Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique

Adebola K. Ojo; Ifechukwude Jude Okafor

REVIEW 4 major objections 6 minor 3 references

Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims an LSTM trained on 20 years of cleaned Nigerian Stock Exchange data predicts the tested equities with accuracy between 93% and 99%.

desk verdict A standard LSTM-application paper whose 93-98% accuracy claim is uninterpretable: the metric is undefined and the error tables are internally impossible. read the letter →

arxiv 2507.01964 v1 pith:S2JTWGZE submitted 2025-05-27 q-fin.ST cs.LG

Adebola K. Ojo , Ifechukwude Jude Okafor This is my paper

classification q-fin.STcs.LG

keywords LSTMstockreturnforecastingNigerianExchangedeeplearningrecurrentneuralnetworksequitypricepredictionperformancemetricsfinancialtimeseries

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a long short-term memory (LSTM) network, trained on cleaned and normalized Nigerian Stock Exchange price data from 2001 to 2022, can forecast future equity prices and returns with accuracy above 90%, averaging about 97% across the eight stocks tested. If true, a single deep-learning architecture using only daily price and volume features would give investors a dependable signal in a market where conventional statistical forecasts have had limited success. The authors also claim the LSTM matches or beats an artificial neural network and a convolutional neural network on most of the same equities. The stakes are practical: a model like this could inform buy, sell, and hold decisions in an emerging market, and it would extend the case for LSTM-based financial time-series forecasting beyond developed markets.

What carries the argument

The load-bearing object is the LSTM recurrent network, whose cell state acts as an internal memory that the forget, input, and output gates update. Each gate is a sigmoid layer with a point-wise multiplication operator that decides what to remove, add, or emit from the memory. The paper's specific configuration — 40 units, an input window of 55 prior observations, dropout 0.2, batch size 35, and 50 epochs — maps normalized opening, high, low, close, and volume values to a predicted closing price. The gates matter because they allow the network to carry relevant price patterns across time steps instead of treating each day in isolation, which the authors argue is what lets it handle the non-linear nature of equity series. Supporting machinery includes MinMaxScaler normalization to a 0-1 range and reshaping the data into the 3D matrix format the LSTM consumes.

What would settle it

Take the LSTM's predicted closing prices for the held-out 20% of any tested stock, form one-day-ahead return predictions, and compare them with the naive forecast that tomorrow's close equals today's close. If the naive forecast has equal or lower RMSE on price levels, or if the LSTM's sign accuracy on returns is near 50%, the central 'over 90% accuracy' claim is refuted.

Watch

Extended reading notes

Core claim

The central discovery claimed is that the LSTM forecasts closing prices for eight Nigerian equities with accuracy scores between 0.9346 and 0.98533, with the accompanying MAE, MSE, and RMSE values interpreted as low loss errors. The specific network uses 40 units, an input shape of 55 time steps by 1 feature, a dropout of 0.2, a batch size of 35, and 50 epochs; it is trained on 80% of each stock's cleaned data and tested on the remaining 20%. From the eight-stock table the authors report an average predictive accuracy of 97% and 3% lost errors. They conclude that LSTM models are useful for financial time-series problems provided the training dataset is reliable, and they recommend hybrid LSTM-CNN models as future work to reduce the risk of relying on a single model.

Load-bearing premise

The claim collapses if the reported accuracy measures how closely the model tracks past closing price levels rather than how well it predicts future returns, because a model that simply repeats the last observed price can score very high on such a metric without forecasting skill.

Editorial extensions

If this is right

Trained on cleaned NSE data covering 2001-2022, the LSTM predicts the tested equities' closing prices with reported accuracy between 93% and 99%, which the authors translate into a 97% average and 3% error.
Against an artificial neural network and a convolutional neural network on the same stocks, the LSTM has higher reported accuracy on most of the eight names, so the recurrent memory structure is claimed to fit these price series better.
Because the model needs only daily price and volume features plus standard hyperparameter values, the method is portable to other Nigerian equities with at least 20 years of consistent data.
Predicted closing prices can be converted into return signals, so an investor who trusts the model would have a concrete input to trading decisions rather than relying on technical or fundamental judgment alone.
The authors' recommended hybrid LSTM-CNN extension follows directly: combining the sequence memory of LSTM with the feature extraction of CNN would target the single-model risk the study acknowledges.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper does not state whether the 80/20 split respects chronological order; if the split is random, the model could see future patterns during training, so the accuracy claim is only meaningful under a time-ordered split.
The reported accuracy is computed against predicted price levels, not against the sign or magnitude of one-day returns; a naive forecast that repeats yesterday's close can score nearly as well on price levels even with no predictive skill for returns.
A direct extension would measure the LSTM's return-direction accuracy against a 'repeat last close' benchmark on the same test window, which would settle whether the model forecasts movements or merely tracks the series.
If the accuracy holds on a time-ordered split with return-based metrics, the same pipeline could be tested across other African exchanges, where thin trading and data gaps would stress the cleaning and normalization steps.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things to know. This is a standard LSTM-on-prices paper applied to eight Nigerian equities, and the headline 93-98% accuracy claim does not survive contact with the paper's own tables. 'Accuracy' is never defined, and the error columns in Table 3 contradict basic arithmetic.

What it does well: the dataset is real — eight NSE-listed stocks with over 20 years of daily data across banking, insurance, manufacturing, and telecom. The workflow is the standard LSTM pipeline (MinMaxScaler, 80/20 split, Keras/TensorFlow, unit=40, dropout=0.2, 50 epochs), and they benchmark LSTM against ANN and CNN, a reasonable structure. The literature review is honest and cites prior Nigerian-market ML studies, so the citation pattern is not a problem.

The soft spots are load-bearing, not cosmetic. Section 3 lists the metrics as 'Accuracy, Mean Absolute Error (MAE), Mean Squared Error (MAE), and Root Mean Squared Error.' The typo is minor; the missing formula for Accuracy is not. The only quantitative gloss is the conclusion's '97% predictive accuracy and 3% lost errors.' The plausible readings — R² or 1 minus a normalized price-level error — are exactly the numbers that land near 0.95-0.98 for an autocorrelated price series even when the model has zero skill on returns. All metrics are computed on closing prices, not on returns, which is what the abstract claims to predict. No persistence or random-walk baseline appears anywhere.

The numbers themselves are impossible. WAPCO shows MAE=4.474 and RMSE=0.7401; for a single error vector, MAE cannot exceed RMSE. AIICO shows RMSE=0.544 with MSE=0.00285, whose square root is about 0.053. BERGER's LSTM RMSE is 0.2776 in Table 3 and 0.2710 in Table 4. The 80/20 split is never documented as chronological, and there is no code or data to check against. These contradictions sit directly under the central claim, so the empirical result is uninterpretable as written.

The question is legitimate — whether deep learning adds genuine out-of-sample skill for an understudied emerging market like Nigeria is worth a careful study. This is not that study. The fair use of this paper is as a cautionary example for a reading group on how evaluation metrics go wrong in financial ML. Nobody should cite the 93-98% numbers as evidence of LSTM skill.

Recommendation: desk-reject. If a venue wants to give the authors a path, the fix list is well-defined — define accuracy, add a persistence baseline, make the split explicitly chronological, correct the tables, and share code. But the submission in front of us has no coherent empirical basis.

Referee Report

4 major / 6 minor

Summary. The paper proposes an LSTM model to forecast Nigerian equity stock returns, using daily price data from 2001 to 2022 for eight stocks listed on the Nigerian Stock Exchange. The model is trained on 80% of the data and tested on 20%, and performance is reported in terms of accuracy, MAE, MSE, and RMSE, with comparisons against ANN and CNN. The central claim, stated in the abstract and conclusion, is that the LSTM predicts future prices and returns with over 90% accuracy, averaging 97%. However, the paper never defines the 'accuracy' metric, and the reported error metrics in Tables 3 and 4 contain internal contradictions.

Significance. If the claimed results were valid, the paper would provide strong evidence that LSTM models can forecast equity returns in an African emerging market with 93-98% accuracy, which would be a notable empirical contribution. The paper deserves credit for using a real multi-decade dataset and for benchmarking three neural architectures. However, the central claim is not supported as stated: 'accuracy' is undefined, the metric values are internally inconsistent, and the evaluation protocol is not specified well enough to rule out leakage. Because the headline result rests on these uninterpretable numbers, the paper cannot be considered a reliable contribution in its current form.

major comments (4)

[Section 3, 'Evaluations Metrics Utilised'] The metric 'Accuracy' is listed among the evaluation metrics but no formula or definition is given anywhere in the paper. The abstract and conclusion use this quantity to assert that the model predicts returns with over 90% accuracy (97% average), yet for a regression task on price levels, 'accuracy' is not a standard metric. Without a definition, the reader cannot tell whether it measures return-direction correctness, R-squared, or some normalized error; under the most plausible level-based definitions, a persistence forecast would also score very high, so the headline claim is uninterpretable.
[Table 3] The WAPCO row reports MAE=4.474 and RMSE=0.7401. For any fixed error vector, MAE cannot exceed RMSE (by the RMS-AM inequality), so these two numbers cannot come from the same set of prediction errors. Similarly, the AIICO row gives MSE=0.00285 and RMSE=0.544, but sqrt(0.00285) is approximately 0.053, not 0.544. These internal contradictions indicate that the metric columns are not a coherent output of the claimed evaluation.
[Tables 3 and 4] The same model and stock appear with different metric values across the two tables: BERGER LSTM RMSE is 0.2776 in Table 3 but 0.2710 in Table 4, and WAPCO accuracy is 0.9739 in Table 3 but 0.97739 in Table 4. Since Table 4 is supposed to report the same LSTM results as Table 3, these discrepancies suggest that the numbers were produced by different runs or were transcribed inconsistently, further undermining the reliability of the reported 93-98% accuracy range.
[Section 3, dataset split] The paper states that 80% of the dataset was used for training and 20% for testing, but it never specifies whether the split is chronological. For a time-series forecasting problem, a random split can allow future information to enter the training set, and the MinMaxScaler is described as being applied before the split, which risks using test-set statistics in scaling. Without a chronological split and leakage-free preprocessing, the out-of-sample nature of the reported errors cannot be verified.

minor comments (6)

[Section 3, 'Evaluations Metrics Utilised'] The phrase 'Mean Squared Error (MAE)' should read 'Mean Squared Error (MSE)'; the same heading also misspells 'Evaluations' as 'Evaluations'.
[Section 4, interpretation paragraph] The sentence referring to 'the first row of Table 2' should instead refer to Table 3, since Table 2 contains a sample of raw stock data, not model predictions.
[Table 4] Stock names are misspelled in the table header and rows: 'GTBAN' should be 'GTBank', 'VAPC' should be 'WAPCO', and the CNN column header is missing a closing parenthesis.
[Literature Review] The phrase 'Recurrent Neutral Network' should be 'Recurrent Neural Network', and 'Hochreiter and Schmid Huber' should be 'Hochreiter and Schmidhuber'.
[Section 3, model architecture] The text 'a unit value of 40, a total input shape of (55,1)' should use the plural 'units', and the relationship between the input shape, lookback window, and the number of units is not explained.
[References] Reference [10] is a Towards Data Science blog post, not a peer-reviewed source; it should be clearly marked as a tutorial or replaced with a primary reference.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation identifiable; undefined 'accuracy' is a verifiability defect, not a circular one.

full rationale

The paper's claimed derivation chain is straightforward: collect Nigerian equity price data, clean and normalize it, split into 80% train and 20% test, train an LSTM, predict test prices, and report Accuracy, MAE, MSE, RMSE. The only step that could be circular is the undefined 'accuracy' metric, but the paper never defines it, and no equation is given that would let us exhibit a reduction of the reported 93-98% accuracy to a fitted input or to the autocorrelation of price levels. The internal inconsistencies (e.g., WAPCO MAE 4.474 exceeds RMSE 0.7401; AIICO RMSE 0.544 is not sqrt(MSE)=0.053; BERGER RMSE differs between Table 3 and Table 4) are arithmetic or reporting failures, not instances of a prediction being equivalent to its inputs by construction. There are no load-bearing self-citations, no imported uniqueness theorems, and no fitted parameter renamed as a prediction. The absence of a stated chronological split and the use of price levels rather than actual returns are correctness risks, but the circularity analyzer requires a specific reduction; none is present in the manuscript.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claim rests on an undefined accuracy metric, six hand-picked hyperparameters, and unstated assumptions about data reliability and split ordering. No code or data is shipped. The paper introduces no new physical or theoretical entities; its empirical contribution is bounded by how well these assumptions hold, and they are not tested.

free parameters (6)

LSTM hidden units = 40
Hand-chosen without tuning or justification; the reported accuracy depends on this architectural choice (Section 3).
Lookback window length = 55 days
Input shape (55,1) is selected without justification or sensitivity analysis (Section 3).
Dropout rate = 0.2
Regularization level chosen without tuning (Section 3).
Batch size = 35
Training batch size chosen without tuning (Section 3).
Epoch count = 50
Number of training epochs chosen without early stopping or tuning (Section 3).
Train/test split ratio = 80/20
Conventional split; whether it is chronological is never stated, which matters for time-series leakage (Section 3).

assumptions (4)

domain assumption The NSE historical dataset is reliable and cleaning it (removing noise and missing data) does not introduce selection bias
The abstract and conclusion condition the headline claim on a 'reliable dataset'; the cleaning step in Section 3 does not report how much data was removed.
domain assumption The metric called 'accuracy' is a valid measure of predictive skill for stock returns
Section 3 lists 'Accuracy' among evaluation metrics but never defines it; the conclusion treats the resulting numbers as evidence of return predictability.
domain assumption The 80/20 train/test split is chronological and leakage-free
Section 3 states the split but not its ordering; random splitting of time series would leak future information into training.
standard math The standard LSTM formulation and its Keras/TensorFlow implementation are correct
The paper relies on the textbook LSTM gate formulation (Section 2, ref [2]) without re-deriving or verifying it.

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Cite this review

Pith. "Pith review of Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique." pith.science (2026). https://pith.science/paper/S2JTWGZE

@misc{pith2026250701964,
  author       = {Pith},
  title        = {Pith review of: Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S2JTWGZE}},
  note         = {Machine review of arXiv:2507.01964}
}

read the original abstract

Investors and stock market analysts face major challenges in predicting stock returns and making wise investment decisions. The predictability of equity stock returns can boost investor confidence, but it remains a difficult task. To address this issue, a study was conducted using a Long Short-term Memory (LSTM) model to predict future stock market movements. The study used a historical dataset from the Nigerian Stock Exchange (NSE), which was cleaned and normalized to design the LSTM model. The model was evaluated using performance metrics and compared with other deep learning models like Artificial and Convolutional Neural Networks (CNN). The experimental results showed that the LSTM model can predict future stock market prices and returns with over 90% accuracy when trained with a reliable dataset. The study concludes that LSTM models can be useful in predicting financial time-series-related problems if well-trained. Future studies should explore combining LSTM models with other deep learning techniques like CNN to create hybrid models that mitigate the risks associated with relying on a single model for future equity stock predictions.

Figures

Figures reproduced from arXiv: 2507.01964 by the authors.

**Figure 1.** A sample of LSTM (RNN variant) [8] LSTM uses the cell state at time-interval to predict the next internal value instances. The cell state vector represents the LSTM memory. Usually, the cell state can be altered by specific gates (Forget, Input, or Output gate respectively). A gate is architecturally a sigmoid neural net layer characterized by a point-wise multiplication operator. It coordinates the information dire… view at source ↗

**Figure 2.** depicts the architectural design of the proposed LSTM model. It helps laid down step by step the processes (methodologies) of how to go about achieving the objectives of this study as enunciated below [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

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Works this paper leans on

3 extracted references · 3 canonical work pages

[1]

Predicting Nigerian Stock Returns Using Technical Analysis and Machine Learning,

[1]. David Oyewola, Emmanuel Gbenga Dada, Ezekiel Olaoluwa Omole and K. A. Al-Mustapha, “Predicting Nigerian Stock Returns Using Technical Analysis and Machine Learning,” European Journal of Electrical and Computer Engineering. 2019;3(2):1-8. [2]. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation. 1997;9(8):1735–1780. [3]. Abubakar S...

work page 2019
[2017]

Chanddrika PV, Sreenvasan KS

[8]. Chanddrika PV, Sreenvasan KS. Application of deep learning techniques on stock market indices. Journal of scientific Engineering. 2020;7:10. [9]. Neenwi S, Asagba PO , Kabari LG. Predicting the nigerian stock market using artificial neural network. European Journal of Computer Science and Information. 2013;1(1):30-39. [10]. Asutosh N. Predicting stoc...

work page 2020
[2021]

[Accessed 13 May 2021]

Available: https://towardsdatascience.com/predicting -stock-price-with-lstm-13af86a74944. [Accessed 13 May 2021]. [11]. Chungu DA. Determinants of Stock Market Performance in Zambia. Journal of Economics, Management and Trade. 2022;28(4):62-76. [12]. XXT Lu M . An efficient time -series recurrent neural network for stock price prediction. Information Scie...

work page 2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Predicting Nigerian Stock Returns Using Technical Analysis and Machine Learning,

[1]. David Oyewola, Emmanuel Gbenga Dada, Ezekiel Olaoluwa Omole and K. A. Al-Mustapha, “Predicting Nigerian Stock Returns Using Technical Analysis and Machine Learning,” European Journal of Electrical and Computer Engineering. 2019;3(2):1-8. [2]. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation. 1997;9(8):1735–1780. [3]. Abubakar S...

work page 2019

[2] [2017]

Chanddrika PV, Sreenvasan KS

[8]. Chanddrika PV, Sreenvasan KS. Application of deep learning techniques on stock market indices. Journal of scientific Engineering. 2020;7:10. [9]. Neenwi S, Asagba PO , Kabari LG. Predicting the nigerian stock market using artificial neural network. European Journal of Computer Science and Information. 2013;1(1):30-39. [10]. Asutosh N. Predicting stoc...

work page 2020

[3] [2021]

[Accessed 13 May 2021]

Available: https://towardsdatascience.com/predicting -stock-price-with-lstm-13af86a74944. [Accessed 13 May 2021]. [11]. Chungu DA. Determinants of Stock Market Performance in Zambia. Journal of Economics, Management and Trade. 2022;28(4):62-76. [12]. XXT Lu M . An efficient time -series recurrent neural network for stock price prediction. Information Scie...

work page 2024