Ritchie Ng

• National University of Singapore
• Researcher at National University of Singapore

Recent studies show that LSTM-based neural optimizers are competitive with state-of-theart hand-designed optimization methods for short horizons. Existing neural optimizers learn how to update the optimizee parameters, namely, predicting the product of learning rates and gradients directly and we suspect it is the reason why the training task becom...

The financial markets are moved by events such as the issuance of administrative orders. The participants in financial markets (e.g., traders) thus pay constant attention to financial news relevant to the financial asset (e.g., oil) of interest. Due to the large scale of news stream, it is time and labor intensive to manually identify influential e...

Financial texts (e.g., economic news) play an important role in predicting stock prices. The effects of texts of different semantics (e.g., launching a product and reporting a small product bug) last for different time horizons. Despite the importance of timing in stock prediction, there is currently no research that accounts for the time horizon o...

Stock portfolios are often exposed to rare consequential events (e.g., 2007 global financial crisis, 2020 COVID-19 stock market crash), as they do not have enough historical information to learn from. Large Language Models (LLMs) now present a possible tool to tackle this problem, as they can generalize across their large corpus of training data an...

Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility, allowing financial institutions to price and hedge derivatives, and banks to quantify the risk in their trading books. Additionally, most financial regulators also require a liquidity horizon of several days for institutional investors to exit thei...

This repository implements the paper "Eigenvectors from Eigenvalues" that "relates the norm squared of the elements of eigenvectors to the eigenvalues and the submatrix eigenvalues." Implementation is in PyTorch, enabling deployment on CPU and GPU for large-scale calculation of eigenvectors through this new succinct method.

From palpable marine debris to microplastics, marine debris pollution has been a perennial problem. In recent years, there is an emergence of large-scale clean-up efforts making its way around the world. Complementary to large-scale clean-up efforts, there is a nascent area in the use of unmanned and remote vehicles for detecting and removing debri...

Typically we attempt to achieve some form of stationarity via a transformation on our time series through common methods including integer differencing. However, integer differencing unnecessarily removes too much memory to achieve stationarity. An alternative, fractional differencing, allows us to achieve stationarity while maintaining the maximum...

Open source guides and codes for mastering deep learning to deploying deep learning in production in PyTorch, RAPIDS, Numba, Python, C++ and more.