Neural Networks in Investments

Neural network in investment I. Summary Investment management companies are often overwhelmed by the huge amount of data they receive from financial markets. Most of the data available is inherently digital and noisy making the decision making process more difficult. These decisions often rely on the integration of statistical measurements that attempt to compress most data and qualitative explanations, such as graphs and bar graphs that include news events and other relevant information. Investment decisions usually involve nonlinear relationships between the various elements of data.

A third-generation neural network, a pointed neural network, aims to fill the gap between neuroscience and machine learning using a biologically realistic neuron model for computation. The spike neural network (SNN) is fundamentally different from the neural network known to the machine learning community. SNN works with spikes. Spikes are not continuous values, but are individual events occurring at some point. The appearance of spikes is determined by differential equations representing various biological processes, the most important of which is the membrane potential of neurons. Essentially, when a neuron reaches a certain potential, it jumps up and the potential of the neuron is reset. The most common model is the Leaky integrated ignition (LIF) model. In addition, SNN is usually loosely coupled and uses a special network topology.

The neural network is a model of machine learning and exists for at least 50 years. The basic unit of a neural network is a node roughly based on biological neurons in the mammalian brain. Connections between neurons also mimic the way biological brains develop these connections over time ("training"). From the mid 1980's to the early 1990's, many important architectural advances were made in neural networks. However, the time and data necessary to achieve good results will slow adoption and reduce interest. In the early 21st century, the computing capacity improved dramatically and the industry witnessed the "Cambrian explosion" of computing technology, which was previously impossible. As an important competitor in this field, the decade of explosive computing growth brought deep learning, and won many important machine learning competitions.