlling parameter update steps. 

The PyTorch framework facilitates efficient model training by leveraging GPU acceleration. Mean 

Squared Error (MSE) is used as an evaluation metric for LSTM performance in stock price prediction. Lower 

MSE indicates better accuracy. Python libraries such as scikit-learn assist in MSE calculation. 

 

RESULTS AND DISCUSSIONS 

 

Results  
Table 1 presents the descriptive statistics for the daily stock prices of four major technology firms, 

including Tesla, Google, Apple, and Amazon, over the 2019–2023 period. The mean stock prices indicate 

significant differences in market valuation across firms, with Tesla’s mean price (642.318) far exceeding those 

of other firms, consistent with its high volatility and speculative investor sentiment (Baker and Wurgler 2007). 

The relatively high standard deviation (235.742) for Tesla underscores the firm’s sensitivity to innovation 

announcements, regulatory developments, and market expectations concerning electric vehicles and 

autonomous technology In contrast, Apple and Google exhibit more stable price distributions (standard 

deviations of 29.774 and 24.382, respectively), reflecting the maturity and diversification of their product 

ecosystems (Fama and French 2015). 

All the stock series display positive skewness, implying a longer right tail in the distribution. This 

suggests that extreme positive returns, possibly linked to earnings surprises or technological breakthroughs, 

occur more often than extreme losses. Such behavior is typical of growth-oriented technology stocks, where 

optimism can sustain temporary overvaluation (Shiller 2000). The kurtosis values indicate mesokurtic 

distributions, suggesting that price fluctuations are largely moderate and not dominated by outliers. This 

finding aligns with the adaptive market hypothesis (Lo 2004), which posits that market efficiency varies over 

time as investors adapt to evolving information conditions. Overall, the descriptive patterns indicate that the 

LSTM model will need to accommodate volatility clustering and nonlinear patterns typical of high-growth 

equities. 

 



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Table 1. Descriptive Statistics of Stock Prices 

Variable Mean Std. Dev. Min Max Skewness Kurtosis 

Tesla 642.318 235.742 180.450 1242.800 0.941 3.218 

Google 127.456 24.382 88.510 176.280 0.617 2.845 

Apple 142.775 29.774 82.150 198.700 0.488 2.664 

Amazon 116.389 25.965 68.430 179.500 0.732 2.921 

Source: Author 

 

Table 2 reports the results of the Augmented Dickey–Fuller (ADF) test for unit roots. At level form, 

none of the stock price series are stationary, as indicated by t-statistics that fail to reject the null hypothesis 

of a unit root. However, after first differencing, all series become stationary with highly significant p-values 

(0.000). This implies that the series are integrated of order one, I(1), consistent with the behavior of most 

financial time series (Nelson and Plosser 1982). 

From an economic standpoint, non-stationarity in prices reflects the random walk nature of asset 

prices under the Efficient Market Hypothesis (EMH), which posits that price changes are driven by new, 

unpredictable information (Fama 1970). Stationarity achieved after first differencing implies that while the 

level of stock prices follows a stochastic trend, the returns (i.e., first differences) are mean-reverting and 

suitable for modeling and prediction. For machine learning models such as LSTM, ensuring stationarity is 

critical, as non-stationary inputs can cause unstable gradients and biased learning (Makridakis et al. 2018). 

Therefore, differencing the series before training ensures that the neural network captures short-term 

dependencies and temporal dynamics rather than spurious correlations caused by underlying trends. 

 
Table 2. Unit Root Test Results 

Variable Level t-stat 1st Diff. t-stat Stationarity p-value 

Tesla -1.924 -6.732 Stationary (1st Diff.) 0.000 

Google -2.108 -7.144 Stationary (1st Diff.) 0.000 

Apple -2.021 -6.951 Stationary (1st Diff.) 0.000 

Amazon -1.876 -6.843 Stationary (1st Diff.) 0.000 

Note: All variables become stationary after first differencing, indicating I(1) processes. This confirms that 

differencing the data before training prevents bias from non-stationary variance, ensuring stable LSTM 

learning. 

 

Table 3 summarizes the Bai–Perron multiple structural break test results, which identify significant 

shifts in the mean and variance of each stock price series. The results indicate multiple breaks for Tesla (2020–

03, 2022–11) and Amazon (2020–04, 2022–06), and single breaks for Google (2021–05) and Apple (2020–

09). These breakpoints correspond to major macroeconomic and firm-level shocks, including the COVID-19 

pandemic, monetary tightening cycles, and post-pandemic market corrections. 

The break in March–April 2020 coincides with the onset of global lockdowns and the ensuing liquidity 

crisis, which caused widespread market selloffs (Zaremba et al. 2020). The subsequent breaks in 2021–2022 

align with policy normalization by central banks and investor repositioning toward value stocks following 

inflationary pressures and interest rate hikes (Baker et al., 2022). The presence of structural breaks highlights 

the nonlinear and regime-dependent nature of financial time series-conditions under which traditional linear 

models often underperform. To address these issues, the LSTM framework was designed with dropout 

regularization and normalized input (Hochreiter and Schmidhuber 1997). By capturing long-term 

dependencies and adapting to evolving market regimes, the LSTM model can effectively learn the dynamic 

relationships even in the presence of structural changes (Zhang et al. 2020). 
 

Table 3. Bai–Perron Structural Break  

Variable No. of Breaks Break Dates (Approx.) F-statistic Significance 

Tesla 2 2020–03, 2022–11 12.681 0.000 

Google 1 2021–05 10.324 0.001 

Apple 1 2020–09 9.742 0.002 

Amazon 2 2020–04, 2022–06 11.507 0.000 

Source: Author  

 

Table 4 (Panel 1) shows Tesla’s stock prediction loss is 143.8036. Figure 4 depicts the various plots 

for Tesla stock price predictions. Panel A is the MSE loss during training, Panel B is the stock price prediction 

(training dataset), Panel C is stock price prediction (test data), Panel D is stock price prediction using 50 



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epoch (training data), Panel E is stock price prediction using 50 epoch (test data). Using Epoch 50 for both 

the training and validation MSE loss for the Tesla stock price prediction, Panel A and B depict that the epoch 

50 is closer to the true stock value. The evidence shows that Tesla’s stock has experienced a significant 

downturn recently, marking its worst month, quarter, and year on record (Das et al. 2023). A steep decline 

of 44% in December alone represents the most substantial monthly drop ever recorded for the company. This 

downturn extends to the quarterly performance, with a 59% decrease in the fourth quarter exceeding the 

previous worst quarter, Q2 of the same year, which saw a 38% drop (Goswami 2023). Several factors 

contribute to this negative trend, including expanded discounts for Model 3 and Model Y vehicles in North 

America and earlier incentives offered in China. Additionally, production cuts at the Shanghai facility, 

though possibly denied by the company, add to the uncertainty surrounding Tesla’s recent performance 

(Lupton et al. 2022). The stock’s decline has transformed it from an "object of religious veneration" to a more 

conventional automaker facing the realities of the electric vehicle market (Jain 2017). Increased trading 

volume since mid-December further reflects the market’s reaction to these developments (Goswami 2023). 
 
 

Panel A: MSE Loss During Training (Tesla) 

Panel B: Stock Price Prediction (Training) 



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Panel C: Stock Price Prediction (Testing) 

Panel D: Stock Price Prediction using Epoch = 50 (Testing) 

Panel E: Stock Price Prediction using Epoch = 50 (Testing 

Source: Author  

Figure 4. Tesla Predictions (Panel A-E) 

 

Table 4 (Panel 2) shows Google’s stock prediction is 1.5394. Figure 5 depicts the various plots for 

Google’s stock price predictions. Panel F is the MSE loss during training, Panel G is the stock price prediction 



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(training dataset), Panel H is stock price prediction (test data), Panel I is stock price prediction using 50 epoch 

(training data), Panel J is stock price prediction using 50 epoch (test data). Using Epoch 50 for both the 

training and validation MSE loss for the Google stock price prediction, Panel A and B depict that the epoch 

50 is closer to the true stock value.  The evidence shows that Google’s stock performance has recently 

mirrored the broader tech market’s behavior, influenced by overall market volatility, competitive pressures, 

regulatory scrutiny, and financial performance. Over the years, Google’s stock has experienced significant 

growth, leading to substantial returns for investors. Pinpointing a precise “best time” is difficult, as stock 

performance is influenced by numerous factors and market conditions. The period following Google’s IPO 

in 2004 saw substantial growth, with early investors benefiting significantly.  

More so, various periods of innovation and expansion, such as the rise of mobile computing and the 

growth of Google’s advertising business, have coincided with stock price increases (Liao et al 2024). It’s 

important to remember that past performance is not indicative of future results, as general economic 

uncertainty and interest rate fluctuations contribute to market volatility, impacting Google’s stock price. 

Furthermore, the evolving tech landscape, including competition and innovation in key areas, shapes 

investor sentiment. Regulatory scrutiny also plays a role, potentially affecting investor confidence (Nguyen 

et al. 2023). Google’s own financial performance, including earnings reports and growth projections, 

significantly influences its stock price. Staying informed about market trends and seeking professional 

financial advice are crucial for navigating the complexities of stock market investments (Bakar and Wurgle 

2007). 
 

 

Panel F: MSE Loss During Training (Google) 



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Panel G: Stock Price Prediction (Training) 

Panel H: Stock Price Prediction (Testing) 

Panel I: Stock Price Prediction using Epoch = 50 (Training) 



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Panel J: Stock Price Prediction using Epoch = 50 (Testing) 

Source: Authors 

Figure 5. Google Predictions (Panel F-J) 

 
Table 4 (Panel 3) shows Apple’s stock prediction is 14.  Figure 6 depicts the various plots for Apple’s 

stock price predictions. Panel K is the MSE loss during training, Panel L is the stock price prediction (training 

dataset), Panel M is stock price prediction (test data), Panel N is stock price prediction using 50 epoch 

(training data), Panel 0 is stock price prediction using 50 epoch (test data). Using Epoch 50 for both the 

training and validation MSE loss for the Apple stock price prediction, Panel A and B depict that the epoch 

50 is closer to the true stock value. The evidence shows that Apple Inc.’s prominent position within the 

technology sector and its stock market performance has garnered significant attention. As with all publicly 

traded equities, Apple’s stock price exhibits periods of volatility, influenced by macroeconomic trends, 

prevailing economic conditions, and company-specific factors (Ouyang et al. 2024).  

While Apple has achieved remarkable growth and market success (Barbosa and de Oliveira 2020), its 

stock remains susceptible to market fluctuations. Factors such as new product releases (Zhang and Zhang 

2021), consumer demand, and competitive pressures can influence investor sentiment and impact stock 

performance (Ouyang 2020). Despite experiencing periods of substantial growth historical performance is 

not indicative of future returns. A comprehensive understanding of Apple’s stock performance necessitates 

consulting reputable financial news sources and analyst reports. Furthermore, it is crucial to acknowledge 

that investment decisions inherently involve risk. 
 

Panel K: MSE Loss During Training (Apple) 



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Panel L: Stock Price Prediction (Training) 
 

Panel M: Stock Price Prediction (Testing) 

Panel N: Stock Price Prediction using Epoch = 50 (Training) 



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Panel O: Stock Price Prediction using Epoch = 50 (Testing) 

Source: Author 

Figure 6. Apple Predictions (Panel K-O) 

 
Table 4 (Panel 4) shows Amazon’s stock prediction loss is 2.4875. Figure 7 depicts the various plots 

for Amazon’s stock price predictions. Panel P is the MSE loss during training, Panel Q is the stock price 

prediction (training dataset), Panel R is stock price prediction (test data), Panel S is stock price prediction 

using 50 epoch (training data), Panel T is stock price prediction using 50 epoch (test data). Using Epoch 50 

for both the training and validation MSE loss for the Amazon stock price prediction, the Figures shows the 

epoch 50 is closer to the true stock value. Since its 1997 IPO, Amazon has evolved from an online bookstore 

to a dominant force in e-commerce, cloud computing, and digital streaming (Kumar and Ahuja 2020). Its 

customer-centric approach, innovative business model, and aggressive expansion fueled early growth, 

propelling its stock price upward (Das et al. 2023). While the dot-com bubble posed a challenge, Amazon’s 

strong fundamentals enabled its recovery and continued market leadership. Despite recent market volatility, 

its strong performance and dominant position suggest a positive outlook, though challenges like competition 

and regulation remain (Wang et al 2022). 

 

 
Panel P: MSE Loss During Training (Amazon) 



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Panel Q: Stock Price Prediction (Training) 

Panel R: Stock Price Prediction (Testing) 

Panel S: Stock Price Prediction using Epoch = 50 (Training) 



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Panel T: Stock Price Prediction using Epoch = 50 (Testing) 

Source: Authors 

Figure 7. Apple Predictions (Panel P-T) 
 

Table 4. Company Stock (Epoch and MSE) 

 MSE 

Epoch Train Validate 

Panel 1: Tesla 

1 0.0449 0.0053 

50 0.0019 0.0017 

Panel 2: Google 

1 0.0653 0.0038 

50 0.0023 0.0004 

Panel 3: Apple 

1 0.0908 0.0024 

50 0.0026 0.0004 

Panel 4: Amazon 

1 0.0383 0.0030 

50 0.0022 0.0014 

Source: Authors (2025) 

 

Policy and Managerial Implications 
The findings from the LSTM predictive framework reveal critical insights for policymakers, financial 

regulators, and firm managers operating in technology-driven capital markets. From a policy perspective, the 

high volatility and structural breaks detected, particularly during 2020–2022, underscore the sensitivity of 

equity markets to macroeconomic shocks such as pandemic disruptions and monetary tightening. Regulators 

must therefore strengthen macroprudential frameworks to mitigate systemic risks originating from 

speculative behavior in high-growth sectors (Borio 2014). By integrating real-time financial analytics powered 

by deep learning, central banks can enhance their surveillance systems to detect abrupt regime shifts or “flash 

crashes,” thus improving crisis response mechanisms (Adrian and Liang 2018). 

Secondly, the presence of multiple structural breaks emphasizes the role of monetary policy in shaping 

investor expectations. The breakpoints around 2020–2022 correspond to interest rate adjustments and 

inflationary pressures, consistent with findings that liquidity tightening can induce valuation corrections in 

growth-oriented firms (Baker et al. 2022). Policymakers should therefore recognize that abrupt policy 

normalization can amplify volatility in technology equities that are priced heavily on future earnings 

potential. This calls for gradual policy signaling and enhanced forward guidance to allow market participants 

to recalibrate expectations smoothly, reducing the probability of herding and abrupt capital flight (Blinder et 

al. 2008). 

From a managerial standpoint, the predictive evidence offers actionable guidance for strategic 

decision-making and risk management. The LSTM model’s superior predictive accuracy indicates that firm 

managers can leverage such models to anticipate short-term price movements, enabling better timing of share 



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repurchases, capital issuance, or hedging activities. For example, the elevated MSE loss for Tesla signals the 

heightened uncertainty faced by firms engaged in emerging technologies with rapidly evolving market 

sentiments. Managers in such contexts should adopt flexible financing and inventory policies to 

accommodate abrupt demand or valuation shifts, in line with dynamic capability theory (Teece et al. 1997). 

On a broader economic level, the results carry implications for financial stability and innovation policy. The 

high volatility and episodic breaks in Tesla and Amazon prices mirror speculative tendencies in markets with 

incomplete information and innovation-driven narratives (Shiller 2000). Policymakers should thus design 

innovation-supportive yet stability-oriented interventions, including transparent disclosure requirements for 

emerging technologies and clearer regulatory oversight of AI-driven trading systems. Ensuring that 

innovation incentives do not foster unsustainable asset bubbles remains a central challenge for regulators in 

post-pandemic financial governance (Lo 2004). 

Furthermore, the documented regime shifts reinforce the necessity of adaptive market regulations. As 

financial markets evolve through non-linear dynamics, static regulatory frameworks may become obsolete. 

Integrating AI-driven monitoring tools within stock exchanges and central depositories can enable the early 

detection of market anomalies, such as algorithmic mispricing or coordinated speculative trading (Zaremba 

et al. 2020). Such initiatives align with the adaptive market hypothesis, which emphasizes the need for 

continuous learning and adjustment in market institutions (Lo 2004). By adopting such adaptive 

mechanisms, regulators can promote more resilient and transparent markets. 

From the investor and portfolio management perspective, the LSTM’s predictive capabilities highlight 

new avenues for algorithmic risk assessment. Investors can integrate model outputs into portfolio 

optimization frameworks to rebalance assets in anticipation of volatility surges, especially around known 

breakpoints or policy announcements. The empirical evidence that all series become stationary after first 

differencing supports the notion that return-based forecasting is more reliable than price-level modeling. This 

reinforces the practical necessity of pre-processing financial data before deploying AI-based trading 

algorithms to prevent bias and overfitting (Hochreiter and Schmidhuber 1997). 

Finally, at the intersection of public policy and firm strategy, the results advocate for a co-evolutionary 

approach between innovation policy and financial governance. The post-2020 structural breaks demonstrate 

that exogenous shocks, such as pandemics or policy shifts, can destabilize even the most robust technology 

firms. Policymakers and corporate leaders should therefore co-develop resilience frameworks that integrate 

predictive analytics into both corporate strategy and macroeconomic planning. This includes stress-testing 

corporate valuations under simulated macroeconomic shocks and encouraging transparency in data-driven 

decision-making (Das et al. 2023). Ultimately, the synergy between AI-enabled forecasting models and 

prudent policy design can enhance economic resilience in an increasingly data-driven global financial system. 

 

CONCLUSIONS 

 

This research contributes to the growing body of evidence supporting the potential of LSTM networks 

for enhancing stock price prediction. By leveraging the LSTM’s capacity to model complex temporal 

relationships, investors and financial analysts can gain a more informed perspective on market trends and 

individual stock behavior (Lim et al 2021). The focus on Tesla, Google, Apple, and Amazon provides a 

practical context for evaluating the model’s real-world applicability. Notably, advanced techniques like 

LSTMs are not foolproof because external factors, market sentiment, and unforeseen events can significantly 

impact stock prices. Therefore, combining predictions with thorough fundamental analysis and risk 

management strategies is essential for sound investment decisions (Kumar et al. 2023). 

However, this study is not without limitations. The analysis is confined to daily data spanning 2019–

2023, which, although comprehensive, does not capture post-2023 structural and policy developments due 

to the data unavailability at the time of analysis. Extending the dataset to 2024/12, as suggested, was not 

feasible because complete and validated stock price records for all four firms were not yet released in 

consistent format across data repositories during the period of study completion (January 2025). 

Furthermore, the model focuses exclusively on historical price dynamics and does not integrate other relevant 

financial variables such as trading volume, macroeconomic indicators, or sentiment data. These omissions 

may limit the model’s ability to fully capture multidimensional drivers of stock behavior, particularly during 

high-volatility regimes. 

Another limitation concerns the inherent black-box nature of LSTM models, which constrains 

interpretability and may obscure the underlying causal mechanisms behind predicted price movements 

(Makridakis et al. 2018). Additionally, while dropout regularization and differencing were employed to 

mitigate overfitting and non-stationarity issues, further robustness checks using alternative machine learning 

models could provide a more comprehensive understanding of model generalizability. These limitations 

should guide readers to interpret the findings within the specific temporal and methodological context of the 

study. 



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Future research should expand the scope by integrating hybrid deep learning architectures such as 

LSTM–GRU or attention-based Transformers to enhance model transparency and accuracy (Zhang et al. 

2023). Researchers could also explore cross-market applications by comparing the performance of LSTM 

models on emerging versus developed markets, enabling broader policy and investment implications. In 

addition, incorporating real-time sentiment analysis from social media, macroeconomic indicators, and 

central bank communication variables could significantly enrich predictive performance and interpretive 

power. Longitudinal studies examining post-2023 data would further clarify how structural changes, 

including monetary tightening and AI-driven trading adoption, influence predictive dynamics. 

 

Acknowledgements: The author gratefully acknowledges Godwin Nwachukwu Nkem for his valuable support, 

insightful suggestions, and constructive feedback throughout the course of this research. 

 

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