We already covered in a previous post, how important it is to deal with uncertainty in financial Deep Learning forecasts. In this post, we'll attempt a first introduction on how we deal with explainability. Neural networks have been applied to various tasks including stock price prediction. Although highly successfully, these models are frequently treated as black boxes. In most cases we know that the performance on the test data is satisfying, but we do not know why the model came up with a specific output.
Artificial Intelligence or AI is a field of Data Science that trains computers to learn from experience, adjust to inputs, and perform tasks of certain cognitive levels. Over the last few years, AI has emerged as a significant data science function and, by utilizing advanced algorithms and computing power, AI is transforming the functional, operational, and strategic landscape of various business domains. AI algorithms are designed to make decisions, often using real-time data. Using sensors, digital data, and even remote inputs, AI algorithms combine information from a variety of different sources, analyze the data instantly, and act on the insights derived from the data. Most AI technologies – from advanced recommendation engines to self-driving cars – rely on diverse deep learning models. By utilizing these complex models, AI professionals are able to train computers to accomplish specific tasks by recognizing patterns in the data. Analytics India Magazine (AIM), in association with Jigsaw Academy, has developed this study on the Artificial Intelligence market to understand the developments of the AI market in India, covering the market in terms of Industry and Company Type. Moreover, the study delves into the market size of the different categories of AI and Analytics startups / boutique firms. As a part of the broad Data Science domain, the Artificial Intelligence technology function has so far been classified as an emerging technology segment. Moreover, the AI market in India has, till now, been dominated by the MNC Technology and the GIC or Captive firms. Domestic firms, Indian startups, and even International Technology startups across various sectors have, so far, not made a significant investment, in terms of operations and scale, in the Indian AI market. Additionally, IT services and Boutique AI & Analytics firms had not, till a couple of years ago, developed full-fledged AI offerings in India for their clients.
The Chinese University of Hong Kong issued a study that calls for a second look at the effectiveness of generating returns from investment strategies based on machine learning. The study found that when applying several well-established deep learning methods to the broader markets, superior value-weighted, risk-adjusted returns could be generated – 0.75-1.87 But in the event of basic exclusions that weigh down benchmark performance – such as microcaps or distressed firms – performance weakens. When microcaps were excluded, adjusted returns fell 62 percent, attributable to small capitalizations and a higher likelihood of low liquidity. Performance also declined when exclusions involved non-rated firms (68 percent) and distressed firms (80 percent). Exclusions aside, the study also highlighted the need to be able to stomach high transaction costs at levels that may not be applicable to most retail investors.
Online Courses Udemy Introduction to Machine Learning & Deep Learning in Python, Regression, Naive Bayes Classifier, Support Vector Machines, Random Forest Classifier and Deep Neural Networks Created by Holczer Balazs Students also bought Cluster Analysis and Unsupervised Machine Learning in Python Feature Engineering for Machine Learning Data Science 2020: Complete Data Science & Machine Learning Machine Learning A-Z: Become Kaggle Master Python for Time Series Data Analysis Ensemble Machine Learning in Python: Random Forest, AdaBoost Preview this course GET COUPON CODE Description This course is about the fundamental concepts of machine learning, focusing on regression, SVM, decision trees and neural networks. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very good guess about stock prices movement in the market. In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with SkLearn, Keras and TensorFlow.
Deep learning technology has rattled the global financial industry in both positive and negative ways. On the one hand, deep learning technology has considerably improved market efficiency. Tomiwa, a big data author and expert, claims to have beaten the stock market average over the past ten years with a program that he developed with Python. The same kind of program could be used by Forex or derivative traders. One of the biggest downsides, though, is that it has giving larger institutional traders with deep pockets an even stronger advantage.
Bitcoin is a very particular asset. Bitcoin is a very particular asset. Its price is sensible to demand and supply rather than external factors, so it may highly depend on perceived trends rather than perceived information. For this category of problems, pattern recognition may prove incredibly useful. Because this problem is very big, from beginning to end, I will begin with the first part of the article by Mining Bitcoin Data.
Convert the Xtrain and Ytrain data set into NumPy array because it will take for training the LSTM model.LSTM model has a 3-Dimensional data set [number of samples, time steps, features]. Therefore, we need to reshape the data from 2-Dimensional to 3-Dimensional. Below the code, snapshot illustrates a clear idea about reshaping the data set.Create the LSTM model which has two LSTM layers that contain fifty neurons also it has 2 Dense layers that one layer contains twenty-five neurons and the other has one neuron. In order to create a model that sequential input of the LSTM model which creates by using Keras library on DNN (Deep Neural Network). The compile LSTM model is using MSE (Mean Squared Error) for loss function and the optimizer to be the "adam".
Is Deep Learning now leading the charge for innovation in finance? Computational Finance, Machine Learning, and Deep Learning have been essential components of the finance sector for many years. The development of these techniques, technologies, and skills have enabled the financial industry to achieve explosive growth over the decades and become more efficient, sharp, and lucrative for its participants. Will this continue to be what drives the future of the financial industry? With the newer deep learning focus, people driving the financial industry have had to adapt by branching out from an understanding of theoretical financial knowledge.
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
In a recent blog post, Nomics announced the release of its 7-day crypto price predictions. Their predictions use a long short-term memory (LSTM) machine learning model. Although the 7-day predictions are still in beta, we were excited to see the development of new strategies for price analysis. This excitement got us questioning how an ML-based portfolio strategy would perform over the course of a few months. To answer that question, we are putting together a study that will benchmark the performance of an ML-based strategy against other strategies like market-cap indexes, holding Bitcoin, and score-based allocations.