Microbes

Microbes are fascinating. They are intriguing. And we're just starting to find out the relationship they have with their hosts (us humans). I recently read 'I contain multitudes' book. It turned out to be much better than my expectations. I attempt to highlight intriguing points from that book along with other things I picked elsewhere.

Why is machine learning in finance so hard?

Financial markets have been one of the earliest adopters of machine learning (ML). People have been using ML to spot patterns in the markets since 1980s. Even though ML has had enormous successes in predicting the market outcomes in the past, the recent advances in deep learning haven’t helped financial market predictions much. While deep learning and other ML techniques have finally made it possible for Alexa, Google Assistant and Google Photos to work, there hasn’t been much progress when it comes to stock markets.

Python - C++ bindings

Python - C++ bindings are useful for several reasons. Performance is one of them. Exposing existing C++ classes to a python module is another important reason.

Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals (Paper Summary)

Factor-based strategies are very common in quant funds. Doing a good job of forecasting the fundamentals directly translates into better returns in the factor strategies. The authors used the US company data from 1970 to 2017. They compare MLP/RNN approach against the linear regression and a naive predictor.

A Brief Review of Reinforcement Learning

Reinforcement Learning is a mathematical framework for experience-driven autonomous learning. An RL agent interacts with its environment and, upon observing the consequences of its actions, can learn to alter its own behaviour in response to the rewards received. The goal of the agent is to learn a policy ππ that maximizes the expected return (cumulative, discounted reward).

Machine Learning for Intraday Stock Price Prediction 2: Neural Networks

This is the second of a series of posts on the task of applying machine learning for intraday stock price/return prediction. Price prediction is extremely crucial to most trading firms. People have been using various prediction techniques for many years. We will explore those techniques as well as recently popular algorithms like neural networks. In this post, we will focus on applying neural networks on the features derived from market data.

Machine Learning for Intraday Stock Price Prediction 1: Linear Models

This is the first of a series of posts on the task of applying machine learning for intraday stock price/return prediction. Price prediction is extremely crucial to most trading firms. People have been using various prediction techniques for many years. We will explore those techniques as well as recently popular algorithms like neural networks. In this post, we will focus on applying linear models on the features derived from market data.

StarSpace: Embed All The Things! (Paper Summary)

This paper describes a way to generate embeddings for various tasks. The algorithm is general enough which enables it to achieve strong results in very diverse tasks.

Deep Neural Networks for Youtube Recommendations (Paper Summary)

Youtube switched their recommender system from matrix factorization to neural networks few years ago. This paper describes the neural network models as well as the overall system around it, including the data processing and deployment aspects.

Deep learning networks for stock market analysis and prediction (Paper Summary)

In this paper, Deep learning techniques are applied to the financial market data directly rather than using any text/alternative data sources. This has been a relatively tricky dataset for any non-linear machine learning technique because of the extremely high noise-to-signal ratio. The authors use a relatively high-frequency dataset sampled at every 5 minutes. They consider 38 stocks from Korea KOSPI.