Anomaly Detection in Equity Stocks: Applying Machine Learning Techniques to Trading Data from the Colombo Stock Exchange

Abstract

ABSTRACT Anomaly Detection in Equity Stocks: Applying Machine Learning Techniques to Trading Data from the Colombo Stock Exchange explores the possibility of adopting machine learning models to identify abnormal trading behaviors that could indicate market manipulation. Motivated by the need to enhance investor confidence by researching methods that can detect market abnormalities, this research leverages publicly available historical market data from the Colombo Stock Exchange. The study adopted two approaches: a baseline time-series forecasting method to detect anomalies, named Facebook Prophet. A series of secondary models had Isolation Forest as a single model, and as hybrid models, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and autoregressive LSTM (AR-LSTM) forecasting models were incorporated with Isolation Forest as the unsupervised anomaly detection method. The research has considered Commercial Leasing and Finance PLC (COLE) as the main focus due to the SEC Sri Lanka confirming manipulations from 9th to 25th of August 2021, along with John Keells PLC (JKH) as a control dataset, which does not have SEC Sri Lanka confirmed manipulation cases. In terms of COLE, the Prophet model achieved the highest F1-Score of 0.1792, balancing precision (0.1111) and recall (0.4615), making Prophet the most effective overall. AR-LSTM + Isolation Forest demonstrated the highest recall of 0.6154, but at the cost of precision 0.0816, resulting in a slightly lower F1-Score of 0.1440. Isolation Forest and CNN+ Isolation Forest showed comparable output, while LSTM + Isolation Forest underperformed with the lowest F1-Score of 0.0740. For JKH, using a baseline assumption that all data points were anomalous, Prophet again emerged as the leading model with an F1-Score of 0.1110, followed by AR-LSTM and LSTM (0.094 each), validating Prophet’s robustness across both verified and unverified anomaly detection contexts. The findings of this study suggest that anomaly detection through machine learning models by solely relying on publicly available data, such as daily closing prices, trading volumes, along with derived technical indicators, is an effective approach that will provide a valuable early-warning signal to market participants. Future work can incorporate expanded features along with sentiment data to enhance anomaly detection using machine learning models. Keywords: Anomaly Detection, Machine Learning, Facebook Prophet, LSTM, Isolation Forest, CNN, AR-LSTM, Stock Market Manipulation, Colombo Stock Exchange, Time-Series Forecasting.