Questions tagged [neural-networks]
The neural-networks tag has no usage guidance.
38
questions
1
vote
1
answer
96
views
Sampling dollar bars for ML model of multiple tickers
I have a Neural Network model that provides predictions for the future returns of a portfolio comprising stocks and cryptocurrencies. The original model operates on standard time bars and generates ...
CommunityBot
- 1
2
votes
1
answer
84
views
Weak Stationarity for Neural Network Input?
I am taking a course that detailed that input data into neural networks should be at least weakly predictive and weakly stationary (stable mean).
Does this principle apply to other ML models like tree-...
- 4,726
0
votes
0
answers
52
views
Heston model calibration to option prices and implied volatility
I hope that you are having a great day,
I am trying to write a research paper on the Heston model deep calibration. I noticed during my literature review that the most common approach is to calibrate ...
- 57
0
votes
0
answers
66
views
Parameters bounds for Heston model calibration
Still working on my master thesis and I have a question I have been looking at for some time but can't find a good reason.
I am looking to follow the steps of Horvath et al. (2019) in order to ...
- 57
1
vote
1
answer
140
views
Deep calibration in the Heston Model
I am doing my master thesis on deep calibration in the Heston Model, and after reading a few academic paper (eg. Horvath et al. 2019) on the subject I understand pretty well the procedure and the ...
- 373
2
votes
1
answer
132
views
Deep vs "shallow" calibration of option pricing models
I am currently investigating the application of deep learning in calibrating option pricing models, specifically, models of rough volatility, such as rBergomi. While there is a lot of research on ...
- 12.5k
1
vote
1
answer
100
views
Neural network time series prediction tool [closed]
What are some of the state of the art time series prediction tool with neural network?
- 36
2
votes
2
answers
2k
views
One-day-ahead prediction of S&P500 with Temporal Convolutional Networks
I'm trying to predict the one-day ahead movement of the S&P 500 with Temporal Convolutional Networks 1 to capture some "memory".
I use daily close data with the loss function $\mathrm{...
- 8,561
0
votes
1
answer
79
views
Optimal Input and Target Variables for Forecasting Using a Deep Neural Network on Daily Stock/Index Data [closed]
What is the optimal input and target variables for forecasting with a deep neural network on daily stock/index data? More specifically I’m training a temporal convolutional network, but a more general ...
- 370
0
votes
1
answer
103
views
In stock prediction with LSTM, is there a need to get a dataset for a specific time period in order to predict future close price?
I am currently trying to predict the close price of the TSLA stock for March 2022 using LSTM model. Initially, I was using TSLA stock data starting from 2012 to of course March 2022. However, I was ...
- 522
1
vote
2
answers
264
views
Theoretical and practical drawbacks of using Deep Learning for calibration and pricing
I am investigating the suitability of using deep learning for pricing and calibration for the full implied volatility surface. Such examples of their application are in papers here and here. During ...
- 5,101
0
votes
0
answers
121
views
Good (non-random walk) financial time series to perform forecasting on
I would like to start with a brief caveat, namely that I am by no means a domain expert in financial markets. Therefore the question I am asking may sound silly to a practitioner but I am asking it ...
0
votes
0
answers
98
views
transforming a model to long short instead of long-only
I am currently trying to adapt a model to a long short portfolio strategy. The model is stated here: A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem by Jiang, Xu,...
- 11.6k
1
vote
1
answer
530
views
Predicting time series using Jump Diffusion model and Neural Networks
I am trying to understand the difference between using Jump diffusion model and Neural Networks or more precisely LSTM to predict time series data regardless what that data contains for example a ...
CommunityBot
- 1
0
votes
1
answer
880
views
Feature engineering for mid-price prediction - quickly changing features
I'm training a fully-connected feed-forward neural network on HFT (limit order book) data to predict the midprice at timepoint $t+\Delta t$ (assuming that $t$ is the current moment, and $\Delta t$ is ...
- 2,952
0
votes
3
answers
935
views
Consistent offset/lag in time-series prediction using Neural Network (all code provided)
I'm using a neural network (keras package) to predict Bitcoin prices 48 hours in advance. The issue is that for some reason, my predictions are "correct" but they are lagging behind the true ...
- 277
2
votes
1
answer
1k
views
Why are my Neural Network predictions “correct”, but offset from true value? Not using any past lagged values
Please bear with me through the whole question - I just want to make it very clear what I've done so far and why I'm so perplexed.
I am working with a neural network with the Keras package in R, ...
- 861
4
votes
1
answer
2k
views
How to use neural network for technical analysis?
I am working on building a Neural network for technical analysis of stocks. The input I have is the open price and two (so far) technical indicators : RSI and William's R - for the past 2 years. I can ...
- 934
1
vote
4
answers
2k
views
What Is A Good Success Rate Using Machine Learning For A Beginner?
I know this question will be quickly destroyed and my account summarily banned, but I just have to ask:
For a trader using machine-learning algorithms (SVMs, ANNs, GAs, Decision Trees) for ...
- 5,101
3
votes
1
answer
200
views
time series data modeling for deep learning
what is the best format to feed the input data, which are time series with varying density over time, to a deep learning network, while at any iteration we want to feed a batch of data including a ...
- 12.5k
-2
votes
2
answers
2k
views
Least-Squares-Monte-Carlo by Neural Network Estimator for pricing American Option Python [closed]
First I did the LSM (Longstaff-Schwartz) to understand how its work to price an American option.
code for standard_normal
...
CommunityBot
- 1
9
votes
2
answers
889
views
Defining an objective function for machine learning task of trading
A simplified example. Given:
asset's price time series
fixed distances to stop and target.
A function of these inputs has two possible output values: $1$ if price is likely to hit the target ...
- 934
4
votes
1
answer
163
views
Neural Networks for Estimation of Unmarked Private Asset Returns from Market Data
Let's assume it is March and my illiquid private assets portfolio is only 50% marked for 12/31, but I want to get the most accurate estimate of my final return for the quarter ended on 12/31.
What is ...
- 11.2k
3
votes
1
answer
882
views
Algorithmic Trading: Normalization and Selection of Technical Indicators for Artificial Neural Networks [closed]
I study on algorithmic trading for a while based on technical indicators. I started to learn about neural networks and want to use technical trading indicators in this approach.
However, I am not ...
- 180
3
votes
3
answers
2k
views
Appropriate way to normalize Bollinger Bands?
I am playing around with using neural nets to make predictions on market trends. I am currently feeding in a portfolio of historical data of many stocks, and am now implementing several technical ...
- 178
10
votes
2
answers
362
views
Reference request: Quantitative approaches to market abuse detection
have been asked to look at some financial timeseries for potential suspicious activity. These are stocks (my background fixed income hybrids trading and not forensic analyst...) and most of the ...
- 27.6k
1
vote
0
answers
344
views
Probability Integral Transform: Standardisation
I've been applying the probability integral transform as shown here to standardise date for input into a neural network:
https://math.stackexchange.com/questions/592076/mapping-cdfs-to-each-other?...
CommunityBot
- 1
1
vote
1
answer
855
views
Design models using adjusted or unadjusted stock prices (time series prediction)?
I'm creating a predictive model for closing price of stocks (using neural network and support vector machines.). Is it appropriate to use adjusted prices or unadjusted prices for this prediction ...
CommunityBot
- 1
2
votes
1
answer
4k
views
When and how to use RNN for stock analysis or trading
I am learning about neural network and created some small networks in feed forwarding network myself. I was curious about Recurrent Neural Networks (RNN) and read some papers about RNN in trading. The ...
CommunityBot
- 1
1
vote
2
answers
548
views
Feature Selection Effect on Deep Multi-Layer-Perceptron for Financial Applications
I am trying to build a machine learning system for financial price prediction.
I am using a 3 layer MLP (a deep network) with 3 outputs (buy,hold,sell).
I am using different features such as price ...
7
votes
2
answers
457
views
Research topics - neural networks and market liquidity
I am a masters student looking for some direction on using neural network on market depth data to help predict market liquidity and bid-ask spreads. Can some of the more experienced people give me ...
- 266
3
votes
1
answer
527
views
Applying Time Delay Neural Network to financial events
I have an IT background and I would like to use data from a forex calendar like this one to predict prices.
The problem is that calendar news impacts can last for days or weeks or even can effect ...
- 2,486
2
votes
0
answers
49
views
Non-overlapping ranges of HCNN' observables and of state transition function
In the artcicle Forecasting and Trading the High-Low Range of Stocks and ETFs with Neural Networks HCNN is used for forecasting of nine time-series, namely:
returns of the lows
returns of the highs
...
- 1,704
4
votes
3
answers
418
views
selecting test data for neural networks
I have been working on a neural network based on certain technical indicators. As people familiar with neural networks would know after developing a hypothesis, the developer is also supposed to ...
CommunityBot
- 1
14
votes
1
answer
8k
views
Multilayer Perceptron (Neural Network) for Time Series Prediction
I have it in mind to build a Multilayer Perceptron for predicting financial time series. I understand the algorithm concepts (linear combiner, activation function, etc). But while trying to build the ...
- 109
1
vote
2
answers
696
views
Machine Learning on matlab 2010
I am trying to develop a trading model. It uses certain technical and fundamental features and the model learns from the past. I have a 3-class output - bullish, neutral and bearish.
On trying neural ...
CommunityBot
- 1
1
vote
2
answers
1k
views
Howto Calculate An Error's Partial Derivative in ANN
This is a follow-on question from this post I made, "Multilayer Perceptron (Neural Network) for Time Series Prediction", a few months back.
I'm constructing a feed-forward artificial neural network, ...
CommunityBot
- 1
3
votes
1
answer
539
views
Econometric vs ANN models for forecast?
I hope this is an appropriate question for this forum... for me it is an obvious query since it intrigues me for a long time.
Ok, assume there are 2 distinct classes of models: econometric (AR, MA, ...
- 13.5k